Why InMoment?

For Teams

Use Cases

Explore InMoment

XI Platform

Enterprise Architecture

Explore InMoment

2023 Consumer Experience Trends

Download the reports to learn what consumers want in 2023

Learn & Connect

Explore InMoment

Become a CX Pro

2023 Consumer Experience Trends

Download the reports to learn what consumers want in 2023

Schedule a Demo

2023 Consumer Experience Trends

Download the reports to learn what consumers want in 2023

 
Schedule a Demo

2023 Consumer Experience Trends

Download the reports to learn what consumers want in 2023

Predictive Analytics

What Is Predictive Analytics
Back to Blog

Unlocking Customer Satisfaction: The Role of Predictive Analytics in CX

By: The InMoment Team

In an era where customer expectations continually morph, businesses must always be a step ahead to keep their clients thrilled and engaged. And the most effective tool brands can use to stay ahead of the curve? Predictive analytics. “What is predictive analytics,” you may ask. Well, by thoroughly analyzing historical data, predictive analytics software can predict future customer needs and behavior, forging a proactive customer experience (CX) strategy.

A Deep Dive Into Predictive Analytics

Before we explore its role in CX, let’s unravel the concept of predictive analytics.

What Is Predictive Analytics?

Predictive analytics is a sophisticated method that combines data, machine learning techniques, and statistical algorithms to predict future happenings based on past data. It provides a glance at the likely future by examining patterns and trends in the current data. Deployed across sectors, from finance to healthcare, predictive analytics helps drive informed decisions and actions.

The Methodology and Technologies of Predictive Analytics

Predictive analytics revolves around three main stages: data gathering, statistical analysis, and deployment. This process starts with accumulating vast amounts of relevant data. This data then undergoes processing and analysis through advanced statistical techniques. Finally, the results are deployed in a practical form—be it a detailed report, a visual data representation, or an automated business operation process.

The Intersection of Predictive Analytics and Customer Data

Customer data forms the foundation for predictive analytics. By scrutinizing past customer behaviors, preferences, and interactions, predictive analytics can forecast future customer actions, preferences, and potential issues. It equips organizations with answers to key questions: who are their most valuable customers, what are their customers’ needs, or which customers are at risk of moving away.

The Role of Predictive Analytics in the Customer Experience

In the maze of business strategies, predictive analytics shines as a beacon, illuminating the path towards superior customer experience. It’s an incredibly powerful tool that can turn a plethora of data into insightful narratives about the customers, allowing businesses to not just meet but anticipate their needs. Let’s delve into how this transformative power reshapes the landscape of customer experience.

The Transformative Power of Predictive Analytics in CX

Predictive analytics revolutionizes customer experience in various ways, leading to impactful changes:

  • Proactive Approach: Predictive analytics transforms CX from being reactive to proactive, enabling businesses to anticipate and address customer needs even before they’re expressed. This proactive stance results in a more streamlined and satisfactory customer journey.
  • Tailored Customer Interactions: By providing insights into customer behaviors and preferences, predictive analytics allows for personalization at an individual level. The result is finely tuned interactions that resonate with customers on a personal level, increasing engagement and loyalty.
  • Improved Product Recommendations: With the help of predictive analytics, businesses can create more accurate and appealing product suggestions. By understanding the preferences and purchasing habits of each customer, product recommendations become significantly more relevant and effective.
  • Timely Customer Service: Predictive analytics can also help in detecting potential issues or queries a customer might have, enabling customer service to address these proactively. This results in timely resolution of issues, improved customer satisfaction, and an enhanced reputation for the business.

Reaping the Rewards of Predictive Analytics in CX

Predictive analytics brings an array of benefits to customer experience management, each one contributing to a more successful business strategy:

  • Enhancing Customer Loyalty and Satisfaction: By predicting what customers want before they even ask for it, businesses can provide a proactive and personalized experience that increases satisfaction and fosters loyalty.
  • Boosting Customer Lifetime Value: Predictive analytics helps identify the most valuable customers and understand their behavior, allowing businesses to implement strategies that maximize the value these customers bring over their lifetime.
  • Reducing Customer Churn: By identifying patterns that indicate a customer is at risk of leaving, businesses can take proactive measures to retain them, thereby reducing customer churn.
  • Enriching Cross-selling and Up-selling Opportunities: Predictive analytics 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.
  • Catalyzing Overall Business Growth: By enhancing the customer experience and making operations more efficient, predictive analytics contributes to accelerated business growth and increased profitability.

Addressing the Complexities and Solutions of Implementing Predictive Analytics

While predictive analytics represents a potent force in sculpting remarkable customer experiences, the path to its successful implementation is not devoid of complexities. These challenges need to be recognized, understood, and navigated strategically to truly unlock the transformative potential of predictive analytics. Let’s first uncover the common hurdles that organizations face on this journey.

Unraveling Common Hurdles in Implementing Predictive Analytics

Navigating the path of implementing predictive analytics involves tackling several challenges:

  • Data Privacy Concerns: Organizations must handle vast amounts of customer data, raising critical concerns about data privacy, security, and compliance with regulations such as GDPR and CCPA.
  • Lack of Skilled Resources: Predictive analytics requires a unique blend of skills in data science, statistical analysis, and machine learning – a skill set that may be scarce within an organization.
  • Integration Issues: Organizations often struggle with incorporating predictive analytics systems into their existing infrastructures, leading to compatibility issues and inefficient operations.
  • Real-Time Analysis and Scalability Problems: For many organizations, processing large volumes of data for real-time insights or scaling their analytics initiatives to accommodate increasing data loads can be a daunting task.

Roadmap to Overcoming these Challenges

Addressing these challenges calls for strategic solutions:

  • Building a Skilled Team: Investing in hiring and training employees in data analytics can help build a proficient team capable of harnessing the power of predictive analytics.
  • Data Quality Assurance: Prioritizing data quality is crucial – cleaner, well-structured data can significantly improve the accuracy of predictive models and forecasts.
  • Investing in Scalable and Integrable Analytics Platforms: Selecting analytics platforms that can seamlessly integrate with existing systems and scale with increasing data volumes can ensure smoother operations and long-term success.
  • Establishing a Robust Data Privacy Policy: Developing a comprehensive data privacy policy, complying with all relevant regulations, can assuage data privacy concerns and safeguard the organization from legal repercussions.

Wrapping Up

In the customer-driven era, predictive analytics has emerged as a linchpin to enhance customer experience. By harnessing data to forecast customer behavior, companies can deliver personalized experiences, leading to heightened customer satisfaction and loyalty.

InMoment is leading the way in integrating predictive analytics into CX strategy. The predictive customer analytics in InMoment’s XI Platform unlocks profound insights into customer behavior, helping businesses create not just reactive but proactive and personalized experiences. Investing in such technology will undoubtedly place companies on the path to sculpting a more engaging and gratifying customer journey.

The horizon of CX lies in predictive analytics. Is your business ready to seize it?

Customer Churn Prediction
Back to Blog

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

By: Ton Luijten, Customer Success Director, Data Science Lead APAC

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”

Change Region

Selecting a different region will change the language and content of inmoment.com

North America
United States/Canada (English)
Europe
DACH (Deutsch) United Kingdom (English) France (français) Italy (Italian)
Asia Pacific
Australia (English) New Zealand (English) Singapore (English)