Response Bias in CX: How to Get Better Feedback

Creating and executing the perfect survey that avoids obtaining misleading feedback can be tricky business. To be successful, you’ve got to watch out for inaccurate data such as sampling bias, framing bias, and most importantly, response bias. 

What is Response Bias?

Response bias is our human tendency to self-report inaccurate (or even false) answers to survey questions. For example, if someone asks you how much you exercise, you might be inclined to say four times a week, even if the answer is two. 

It doesn’t matter if it’s intentional or accidental—if humans are inaccurately reporting on their experiences, this false information can negatively affect the data gathered from your survey. Unfortunately, if you aren’t getting accurate data from your surveys because of response bias, you can’t rely on it to improve experiences for your customers and employees. 

How Common Is Response Bias?

Response bias can occur to varying degrees in almost any survey or study involving human participants. Its occurrence depends on several factors, including the nature of the questions asked, the survey methodology used, the characteristics of the sample population, and the efforts made to minimize bias.

In some cases, response bias may be minimal, especially if researchers take proactive steps to mitigate it, such as ensuring anonymity, using randomized sampling, and designing neutral survey questions. However, in other cases, response bias can be more pronounced, particularly if the survey topic is sensitive or if participants have strong opinions or motivations that influence their responses.

It’s essential to recognize that while response bias can never be eliminated, but researchers can take steps to minimize its impact. By employing best practices in survey design, sampling, and data analysis, researchers can reduce the likelihood of response bias and improve the accuracy and reliability of their findings.

Can A High Response Rate Decrease Response Bias?

A high response rate doesn’t necessarily guarantee a decrease in response bias, although it can help mitigate certain types of bias.

When a survey achieves a high response rate, it generally means that a larger proportion of the target population has participated. This can reduce voluntary bias, as a more representative sample of the population is likely to have been captured. However, even with a high response rate, other forms of response bias, such as social desirability bias or acquiescence bias, may still be present if respondents provide inaccurate or misleading answers.

To reduce response bias effectively, researchers should focus on employing strategies aimed specifically at minimizing bias. While a high response rate can be an indicator of a more representative sample, it’s not a standalone solution for addressing response bias.

What Causes Response Bias?

Response bias can stem from several sources. Respondents may alter their responses to present themselves in a more favorable light or conform to social norms. Similarly, some individuals tend to agree with statements regardless of their content. Both of these things can skew results from a questionnaire. 

Can the Wording of A Question Create Response Bias?

Yes, the wording of a question can create response bias. The way a question is phrased can influence how respondents interpret it and, consequently, how they choose to answer. 

For example, leading questions that suggest a particular answer or contain biased language can lead respondents to answer in a way that aligns with the wording of the question. An example of this would be the question “Don’t you agree that product X is better than product Y?” This question implies that one product is better than the other, which may lead the respondent to agree. 

What Are the Different Types of Response Biases? 

According to the experts, there are lots of response biases. Here is an overview of the seven major types of response biases and an example of each.

  • Social response bias: This happens when survey respondents overreport on good behaviors and under-report on bad behaviors. Think about it this way—few people will admit they eat 5-6 bars of chocolate per week, so respondents will tend to under-report their chocolate consumption, skewing the data in the process.
  • Non-response bias: This happens when a survey sample isn’t representative of the target population. Imagine you’re trying to survey customers who attended a grand opening via email. However, half of the surveys went to the attendees’ spam folder. So, the responses you receive only represent half of the attendees. 
  • Hostility bias: When you ask survey respondents about unpleasant memories or negative experiences, the responses might become hostile. An example of this is asking emergency room hospital patients how their experience was—they might only remember their pain and suffering.
  • Satisficing: Respondents who display this form of response bias are likely to leave questions unanswered or to answer dishonestly. These types of customers are trying to satisfy the task of completing the survey, and typically rushing through questions, instead of carefully reflecting on their experiences and providing valuable feedback. 
  • Recency bias: This is a psychological phenomenon of simply picking the answer the respondent reads—one of the reasons why it’s so important to randomize choices.
  • Wording bias: When it comes to surveys, wording matters. Even something as simple as greeting a customer by name can psychologically influence their responses—they might assume someone is personally reading the survey, and be less likely to provide honest feedback
  • Voluntary Bias:Voluntary response bias occurs when individuals choose whether or not to participate in a survey or study, and their decision to participate is influenced by factors related to the topic being studied or their own characteristics.

How to Check for  Response Bias

To identify response bias, you will need to cross-reference survey responses with other data. For example, comparing self-reported behaviors with objective measures or official records can reveal inconsistencies. 

Another way to identify response bias is by examining response patterns within the survey data that can uncover indications of bias. Trends such as consistently high or low ratings, excessive agreement or disagreement with statements, or patterns related to question order.

How Can CX Leaders Reduce Response Bias?

Now, let’s take a break down some ideas for reducing response bias. Here are some tips from our InMoment experts:

  • Understand Your Demographic. This should be the first exercise your brand takes on when designing a survey. Ask yourself who are you surveying, what kind of information are you looking for, and what might be some of the obstacles in the way of an accurate data set. Of course, we recommend running this workshop with an expert in human behavior to make sure you have all your bases covered! 
  • Diversify Questions. No one likes to fill out a repetitive survey— change up the questions and the formats to keep your respondents engaged. You can swap between a mix of binary response questions (yes or no) with those that offer a range of responses with checkboxes, which will keep the participants’ brains focused on the task at hand instead of zoning out.
  • Allow Participants to Say “No.” This is a simple, yet powerful solution to ensure you’re getting accurate data. Allowing participants the power to say “no” or “I’m not sure,” will help you avoid those respondents who don’t have insightful commentary on this particular question.
  • Avoid Question-Wording Bias. The best way to avoid this type of bias is to balance the response options between an equal amount of positive and negative options. If you ask a survey respondent a question like, “It’s ok to eat ice cream once a week, right?” This will surely solicit a skewed and biased response.
  • Avoid Emotionally Charged Terms. Ideally, surveys are designed with neutral language wherever possible. Adding in active verbs like “smash” or “brutalized” are sure to elicit an emotional response from a participant. 

Why You Want to Avoid Response Bias

Avoiding response bias is crucial because it compromises the validity and reliability of survey data, which leads to inaccurate conclusions and potentially misleading insights. Response bias distorts the true representation of the population being studied, as it skews the distribution of responses away from what would be expected in the absence of bias. This undermines the integrity of the research findings and can have serious implications for decision-making based on the survey results.

Is Response Bias a Sampling Error?

Response bias is not classified as a sampling error per se. Sampling error refers to the variability that occurs when a sample is selected from a population rather than surveying the entire population. It is inherent in any sampling process and can be quantified using statistical methods. 

Response bias, on the other hand, occurs when the responses from the selected sample do not accurately reflect the true characteristics or opinions of the population due to systematic errors in the data collection process. While both sampling error and response bias can affect the accuracy of survey results, they arise from different sources and may require distinct strategies for mitigation.

How to Minimize Response Bias

Eliminating response bias can be almost impossible. However, some measures can be taken to minimize response bias and ensure that the data you are collecting is as accurate as it can be. Here are some steps you can take to minimize response bias: 

  • Use randomized sampling techniques: Employing sampling methods such as systematic sampling helps ensure that every member of the population has an equal chance of being selected for the survey. This reduces the likelihood of voluntary bias and ensures that the sample is representative of the population.
  • Guarantee anonymity and confidentiality: Assure respondents that their responses will remain confidential and anonymous. This can encourage honesty and reduce social desirability bias, as respondents feel more comfortable providing truthful answers without fear of judgment.
  • Use multiple response formats: Incorporate a variety of response formats, such as multiple-choice, open-ended, and Likert scales. Offering different response options allows respondents to choose the format that best reflects their opinions or experiences, reducing the likelihood of response bias.

By implementing these strategies, researchers can help mitigate response bias and improve the quality and reliability of survey data.

Let InMoment Reduce Response for You

At InMoment, we believe the best experience programs are a combination of best-in-class technology + human expertise. And at InMoment, we’ve got both of these covered:

Solution #1: Take Advantage of InMoment’s Best-In-Class Tech

InMoment’s Experience Improvement (XI) Platform is made up of a series of applications to help you design the perfect survey and come up with results that help your business actually improve experiences for customers and employees.

Specifically, InMoment’s patented, AI-powered Active Listening Studio is used to, well, actively listen to survey respondents when they are filling in their answers. The application is designed to encourage rich conversations by listening and responding to customers in real-time, eliciting not only more, but more valuable responses. One example of this is prompting the respondent to “share more.” For instance, if the question is, “Tell me about your experience in-store today,” you can set up parameters and triggers in the Active Listening Studio to ensure the customer provides feedback of 200+ characters.

Wondering what kind of results InMoment clients have seen? Using the Active Listening Studio to revamp its listening, a leading global retailer increased survey response rates by 37% and response length by 38%. This meant more and better customer-sourced guidance for every department across the organization.

Solution #2: Leverage InMoment’s Human Expertise

 At this stage in the customer experience landscape, we can’t solve problems using technology alone. True innovation often requires a human touch. 

InMoment is made up of lots of people, and some of those are data science experts. Yes, we’re experts in CX, EX, and MX, but more importantly—we’re experts in human behavior. Our data scientist- and PhD-led Strategic Insights team are the best in the business when it comes to helping our clients design and execute surveys without response bias. Don’t take our word for it though! In a recent Forrester Wave report, InMoment was classified as a leader!

Response Bias: A Recap

When it comes to surveys, customer experience practitioners should avoid response bias at all costs. And there are so many types to look out for! Whether it’s hostility bias, recency bias, or non-response bias, all types of bias can skew your data set and affect your overall experience improvement goals. The end result of a survey without response bias is cleaner data, more accurate insights, and a clearer picture of how to improve experiences for customers and employees. 

InMoment is here to help you every step of the way with our award-winning customer experience platform that combines best-in-class technology and experts in data science and human behavior. 

Digital Experience: Meeting Customer Expectations

For as long as there has been business, there have been customers. And for as long as there have been customers, businesses have had to prioritize creating a positive customer experience. The customer experience—how buyers feel about their collective interactions with a brand—defines and motivates customer action. It sets the tone for the entire company/consumer relationship. But with the advent of the information age and the communication technologies that support it, a new facet of customer experience has emerged: digital experience

What Is Digital Experience?

Digital experience (also called digital customer experience or abbreviated as DX) is an extension of traditional customer experience. But where customer experience takes every interaction into account, the digital experience focuses more specifically on those interactions that occur with digital touchpoints. This includes communicating with a brand via social media, making a purchase online, receiving an email or text confirming an order, providing user feedback on online channels, using a company app, operating a digital kiosk or point-of-sale terminal, or even simply visiting an organization’s webpage. 

In other words, these touchpoints include a broad range of interactions and are constantly growing to encompass an ever-greater portion of the customer journey. As such, a digital experience is a powerful tool for shaping essential interactions. 

Why is Digital Customer Experience Important?

The digital customer experience has become an essential part of the way that consumers navigate the marketplace. It is common for consumers to switch channels multiple times throughout the course of an interaction. They don’t view a transaction as an online experience, a mobile app experience, or a customer service experience, they view it as one continuous experience that they had. Your brand needs to deliver on all fronts in order to create a memorable experience for these customers. Not only will it make your business better, but in the coming years, it will be the only way your business can survive. Let’s get into the details.

Customer Expectations

First of all, customers want and are coming to rely on the digital experience. They have high expectations for a brand’s online experience and use your online presence and quality to gauge how trustworthy your brand is. They need and want websites that are easy to navigate, have quick load times, and offer a personalized experience.

Competitive Advantage

Creating a unique and positive digital experience helps brands set themselves apart from competitors, too. When people start shopping for something, like say a new pair of headphones, they are going to first go online to see what is available to them, research pricing, and compare brands. Your brand has to have an online presence to be a competitor, and if you want customers to actually choose your product or service, you need to have a robust and thoughtful digital customer experience.

Being online with your business evens the playing field; having a great digital customer experience puts your brand on top. Customers will go with well-designed websites, brands that pay to be more visible through SEO efforts, companies with helpful and high-ranking content, and services with plenty of customer reviews and other types of social proof. If you want your company’s services to rise to the top, then you need to provide a seamless, engaging, and robust digital customer experience that will make consumers choose you over other competitors that are just one click away.

Brand Reputation

A good digital customer experience is critical to a brand’s reputation. Word spreads fast in the age of social media and a bad digital experience can hurt a brand’s reputation. The better customer experiences your consumers have, the more you can fortify your brand reputation. This means prioritizing positive interactions with products and services, engaging with people online, implementing customer feedback, and more. From the first ad or social media content a customer engages with to the checkout button on your website, the digital customer experience is what shapes your presence online and ensures that your brand reputation is protected.

A well-designed digital experience can increase customer engagement and interaction with a brand. Interactive features, personalized content, and social media integration can foster meaningful connections and encourage customers to actively participate and share their experiences with others.

InMoment’s digital experience software can help lay out a well-designed online experience that increases site conversions, identifies web experiences that impede user interactions, and ultimately creates a higher customer lifetime value. 

Factors that Impact Digital Experience

Because digital experience is an extension of customer experience, it is shaped by many of the same factors. These include the following:

  • Usability
    How much effort does the interaction demand on the part of the user? Is the digital interface intuitive, or are there areas where a user might become confused or frustrated?
  • Availability
    Does the touchpoint conform to user time demands? Is it accessible when the user needs it?
  • Performance
    What kind of time commitment does the touchpoint represent? Does it load quickly? Is it responsive to user commands?
  • Achievement
    How well does the touchpoint help the user achieve their goals? Do they come away from the experience feeling as though it was successful?
  • Pain Points
    Are there any errors, non-functioning components, frustrating interactions, or other hurdles standing in the users’ way as they attempt to interact with the touchpoint?
  • Emotion
    How did the customer feel about the interaction overall? Would they consider it positive, or was it a negative experience? 

Taken together, each of these factors points toward a single element vital to digital experience: seamlessness.

Why Is It Important to Offer a Seamless Digital Experience?

As previously addressed, the number of digital touchpoints a customer may choose to interact with is constantly growing. And as digital communications channels continue to supplant traditional ones, even the most basic of interactions will soon depend at least partially on digital technologies. Perhaps even more relevant is the fluidity with which customers now transition between channels. The customer journey is no longer a single, straight path; it’s a complex web that must coalesce every digital channel into a unified experience. 

But as intricate as these new journeys may be, it is essential that this increased complexity not get in the way of the customer’s ability to accomplish their goals. Users aren’t interested in the differences between channels or the specific challenges associated with digital interactions — they simply want to make a purchase or investigate a solution, and they will use whatever channels are most convenient to them at that moment. 

With this in mind, the importance of offering a seamless digital experience becomes much clearer. Customers don’t want to restart (or even slow down) their journey simply because they decided to jump on their smart device or transition to a social platform. They crave consistency, efficiency, and flow, and any interruption is an unwanted intrusion

What does this mean for businesses? It means that no matter how or where a customer decides to make contact, they should ultimately encounter the same, seamless experience where they can resume their journey without having to reconstruct the path.

Of course, while meeting customer goals and fulfilling their expectations is a worthy objective in and of itself, the right approach to digital experience also carries with it a number of business advantages:

  • A seamless digital experience provides increased opportunities for gathering customer data and user feedback. By expanding on digital touchpoints and incorporating online channels smoothly into the customer journey, you not only give a voice to those who might otherwise go unheard, but you also exponentially increase the amount of user data available to apply toward marketing, sales, and overall company strategy.
  • Digital interactions are more than just another way to connect with a business — they’re the most popular way. Unmatched in terms of convenience and capability, digital channels are the preferred method, a position that has only become more stable following the COVID-19 pandemic and subsequent lockdown. Today, 8 in 10 consumers (or 81%) plan to increase or maintain their online usage even after COVID restrictions have been fully lifted. Improving the digital experience, therefore, is more important than ever for companies that want to meet customers on their terms.
  • You might not suspect this from the flood of advertisements that clog news feeds and interrupt television programs, but traditional advertising is not as effective as it once was. In fact, Forbes reports that a staggering 96% of people are distrustful of ads. A positive digital experience, on the other hand, gives companies a more direct and less suspect avenue for engaging potential buyers. Instead of telling customers what they should do, the organization becomes a trusted partner working with the consumer to reach a satisfactory solution.
  • The days when customers would accept subpar digital experiences are long gone. Today’s buyers expect seamlessness, consistency, and personalization when they interact with businesses online. Simply put, the bar has been raised, and those businesses that fall short in the digital experience department are likely to be left behind. Brands with higher user satisfaction rankings for at least three years grow revenues 2.5 times faster and deliver up to 500% more shareholder returns over the following decade.

Each of these advantages leads to and reinforces the same outcome: Increased revenue and improved ROI on customer-facing initiatives. 

What Factors Make a Good Digital Experience? 

Most businesses agree that a positive digital experience is essential. Still, there’s a big difference between recognizing the importance of a good digital experience and knowing how to provide one. Here, we highlight several elements intrinsic to meeting and surpassing customer expectations online:

Making Omnichannel Your Top Priority

Creating an omnichannel customer experience may be the biggest factor determining the success of any modern digital experience plan. But effective omnichannel doesn’t happen on its own; it takes dedicated planning and constant refinement to ensure that every channel is connected, consistent, and capable of providing a seamless user experience. Standardize your brand presence and pick up the thread of the customer journey regardless of when, where, or how your customers reach out.

Following Up on Customer Feedback

Customer feedback is essential to improving the digital experience — if something is failing to meet expectations or if a change significantly improves the experience, the customers are the ones who can give you the most objective reports. So, when they come to you to discuss the situation, don’t let it hang in the air. Follow up on every piece of customer feedback. This may be particularly important when addressing negative issues; working together to resolve concerns and then reaching out afterward to make sure that the customer is satisfied can help turn a potentially bad experience into a good one. 

With InMoment, getting and analyzing customer feedback has never been easier. Our customer experience platform gives you the ability to collect the strongest signals, generate the richest insights, and drive the smartest actions. 

Investing in Long-Term Success

Digital experience is big. The touchpoints it includes are many. The possible ramifications are extensive. On the other hand, this means that seeing the results of any changes to or investments in your digital experience strategy can take time. Make sure that you and any stakeholders within the company are aware of this fact, and that everyone involved has the correct long-term mindset.

Being Transparent with Customers

Business earnings aside, those who benefit most directly from improved digital experience are the customers. As you invest in smoothing out their journey, don’t be shy about sharing. Tell your customers what you’re doing and why. Communicate with them and let them know how a feedback loop is helping optimize their experience. Above all, treat your audience as a partnership; when customers feel like they have a more active role in the business, they become invested in the success of the brand. 

Breaking Down Silos

Providing a good digital experience demands buy-in and coordination throughout your entire organization. This requires unrestricted access to relevant information, which simply is not possible when departments and tools are siloed. Data and communication silos are the natural enemies of centralizing and optimizing the customer digital experience—if you’re going to work toward omnichannel, those silos will have to go. 

Improving Constantly

The digital landscape is constantly evolving—your strategy needs to evolve along with it. Always be on the lookout for areas where you could be improving. This means collecting customer feedback and analyzing customer experience metrics, but it also means trying new layouts and approaches and charting their effectiveness in terms of helping the buyer achieve their goals. This will give you the insights you need to keep your strategy moving forward. 

Features to Improve Your Digital Customer Experience

While it is important to invest in long-term success, be transparent with customers, and break down silos, there are also some technical aspects that need to be addressed to ensure that you are actively working to improve your digital customer experience.

Intuitive Interface

A user-friendly interface that is easy to navigate and understand can significantly enhance the digital experience. Make it easy for customers to find what they are looking for and answer their questions with little effort. If your website looks, feels, or moves in a clunky or confusing manner, people won’t even become frustrated—they will simply leave and head to a website that is easier to navigate. Clear navigation, consistent design elements, and logical layouts contribute to ease of use. 

Tip: Try providing a searchable database for FAQs and helpful resources and make sure your navigation bar offers the right shortcuts and links that will address your consumer’s pain points. Make sure checking out is easy and logical and that consumers know exactly what they are getting by engaging with your brand.

Responsive Design

Ensuring that the digital platform works seamlessly across various devices and screen sizes improves accessibility and usability. Responsive design adapts the layout and functionality to provide an optimal experience on smartphones, tablets, and desktops.

Fast Loading Times

Slow loading speeds can frustrate users and lead to abandonment. Optimizing performance through efficient coding, content delivery networks (CDNs), and image compression can improve loading times and overall user satisfaction.

Accessibility Features

Making the digital experience accessible to users with disabilities is essential for inclusivity. Features such as screen reader compatibility, keyboard navigation, and alt text for images enable individuals with diverse needs to access and interact with the platform.

What Factors Get in the Way of a Good Digital Experience?

Unfortunately, there are a lot of hurdles that can trip up an otherwise positive digital experience. Here’s a list of some of the most common that you will need to watch out for:

  • Confusing user interface (UI)
  • Content not optimized for mobile screens
  • Difficult or non-intuitive navigation
  • Difficulty resolving support requests
  • Distracting pop-up messages or advertisements
  • Hidden fees or other unexpected costs
  • Illegal or secretive data collection practices
  • Improperly coded input fields
  • Inconsistent brand voice between channels
  • Delayed responses to queries
  • Lack of payment options
  • Language barriers
  • Lost or delayed orders
  • Poor web performance
  • Scrolling issues on mobile devices
  • Touchpoint outages
  • Unfriendly or unhelpful brand representatives
  • Unwanted or overly frequent email or text messages

Obviously, this is not an exhaustive list, which is why it’s so important to be open and responsive to customer feedback. Your customers will tell you where the journey runs into problems. Then it’s simply a matter of revising the digital experience to meet the users’ expectations. 

How to Measure Digital Customer Experience

In order to measure the success of your digital customer experience, you first need to decide on the metrics that matter the most to your business. By aligning digital customer experience metrics with overarching business strategies, your organization can ensure that your measurement efforts are not only meaningful but also actionable. For example, if you are prioritizing customer retention, you may focus on metrics such as customer lifetime value, repeat purchase rate, and Net Promoter Score (NPS) to gauge the effectiveness of your digital interactions in fostering long-term loyalty and advocacy.

Moreover, as digital customer experience continues to evolve, you must remain agile in your approach to measurement, regularly reassessing and refining your chosen metrics to adapt to changing market dynamics and consumer preferences. By adopting a data-driven mindset and leveraging insights gleaned from these key performance indicators, organizations can iteratively enhance their digital strategies and deliver exceptional experiences that resonate with customers and drive sustainable growth.

What A Great Digital Customer Experience Looks Like in Action 

A great digital customer experience seamlessly blends various elements to create a harmonious interaction. In a perfect world, the customer accesses a digital platform that has an intuitive and easy-to-use interface, the experience feels tailored to them, and their experience is consistent across channels. 

To exemplify what a great digital customer experience looks like, let’s look at a guest’s experience with a hospitality brand. The guest visits the hotel’s mobile website or app to book a room. The interface is user-friendly, with clear descriptions, images, and pricing. The guest receives personalized recommendations based on their preferences, such as room type, amenities, and location.

After booking, the guest receives a confirmation email or message with all the details of their reservation. They also receive a pre-arrival guide with information about the hotel, local attractions, and transportation options.

Upon arrival, the guest can choose between traditional check-in at the front desk or using a mobile check-in feature on the hotel’s app. If they opt for mobile check-in, they receive a digital room key on their smartphone, allowing them to bypass the front desk and go straight to their room.

In their room, the guest finds a personalized welcome message on the TV screen or a digital tablet. If the guest needs assistance or recommendations during their stay, they can easily reach out to the hotel’s virtual concierge through the app or website. 

This experience represents an experience where the business met the customer where they were and did everything to accommodate their various needs and travel references. When creating a digital experience for your brand, keep in mind the different ways in which customers may want to engage with you, and be sure to set up your customer experience program to account for that.  

Why Do You Need A Digital Customer Experience Strategy?

A digital customer experience strategy is a must-have for organizations that are looking to increase customer acquisition and retention. Customers increasingly prefer to interact with businesses digitally, whether it’s through websites, mobile apps, social media, or other online channels. As a matter of fact, 74% of customers expect any service that would be available in person or over the phone to be available online. 

A well-executed digital customer experience strategy can differentiate a business from its competitors by delivering exceptional experiences that delight customers and keep them coming back. Furthermore, The quality of a business’s digital customer experience directly impacts its image and brand reputation. A positive experience can enhance brand perception, increase customer trust, and generate positive word-of-mouth referrals. 

A digital customer experience strategy also offers businesses the flexibility to adapt quickly to changing market conditions, customer preferences, and emerging technologies. This enables businesses to stay agile and responsive, ensuring that they can pivot and evolve to meet evolving customer needs.

How to Improve Your Digital Customer Experience Strategy

If you already have a digital experience strategy, then you are on the right track. However, these strategies are not a one-and-done thing. They need to be continuously monitored and improved in order to realize the best results for your business. Here are some things you can do to improve your digital customer experience strategy: 

  • Data-driven Insights: Leverage data and predictive customer analytics to gain insights into customer behavior, interactions, and engagement across your digital touchpoints. You can use these insights to identify and prioritize areas of improvement. 
  • Optimize the User Experience (UX): No matter your product or service, you will not succeed. You can optimize the user experience of your digital platforms by conducting usability tests, A/B testing, and user research. 
  • Create Personalized Experiences: By leveraging customer data and insights, businesses can deliver tailored content, recommendations, and offers that resonate with individual customers. This can be achieved through techniques such as dynamic website content, personalized emails, targeted advertising, and product recommendations based on past behavior. The goal is to make customers feel understood and valued, leading to increased engagement and satisfaction.
  • Employee Training and Engagement: It is important to invest in training and empowering employees to deliver results. Ensure that frontline employees are knowledgeable about digital platforms and best practices for engaging and assisting customers across digital channels. 

By following these steps and fostering a culture of continuous improvement, your business can enhance the existing digital customer experience strategy to deliver exceptional experiences that drive customer satisfaction, loyalty, and business growth.

Make Every Customer Experience an Omnichannel Experience

Digital devices, channels, and expectations are growing, and will likely continue to do so for the foreseeable future. As such, the line that once separated customer experience from digital experience no longer exists. Instead, the modern customer journey is all-encompassing — an omnichannel experience to help ensure a streamlined and supportive customer journey for every buyer. By optimizing the digital experience, you’ll be setting the groundwork for satisfied customers and improved returns for your business. 

Want to learn more about digital experience transformation and how your organization can develop a successful digital strategy? Access Your Digital Experience Transformation Roadmap ebook and if you’re ready to take the digital experience further than ever before, book a demo with InMoment today! 


Fullstory. Investing in your digital experience matters: 3 takeaways from our consumer survey. ( Access 3/12/2024.  

Forbes. 7 Reasons People Hate Your Ads And What Do About It. ( Access 3/12/2024. 

Harvard Business Review. Are You Undervaluing Your Customers? ( Access 3/12/2024. 

Salesforce. State of the Connected Customer Report. ( Accessed 3/21/2024.

Creating Fans, Not Just Customers: Metro Bank’s Journey to Customer-Centricity

In the midst of a financial industry crisis, Metro Bank emerged in 2010 with a bold vision—to create fans, not just customers. Breaking the mould of traditional banking, Metro Bank embarked on a mission to revolutionise the banking experience in the UK. From the outset, Metro Bank sought to rewrite the rules of banking. They envisioned a landscape where exceptional customer service, inviting environments, and a commitment to personal connections prevailed over the conventional transactional approach. In this blog we break down the key components which remain the driving force behind their banking revolution!

Championing a Retail-Like Banking Experience

Metro Bank’s innovative approach emphasised “stores” over traditional “branches.” They’ve introduced safe deposit boxes, on-the-spot card printing and fostered an inclusive environment that extends a warm welcome to both dogs and children. Metro Bank has embraced a retail-inspired model, setting it apart from traditional banking structures.

Adapting to Evolving Customer Needs

The banking sector evolved rapidly, especially with the accelerated shift to online transactions during the pandemic. Despite these changes, Metro Bank remained committed to their service-based model. They placed a premium on personal connections while adapting to excel in online channels, ensuring a seamless transition for customers.

Customer-Centric Approach and Transformation

In 2020, Metro Bank established a dedicated team focused on cultivating fans. Their three key priorities—ensuring excellent customer outcomes, empowering colleagues to create fan-worthy experiences, and driving innovation through programmes like ‘Magic Makers’—underscored the bank’s commitment to exceptional customer experiences.

Revamping Customer Insight with InMoment’s Partnership

Recognising the importance of customer feedback and the need for an improved insight programme, Metro Bank partnered with InMoment to overhaul its approach. The collaborative effort aimed to redefine their insight strategy by incorporating competitive benchmarking, relationship surveys, and touchpoint surveys to understand customer “moments of truth.”

Data-Driven Success and Achievements

Metro Bank have successfully Increased Net Promoter Score (NPS) scores and are seeing positive results. Their contact centre and store surveys have also tripled response rates! Leveraging tools like active listening enabled them to dive deeper into customer feedback, revealing invaluable insights that guided their priorities.

InMoment’s partnership played a pivotal role in Metro Bank’s journey toward customer-centricity:

  • Data Transformation: InMoment assisted Metro Bank in transforming their customer insight programme.
  • Tool Implementation and Adoption: InMoment provided tools and methodologies for data collection, analysis, and dissemination.
  • Survey Optimisation: InMoment’s guidance has helped Metro Bank to optimise survey placement and design.
  • Insightful Analytics: InMoment’s analytics capabilities has helped Metro Bank identify patterns, pain points, and prioritise actions based on data-driven insights.

As Metro Bank forges ahead, their dedication to evolving in response to customer dynamics remains unwavering, ensuring that their mission of creating fans, not just customers.

Read the full story here!

Unveiling Integrated CX Part 2: Richest Insights for Transformative Results

True excellence in CX demands a transformative strategy that seamlessly integrates across the organisation. That’s where InMoment steps in, offering a simplified pathway to success with our Integrated CX approach: Strongest Signals, Richer Insights, and Smarter Actions.

Integrated customer experience ensures understanding, prioritising, and taking action for amplified customer satisfaction and business growth. Now that we’ve explored capturing Strongest Signals in our previous blog post, it’s time to dive into the heart of Integrated CX—unlocking the Richest Insights. This phase goes beyond mere data collection; it’s about turning that data into a goldmine of insights that guide your path to success. 

Unlocking Richest Insights: Analysing the Unstructured Goldmine

Seeing the Bigger Picture

Analysing structured data is essential, but the real treasure lies within unstructured data, providing the qualitative insights that set CX leaders apart. One of the key aspects of obtaining richer insights lies in the ability to see the bigger picture. In most businesses, data is scattered across various sources and formats, residing in different departments. Bringing all this data together is akin to assembling a jigsaw puzzle, revealing the complete image of customer interactions. By consolidating data from diverse channels, businesses can unlock a fuller understanding of customer preferences, pain points, and behaviours.

Moving Beyond Surveys: Making Sense of Unstructured Data

Embracing the capabilities of AI, breaking free from survey-only methodologies, and integrating data on different levels empowers organisations to see the bigger picture, make sense of unstructured data, and predict the future of customer interactions.

Tapping into Unlimited Insights with AI

While traditional surveys have long been a staple in understanding customer sentiment, relying solely on this method can limit the depth of insights. AI introduces a paradigm shift by enabling businesses to tap into unlimited insights. Conversational intelligence allows organisations to delve into unstructured data, breaking down every customer comment to uncover nuanced information. This move away from surveys-only methodologies paves the way for a more dynamic and comprehensive understanding of customer experiences.

The Power of Conversational Intelligence

Conversational intelligence, powered by AI, equips businesses with the tools to analyse textual and verbal interactions. Every customer comment, whether from emails, social media, or customer service interactions, can be dissected for sentiment and emotion analysis. This depth of understanding goes beyond the surface, allowing businesses to respond to customer needs on a more personal level.

Predicting the Future with Data

Harnessing the power of richer insights goes beyond understanding the present; it involves predicting the future. By identifying trends and patterns through data analysis, businesses can make informed decisions that anticipate customer needs. 

Richer Insights unlock:

  • Descriptive Insights
  • Predictive Insights
  • Prescriptive Insights

This forward-thinking approach is a strategic advantage, allowing companies to stay ahead of the curve and proactively shape customer experiences.

From Data to Action: The Critical Next Step

Collecting diverse data types and understanding customer sentiments is vital, but without action, it’s merely analysis. A fragmented CX approach lacks clarity on where to focus, leading to isolated initiatives and slow, expensive actions. InMoment brings clarity, aligning cross-functional teams to drive the biggest impact on your business and overall customer experiences.

Until now, CX faced challenges that hindered its role as an indisputable growth strategy:

  • Survey-centric Approach: Historically, CX has been synonymous with surveys, leaving out valuable voices that don’t participate in surveys.
  • Unstructured Data Complexity: Technology struggled to process unstructured data effectively, leading to challenges in analysing qualitative feedback.
  • Siloed Teams and Systems: Teams operated in silos, utilising fragmented data to make decisions, limiting the holistic view of the customer journey.

However, 2023 marked a turning point –  InMoment unlocked major innovations to overcome these challenges:

  • Multi-Signal Collection and Conversational AI: InMoment’s open technology ingests all data types, soliciting actionable feedback through conversational interactions.
  • Advancements in NLP: AI-driven natural language understanding has revolutionised the analysis of structured and unstructured data.
  • Connected Teams Driving CX Impact: With predictive simulations and strategic ROI alignment, teams can leverage 100% of CX data to drive impactful initiatives.

How InMoment Can Help

Don’t settle for fragmented CX tactics, embark on a transformative journey toward unparalleled customer satisfaction and business success! InMoment empowers businesses to not only listen to customers across various channels but also make sense of their feedback with the utmost precision. However, the journey doesn’t end here. Without action, even the strongest signals and richest insights are futile. It’s imperative to align cross-functional teams and focus on initiatives that drive the most significant impact on both your business and customer experiences.

To learn more about InMoment’s Integrated CX approach, watch our recent webinar recording on Richest Insights here
Stay tuned for Part 3: Smarter Actions, where we explore the critical role of actionable insights in driving tangible business outcomes! Register for the Smarter Actions webinar today!

Likert Scale: Gauging the Attitudes of Your Customers

It is hard to talk about survey methodology and practices without mentioning the Likert scale. While some may think the Likert scale is only used in academic research, it is a cornerstone of survey strategies across various industries such as travel & hospitality, automotive, and financial services.

What is the Likert Scale?

The Likert Scale, named after psychologist Rensis Likert, is a widely used tool in social science research and survey methodology for measuring attitudes, opinions, and perceptions of respondents. The Likert Scale usually ranges from five to seven points, with respondents selecting a response that best reflects their agreement or disagreement with each statement. The typical format includes options such as “Strongly Disagree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.” In some cases, scales may also include “Don’t Know” or “Not Applicable” options.

Researchers analyze the responses to calculate measures of central tendency (like mean or median) and dispersion (like standard deviation) to understand the distribution of opinions or attitudes within the sample population. This scale provides a structured way to quantify subjective opinions, making it easier to analyze and compare data across respondents and groups.

What are the Different Types of Likert Scales?

There are several variations of Likert scales, differing primarily in the number of response options provided to respondents. The two most common types are the 5-point Likert scale and the 7-point Likert scale.

5-Point Likert Scale:

In this scale, respondents are typically presented with a statement and five response options ranging from “Strongly Disagree” to “Strongly Agree.” The options might look like this:

  • Strongly Disagree
  • Disagree
  • Neither Agree nor Disagree (Neutral)
  • Agree
  • Strongly Agree

7-Point Likert Scale:

The 7-point Likert scale expands on the 5-point scale by providing additional response options, usually to offer more nuanced distinctions between levels of agreement and disagreement. The options might look like this:

  • Strongly Disagree
  • Disagree
  • Somewhat Disagree
  • Neither Agree nor Disagree (Neutral)
  • Somewhat Agree
  • Agree
  • Strongly Agree

Both scales serve the same purpose of measuring attitudes or opinions, but the 7-point Likert scale allows for a finer granularity of responses, which can sometimes provide more detailed insights into respondents’ attitudes or perceptions. The choice between the two scales depends on the specific needs of the research or survey design and the level of detail desired in the responses.

What is the Best Type of Likert Scale to Use?

The choice of which Likert scale to use depends on several factors, including the research objectives, the nature of the survey questions, and the preferences of the researcher or organization conducting the survey. There isn’t a universally “best” type of Likert scale; rather, it’s about selecting the most appropriate scale for the specific context. Here are some considerations to keep in mind when choosing a Likert scale:

Research Objectives

Consider the goals of your research and the type of data you need to collect. If you require more nuanced responses to accurately capture the variability in respondents’ attitudes or opinions, a 7-point Likert scale might be more suitable. However, if simplicity and ease of interpretation are priorities, a 5-point Likert scale could suffice.

Question Complexity

The complexity of the survey questions can influence the choice of the Likert scale. If the questions are straightforward and do not require fine-grained distinctions in responses, a simpler scale like the 5-point Likert scale may be sufficient. On the other hand, if the questions are more complex or cover a wide range of opinions, a 7-point Likert scale might provide more flexibility.

Response Bias

Consider the potential for response bias in your survey. Providing more response options (e.g., with a 7-point Likert scale) can sometimes reduce the likelihood of respondents selecting neutral options as a default. However, too many response options could overwhelm respondents and lead to careless responses.

Comparison with Existing Data

If you have existing data or are conducting research in a field where a particular Likert scale is commonly used, it may be advantageous to maintain consistency for easier comparison and analysis across studies.

Ultimately, the choice of the Likert scale should be made thoughtfully, taking into account the specific requirements of the research, the characteristics of the respondents, and the overall survey design. It’s often beneficial to pilot test different versions of the Likert scale to gauge respondent understanding and ensure the scale effectively captures the intended attitudes or opinions.

Examples of Likert Scale Questions

Writing effective Likert scale questions involves careful consideration of the topic, clarity of language, and ensuring that response options adequately capture the range of attitudes or opinions you want to measure. These factors are of the utmost importance to limit any type of voluntary response bias in sampling. Remember, whoever answers the question will be answering by selecting a range of emotions such as “satisfied/agree” or “not satisfied/disagree.” So, more often than not, these questions will be statements that reflect aspects of the topic you are trying to assess. Here are some examples of Likert scale questions:

  • I am likely to recommend this product to others.
  • The quality of the product meets my expectations.
  • I am happy with the level of support provided by customer service.
  • How pleased are you with your job?
  • I thought this system was easy to use.

These examples represent Likert questions that can be direct questions or statements about a range of products and services. 

Examples of Bad Likert Scale Questions

Poorly constructed Likert questions often consist of double-barreled statements that contain ambiguous language that causes them to be biased or misleading. Consider the following examples:

  • “Do you agree that the product is excellent and worth recommending?”

This question is double-barreled, combining two distinct concepts (“excellent” and “worth recommending”) into a single statement. This question would not yield a meaningful response as the question is comparing two items into one question. 

  • “How much do you like the product: very much, much, somewhat, little, very little?”

This question lacks a clear direction or anchor for respondents to understand the meaning of each response option. It also uses imprecise language (e.g., “somewhat”) that may be interpreted differently by respondents. This question would also not yield a meaningful response. 

How to Analyze Likert Scale Data

After your surveys have been completed, it is time to analyze the data. When it comes to analyzing Likert scale data, there are a number of ways to segment the data. Which method you choose will ultimately end on the initial research questions. Some examples of this data analysis are descriptive, frequency, and regression analysis. 

  • Descriptive analysis: Calculate the mean, median, mode, and standard deviation for each response on the Likert scale for a quick summarization of the data. 
  • Frequency analysis: Total the number of items each response was selected and use the quantitative data to create tables or charts to show the distribution of each answer. 
  • Regression analysis: Depending on the objective of the survey, you may be able to analyze the relationship between the various Likert responses and an independent variable. 

Advantages of Using the Likert Scale

The Likert scale offers several advantages for organizations that are looking to implement a simple, effective survey methodology. Likert scales are straightforward and easy to understand for both respondents and researchers. Along with ease of use, here are some other benefits of utilizing the Likert scale: 

  • Flexibility: Likert scales can be adapted to measure a wide range of constructs, including attitudes, opinions, behaviors, satisfaction levels, and more. Researchers can customize Likert scale questions to fit their specific research objectives and contexts.
  • Comparability: Likert scale data enables researchers to compare responses across different groups, variables, or time points. This comparability facilitates meaningful analysis of trends, differences, or relationships within the data.
  • Standardization: Likert scales provide a standardized format for measuring attitudes or opinions, enhancing the consistency and replicability of research findings. This standardization allows for easier comparison of results across studies and populations.

Limitations of the Likert Scale

The Likert scale offers many advantages, but those are not without a small set of limitations. One of the biggest limitations of the Likert scale is the finite number of responses that respondents are limited to. These may not fully capture the complexity of respondents’ attitudes or opinions. This can lead to oversimplification or loss of nuance in the data.

Along with this, respondents may exhibit response bias, such as acquiescence bias (tendency to agree with statements) or social desirability bias (tendency to provide socially acceptable responses), particularly if the scale lacks anonymity or if respondents feel pressured to conform to perceived norms.

Despite these limitations, the Likert scale remains a widely used and valuable tool for measuring attitudes, opinions, and perceptions in various research settings. Researchers should carefully consider these limitations and take steps to mitigate potential biases and challenges when designing and interpreting Likert scale surveys.

When to Use the Likert Scale

Likert scales are well-suited for assessing individuals’ attitudes or opinions toward specific topics, issues, products, services, or experiences. This can come in the form of a Net Promoter Score (NPS) survey or a Customer Satisfaction Survey (CSAT). For example, they can be used to gauge satisfaction with customer service or perceptions of organizational culture. 

Furthermore, Likert scales are effective in quantifying subjective perceptions or experiences. Researchers can use Likert scales to measure perceptions of quality, trust, reliability, fairness, or effectiveness in various domains. This can be used to ask customers about their personal experiences with an organization and make those answers measurable. 

How the Likert Scale Effects Your CX Efforts

The Likert scale is a great tool to be utilized in your customer experience efforts. They are a great way to provide a structured method for measuring customer satisfaction across various touchpoints in the customer journey. By asking customers to rate their satisfaction levels with specific aspects of their experience (e.g., product quality, service responsiveness, website usability), organizations can identify areas of strength and areas for improvement.

Similarly, Likert scale data provides valuable insights that can inform strategic decision-making and resource allocation. By identifying areas with low satisfaction scores or high variability in responses, organizations can prioritize investments in CX improvement initiatives that are most likely to have a positive impact on customer loyalty and retention. 

Involving customers in the feedback process through Likert scale surveys can enhance engagement and satisfaction. By demonstrating a commitment to listening to customer feedback and taking action based on their responses, organizations can build trust, loyalty, and advocacy among their customer base.

Utilize the Likert Scale with InMoment

InMoment’s XI Platform allows you to utilize the Likert Scale to gather actionable feedback, measure satisfaction, and drive meaningful improvements. Schedule a demo today to see how we can help your business. 

Unleashing the Power of Integrated CX: Strongest Signals

Understanding and responding to customer needs is paramount for business success, however, many businesses find themselves grappling with the challenge of harnessing the full spectrum of customer voices. Surprisingly, a staggering 85% of valuable customer data remains untapped, residing in various teams, systems, and silos. This fragmented approach to CX makes it difficult to track customer behaviour across different channels and identify crucial opportunities for improvement. This is where the need for a fully integrated transformative business strategy becomes apparent.

Enter Integrated CX—a transformative business strategy that aligns organisations to unlock the full potential of customer data.This approach revolves around three key pillars: Strongest Signals, Richest Insights, and Smartest Actions.

In this blog post, we delve into the first pillar – Strongest Signals – and explore how InMoment’s Integrated CX leverages AI and expert services to collect and connect experience data from every touchpoint in the multi-channel customer journey, forming  the foundation of a holistic understanding of customer experiences.

Understanding Strongest Signals

Collecting Experience Data from Everywhere

To truly grasp customer sentiment, you must take a holistic approach. Integrated customer experience, championed by InMoment, integrates AI and expert services to collect and connect experience data from every touchpoint in the multi-channel customer journey. Strongest Signals encapsulates the essence of this methodology, focusing on collecting and connecting experience data in every format and from every conceivable touchpoint. This comprehensive method ensures that no valuable piece of information is left unexplored.

Decoding Emotions and Behaviours

Meaningful customer feedback extends beyond traditional surveys. Having stronger signals allows organisations to tap into an almost unlimited amount of customer data and broadens the scope to include both solicited and unsolicited feedback from digital and non-digital channels. By incorporating call centre and chat transcriptions, social ratings, reviews, and other customer signals, businesses gain deeper insights into customer sentiments, preferences, and pain points. This diverse data set helps decode emotions, understand behaviours, and enable informed actions.

A Holistic View of Customer Experience

By aggregating these diverse data points into a unified platform, organisations can achieve a comprehensive and holistic view of the customer journey. This unified view allows businesses to leverage the full spectrum of customer data, allowing organisations to  identify opportunities for innovation, improve operational efficiency, and deliver personalised experiences that resonate with customers on a profound level.

AI-Driven Listening Strategy

The Role of AI in Enriching Customer Interactions

Harnessing the power of AI, businesses can move beyond traditional feedback mechanisms to engage customers in conversations that matter. By analysing conversational patterns, AI customer experience systems can understand customer sentiments, preferences, and pain points, enabling organisations to respond in real-time. This dynamic interaction not only fosters a more personalised and engaging experience but also encourages customers to share detailed insights.

Moving Beyond Surveys

Embracing Contact Channels

To truly understand and address customer needs, businesses are encouraged to broaden their horizons beyond traditional surveys. Recognising the pivotal role of contact channels, consider incorporating call centre and chat transcriptions into your data collection strategy. This step not only brings the most significant contact channel into the mix but also captures valuable insights from direct customer interactions, providing a deeper and more comprehensive understanding of customer experiences.

Connecting Social Ratings and Reviews

A forward-thinking approach to customer experience involves seamlessly integrating social ratings and reviews into a cohesive programme. By actively tapping into both complaints and compliments data across various platforms, businesses can uncover a wealth of insights. This holistic perspective allows organisations to gain a nuanced understanding of customer sentiments expressed on social media, review sites, and other channels. Embracing this integrated approach ensures a more comprehensive view of the customer landscape and provides valuable information for informed decision-making.

How InMoment Can Help

InMoment’s Strongest Signals approach to Integrated CX is a beacon for businesses seeking to harness the full potential of customer voices. By collecting and connecting data from diverse channels, decoding emotions, and utilising AI-driven strategies, businesses can move beyond traditional surveys and unlock a deeper understanding of customer experiences. 

To learn more about InMoment’s Integrated CX approach, starting with Strongest Signals, watch our recent webinar recording here.

In the next parts of this blog series, we will explore how InMoment transforms this wealth of data into Richest Insights and Smartest Actions, driving businesses towards sustained growth and profitability.

Why You Should Build an Omnichannel Customer Experience

It’s easy to fall into the trap of thinking that customer expectations are always changing, and you’ll never be able to keep up. However, all your customers want the same thing: to be able to interact with their brand on their terms. In order to do that, you need to work to create an omnichannel customer experience that allows customers to contact you when they want, how they want, and where they want. 

What is Omnichannel Customer Experience?

Omnichannel customer experience refers to providing a seamless and integrated experience for customers across multiple channels or touchpoints. This approach ensures that customers can interact with a business or brand consistently regardless of whether they are using a website, a mobile app, social media, a physical store, or any other channel.

Omnichannel vs Multichannel

While both omnichannel and multichannel strategies involve leveraging multiple channels, they differ significantly in their approach and execution. Omnichannel strategies prioritize seamless integration and consistency across all customer touchpoints. They aim to provide a unified experience where customers can transition between channels effortlessly, without experiencing any disconnect. Whether a customer interacts via a website, mobile app, social media, or in-person, the experience remains consistent and interconnected. 

Multichannel strategies, on the other hand, may involve using multiple channels independently. While they offer customers various avenues to engage with the brand, there may be less emphasis on integration and consistency between these channels. Customers might have different experiences or encounter discrepancies when switching between channels.

What are the Advantages of Omnichannel Customer Experience?

Omnichannel experiences make it easier for customers to engage with a brand by providing multiple channels through which they can interact. Whether they prefer to shop online, visit a physical store, or contact customer service via social media, customers have the flexibility to choose the most convenient option for them. Aside from convenience, there are many other advantages of implementing an omnichannel customer experience strategy. 


Omnichannel strategies ensure a consistent experience across all channels, which builds trust and loyalty among customers. Whether they interact with the brand online, in-store, or through a mobile app, customers receive the same level of service and messaging.


By integrating data from various channels, businesses can create more personalized experiences for customers. They can tailor product recommendations, promotions, and communications based on individual preferences and behaviors, leading to higher engagement and conversion rates.

Seamless Transitions

Omnichannel strategies enable seamless transitions between channels, allowing customers to start an interaction on one channel and continue it on another without any disruptions. For example, a customer may research a product online and then visit a physical store to make a purchase, with their shopping cart and preferences already synced.

Insights and Analytics

Omnichannel strategies generate valuable data and insights about customer behavior and preferences across different channels. By analyzing this data, businesses can gain a deeper understanding of their customers and make informed decisions to improve their marketing, sales, and customer service efforts.

Increased Customer Satisfaction

By providing a cohesive and personalized experience across all channels, businesses can enhance customer satisfaction and loyalty. When customers feel valued and understood, they are more likely to return for future purchases and recommend the brand to others.

How Omnichannel Customer Experience Impacts Your Bottom Line

An omnichannel customer experience can drive revenue growth, improve profitability, and position a business for long-term success by delivering value to customers at every stage of their journey. Strategies like these can impact your business in many different ways, such as:

Increased Sales

Omnichannel strategies can lead to higher sales by providing customers with more opportunities to engage with the brand and make purchases. By offering seamless integration between online and offline channels, businesses can capture sales from customers who prefer to shop across multiple channels, resulting in increased revenue.

Improved Customer Retention 

Omnichannel experiences enhance customer satisfaction and loyalty by providing consistent and personalized interactions across all channels. Satisfied customers are more likely to return for future purchases and become advocates for the brand, leading to higher customer retention rates and reduced churn.

Cost Savings

Omnichannel strategies can lead to cost savings by optimizing operational efficiencies and reducing redundant processes. For example, integrating inventory management systems across online and offline channels can minimize inventory holding costs and prevent stockouts or overstock situations. Additionally, providing self-service options through digital channels can reduce the need for expensive customer support resources.

Enhanced Brand Reputation

Positive omnichannel experiences can play a vital role in your brand reputation management, and help you increase customer trust. When customers receive consistent and personalized interactions across all channels, they are more likely to perceive the brand positively and recommend it to others, driving word-of-mouth referrals and organic growth.

Competitive Advantage

Offering a seamless and personalized omnichannel experience can differentiate your business from your competitors. Customers are more likely to choose a brand that provides a convenient and consistent experience across all touchpoints, leading to increased market share and sustainable growth.

Omnichannel Customer Experience Examples

When companies effectively implement an omnichannel experience, they break down the silos between online and offline touch points. In doing so, they can create a cohesive journey that enhances customer satisfaction and drives engagement and loyalty. Here are some examples of what an omnichannel customer experience can look like in action. 

Buy Online, Pickup in Store

A customer browses products on a retailer’s website, adds items to their online shopping cart, and selects the option for in-store pickup. When they arrive at the store, they receive a notification on their mobile app, directing them to the designated pickup area. The customer can seamlessly transition from the online shopping experience to the physical store, with their order ready for pickup upon arrival.

Mobile App Integrations

A grocery chain offers a mobile app that allows customers to create shopping lists, view digital coupons, and locate products in-store via a digital map. Customers can scan items using their smartphone as they shop, adding them to their digital cart for a faster checkout experience. The app also provides personalized recommendations and offers based on the customer’s shopping history, enhancing the overall shopping experience.

Customer Profiles

An insurance company integrates its communication channels into a customer’s profile. So, if a customer calls customer service to file a claim, the customer service agent can put the details of the specific call into the customer’s profile. That way, when the customer enters a branch to follow up on the claim, the branch agent can pull up the customer’s profile and be completely up to speed with the customer’s experience up to that point. 

How to Build an Omnichannel Customer Experience

Implementing a strategy that creates an omnichannel customer experience can seem like a complex process. But, it may not be as hard as you think. Building an omnichannel experience requires creating a customer-focused culture, and software to support it. Here are the steps to get you started building an integrated customer experience

1. Understand Your Customers

Start by gaining a deep understanding of your customers’ preferences, behaviors, and expectations across different channels. Conduct market research, analyze customer data, and gather feedback to identify their preferred channels of interaction, pain points, and opportunities for improvement.

2. Integrate Channels

Choose the best customer experience management software that enables seamless integration and communication between online and offline channels. This may involve integrating data from various systems, such as CRM, e-commerce platforms, POS systems, and customer service software, to create a unified view of the customer journey. One of the most important parts of the omnichannel customer experience is having all your data in one place. 

3. Personalize Interactions

Leverage data and technology to deliver personalized experiences to your customers. Use customer data to segment your audience and tailor your marketing messages, product recommendations, and promotions to individual preferences and behaviors. Personalization can help deepen customer engagement and drive conversions.

4. Offer Seamless Transitions 

Enable customers to transition seamlessly between channels without losing context or experiencing disruptions. For example, allow customers to start an interaction on one channel (e.g., browsing products online) and continue it on another (e.g., completing the purchase in-store) without having to repeat information or restart the process.

5. Measure and Optimize 

Continuously monitor key metrics, such as customer satisfaction, conversion rates, and revenue per channel, to assess the effectiveness of your omnichannel strategy. Use data analytics and A/B testing to identify areas for improvement and optimize the customer experience across all channels.

Build an Omnichannel Customer Experience with InMoment

Ready to take your customer experience to the next level? Partner with InMoment to build a seamless omnichannel experience that delights your customers across all touchpoints. With our advanced technology solutions and expert guidance, you can integrate channels, personalize interactions, and optimize the customer journey to drive satisfaction and loyalty. Schedule a demo today!

InMoment Advanced AI: Supercharging CX

Data is gold. Data is truth… but Data is useless if you can’t rely on it. 

Understanding customer and employee sentiment is more than just a competitive edge—it’s essential, with companies in every industry and sector focusing resources on comprehending it. 

We have a revolutionary tool that we’d like to share, one that has helped businesses large and small navigate this space. InMoment Advanced AI turns diverse data streams into valuable insights companies can use for their strategy. It’s been the change clients in various fields have relied on. So for starters…

What is InMoment Advanced AI??

InMoment Advanced AI is a comprehensive data analytics tool that integrates and analyzes structured and unstructured data using advanced Natural Language Processing (NLP) and AI. It offers a deep understanding of customer and employee feedback, transforming complex data into clear and actionable insights. 

Central to InMoment Advanced AI’s functionality are predictive analytics and customizable dashboards, which enable businesses to understand current data trends and anticipate future customer patterns and behaviors across these data sets. 

InMoment Advanced AI’s power lies in its ability to analyze both historical customer experience data and real-time data sources like social media and reviews. This dual capability offers businesses an advantage over competitors who may excel in historical data analysis or current data interpretation, but struggle to integrate both into timely insights. InMoment Advanced AI’s integrated approach provides a comprehensive view, turning past and present data into powerful, actionable insights for immediate strategic impact.

InMoment Advanced AI enables businesses to process virtually any type of content, enrich and understand that content, and visualize it through a powerful set of dashboarding tools. The engine that enables this enrichment uses AI and NLP to understand the content and derive valuable metadata, including: intent prediction, effort signals, and emotion detection. 

Let’s go over what these are and their broader implications.

Intent Prediction

Intent prediction is a crucial component of data analysis, focusing on deciphering the underlying intentions behind customer interactions. This technology uses deep learning models to predict a customer’s future actions or needs. 

For example, in customer service interactions, intent prediction can determine whether a customer is likely to purchase, seek support, or churn. By understanding these intentions, businesses can proactively address customer needs, enhancing the overall customer experience and increasing sales and customer satisfaction.

Effort Signals

Effort signals involve analyzing customer interactions to gauge the degree of effort a customer exerts in their journey. This metric is key in understanding customer satisfaction and loyalty, as higher effort levels correlate with negative customer experiences. 

By analyzing data such as the length and complexity of customer service interactions, businesses can identify areas where customers face difficulties. Addressing these high-effort points can significantly improve the customer experience, increasing satisfaction and loyalty.

Emotion Detection

Emotion detection is identifying and analyzing emotional states in customer interactions. This aspect of sentiment analysis uses a BERT deep learning model to assign an emotion to the speaker or subject of a sentence or thought. 

This technology can distinguish between emotions like happiness, frustration, or disappointment. Emotion detection helps businesses tailor their responses and strategies to align with customer emotions, enhancing personalized customer experiences and building stronger emotional connections with the brand.

Types of Data

Structured: The Backbone of Predictability

Structured data is the cornerstone of conventional data analysis, representing the world of quantifiable and measurable information. Characterized by its specific, organized format, structured data neatly aligns in rows and columns, reminiscent of spreadsheets or relational databases. This meticulous arrangement makes it well-suited for quantitative analysis, offering clear, objective, and mathematical insights into various aspects of business and customer behavior.

It is the language of logic and mathematics, offering a clear, structured view of the world that is easily interpreted by computers. Its strength lies in its straightforward aggregation and manipulation, allowing businesses to accurately quantify and measure trends, performance metrics, and other key indicators.

This data type is the foundation of data-driven decision-making, enabling enterprises to translate complex phenomena into understandable metrics. While it might lack the nuanced storytelling of unstructured data (we’ll get there in a second), structured data offers the definitive “what” in the story of customer and business interactions—the concrete, quantifiable facts that are essential for informed strategy and planning.

Unstructured: The Streaming Thoughts of Your Everyday Life

Unstructured data, the most raw and unrefined form, is abundant and profoundly human by nature. Emerging from sources rich in personal expression like open-ended survey questions, reviews, social media, and SMS messages, this data type offers a window into the authentic human experience. 

According to IDC, The Digital Source, 85% of customer data is unstructured and it’s growing at 55% per year, highlighting the vast and rapidly expanding landscape of human communication that structured data cannot capture. Tools like InMoment’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

What sets unstructured data apart is its embodiment of language. It directly reflects our unfiltered and unstructured thoughts in their most natural state. While structured data can be seen as the mathematics of human behavior, unstructured data is pure, unadulterated human communication.

This richness, however, presents a challenge: unstructured data is the hardest for computers to decipher, as it requires understanding nuances, context, and the subtleties of human language. Despite this complexity, our deepest and most meaningful insights lie in these unstructured narratives. Tools like InMoment’s Advanced AI are essential in harnessing this wealth of information, translating natural language complexities into actionable insights, and unlocking the deepest understanding of customer experiences and needs.

Bringing Them Together: The Full Story

Integrating structured and unstructured data is a key aspect of InMoment Advanced AI and, arguably, its strongest feature. Structured data provides precise, quantifiable insights, such as the exact factors contributing to customer churn

While structured data gives you the numbers, unstructured data provides the “why” behind these figures. It’s found in customer verbatims and feedback, revealing the customers’ personal stories, opinions, and suggestions. It’s the narrative that puts context and meaning behind the numbers. But on its own, unstructured data can be overwhelming and hard to navigate to find the most impactful insights.

Combining structured and unstructured data tells the full story. This integration allows businesses to quantify aspects of the customer experience and understand the underlying reasons behind these metrics. With InMoment Advanced AI, companies can sift through the rich, detailed narratives in unstructured data, guided by clear, actionable insights from structured data. This holistic approach enables a deeper understanding of customer needs and preferences, leading to more informed and effective business decisions.

InMoment Advanced AI bridges the gap. 

Spotlight Addresses Key Business Challenges

Understanding and Predicting Customer Behavior

We mentioned this earlier, but we’d like to go more in-depth—this one’s important. One of the paramount challenges businesses face today is their inability to predict future customer behaviors. InMoment Advanced AI  excels in this area using AI-powered, advanced analytics and machine learning algorithms. 

According to Gartner, by 2025, customer service organizations that embed AI in their multichannel customer engagement platform will elevate operational efficiency by 25%, underscoring the efficiency gains possible with advanced AI solutions. This capability enables businesses to move beyond surface-level insights, delving into predictive analysis that anticipates future customer actions and preferences.

By understanding these predictive patterns, companies can tailor their strategies proactively, ensuring they are always one step ahead in meeting customer needs and expectations. This forward-looking approach is vital for maintaining competitive advantage and fostering customer loyalty.

Data Unification and Analyzation: A Single Source of Truth

Data silos are a significant barrier to effective decision-making in many organizations. 

Tyler Saxey, Director of CX at Foot Locker, states, “InMoment now ticks all of the boxes. InMoment AI solves for any previous text analytics issues. Analyzing call transcripts and getting to the root cause brings a big ROI.” InMoment Advanced AI addresses this issue head-on by offering data unification capabilities, consolidating data from various sources and providing a comprehensive and unified view of customer information. This holistic approach is vital for creating consistent and effective customer experiences across all touchpoints.

By breaking down these silos, InMoment Advanced AI ensures that all decisions involve a complete and accurate picture of customer data—no decisions are made in isolation. This unified view is invaluable for creating consistent and effective customer experiences across all touchpoints.

Regulatory Compliance: Ensuring Communication Standards

We live in a time with increased scrutiny of companies’ regulatory compliance. InMoment Advanced AI is essential in ensuring that customer communications meet the necessary standards. This aspect is crucial for highly-regulated businesses in industries like finance, healthcare, and telecommunications. 

InMoment Advanced AI can help monitor and analyze customer communications, ensuring they adhere to industry regulations and standards. This compliance monitoring not only helps avoid potential legal issues but instills trust among customers, who are increasingly concerned about how their data is handled and used. With nearly 65% of the world’s population expected to have its personal data covered under modern privacy regulations by 2023, up from 10% today, according to Gartner, the importance of incorporating advanced AI for regulatory compliance cannot be overstated.

Why Spotlight is Essential for All Businesses 

Enhancing Experiences: Tailoring Strategies for Satisfaction and Loyalty

InMoment Advanced AI significantly enhances customer and employee experiences. 

Tony Darden, COO of Jack in the Box, shares, “The use of the InMoment AI solution will allow us to easily analyze feedback in all its forms to receive more detailed and immediate insight from a wider variety of guest experiences. Our team is focused on using the additional insight to make business decisions without delay—having a faster time to guest improvement that will positively influence their experience with our brand leading to increased loyalty.” 

By leveraging advanced analytics to understand sentiment and feedback, businesses can tailor their strategies and offerings to better meet their customers’ and employees’ needs and expectations.

Reducing Churn: Anticipating and Addressing Customer Needs

Customer and employee churn is a major challenge for businesses, resulting in lost revenue and increased recruitment and training costs. InMoment Advanced AI’s predictive analytics capabilities play a vital role in identifying the early signs of dissatisfaction or disengagement. By anticipating these factors, businesses can proactively address issues before they lead to churn. This proactive approach helps retain customers and ensures that employees feel valued and engaged, reducing the likelihood of them seeking opportunities elsewhere.

Strategic Decision-Making: Prioritizing Initiatives for Maximum Impact

Data-driven decision-making is at the heart of modern business strategies. InMoment Advanced AI provides comprehensive insights that help businesses prioritize their initiatives, focusing on areas yielding the greatest cost savings or revenue increases. These insights guide businesses in allocating resources effectively, whether it’s refining marketing strategies, optimizing operational processes, or enhancing customer service. By basing decisions on solid data, businesses can maximize their ROI and align their strategies with their overall goals.

The Takeaway: A Holistic Approach for a Winning Strategy

InMoment Advanced AI’s ability to integrate data across multiple channels is a game-changer, providing a unified view of information from various sources. This cross-platform integration is crucial for strategic planning and executive decision-making. It allows businesses to make informed decisions based on a comprehensive understanding of their operations, market trends, and customer behaviors. 

By breaking down data silos, InMoment Advanced AI ensures that a complete and accurate picture of the business landscape backs every decision. A study by McKinsey & Company found that companies that utilize customer analytics comprehensively are 23 times more likely to outperform competitors in terms of new-customer acquisition and nine times more likely to surpass them in customer loyalty.

InMoment Advanced AI’s ability to transform this unified data into actionable strategies makes it indispensable. Its benefits are wide-ranging and impactful, from enhancing experiences and reducing churn to aiding in strategic decision-making and facilitating cross-platform data integration. Adopting InMoment Advanced AI is not just a step towards better data analysis, but a leap towards a more informed, customer-centric, and efficient business model.

For businesses considering Spotlight:

  • How are you currently gathering and interpreting customer and employee feedback?
  • What tools are in use for understanding customer and employee experience?
  • How is this data being used to drive experience initiatives?

A Final Word

InMoment’s InMoment Advanced AI stands out in the realm of customer experience management. Its ability to harness structured and unstructured data, combined with advanced analytics, positions it as an indispensable tool for businesses aiming to enhance customer engagement and make data-driven decisions. 

Adopting InMoment Advanced AI translates into not just collecting feedback but transforming it into a strategic roadmap for business success. Stay ahead of the pack and contact us to learn more about how InMoment Advanced AI can directly impact your business.

How We Think The Gartner® Magic Quadrant™ Can Help Shape Your CX Strategy

When it comes to creating your customer experience strategy, and deciding which partner to execute that strategy with, it is always important to look at third-party research. That is where InMoment believes that evaluative Analyst reports like the Gartner® Magic Quadrant™ can come in. 

What is the Gartner Magic Quadrant?

Gartner defines this report as: “A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors.”* Based on InMoment’s experience, the Gartner Magic Quadrant is a research methodology and visualization tool that can provide a graphical representation of a market’s direction, maturity, and participants developed by the research and advisory firm Gartner, Inc. The Magic Quadrant evaluates technology providers in a specific market based on their ability to execute and their completeness of vision. 

How does a Gartner Magic Quadrant work?

“A Magic Quadrant provides a graphical competitive positioning of four types of technology providers, in markets where growth is high and provider differentiation is distinct. Use a Gartner Magic Quadrant as a first step to understanding the technology providers you might consider for a specific investment opportunity.”*

The Magic Quadrant employs a distinctive methodology to assess and categorize technology vendors within a specific market segment. This categorization involves placing these vendors into one of four quadrants on a two-dimensional graph, a representation that serves as a visual aid for understanding their relative positions in the market landscape. The placement is determined by evaluating the vendor’s “Ability to Execute” and their “Completeness of Vision.” 

The vertical axis typically represents the vendor’s ability to execute, encompassing evaluation criteria such as product or service, sales execution/pricing, and overall viability. On the horizontal axis, the completeness of vision is assessed, considering a vendor’s sales strategy, vertical/industry strategy, innovation, and market understanding. The resulting quadrants are labeled as:

  • Leaders: Leaders execute well against their current vision and are well positioned for tomorrow.
  • Challengers: Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.
  • Visionaries: Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.
  • Niche Players: Niche Players focus successfully on a small segment, or are unfocused and do not out-innovate or outperform others.*

How the Gartner Magic Quadrant Can Provide Value 

InMoment believes that the Gartner Magic Quadrant serves a multifaceted purpose that can benefit both technology buyers and providers. For buyers, it acts as an evaluation tool that offers a visual representation of how different technology providers compare within a specific market which can aid in informed decision-making. Moreover, the Magic Quadrant provides a market description and overview helping organizations understand the broader dynamics of a market for strategic planning. 

For technology providers, the Magic Quadrant offers insights into competitive positioning which can guide strategic planning and marketing efforts. Overall, the Magic Quadrant enhances market transparency, providing an objective framework for evaluating vendors in complex and evolving technology landscapes, empowering organizations to make well-informed decisions.

Do We Think the Gartner Magic Quadrant is Reliable?

For customer experience professionals, InMoment recommends reading the Gartner Magic Quadrant within specific industry trends and considerations. As a snapshot of the market at a given moment, it can provide valuable insights into technology vendors’ positions based on execution ability and vision completeness. 

However, InMoment cautions users to be mindful of the dynamic nature of markets as vendor evaluation can change due to various factors. The subjectivity inherent in the evaluation process, based on analyst judgment, adds another element of interpretation. We think it’s crucial for users to comprehend the methodology and criteria used in each Magic Quadrant and recognize the limited scope of the assessment, which may not capture all relevant aspects for every organization. 

While the Magic Quadrant serves as a useful reference point, it should be complemented with ongoing research, customer references, and a broader set of data points for a comprehensive decision-making process. Users should view it as part of a larger toolkit rather than a singular determinant in their technology procurement strategy.

How InMoment Recommends Leveraging the Magic Quadrant in Your Evaluation Process

In our opinion, the Gartner Magic Quadrant can be a valuable resource for buyers navigating the complex landscape of technology solutions. Incorporating the Magic Quadrant into your evaluation process can be particularly beneficial during key stages of vendor selection. Here are instances when we believe referencing the Magic Quadrant can provide valuable insights:

Market Landscape Understanding

We know that in early stages of vendor selection processes, the Magic Quadrant offers a concise overview of the market landscape. It helps you identify some of the players in the market, their positions, and the overall maturity of the market. We think this foundational understanding can inform your initial considerations and narrow down the list of potential vendors.

Shortlisting Potential Vendors

Once you have a list of potential vendors, we feel the Magic Quadrant can become a powerful tool for narrowing down choices. By assessing vendors based on their Ability to Execute and Completeness of vision, you can identify those that align closely with your organization’s needs and objectives.

Understanding Vendor Strengths and Weaknesses

Dive into the details of the Magic Quadrant when you need a more granular understanding of a vendor’s strengths and weaknesses. “Completeness of Vision reflects the vendor’s innovation, whether the vendor drives or follows the market, and if the vendor’s view of how the market will develop matches Gartner’s perspective. Ability to Execute summarizes factors such as the vendor’s financial viability, market responsiveness, product development, sales channels, and customer base.”** These insights aid in a comprehensive evaluation.

Risk Mitigation

As you move closer to the final stages of your decision-making process, we think organizations and procurement departments can use the Magic Quadrant to mitigate risks by assessing vendors’ positions in terms of Ability to Execute and Completeness of Vision.

Remember, while the Magic Quadrant is a valuable tool, it should not be the sole factor in your decision-making process. InMoment strongly recommends combining this with other research, customer references, and a thorough understanding of your organization’s specific requirements for a comprehensive and well-informed vendor selection.

InMoment’s Recommendations for Utilizing Your Takeaways from the Gartner Magic Quadrant in Vendor Discussions

Engaging in discussions with technology vendors can be a nuanced process. Here’s how InMoment suggests organizations leverage your Magic Quadrant learnings effectively in conversations with vendors:

Set Expectations Clearly

Share with vendors the specific criteria that are significant for your organization. This might include factors such as product or service, market responsiveness/record, and alignment with your strategic objectives. We recommend CX professionals use the Magic Quadrant as a reference point to clarify the importance of these criteria in your decision-making process.

Seek Vendor Perspective

If you’re engaging with vendors who are featured in the report, you should encourage those vendors to share their perspectives on their placement in the Magic Quadrant. This can provide valuable insights into their self-awareness, strategic vision, and plans for improvement. Ask about their roadmap and how they plan to enhance both their ability to execute and completeness of vision.

Explore Future Innovations

Discuss a vendor’s future vision and innovation roadmap. Understand how they perceive market trends, and inquire about upcoming features, technologies, or strategies that align with your organization’s long-term goals. This can help you assess the vendor’s potential to stay ahead in a dynamic market.

Build a Collaborative Relationship

Approach vendor discussions with a collaborative mindset. Use Analyst reports like the IDC MarketScape, Forrester Wave, or Gartner Magic Quadrant as shared reference points to collaboratively identify areas for improvement and opportunities for mutual growth. A transparent and constructive dialogue can contribute to building a strong, long-term partnership.

By integrating your Gartner Magic Quadrant takeaways into your vendor discussions, you empower your organization to make well-informed decisions and foster transparent and collaborative relationships with technology providers. Remember to balance the Magic Quadrant insights with a holistic evaluation that considers your unique requirements, user feedback, and the vendor’s responsiveness to your organization’s needs.

How We Think Gartner’s Magic Quadrant Can Help Influence Your CX Strategy

Choosing a CX vendor based on evaluative Analyst reports isn’t solely about improving customer interactions—it’s about crafting a holistic customer experience. By exploring vendors positioned in the research, you not only ensure they meet the criteria for execution and vision, but might also lay the foundation for a comprehensive CX strategy that addresses diverse touchpoints and facets of the customer journey.

We believe the insights from the Garter CX Magic Quadrant will aid you in how to choose a CX vendor.  According to us, it helps in finding not only a technological fit for but also a strategic partner.  It empowers organizations to make decisions that resonate with their unique objectives and ensures that the chosen CX vendor is not only a technological fit but a strategic partner in achieving comprehensive success.


* Gartner,, as of January 22,2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved


**Gartner, How Markets and Vendors Are Evaluated in Gartner Magic Quadrants 10 October 2022

AI in Financial CX: The Future of Banking Experiences

The financial sector is at the forefront of a significant transformation, driven largely by the buzzword of the decade: artificial intelligence. This somewhat novel tech isn’t just a tool, but a revolution of sorts. CX will be forever changed in the world of retail banking in the coming years by the unstoppable wake of AI. Our take: most industries are already there in large part. 

AI’s ability to analyze and interpret vast data sets is redefining how financial institutions interact with their customers, offering more personalized, efficient, and secure services. 

We’ll explore the multifaceted role of AI and integrated CX in reshaping the financial customer experience, highlighting how it’s paving the way for a future where banking is not only about transactions, but intelligent, customer-centric experiences. 

Hyper-personalization at Scale

AI’s role in personalizing the banking experience is, in a word, profound. By integrating customer signals from a multitude of sources, such as surveys, reviews, social media interactions, and transactional data, we’re now provided with a 360-degree view of the customer. 

AI uses these three types of data to further aid banks in personalizing their customer experience: 

  • Descriptive Insights: These insights offer a detailed view into the customer’s financial dealings, including transaction history, spending trends, asset holdings, and the performance of their financial portfolio. This level of detail helps sketch a comprehensive picture of the customer’s financial activities.
  • Diagnostic Insights: These insights delve into understanding the reasons and mechanisms behind customer behaviors. They provide answers to the ‘why’ and ‘how’ of financial behaviors, giving banks a deeper understanding of their customers’ financial habits and preferences.
  • Predictive Insights: These are forward-looking insights that help banks predict future financial scenarios for their customers. They can alert customers about potential financial challenges like cash flow issues, unexpected large payments, or even opportunities for advantageous loan settlements. Predictive insights are also key in identifying and preventing potential fraudulent activities by recognizing patterns in customer data.

It won’t replace financial advisors in offering financial advice–at least for now–but it can expedite the process of providing customers personalization at scale; a windfall for institutions with a large clientele. 

This level of personalization not only enhances customer satisfaction but can also significantly boost engagement and loyalty. Banks are now moving beyond one-size-fits-all products to create unique, individualized banking experiences for each customer.

Efficiency and Accessibility

The efficiency and accessibility of banking services is changing, too.  

AI-powered chatbots and virtual assistants are capable of handling a multitude of customer queries in real-time, from balance inquiries to complex transactional queries. It’s not just about efficiency, but about redefining accessibility. Customers can now access banking services outside traditional banking hours, from the comfort of their homes, and in their preferred language. 

Implementing these chatbots and virtual assistants enables banks to offer a continuous, tailored experience to their customers. These conversational AI tools not only facilitate seamless interactions but also efficiently discern when a customer’s query necessitates human intervention, directing them to the right staff member. This approach significantly cuts down on waiting times and enhances customer satisfaction.

A prime instance of this technology in action is Bank of America’s Erica, a digital financial assistant. Erica engages customers in customized, forward-thinking, and insightful dialogues, drawing on data like account balances, previous transactions, spending habits, payment notifications, and instances of double charges. In a similar vein, the UK’s Monzo bank utilizes user behavior analysis to pinpoint specific issues customers face. This strategy enables their customer service team to resolve 85% of daily inquiries independently, reducing their reliance on Monzo’s data team for assistance.

This proactive approach, fueled by an integrated understanding of customer data from various touchpoints, is setting new standards in customer-centric banking.

Security and Advanced Fraud Detection

Security is the fortress of financial institutions; AI is the standing army. 

By leveraging machine learning algorithms, AI systems can detect and analyze patterns across a vast array of transactions to identify potential fraud. This capability is enhanced by integrating data from various customer interaction points, providing comprehensive risk assessment. 

AI’s real-time processing abilities mean that suspicious activities are detected and addressed quicker than ever before, safeguarding customer assets effectively. 

The advent of AI-driven biometric technologies, like facial recognition and fingerprint scanning, has introduced a new era of secure customer authentication, adding a robust layer to the overall security framework in banking. 

The encryption will get even more sophisticated in time, constructing an impregnable wall between valuable customers and bad actors’s incessant, attempted breachings.  

Challenges and Ethical Considerations

The integration of AI in banking, while transformative, is not without its challenges. 

Key among these is the balance between personalization and privacy. Banks must ensure that the use of AI in analyzing customer data from various sources, including social media and personal transactions, adheres to strict data privacy standards. 

Three Key Things to Know:

  • AI is a “Black Box”: Where does the information come from? What datasets and algorithms are being implemented? What happens when the wrong person has control? With progressively more large institutions constructing and implementing their own, internal machine learning algorithms, the information output can be better understood–corrected quickly if necessary. 
  • AI is Everywhere: From pet communication devices, smart toilets, dating coaches–AI is becoming intertwined with every facet of society. With its reach touching virtually everything, what happens when this technological web is used for mal intent? It’s a precarious line we tote, and one that needs hurried, buttressed policy support. 
  • AI is Biased: AI can be biased, or in worst-case-scenarios, give incorrect information entirely. It can be the most problematic for banks when using external systems & software, that, as the aforementioned paragraph suggested, is less understood, and can’t be as easily corrected. As AI is used more frequently by financial advisors, the assurance of accurate and objective information is paramount; especially when this novel tech begins to play a bigger role in helping advisors give financial advice. 

Ensuring transparency in AI processes and maintaining an ethical framework is essential in building and retaining customer trust. As AI continues to evolve, banks must remain vigilant and proactive in addressing these challenges to harness AI’s full potential responsibly. We could say the same for any industry. 

A Final Word

AI’s role in transforming the financial customer experience is undeniable and growing. 

By leveraging AI to integrate and analyze customer data from diverse sources, banks are offering unprecedented levels of personalization, efficiency, and security. 

This revolution is not just enhancing the way customers interact with their banks but is also redefining the very essence of customer experience in the financial sector. 

As we move forward, the successful integration of AI in banking will hinge on balancing innovation with ethical and responsible use of technology. The future of banking, therefore, lies in creating AI-driven CX that are not only intelligent and personalized but also equitable and secure, fostering a new era of trust and engagement in the financial world.

If you want to build your CX strategy and understand it in a broader context, InMoment’s unified dashboard compiles your customer signals from across the web to give you a holistic view. It structures all of your data, ensuring that every decision you make for your CX strategy keeps the bigger picture in mind. 

The Impact of Prescriptive Analytics in Business Decisions

In an increasingly data-driven world, it is no longer enough to understand the past. Successful businesses utilize prescriptive analytics to aid in data-driven decisions that will improve their bottom line. Whether it’s reducing your cost to acquire a customer, decreasing your churn rate, or anything in between. Your business can use analytics to drive success and improve customer experiences

What is Prescriptive Analytics?

Prescriptive analytics is a type of advanced analytics that focuses on providing recommendations and insights to optimize decision-making. Prescriptive analytics uses a combination of mathematical models, algorithms, and business rules to analyze data and generate recommendations. It takes into account various factors, constraints, and objectives to suggest the best course of action in a given situation. This type of analytics is particularly valuable in complex and dynamic environments where decision-makers need guidance on how to respond to different scenarios.

What is the Primary Goal of Prescriptive Analytics?

The primary goal of prescriptive analytics is to provide actionable recommendations that help organizations or individuals make informed decisions to achieve desired outcomes. Unlike descriptive analytics, which focuses on summarizing historical data, and predictive analytics, which forecasts future events, prescriptive analytics takes it a step further by suggesting the best course of action.

With InMoment’s award-winning XI Platform, businesses have access to comprehensive analytics and prescriptive actions represented in customizable dashboards. These tools allow your business to digest analytical insights in the way that makes the most sense for your company. 

How Does Prescriptive Analytics Work?

Prescriptive analytics works by using advanced analytical techniques to recommend actions that optimize decision-making. It involves analyzing data, creating mathematical models, and considering various constraints and objectives to suggest the best course of action. 

Consider a retail company using prescriptive analytics to optimize its pricing strategy. The process would involve analyzing historical sales data, market trends, and external factors. Predictive analytics forecasts the demand for products under different price points. The optimization models factor in costs, competitor prices, and revenue objectives.

The prescriptive analytics system then recommends specific pricing adjustments for each product to maximize overall revenue while considering factors like customer demand elasticity and market conditions. The recommendations are not only based on historical data and predictions but also on the optimization of pricing strategies to achieve the desired financial outcomes for the company. Decision-makers can follow these recommendations to adjust prices and potentially improve the company’s profitability.

How Does Prescriptive Analytics Differ From Other Types of Analytics?

When it comes to data analytics, organizations leverage various approaches to extract meaningful insights and guide decision-making. Each type of analytics serves a distinct purpose, contributing to a comprehensive understanding of data. There are four key types of analytics: prescriptive, predictive, diagnostic, and descriptive. 

Predictive vs Prescriptive Analytics

While predictive analytics is instrumental in understanding likely future scenarios, prescriptive analytics takes it a step further by providing actionable insights that empower organizations to make optimal decisions and drive positive outcomes. Both methodologies, when used in tandem, contribute to a comprehensive and strategic approach to data-driven decision-making. Here are some key differences between the two types of analytics:

  • Predictive analytics focuses on forecasting future events, while prescriptive analytics recommends actions to influence or achieve desired outcomes.
  • Predictive analytics is more concerned with “what will happen,” while prescriptive analytics is concerned with “what to do about it.”
  • Predictive analytics helps organizations prepare for the future, while prescriptive analytics guides decision-makers in shaping the future.

Diagnostic Analytics vs Prescriptive Analytics

Diagnostic analytics and prescriptive analytics are two distinct approaches within the realm of data analytics, each serving different purposes in the decision-making process. Diagnostic analytics is focused on understanding the reasons behind past events or performance.  Diagnostic analytics helps organizations understand the factors contributing to past outcomes, prescriptive analytics empowers decision-makers with actionable recommendations to shape future events. Here is a breakdown of some differences between these two types of analytics:

  • Diagnostic analytics looks backward to understand past events, while prescriptive analytics looks forward to recommending actions for future outcomes.
  • Diagnostic analytics is retrospective and focused on root cause analysis, while prescriptive analytics is proactive and provides actionable insights for decision-making.
  • Diagnostic analytics helps uncover the “why” behind events, while prescriptive analytics guides decision-makers on “what to do” to achieve desired results.

As an example, consider an e-commerce platform employing diagnostic analytics to investigate a sudden drop in website traffic, examining factors such as marketing campaigns, website usability, and external events.

Descriptive and Prescriptive Analytics 

Descriptive analytics is centered around summarizing historical data to provide a comprehensive understanding of past events and trends. It seeks to answer the question, “What has happened?” by using data aggregation, visualization, and statistical analysis to present a clear and concise overview of historical performance. Prescriptive analytics takes a more proactive stance by answering the question, “What should be done?” Integrating both approaches offers a comprehensive strategy for effective decision-making.

Consider a retail company that uses descriptive analytics to review sales reports from the previous quarter, visualizing key metrics such as total sales, product categories, and customer demographics. By integrating prescriptive analytics into the decision-making process, the retail company can move beyond analyzing past performance to actively shaping and optimizing its strategies for the future. 

Advantages of Prescriptive Analytics

Prescriptive analytics offers several compelling advantages that make it a valuable tool for organizations seeking to optimize decision-making. One key benefit is its ability to provide actionable insights. By leveraging optimization models and algorithms, prescriptive analytics goes beyond predicting outcomes and offers specific recommendations on the best course of action. This empowers decision-makers with a clear and informed path forward, leading to more effective and strategic choices.

Another advantage is adaptability. Prescriptive analytics systems often incorporate feedback loops, allowing them to continuously learn and refine their recommendations based on new data and changing conditions. This adaptability enhances the relevance and accuracy of the insights provided, particularly in dynamic and evolving environments.

Additionally, prescriptive analytics enables organizations to consider various factors and constraints simultaneously. This holistic approach ensures that decisions align with multiple objectives, leading to outcomes that are not only optimized for a specific goal but also take into account broader organizational priorities.

Disadvantages of Prescriptive Analytics

Despite its benefits, prescriptive analytics comes with certain challenges and disadvantages. One notable concern is the complexity of implementation. Building and maintaining prescriptive analytics models requires expertise in advanced analytics, mathematics, and algorithmic development. Organizations may face obstacles in terms of the technical skills needed, as well as the resources required for ongoing maintenance.

Another challenge is the reliance on data quality. Prescriptive analytics heavily depends on accurate and relevant data. If the input data is incomplete, inaccurate, or biased, it can compromise the reliability of the recommendations. Ensuring data quality becomes a critical aspect of successful prescriptive analytics implementation.

Privacy and ethical considerations also come into play. Prescriptive analytics often involves processing sensitive information, raising concerns about data privacy and the ethical implications of decision-making based on algorithmic recommendations. Striking the right balance between data-driven insights and ethical considerations becomes essential in the deployment of prescriptive analytics.

While prescriptive analytics offers powerful advantages in guiding optimal decision-making, organizations must navigate the complexities of implementation, address data quality concerns, and carefully consider ethical implications to fully realize its potential benefits.

How Does Prescriptive Analytics Help in Business Decision-Making?

Prescriptive analytics plays a pivotal role in enhancing business decision-making by providing valuable insights and actionable recommendations. One key contribution lies in its ability to optimize choices. By leveraging sophisticated mathematical models and algorithms, prescriptive analytics evaluates multiple decision scenarios, considering various factors, constraints, and objectives. This process goes beyond predicting outcomes; it guides decision-makers on the most effective actions to take, ultimately leading to decisions that align with organizational goals.

Moreover, prescriptive analytics contributes to strategic planning and resource allocation. Organizations face numerous challenges in allocating resources efficiently, whether it’s managing production schedules, optimizing supply chain logistics, or determining marketing budgets. Prescriptive analytics analyzes complex datasets to offer recommendations that maximize efficiency, minimize costs, and improve overall resource utilization. This, in turn, enables businesses to allocate resources in a manner that yields the greatest return on investment.

Prescriptive analytics also facilitates risk management. By considering potential scenarios and their associated risks, decision-makers can make informed choices that mitigate uncertainties. Whether it’s in financial investments, supply chain disruptions, or market fluctuations, prescriptive analytics aids in identifying and addressing risks preemptively. This proactive approach empowers businesses to navigate volatile environments more effectively.

How to Get Started with Prescriptive Analytics

Implementing prescriptive analytics involves a strategic approach to harness its benefits effectively. The first step is to establish a clear understanding of organizational goals and challenges. Identify specific decision areas where prescriptive analytics can make a significant impact, such as resource allocation, supply chain optimization, or pricing strategies.

Once the focus areas are defined, the next step is to gather and prepare the necessary data. Data quality is paramount in prescriptive analytics, so organizations should ensure that they have access to accurate, relevant, and comprehensive data. Integration of data from various sources may be necessary, requiring robust data preprocessing to create a unified dataset suitable for analysis.

Collaboration between data scientists, domain experts, and decision-makers is essential throughout the process. Domain experts provide valuable insights into the contextual nuances of decision-making, while data scientists leverage their analytical expertise to build and refine prescriptive analytics models. A collaborative approach ensures that the models align with organizational objectives and are practical in real-world scenarios.

Pilot projects can be instrumental in validating the effectiveness of prescriptive analytics in a controlled environment. Start with a specific use case, implement the prescriptive analytics model, and assess the outcomes. The insights gained from the pilot project can inform adjustments and improvements before scaling up to broader applications.

Continuous monitoring and optimization are critical components of a successful prescriptive analytics initiative. Regularly review the performance of models, update them with new data, and refine algorithms based on feedback. This iterative approach ensures that the prescriptive analytics system remains adaptive and aligned with evolving business needs.

Utilize Prescriptive Analytics with InMoment

InMoment’s prescriptive analytics capabilities give your business the ability to transform raw data into strategic insights. These insights provide actionable recommendations that empower decision-makers to make informed choices and drive optimal outcomes. With the power of prescriptive analytics at your fingertips, you can not only understand historical performance but also proactively shape future strategies, enhance operational efficiency, and stay ahead of the competition in today’s dynamic business landscape. Schedule a demo to learn more today! 

A Deep Dive into Conversational Intelligence

The advent of conversational intelligence has become a catalyst for transformation in the way that businesses communicate with consumers. More than just a technological innovation, conversational intelligence represents a paradigm shift how organizations understand, interpret, and harness the power of human conversations. 

What is Conversation Intelligence

Conversation intelligence refers to the ability to understand, analyze, and optimize human conversations, particularly in the context of business interactions. It involves leveraging software such as natural language processing (NLP), machine learning, and artificial intelligence to gain insights from spoken or written conversations. The goal of conversation intelligence is to enhance communication, improve collaboration, and extract valuable information from interactions.

Why is Conversational Intelligence Important? 

One of the primary reasons conversation intelligence is crucial lies in its ability to enhance the customer experience. By deciphering the nuances of customer interactions, businesses can gain valuable insights into preferences, sentiments, and pain points. This information, in turn, empowers companies to tailor their products, services, and communication strategies to meet the evolving needs of their customer base.

Conversation intelligence also provides a data-driven foundation for decision-making. Analyzing conversations allows businesses to identify trends, evaluate employee performance, and make informed decisions that align with organizational goals. This data-driven approach fosters agility and adaptability, which is crucial in today’s fast-paced business environment.

How Does Conversation Intelligence Work?

Conversation intelligence works through the integration of advanced software, primarily natural language processing (NLP), machine learning (ML), and artificial intelligence (AI). Here is a breakdown of how conversation intelligence operates:

  • Data Collection: Conversation intelligence platforms can work as part of an omnichannel contact center to gather data from various sources, including audio recordings of phone calls, video conferences, chat transcripts, emails, and other written communication channels.
  • Preprocessing: Raw data often contains noise, irrelevant information, or inconsistencies. Preprocessing involves cleaning and organizing the data to ensure accuracy and reliability.
  • Audio Transcription: Audio data is typically transcribed into text using speech-to-text technology. This step is crucial for analyzing spoken conversations.
  • Understanding Language: NLP algorithms are employed to understand the structure and meaning of human language. This involves tasks such as tokenization (breaking text into words), syntactic analysis (parsing sentence structure), and semantic analysis (interpreting meaning).
  • Analytics and Reporting: Conversation intelligence platforms can aid in contact center analytics and reporting features that summarize key metrics, trends, and insights derived from the analyzed conversations. This information is valuable for making data-driven decisions and optimizing business processes.

In essence, conversation intelligence involves a multi-step process of data collection, preprocessing, language understanding, sentiment analysis, machine learning, and real-time analytics. It leverages advanced technologies to extract meaningful insights from conversations, providing businesses with the tools to enhance customer experiences, improve communication strategies, and make informed decisions.

What’s the Difference Between Conversation Intelligence vs Call Tracking Software?

While conversation intelligence and call tracking software both deal with analyzing interactions, they serve different purposes. Call tracking software primarily focuses on monitoring and analyzing call metrics, such as call duration, source, and outcome. On the other hand, conversation intelligence encompasses a broader spectrum that includes the analysis of both spoken and written interactions. It incorporates advanced features such as sentiment analysis, data extraction, and real-time analytics to provide a comprehensive understanding of communication across various channels.

Benefits of Conversational Intelligence

The adoption of conversational intelligence has become a strategic imperative for businesses aiming to stay ahead in the digital era. This transformative technology not only revolutionizes the way organizations understand customer interactions but also brings forth a host of tangible benefits. From optimizing call center performance and nurturing agents to deriving valuable insights and enhancing productivity, conversational intelligence is reshaping the dynamics of customer service. Here is a deeper look into some of the benefits of conversational intelligence.

1. Optimizing Call Center Performance

Conversational intelligence proves to be a game-changer for call centers by providing real-time insights into customer interactions. Monitoring and analyzing calls help identify areas for improvement, ensuring that customer service representatives deliver a consistent and high-quality experience. This optimization leads to increased customer satisfaction and loyalty.

2. Agent Nurturing and Training

Conversational intelligence serves as a powerful tool for nurturing and training customer service agents. These platforms offer customer experience metrics such as response times, call resolution rates, customer satisfaction scores, and agent performance indicators. By analyzing successful interactions, managers can identify best practices and provide targeted coaching to enhance the skills of their teams. This fosters continuous improvement, boosts agent confidence, and contributes to a more positive and proficient workforce.

3. Extracting Valuable Insights

Extracting valuable insights from customer conversations is a key benefit of conversational intelligence. The platform’s ability to perform sentiment analysis, entity recognition, and data extraction enables businesses to understand customer preferences, pain points, and emerging trends. These insights are invaluable for making informed business decisions, refining strategies, and staying ahead in a competitive market.

InMoment’s XI Platform utilizes AI technology to generate auto-generated conversation summaries. These insights give users access to conversation themes, product or service mentions, and can also help track agent performance. This feature has been shown to reduce the average handle time (AHT) by up to 33%.

4. Personalized Customer Interactions

By understanding the nuances of conversations, conversational intelligence enables businesses to personalize customer interactions. This personalization goes beyond addressing customers by name; it involves tailoring responses based on historical interactions, preferences, and previous purchase behavior. The result is a more engaging and customer-centric experience that fosters brand loyalty.

5. Efficient Resource Allocation

Understanding the dynamics of customer conversations helps businesses allocate resources more efficiently. By identifying peak hours, common customer queries, and areas of high demand, organizations can optimize staffing levels, ensuring that they have the right resources in place to handle customer interactions effectively.

Conversation Intelligence Examples

When examining conversation intelligence, it becomes evident that the impact of this transformative technology extends across various industries by reshaping communication dynamics and optimizing operational processes. Let’s look at a few industry-specific examples. 

1. Healthcare: Optimizing Patient Interactions

In the healthcare industry, conversational intelligence proves to be invaluable for optimizing patient interactions. By analyzing conversations between healthcare professionals and patients, organizations can enhance communication effectiveness. This includes understanding patient concerns, improving appointment scheduling processes, and ensuring compliance with healthcare regulations. The insights derived contribute to a more patient-centric approach, fostering improved healthcare outcomes and patient satisfaction.

2. E-Commerce: Enhancing Customer Support and Sales

In the e-commerce sector, conversation intelligence plays a crucial role in enhancing customer support and driving sales. By analyzing customer interactions across various channels, such as live chat, emails, and phone calls, businesses gain insights into customer preferences, product feedback, and common pain points. This information is instrumental in tailoring marketing strategies, improving the online shopping experience, and providing personalized recommendations, ultimately boosting customer satisfaction and loyalty.

3. Financial Services: Ensuring Regulatory Compliance

In the highly regulated financial services industry, conversation intelligence is essential for ensuring regulatory compliance. By analyzing conversations in customer interactions, financial institutions can monitor and enforce adherence to compliance standards and legal requirements. This proactive approach helps mitigate risks, avoid regulatory penalties, and maintain the integrity of financial operations.

These industry-specific examples showcase the targeted application of conversation intelligence, demonstrating its ability to address unique challenges and opportunities within different sectors. Whether it’s optimizing patient care in healthcare, enhancing customer support in e-commerce, or ensuring regulatory compliance in financial services, conversation intelligence proves to be a versatile tool with applications tailored to industry-specific needs.

Conversation Intelligence with InMoment

The adoption of conversational intelligence, powered by advanced technologies like natural language processing and machine learning, is not merely a strategic choice but a necessity in the contemporary business landscape. InMoment’s conversation intelligence technology empowers organizations to navigate the intricacies of conversations and turn insights into actions, and ultimately drive business success. Schedule a demo to learn more today! 

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