Predictive Analytics Archives

Organizations should take a closer look at predictive analytics examples to discover the myriad of ways that data and artificial intelligence (AI) can power more personalized customer experiences and enhance brand loyalty and customer retention. From a cost and ROI perspective, the impact and benefits of predictive analytics in customer experience management cannot be ignored.

It’s an opportunity that your company can capitalize on today. According to Forrester, fewer than 10% of enterprises are advanced in their insights-driven capabilities. By equipping your organization with predictive analytics tools, you can gain rich insights into customer behavior, make data-driven decisions, and optimize business operations.

What is Predictive Analytics?

Predictive analytics is a category of data analytics and the process of using data, statistical algorithms, AI, and machine learning techniques to identify the likelihood of future outcomes based on historical data. Put simply: it involves analyzing current and historical data to make predictions about future events or trends.

Advancements in computing power, storage, and algorithms, along with the rise of AI, have made predictive analytics more feasible and accessible to businesses of all sizes. Machine learning algorithms can analyze large datasets quickly and efficiently, enabling businesses to derive insights in real time.

In other fields, the application of predictive analytics is making a big impact. Scientists are combining linguistics, robotics, machine learning, and camera engineering to decode the language of sperm whales. Meanwhile, healthcare algorithms are being used to detect the warning signs of serious illness.

Benefits of Predictive Analytics in CX

Predictive analytics is also making an impact on the way companies manage the customer experience. By leveraging data-driven insights from predictive analytics, your company can foster meaningful connections with customers and achieve differentiation in today’s competitive marketplace. The wide-ranging benefits of predictive analytics applications in customer experience management include:

Enhanced customer loyalty and satisfaction. By predicting what customers want before they even ask for it, your company can provide a proactive and personalized experience that increases satisfaction and fosters loyalty.
Improve customer lifetime value. Predictive analytics helps identify the most valuable customers and understand their behavior, allowing you to implement strategies that maximize the value these customers bring over their lifetime.
Reduce customer churn. By identifying patterns that indicate a customer is at risk of leaving, you can take proactive measures to retain them, thereby reducing customer churn.
Enhance cross-selling and up-selling opportunities. With predictive analytics, marketers can identify which customers are most likely to be interested in additional products or services, creating more opportunities for successful cross-selling and up-selling.
Accelerate operational improvement. By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

Examples of Predictive Analytics: Applications in Customer Experience Management

A number of predictive analytics examples show how the process is being applied by companies looking to better understand their customers, anticipate their needs, and deliver personalized and proactive experiences that drive satisfaction, loyalty, and ultimately, business success.

Predict Behavior and CLV

More and more retail brands are deploying predictive analytics software to forecast customer behavior and monitor market trends.

A classic example comes from Amazon. The e-commerce and retail giant implements predictive analytics techniques across various facets of its operations, from its powerful product recommendation engine (“Customers also bought these items”) to dynamic pricing adjustments.

Retailers can personalize the shopping experience and increase sales by analyzing information such as past purchase history, browsing behavior, and demographic data. Brands can also leverage predictive analytics algorithms to analyze historical data and market trends, helping predict the optimal price points for products in order to maximize revenue while remaining competitive.

By recommending relevant products, delivering personalized content, and identifying cross-selling and up-selling opportunities based on individual customer profiles and purchase history, brands can create highly personalized retail experiences that drive customer lifetime value (CLV).

The key is to connect customer experience data from every touchpoint and channel for a complete view of the customer journey. Jim Katzman, Principal of CX Strategy & Enablement for InMoment, suggests that companies should “expand the data sources that you use to understand what your customers are saying and how they perceive you. While surveys will continue to be important, they only give you part of the picture. Expanding your data repertoire to such sources as purchasing data, location-tracking data, web searches, social media, and online reviews is a must.”

The next step is to take a long view when looking at customer relationships. Adds Katzman, “You’ll be surprised at how many brands get caught up in the lure of ‘What can I sell you today?’ without considering what seeds to plant for even more success tomorrow.”

“Equally important is to understand how your competitors view this dynamic and what, if anything, they’re also doing to be proactive when it comes to building lifetime value.

Score Leads by Analyzing Customer Data

Another great application example of predictive is lead-scoring marketers leveraging historical data and machine learning algorithms to predict the likelihood of leads converting into customers. Today more than ever, marketers are empowered to make data-driven decisions when scoring and prioritizing leads, resulting in more effective lead management, higher conversion rates, and improved overall sales and marketing performance.

Identify Ideal Customer Profiles (ICPs). Predictive customer analytics tools can analyze historical data to identify patterns and characteristics common among high-value customers. By identifying these attributes, marketers can create an ideal customer profile (ICP) that serves as a benchmark for scoring leads based on their similarity to the ICP.
Assign predictive lead scores. Marketers are also utilizing statistical algorithms to analyze various data points such as demographics, firmographics, online behaviors, engagement with marketing content, and past purchase history to assign a predictive score to each lead. This score indicates the likelihood of a lead becoming a customer based on similarities to past successful conversions.
Prioritize sales efforts. Marketers can use predictive analytics to prioritize leads based on their likelihood to convert. Leads with higher predictive scores can be routed to sales teams for immediate follow-up, while leads with lower scores can be nurtured through targeted marketing campaigns until they demonstrate stronger buying signals.
Reduce sales cycle length. Predictive lead scoring enables marketers to identify leads that are further along in the buying process and more likely to make a purchase. By prioritizing these leads for immediate engagement, marketers can accelerate the sales cycle and shorten the time to conversion, leading to faster revenue generation and increased productivity for sales teams.

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Predictive analytics can also have a significant impact on brand reputation management efforts, helping companies anticipate, monitor, and respond to potential reputation threats more effectively.

Algorithms, for example, can analyze large volumes of data from various sources such as social media and online reviews to gauge customer sentiment toward the brand. By identifying patterns and trends in sentiment data, teams can proactively address emerging issues or negative perceptions before they escalate into major reputation crises.

These analytical techniques help crystallize information contained in reviews into insights — helping companies achieve a more accurate, complete, and unified view of the customer.

With online reputation management software, companies can also analyze customer feedback and sentiment data to identify areas for improvement and proactively address customer concerns. By identifying recurring themes or issues in customer feedback, brands can take corrective actions to improve products, services, and overall customer experience, which in turn enhances brand reputation.

InMoment’s approach is based on machine learning, a method of data analysis that allows companies to find patterns and unlock insights as it is exposed to new review and feedback data. This approach is fast, consistent, and programmable, helping teams quickly understand — at a glance and at scale — exactly what customers are saying. Proprietary relevancy scores for sentiment analysis also provide measurement of positive and negative language, with unparalleled accuracy.

Use AI to Improve Personalization

Predictive analytics empowers companies to better understand their customers, anticipate their needs, and deliver personalized experiences. It’s a particularly powerful tool for curating content based on historical customer data.

One of the best predictive analytics examples comes from streaming giant Netflix, which has a powerful personalized content recommendation engine. The company analyzes user data, including viewing history, ratings, and browsing behavior, to make predictions about what users might want to watch next. This is all reflected as soon as viewers land on Netflix’s home page, which displays content tailored to individual users, improving user engagement and satisfaction.

With predictive analytics, teams can dynamically customize website content, email marketing campaigns, and other communication channels based on individual customer preferences and behaviors. By delivering content that is relevant and timely, businesses can improve personalization, create more engaging customer experiences, and drive higher conversion rates.

Extract Insights from Reviews and Social Media Data

Online reviews and social media data provide a wealth of insights for a business but can be labor-intensive to read through and digest. There are many ways to try to automate this task. Currently, the leading approaches use deep learning models that extract many different kinds of keywords, predict their sentiment, and classify them into relevant categories. This allows companies to improve operations, make better decisions, and elevate the customer experience with data.

Using AI and advanced machine learning techniques, predictive analytics tools can read through thousands of reviews, comments, and feedback in the time it would take a human to read through just a few. The right technology will provide valuable insights, summaries, trends, and statistics that can be applied to support data-driven decision-making and customer-centric innovations.

Rural King, a family-owned farm supply store with 128 stores across 13 states, is no stranger to leveraging predictive analytics in order to create memorable customer experiences. The company regularly analyzes massive amounts of unsolicited feedback to unlock the potential of all its stores’ review data.

“We are hearing directly from customers about the store experience as well as pricing and product challenges,” says Kirk Waidelich, VP of Marketing for Rural King. “This allows us to narrow in on the stores that are experiencing issues — and to target and understand these issues versus simply guessing.”

Learn more about how Rural King is harnessing review data to create memorable in-store experiences.

Let InMoment’s AI Take the Guesswork Out of Your CX Program

The above examples of predictive analytics in action demonstrate that the right use of data and AI can take the guesswork out of any organization’s customer experience program. The world’s top brands partner with InMoment AI, the leading predictive customer analytics solution, to facilitate the discovery of real-time insights, drive individual customer recovery, and turn unstructured feedback into a predictable source of business growth.

References

Forrester. “Data Governance Unlocks The Impact Of Analytics: Data Strategy & Insights 2023” (https://www.forrester.com/blogs/data-governance-unlocks-the-impact-of-analytics-data-strategy-insights-2023/). Access 03/16/2024.

Customer behaviour prediction—including customer churn prediction—is at the top of our clients’ agenda—and for good reason. Who doesn’t want to be able to predict the future for their customers, employees, and business?

What Is Predictive Modelling?

In the world of customer experience, predictive modelling means using data to predict the future needs, wants, and behaviours of your customers and employees.

My name is Ton Luijten, and I’m a Customer Success Director for InMoment, as well as the Data Science Lead for the APAC region. I’ve come across many interesting case studies that show how predictive models can be really powerful when trying to sell products or services to your consumers. However, when it comes to actually improving the experiences of your customers, it becomes more complex.

In order to take action and make the right improvements to your CX, it’s vital to understand why something will happen. If you do not have those actionable insights, you will know what or who to target, but you don’t know how best to target them. In this post, I’ll take you through why you need both prediction and interpretation to make the best business decisions.

What’s the Difference Between Prediction and Interpretation?

Let’s take a step back and talk about the difference between prediction and interpretation. In data science, there’s a trade off between prediction accuracy and model interpretability. We have very flexible approaches that tend to come with great prediction accuracy, we’ll call these “black box” models. We also have more restrictive approaches that lend itself to better interpretation, which we’ll call “white box” models. While at first glance it might be appealing to always go for black box models (i.e. the flexible approach with the higher prediction accuracy), you might want to opt for white box models, which leave room for greater interpretation.

To Decide Which Prediction Model, Identify Your Goal

The best model for your business will depend on what you’re trying to achieve. If you’re in a situation where you just want to be able to predict who will buy your products or services, then you don’t really have a need for interpretation, because you just need to target that audience with your ads. However, if you need to have a conversation with a customer that’s very likely to churn, it might be useful to understand why they’re going to leave, so you can have a more relevant conversation.

Bringing Employee and Customer Churn Prediction to Life

The most common use case for predictive models in CX and EX tends to be employee or customer churn, which means customers or employees are intending to leave your brand. Of course businesses are motivated to retain their customers and employees, as it takes time and money to replace both customers and talent.

When we build predictive models for churn, I typically create at least two—one black box model, where I use a flexible approach that tends to achieve good prediction accuracy and a white box model that provides more insights. When we do this, it becomes very easy for clients to understand why it’s important to have interpretation alongside your prediction accuracy.

Recently we went through this exercise with one of our clients and the black box model provided a great fit, however the only output it provided was relative importance of the variables. In this case it showed tenure as the most important driver. Now this might not be a surprise for most of you, as tenure tends to be quite important when it comes to churn. It’s also not very useful and just throws up more questions; the key question would be at what tenure do my clients start to churn?

Taking Action Post-Churn Prediction

The most important part of predicting churn is taking action on those insights. Churn prediction won’t give you all the answers to why customers or employees might be leaving, but it will direct you where to focus. You’ll need to identify the best way to avoid the churn—and there are right ways and wrong ways of actioning your churn insights.

The wrong way of taking action might look like contacting your at-risk customers and explaining why they shouldn’t leave, or perhaps explain how easy it is to use our product or service. It’s also a bad idea to call at-risk customers to confirm they are leaving, then try and talk them out of it.

These approaches are highly problematic and could cause customers or employees who weren’t actually going to leave to consider doing so. After all, some customers or employees are not looking to leave but are also not very engaged or loyal, so these types of actions could make them rethink the relationship.

The right way to take action on churn insights is to think broader and make a proactive plan. From the “white box” approach, we could actually see that there were high churn groups across the tenure range. At one end there was a group with very low tenure (less than 1 year) who never really used the service and on the other end we have clients who had been with the company for many years and had done many transactions, but they never bothered to use certain services, which made the service harder to use.

Now this obviously gives us a much better idea of how to take action and reduce churn. For new customers, you might consider introducing incentive programs to start using the service when they sign up, while for customers with a longer tenure, you could intervene and make them aware of the services they could take advantage of to make their lives easier.

So, Do You Need Prediction, Interpretation, or Both?

When it comes to Experience Improvement, we need both prediction and interpretation. We want to be as accurate as possible when we predict churning customers or employees but we also want to understand why they’re leaving—and this is not just a one size fits all.

Different segments might be leaving for different reasons and have different propensities to leave. Having insights into why customers or employees might be leaving gives you a better idea of what to do about it. Of course, this might lead to a slightly less accurate predictive model, but the trade off is worth it, because what good is an accurate prediction if you cannot take effective action on the back of it?

Want to learn more about how you can reduce employee and customer churn with your experience program efforts? Check out this eBook, “How to Improve Customer Retention & Generate Revenue with Your CX Program”

Predictive Analytics

Predictive Analytics Examples that Demonstrate Its Impact on Customer Experience

What is Predictive Analytics?

Benefits of Predictive Analytics in CX

Examples of Predictive Analytics: Applications in Customer Experience Management

Predict Behavior and CLV

Score Leads by Analyzing Customer Data

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Use AI to Improve Personalization

Extract Insights from Reviews and Social Media Data

Let InMoment’s AI Take the Guesswork Out of Your CX Program

Reducing Customer Churn: Do You Need Prediction, Interpretation, or Both?

What Is Predictive Modelling?

What’s the Difference Between Prediction and Interpretation?

To Decide Which Prediction Model, Identify Your Goal

Bringing Employee and Customer Churn Prediction to Life

Taking Action Post-Churn Prediction

So, Do You Need Prediction, Interpretation, or Both?

Build an Integrated CX Program

2024 Gartner CX Magic Quadrant for VoC

2024 Online Reputation Benchmark Report

What is Predictive Analytics?

Benefits of Predictive Analytics in CX

Examples of Predictive Analytics: Applications in Customer Experience Management

Predict Behavior and CLV

Score Leads by Analyzing Customer Data

Harness NLP and Sentiment Analysis to Monitor Brand Reputation

Use AI to Improve Personalization

Extract Insights from Reviews and Social Media Data

Let InMoment’s AI Take the Guesswork Out of Your CX Program

What Is Predictive Modelling?

What’s the Difference Between Prediction and Interpretation?

To Decide Which Prediction Model, Identify Your Goal

Bringing Employee and Customer Churn Prediction to Life

Taking Action Post-Churn Prediction

So, Do You Need Prediction, Interpretation, or Both?

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