How To Use Predictive Customer Analytics To Drive Retention and Increase Satisfaction?

Looking to use predictive customer analytics to drive growth and gain a competitive advantage for your business?

Predictive customer analytics can help you engage customers and generate more revenue with accurate predictions. It can lead to greater customer retention and satisfaction in the long run.

In this article, we dive deep to discuss predictive customer analytics, its benefits, and its drawbacks. You will also know how to track and use it for your SaaS business.

Let’s start!

What is predictive customer analytics?

Predictive customer analytics refers to the process of analyzing your customers’ past behavior and predicting their actions in the future. It involves using historical data, statistical algorithms, and machine-learning techniques.

Benefits of predictive customer analytics

Predictive customer analytics can help you with several integral sides of SaaS businesses. You can leverage it to stay ahead of the curve and respond to market changes quickly and effectively.

Let’s see how predictive analytics helps your SaaS business.

Understand customer behavior and predict future events

Predictive customer analytics allows you to dive deep into customer data and identify trends in user behavior. You can learn how customers behave with your product by analyzing it with the customer journey. It can then help you better understand your customers’ needs and wants.

You can also build detailed customer personas to create a holistic view of your customers. You can also use it to forecast which users are likely to be most valuable to their business over time and put efforts into retaining them.

Identify high-risk customers who are stuck in the customer journey

You can use predictive customer models to identify at-risk customer segments and proactively avoid churn. It helps you address the root causes of customer dissatisfaction and improve the overall customer experience.

With predictive analytics, you can focus on customers more likely to part ways with your brand. You can identify the pain points of their user journeys and remove them to enhance customer engagement. It can eventually lower customer churn and increase your customer retention rate.

Increase customer satisfaction and loyalty

By using predictive analytics, you can dive deeper into customer satisfaction and loyalty. You can understand and track their key drivers through real-time customer engagement analytics.

You can use this data to understand what satisfies your customers and what doesn’t. It can help you prioritize what is working for you and make adjustments to improve customer satisfaction and loyalty.

Another advantage of predictive customer analytics is combining it with additional data from various customer surveys, like CSAT, NPS, etc. You can get more comprehensive insights into your customers’ needs and wants.

Drawbacks of predictive customer analytics

Predictive analytics tools can be handy for SaaS businesses seeking deeper insights into customer behavior. But there are a few drawbacks that you should be aware of.

One of the major limitations of predictive customer analytics is its limited ability to predict complex human behavior. Predictive analytics algorithms struggle to fully capture many factors, like emotions and personal motivations, that often influence human behavior.

The constant need for consistent data updates is another troublesome step for many SaaS businesses in predictive customer analytics. Without a system to regularly collect and update data sets, you cannot expect accurate predictions from predictive analytics tools.

Companies using predictive analysis assume enough data is available to generate valuable insights. But when the data set is incomplete, it will skew the insights and provide unreliable results. Incomplete data sets also increase your risks because the decisions you make depending on them might be ineffective.

Another huge challenge here is the accuracy of the data. Companies relying on surveys to gather data know that not every customer provides honest or accurate information. It is not that people are dishonest, but it is primarily due to their personal reservations that they may not be compelled to answer genuinely. The data can lead to inaccurate insights, incorrect predictions, and potentially unsuccessful business decisions.

5 ways to collect data for predictive analytics

Now that you know about the ins and outs of predictive customer analytics let’s see how you can collect data for it.

Use feature tagging to track in-app customer interactions

Predictive customer analytics depend on accurate and relevant data. And one of the best ways to collect this data is by using feature tagging in your in-app customer interactions.

First, you must understand how your customers use and interact with your product’s features. It can help you gain insights into which features are most popular, where users are struggling, and what might be causing them to drop off.

You should track not only feature usage but also clicks, hovers, and text inputs regularly. Tracking these specific elements within your product will help you comprehensively view your in-app engagement.

Combine product usage data with heatmaps to unlock more customer insights

Product usage helps you understand who is using your product and how often, but it is only a part of the customer insights story. You can combine feature and product usage data with heatmap analysis to unlock more insights.

Heatmaps are slightly different from the overall usage data. They try to break down “how” a user engages with any specific element, like clicks, hovers, etc. You can use it to understand a customer’s path to value and remove any friction point one faces while engaging with your product.

But like feature and product usage, heatmaps can also help you better identify customer trends and patterns. You can then understand how different customer segments use any feature. For example, power users will likely have clear product usage patterns from which other users might learn.

The heatmap calculation depends on the event occurrence on the same page.

We get the average by dividing the total events per feature tag by the highest feature tag events. Then, we multiply the ratio by 100 to get the percentage.

Let’s look at some examples to help you get a better grasp of this calculation.

Suppose you have three feature tags as follows on the same page:

• Feature tag no. 1: 40 times occurred → 40/100 → 40% (its color will be between light blue and green).
• Feature tag no. 2: 60 times occurred → 60/100 → 60% (the color will be between green and yellow).
• Feature tag no. 3: 100 times occurred → 100/100 → 100% (it will show red color).

Send microsurveys to collect customer feedback and measure customer loyalty

Sending microsurveys can be an effective way to collect data for predictive analytics and measure customer loyalty. You should use different microsurveys, like NPS, CES, CSAT, etc., for it.

Collecting feedback on an ongoing basis throughout the customer journey should be a process within your product team. It can help you better understand customer satisfaction and the drivers of complaints and negative feedback.

With this understanding, you can easily spot issues and friction in your product and work on them. It will not only improve customer satisfaction but also play a role in reducing customer churn.

You should dive deep into NPS analytics and identify promoters, passives, and detractors. Adding an NPS follow-up question will be more beneficial for you as you will know the reason and what’s next to do.

Perform behavioral segmentation to group customers into cohorts

With behavioral segmentation, you can unlock valuable insights and make more informed decisions to improve the customer experience. You can collect data for predictive analytics by grouping customers into cohorts based on their in-app behavior.

You can dive deep into user segments’ data and understand why specific segments behave differently. Then, you can develop actionable strategies for different segments by identifying the reasons. It can help you increase customer satisfaction and drive growth for your SaaS business.

Analyze historical data and detect event trends

Analyzing historical data and detecting event trends can effectively help you collect data for predictive customer analytics. This approach utilizes past customer behavior data to predict future customer behavior.

You can get your hands on historical data and compare customer segments’ behavior over time. And as you identify event trends and patterns from this data, you’ll be able to make more data-driven decisions about your product as per your customers’ needs.

Another benefit is that it provides a comprehensive view of customer behavior over a period of time. So you get to identify patterns and trends that may not be immediately apparent in real-time data. With it, you can predict future customer behavior more accurately.

How to use predictive data analytics to retain existing customers and improve customer experience

You can use predictive data analytics to increase customer confidence and get the optimum customer retention and experience results. Here is how you can do that.

Trigger personalized in-app experiences at the right time to the right users

You can make the most of predictive customer analytics by triggering personalized in-app customer experiences. It can help you get more customer engagement and opportunities for driving revenue to your business.

You can also predict customers’ future behavior by tracking in-app engagement for predictive customer analytics. And using that data can help you trigger personalized in-app experiences for your users. Your users will feel more valued when they get experiences that are contextual and relevant to them.

You can use it for several purposes: to drive engagement, upsell, cross-sell, collect feedback, introduce a new feature, etc. With personalized experiences, customers are more likely to grab these expansion opportunities.

If you can leverage predictive data analytics, you will also be able to develop a more seamless and intuitive user experience within your product.

Adapt your in-app messaging based on customer preferences and needs

Predictive data analytics can help you to improve your in-app messaging. When you track customer behavior and preferences, you can easily develop in-app messaging and guidance aligning with customers’ specific needs.

With predictive analytics, you have data about your customers’ future course of action regarding your products. You know which features will be the most popular and which might cause some trouble to your customers.

You can then make personalized adjustments to your in-app messaging, like one type of in-app messaging for the popular features and another for the possibly less-used ones.

In-app marketing strategies can also benefit from predictive data. You can develop targeted in-app marketing campaigns that will likely resonate with every customer. Predictive customer data will provide you with the best time and customer segment to send these promotional messages or offers.

Proactively reach out to at-risk customers to avoid customer attrition

You can use predictive customer analytics to predict customer churn more efficiently. When you have the data from in-app engagement and customer feedback, you can easily identify customers at a high risk of churning.

After identifying these at-risk customers, you can proactively try to re-engage them with your product and prevent them from churning. You can offer personalized help and incentives, such as exclusive promotions or discounts. It can help you retain these customers before it is too late.

Conclusion

Predictive customer analytics allows SaaS businesses to get better insights into customer data and predict their future actions. With these advantages, you can improve your products or services by knowing your customers’ wants and needs beforehand.

Want to track in-app user behavior and collect data for predictive customer analytics? Get a Userpilot Demo to track and use predictive customer analytics code-free.