When it comes to experience programs, text analytics software has been revolutionising data interpretation since the capability arrived on the scene. I’m Siobhan May Jones, one of InMoment’s Customer Success Directors, and over my career, I’ve seen this transition up close.
One of my first jobs whilst studying at university was manually coding thousands of verbatims about pet food. While this was great financially because I got paid by the hour, it wasn’t a good use of time by today’s standards. Over the next five years, I worked in the market research industry and found that too many tasks are manual process-rich, as well as subject to human error. It has taken years of discipline to rewire my brain from manual work to working with experts and tools to achieve the right goal.
Let me give you an example—let’s say you need to understand what customers are saying about your employees each month. Your goal is to track which employees you need to support, and which ones need to be celebrated.
You have two options:
1) Download a raw extract of the verbatim and read through it month by month, gain an understanding of what customers are saying, then talk to the team about it.
2) Use natural language processing tools to visualise where and why these comments are showing excellence or areas requiring improvement.
It’s not really a choice between these two options, as the first scenario has you spending hours clicking buttons and cleaning or filtering data, while the second forces you to make an action plan.
So how can you optimise your text analytics software and, ultimately, strengthen your customer experience (CX) program? I have three tips for you:
Tip #1: Confirm You’re Using the Latest and Greatest Software
Before taking any action with text analytics, we recommend chatting with experts in your field to make sure you have the latest tools, processes, software, and overall capability. Your text analytics software should have these four features:
A solution that supports all of the countries and languages your customers work and buy in—at an acceptable level of quality and price.
Your text analytics solution must be able to surface important trends and patterns based on individual comments and the sentiments behind them.
You need a layer of sophisticated analytics that can add tags and themes on a granular level, uncover sentiment, assign categories, identify intent, spot legal issues, and pick up on possible customer churn.
A solution with real-time analysis, reporting and action. This is specifically relevant when considering translations for global companies.
If your text analytics software is missing any of these features, you’ll be starting at a disadvantage. Here at InMoment, we’re constantly innovating based on clients’ specific needs to ensure we’re helping reduce processes and increase action.
Tip #2: Keep Your Goal Front of Mind When Processing Customer Feedback
When you designed your customer experience program, you no doubt started with a goal in mind. And when it comes to processing thousands of unstructured pieces of customer feedback, it can be easy to lose sight of the original goal.
We recommend being honest and clear with your team (and yourself) about what your primary goal is, then using the right approach for that goal. Are you looking to add qualitative information to bring life to your metrics, trying to understand what makes customers angry or frustrated, or are you looking to track a recent frontline training initiative and see if customers noticed enough to talk about it?
Alternatively, are you looking to set up alerts based on topics (regardless of the many possible typos)? Text analytics is a powerful tool that will help you with any of the above goals.
Tip #3: Be On The Lookout For New Updates
When it comes to text analytics software, there will always be new updates, new features, and new opportunities. We recommend adding a biannual calendar note to yourself to proactively identify how text analytics software is changing over time. By being open to change and by constantly onboarding new features, you have a real opportunity to stay ahead of the competition by keeping focus on continuous Experience Improvement (XI).
For more information on text analytics, check out this eBook!
Every holiday season, we at team InMoment like to look back and reflect on what we’ve learned about employee and customer Experience Improvement, and then put those top learnings into a “cheat sheet” of sorts for our readers. And let’s be honest, that refresher is exactly what we all need after the holiday break.
So, are you looking for some inspiration to start your experience mindset off on the right foot in 2022? You’ve come to the right place!
The Continuous Improvement Framework
Your Path to Employee & Customer Experience Improvement Success
The key to a successful experience program is to move beyond merely monitoring employee and customer feedback. Instead, experience professionals need to focus on using that feedback to inform action plans. Customer narratives are a goldmine for companies looking to eradicate superficial and deep-seated problems. Their feedback allows you to identify issues, define remedies that positively impact the bottom line, and ultimately create more meaningful experiences.
Brands can achieve all of this by sticking to a simple, five-step framework that we call the Continuous Improvement Framework: define, listen, understand, transform, realize.
Step #1: Design
When folks start up their employee and customer Experience Improvement programs, they’re often tempted to start listening right off the bat. However, it is absolutely essential that experience professionals design their programs before they launch listening posts.
Here are some notes from InMoment expert Andrew Park on the subject:
“Listening to customers is obviously an integral part of any well-built experience program, but it isn’t enough on its own, especially when brands don’t truly know what they’re listening for. Listening broadly can be helpful, but far more useful is the capability (and the willingness) to listen purposefully.
There are mountains of data out there, and the only way for companies to own the moments that matter (when business, customer, and employee needs intersect) and thus achieve transformational success is to figure out how to listen purposefully. That’s why it’s important for brands to design their experience program’s goals, objectives, and other factors before turning the listening posts on.”
Now that you know what you’re listening for, you can start setting up your listening posts. And whenever most of us think about employee and customer listening, we tend to also think about surveys. But what are the best practices and philosophies successful listening programs follow?
Here’s Andrew Park again:
“Traditional forms of listening usually involve long-winded surveys that focus on single points within brand channels. These surveys may also take a spray-and-pray approach, asking about everything the brand cares about—but that customers may not. Finally, brands may also spend too much time focusing solely on solicited customer feedback, which results in fragmented data. Fortunately, brands can be more versatile when it comes to collecting feedback.”
You’ve collected data at strategic touchpoints using best practices. Now it’s time to leverage analytics to get to the actionable insights in your data. That’s when text analytics come into the picture.
Text analytics are vital to your brand’s ability to understand your customer and employee experiences. You can have listening posts across every channel and at every point in the customer journey, but if you don’t have the best-possible text analytics solution in place, your ability to derive actionable intelligence from that data is essentially moot. And your ability to create transformational change across the organization and drive business growth? That’d be a non-starter without effective text analytics. Without them, all you have is a score, not any context or information on what actually went well or needs improvement.
It’s obvious that text analytics are vital, but in an industry full of jargon, claims about accuracy, and a huge amount of conflicting data, how can you tell what solution attributes will be the best for your company?
In our experience, we’ve found that the hardest step for programs to conquer is going from insights to action—and therefore, to transformation. This is also arguably the most important step on the path to employee and customer Experience Improvement.
Transformation is an important step of the process not just because brands can actively improve themselves, but also because it’s what your customers expect is happening. Customers wouldn’t provide feedback if they didn’t expect brands to do something about it, so bear this in mind when working toward providing the best experience for them.
This is what you’ve been building toward all along: realizing employee and customer Experience Improvement. But what does true success look like? How do you prove it to your business stakeholders?
Here are some thoughts from InMoment XI Strategist Jim Katzman: “Realizing success occurs when you can evaluate how well your program is hitting goals and when you can quantify the results. Even if you don’t hit a homerun against all your goals, evaluating what you have achieved—and what you haven’t—still gives you a great idea of what exactly about your program might need tweaking.
There’s another, more profound way to evaluate your experience program’s impact on the business, and that’s through the lens of four economic pillars. The handy thing about our model is that it’s broad enough to be of use to any company regardless of size, brand, or industry while also giving experience practitioners a foundation from which to evaluate additional financial metrics.”
Over the last few weeks, there have been several announcements from large tech players in the world of VoC (voice of customer) and CX (customer experience). My name is Melanie Disse and I have over 10 years of industry experience—most recently in a VoC role at Mercury New Zealand. I thought I’d spend a moment explaining what these announcements mean for those of us who are VoC, CX and Customer Insights professionals. Before we get started, let’s check out what I’m referring to:
You might be thinking, so what? Should I be excited about this? Let’s look into it.
What the Acquisition and Partnership Means in a Nutshell
In a nutshell, Lexalytics, and Tethr are data analytics platforms focusing on structured and unstructured customer data, as well as solicited and unsolicited feedback. With such acquisition/partnership, companies like InMoment strengthen their capabilities in the “text analytics” space, meaning their ability to analyze unstructured data and extract meaning and actionable insights. But also in a broader way to be able to connect unstructured and structured data sources to generate insights from within one platform.
The Humble Beginnings of Surveys
Before I jump into the deep end, let’s start at the beginning. Not that long ago, if we wanted to know what a customer thought, how they felt about interacting with your brand (website, store, call center, etc.), or how loyal they are to you, we had to ask them. We sent a survey and asked them what we wanted to know. In fact, almost every company sent surveys, to an extent that customers got rather fed up with it. We ran into the problem of survey fatigue, which plagues many of us.
But it’s not just survey fatigue that challenges the trusted old survey, it’s also the accuracy of insights we gain from it. We sometimes ask questions the customer may or may not know the answer to—for example, did we resolve your issue today? The customer is likely thinking “hmm, well I hope so, the agent promised me to fix it..” We also ask questions we should know the answer to, like “did you travel with us in the last 30 days?” And finally, we ask questions that seem irrelevant or unimportant to the customer, but we want to know more about it, like “did you remember seeing any advertisements on your flight today?” So, we kinda capture the “voice of the customer”, at least on things that are important to the company, and from those customers that can be bothered to respond.
In addition to that, we tend to look at survey results in isolation, and then look at things like financial results, churn reporting, or customer complaints data, in isolation as well. Depending on the data maturity level in your business, you may combine some of your data, but not all of it. You may analyze some of your data, but not all of it—which we know is limiting, as data is best utilized when combined with various sources, rather than analyzed in isolation.
So that’s why I’m excited about the recent announcements. It’s not that I oppose using surveys—absolutely not. They are a great tool in our toolbox, but they are only one tool, not THE tool.
Extracting Meaning from Unstructured Data
There’s one resource that has long been underutilized for mining data—the contact centre! The contact centre is an absolute treasure trove of customer insights and has long been underutilized from a customer insights perspective. It’s an amazing source of customer feedback. We have agents on the phone, email, live chat, and social media messaging. We have bots, call notes, and so much more. So instead of sending a survey, we can now analyze the data we already have, and potentially supplement what’s missing with a survey.
Conversational analytics is also powerful as we are no longer limited to low numbers of survey responses, or hearing only from those customers that take the time to respond. Analyzing the conversation that just took place between your company and a customer means we have 100% of the conversation to use to generate insights from. It means more volume, but also a deeper understanding of your customers’ experiences, as we “hear” from all customers that interact with us.
With acquisitions and partnerships, companies like InMoment strengthened their capabilities in this space, using ML (Machine Learning) and NLP (Natural Language Processing) to extract as much insight as possible from those unstructured data sources to tell us what the conversation was about, how the customer (and agent) felt about the interaction, and even predict what the experience was like (e.g. customer effort). Effort and ease, or CES (Customer Effort Score), is a super valuable metric to use in the interaction environment, as it tells us so much about how an experience went from a customer point of view, and is strongly correlated to customer loyalty. Based on unstructured data (the conversation that just took place between agent and customer) as well as operational data (e.g. call history, wait times, transfers, channel hopping) we can predict the level of effort the customer had to put forth to get their query resolved, all without a survey.
Analyzing call or chat data helps us understand the conversation that took place, but also what it was all about. It allows you to narrow down on your customer “intent”, or reason for contacting. While we typically rely on agents to choose a “call reason” from a drop down menu, if you work in this space you probably know the accuracy levels of that data. That’s not just because an agent may opt to take a short cut and choose whatever option is right at the top of the drop down menu, it’s also limited to the options we provide, and one option only. Often calls may cover more than one reason, or the contact reason differs from the actual problem that needs to get addressed. Some telephone platforms now offer “intent recognition” and we can also get that information from our VoC platforms if we ingest that data.
Beyond our contact centre data we can also leverage external sources such as social media or reviews. It’s another source of “free” customer feedback we can leverage to better understand our customers, their needs, and potential improvement areas. And again, we pull it into the same platform to have it in the same place as our other customer feedback data for enriched analytical capabilities.
The Power and Limitations of Technology
While those VoC platform announcements are super exciting, it’s not as simple as plugging them into our company tech environment and we have full access to all the shiny toys. You may end up with an (expensive) Ferrari in the garage, unable to drive it. The more data we can ingest into these VoC platforms, the better the quality of our customer and employee insights. However, which data we can share—from a policy, privacy, or tech point of view—determines to what extent we can leverage the tools. If you’re faced with a stack of legacy systems that don’t integrate easily, or can’t even connect the (data) dots between your systems, things become more challenging.
Another incredibly exciting area is predicting experiences, or rather experience metrics. A word of caution here as well—we all know how unique and unpredictable we are as humans. A lot of testing is required before you have satisfactory accuracy levels for your particular organization (similar to intent work). So again, a great example of how we can leverage survey data to gain insights into customers’ perceptions of experiences. Expectations and perceptions make predicting experiences rather interesting.
So to wrap up, from a conversational analytics point of view, we’re heading to a state where we know why the customer got in touch and what the interaction was about, what the experience was like from a customer point of view, how the customer felt (emotion and sentiment), and the impact the agent may have had. It’s pretty powerful to have that all in one place, but what do we do with this information?
Firstly, we can enrich it even further with not “just” unstructured data from internal sources, but external sources like social media as well. We can also add key operational or financial data we have on the customer (e.g. call metrics such as handle time, customer tenure, value segments, churn risk, and others).
Secondly, when we bring it all together we see a picture emerging on two levels, the operational level and the strategic level.
On an operational level we may gain insights to help us train our agents or uncover root causes that we can tackle. Those are typically limited to a specific area, e.g. a call center team, and smaller in nature.
On a strategic level we are able to uncover an end-to-end view of the customer experience, enabling us to look at company-wide experience improvement areas. Whether that’s overall, or broken down e.g. by specific journey stages. Again, effort is a great metric to use here as you can map out friction areas (aka areas for improvement) across journey stages by channel, or intent. You can also view this by e.g. product or specific services, overlay churn risk or value segments, the list is endless. It should give you a clear idea where to focus your improvement efforts and track performance over time.
Many VoC tools can do parts of what I outlined here, but what we’re seeing now is a strong focus within our industry to mature our capabilities further, particularly in the conversational analytics space. It enables us to use the data we already have and use surveys only when we really need them. And that, in my humble opinion, is fantastic!
Today, we announced that Lexalytics is joining InMoment. As the leader in Experience Improvement (XI) this is another step in our continued effort to bring the best and the brightest in technology and expertise to our customers.
Wondering Who Lexalytics Is?
Since 2003, Lexalytics has been the leader and pioneer in structured and unstructured analytics —translating text into profitable decisions. Their expertise focuses on delivering natural language processing (NLP), and machine-learning (ML) solutions for the world’s most customer-centric brands.
Well, metrics can be limited in their actionability, and the world is moving increasingly beyond using just structured surveys into conversations and rich forms of customer, employee and market experience data. The complexity of doing this at scale, in multiple languages across a variety of data forms (short, long) with velocity is more important today than ever before.
Superior text and unstructured analytics is core to the future of feedback. Together we can offer our customers even more in-depth unstructured analytics that includes the ability to handle social, call center, chat logs, reviews, and other unstructured data, more advanced machine learning models with context provided by industry, and the ability to deploy text analytics on premise for sensitive data.
What Does This Mean to Our Customers?
It means we’re continually striving to bring world-class technology and expertise together to enhance the experiences that impact our customers’ customers and positively impact their business outcomes.
We thank our customers, partners, and our team for their support and for choosing us for their experience improvement journey.
Together, we will make 2021 and beyond the year of exceptional experiences.
Creating a customer journey map is the first step toward designing a superior customer experience (CX) that drives end-user growth. Rather than rushing in and narrowly focusing on a single touchpoint to measure success, a customer journey map helps you evaluate the journey as a whole—providing a bird’s-eye view of the experience your brand delivers.
So You’ve Already Mapped Out the Customer Journey! What’s Next?
The urgent question then becomes, how do you take that big picture view and start asking your customers about their experience?
To move forward, you need to figure out which specific touchpoints you want to study, which metrics you want to gather, what questions you want to ask, and which channels are the most effective to collect that data.
Your customers are more than willing to tell you about the bottlenecks in their journey, but you’ll want to be thoughtful in your approach. So, before you start sending out surveys, think through your voice of customer (VoC) strategy using your journey map as a guide.
That’s what this post is all about. It will help you develop a strategy for gathering feedback at key points within your customer journey so you can take actionable steps toward optimizing your customer experience. Now let’s get started!
When in the Customer Journey Should You Ask for Feedback?
Before you begin asking away, it’s important to determine which pivotal touchpoints (otherwise known as make-or-break moments) within the B2B customer journey are ideal times to gather feedback.
Just to clarify, we are giving you a general idea of when to ask these questions, but this is not a turnkey solution. Every company’s customer journey map looks different, and your approach to asking the right questions at the right time will differ.
In fact, in all likelihood, you already have some sense of where the bottlenecks are in your customer journey and what needs improvement, so trust your intuition there.
And if you’ve got any doubt? The following touchpoints represent good places to start.
1. Onboarding Completion
Why is onboarding a make-or-break moment? Signing up for a new service always takes effort because you’re asking new customers to open their minds, learn about your product, and make a change by integrating your product into their lives. The more seamless you can make this stage, the more likely you are to gain a loyal customer.
2. Support Interaction
Why are support interactions make-or-break moments? We often think of customer support as its own thing, but it’s a vital part of the customer journey. The bane of product-led growth is friction, and by definition, a support interaction is a point of friction. No matter how usable your product is, some people will struggle with it.
Asking for feedback after a support case is closed will give you feedback on how your support team is doing. This will help determine resources support may need to speed customers through this touchpoint, identify bugs and usability issues, and draw attention to possible feature improvements.
3. Product Experience (Usually In-App)
Why is product use a make-or-break moment? This feedback will tell you what’s working as anticipated and what needs to be reconsidered. Customer feedback can and should influence your roadmap and guide the prioritization of development resources. Plus, SaaS companies are always trying out new features, and there’s no better time to survey your customers about those features than at the very moment they’re using them.
4. First Experience of Value and/or Pre-Renewal (Loyalty Check)
Why is the incomplete and/or pre-renewal experience a make-or-break moment? After a user has been up and running for a bit, they should be experiencing the benefits of using your product and services. It’s time to make sure they are. Asking for feedback at this touchpoint is meant to surface all kinds of things about their relationship with you (that you won’t hear after a support interaction, for example). Product, service, pricing, you name it. A survey response might give you the opportunity to fix an issue you didn’t know about and retain their business. And make sure to ask again pre-renewal to make sure your relationship is still on the right track.
How Might Your Approach Vary Depending on Your Business Model?
Let’s say you’ve got a self-serve product where customers get started quickly and they can see your product’s value upfront. In that case, it makes sense to ask the loyalty question (Net Promoter Score) early on in the customer journey because they’ve reached a point where they understand your value proposition.
On the other hand, if you send in consultants who spend weeks or more helping your enterprise users get up to speed with your product, you’ll probably want to wait a while to send that first NPS survey.
Just make sure that, whenever you ask the question, it makes sense to do so at that time. For example, asking someone how they feel about a new feature (PSAT) when they’re not currently using that feature makes no sense. Instead, ask them about the feature using an in-app survey, while they’re engaging the product. And of course, you wouldn’t want to ask someone about a support experience they had weeks earlier. Use common sense and put yourself in the customers’ shoes to deliver surveys that flow with their experience.
Remember: Rome Wasn’t Built in a Day, and Neither Is a Mature CX Program
Companies don’t generally implement voice of customer surveys at multiple journey points all at once—they roll out gradually, sometimes over 2-3 years. They might do one, then add another 6+ months later. A helpful tip is to start with the touchpoint that will give you the biggest bang for your buck in terms of learning, retention, and driving Customer Lifetime Value (CLV).
So What Questions Do You Ask?
When gathering voice of customer data, the most common feedback questions revolve around the things that drive product-led growth—like ease of use, customer satisfaction, and brand loyalty. With this in mind, the following metrics can help you assess these elements at key touchpoints: Customer Effort Score, Customer or Product Satisfaction, and Net Promoter Score. You’ll typically want to follow the rating question with an open-ended one asking the customer to explain the reasoning behind their score.
Now, you may be wondering: why not simply make up your own customer feedback questions tailored to your business, products, and customer experience? It may be tempting, but these metrics will give you a benchmark and scores that you can monitor over time to track whether you’re improving.
Need a Few More Reasons to Use Standard Metrics?
It’s much easier to get internal buy-in when using tried-and-true metrics employed by companies around the world. People can waste countless hours arguing over what questions to ask, but using established metrics can instantly end that debate.
These metrics are also extendable and extensible. In other words, you can extend the same questions to different products and features without reinventing the wheel. This makes it easier to roll out a CX program across a portfolio of products and brands.
And finally, these established metrics will stand the test of time, surviving personnel changes. Simply put, it’s an evergreen survey strategy.
What are the Metrics You Ought to Consider Tracking?
Choose your metric based on what you want to learn, and whether it will make sense to your customer in context. Remember, a survey is part of your customer’s experience.
What Is the Customer Effort Score (CES)?
Customer Effort Score (CES) lets you know how much work it takes for customers to accomplish something (e.g., onboarding, solving a problem).
CES surveys ask the customer “How easy was it to ________?” and is scored on a numeric scale. It’s a metric that is used to improve systems that may otherwise frustrate customers.
As a CX metric, CES helps with that “ease of use” component that increases Customer Lifetime Value (CLV). And while there’s no standard format for CES surveys, they usually look like a 5- or 7-point scale asking how easy it was for a customer to achieve whatever goal they were trying to accomplish. Take a look at our post abouthow to use CES to evaluate your onboarding experience for more details.
What Is Customer Satisfaction (CSAT)?
SaaS companies typically use CSAT surveys to get a read on specific interactions, such as a recently closed support ticket or a fresh purchase. You can format your CSAT survey as a numeric scale (e.g., 5- or 7-point). You’ll typically want to follow the rating question with an open-ended one asking the customer to explain their score.
What Is Product Satisfaction (PSAT)?
A Product Satisfaction (PSAT) survey measures customer satisfaction with your product or a specific feature, and you’ll often ask it with an in-app survey. Like CSAT, it’s flexible and you can ask the question in a variety of formats (binary +/- or on a 5- or 7-point scale).
What Is the Net Promoter Score (NPS)
There’s an excellent chance you’re already conducting NPS surveys at regular intervals, and that’s great! By combining NPS data with other key metrics listed here, you can get a good sense of the customer experience you offer across the entire journey. If you aren’t already using NPS, ask it after your customer has had a chance to experience value from your product or ask it prior to renewal. Both are good times to assess user loyalty.
Net Promoter Score measures brand loyalty, and unlike the other three metrics listed, it follows a standard format, which allows you to compare your results against industry leaders in your field. The standard NPS question asks: “On a scale of 0-10, how likely are you to recommend us to a friend or colleague?”
That NPS question should always be followed by an open-ended question asking respondents why they gave you the score they did. You will then use their answers to (1) have a customer service agent or a success manager follow up with the detractors to try to fix the problem and (2) use the response to improve your customer experience.
Remember: Less Is More
Have you ever taken a “brief” survey that stretched on far longer than promised? Most customers don’t want to take 3-4 minute surveys, and you can reduce friction and improve your survey response rate by using microsurveys.
Let people write a novel in response to your open-ended questions, that’s great—you’ll learn a lot from them! But put yourself in your customers’ shoes and keep your surveys short and sweet, gathering a relevant metric upfront.
Which Distribution Channels Should You Use to Gather Feedback?
Emails, SMS, and in-app surveys are the three main survey channels typically used to gather customer feedback on a post-acquisition journey. Once again, use common sense and think about the channel that makes the most sense for the user. Product experience, as mentioned above, is almost always best asked through in-app surveys at the moment they’re in your platform or app. Support experience is often assessed with an email survey. SMS can be a great channel for gathering feedback if you’re already communicating with customers via phone—for example, following up on a cable technician’s visit.
Just like the question of “when” to collect data, the question about how to distribute surveys will sometimes produce different answers based on your business model. For instance, if you’ve got a more complicated onboarding process where end-users interact with customer success a fair bit, they won’t be surprised to receive a survey via email. On the other hand, if you have a largely self-serve product where onboarding is straightforward, it makes sense to conduct the CES survey in-app.
As you gather data and begin to analyze it, it’s important to remember that none of these metrics or the touchpoints they evaluate exist in isolation. The real secret to a successful CX strategy is to take a step back and look at the entire journey—understanding how it’s all interconnected.
This is where it helps to have a cross-functional team, often led by someone with responsibility for CX operations, that can step back and look at an implementation plan. They can then unify all the data and connect information across the tech stack (e.g., Zendesk, Salesforce, Gainsight, InMoment.
Without this holistic approach, it’s easy to develop departmental silos (where everyone focuses exclusively on their own touchpoints) and technological silos (e.g., the Sales team sees what’s in Salesforce, and the Customer Support team sees what’s in Zendesk, but nobody sees the big picture).
Tasking a team with developing a big picture approach to evaluating the entire customer journey is an essential ingredient in creating a consistent customer experience. And a consistent, seamless, enjoyable experience will build loyalty and boost customer value in the months and years to come.
Each key touchpoint throughout the customer journey plays a huge role in how a customer judges their experience as a whole. This means that at every touchpoint, the stakes are high and there’s a risk of damaging your brands’ reputation. And the scariest thing? It’s not enough to do the work to understand the customer journey at one point in time; businesses need to constantly keep up because customer journeys evolve overtime. That’s where customer journey analytics can come into the picture!
The customer journey can often feel like a never-ending puzzle. How do we create the best experience for a bunch of strangers? Well, that’s correct, customers are technically strangers, which makes it infinitely harder to cater to them. A logical first step then is to get to know your customers!
With powerful customer experience technology, InMoment can help your brand eliminate silos and combine data according to segmented groups, so you can feasibly sort through all sources of customer feedback, whether they’re solicited (phone, email, or text surveys etc.) or unsolicited (social, third party review sites, and more). Seeing these data points altogether can give you a general idea of how your customers behave, what they care about, and more. When you have an inside scoop on how your customers are interacting with your brand, suddenly, customers aren’t strangers anymore but people you can get to know better and better!
Step #2: Pinpoint the Target Areas
One of the benefits of having data collected from a myriad of sources is the ability to statistically analyze trends, patterns, and anomalies. By measuring what topics have the most traffic, your company can focus its priorities on the issues that matter. Leveraging customer journey analytics to identify the impact of a topic often proves to be a big time saver!
InMoment’s advanced analytics can generate all the associated comments and details about an issue, where it’s happening, the words and themes most commonly associated with it, how widespread it is, what impact it has on your business, and more. It can also generate actionable alerts so you can closely monitor problems that arise—and take action.
Step #3: Strategize for the Future
With so much data to manage, businesses often forget the potential for feedback to predict customer concerns and behavior. These predictions allow brands to execute dynamic offers, personalized incentives, and customer-focused policies that build loyalty and drive new business. By utilizing your customer journey analytics to predict future problem points and subsequently implement an effective strategy, your company can proactively meet customers’ needs.
Predictive models work the best when they forecast risks and opportunities, including churn/attrition, revenue, customer segments, likelihood to return/recommend, and potential cross-sell and upsell opportunities. With these forecasts, your brand can maintain an informed and preemptive action plan that will keep customers loyal. Customer journey analytics are not only useful today, but for making business improvements in the long run!
You’ve just learned a bit about how to leverage customer journey analytics in your CX Program—but if you’re looking for a more in depth guide to understanding and predicting customers’ needs, read our eBook!
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