How to Leverage Historical Data & Predictive Analytics to Improve Customer Experience

5 start customer experience rating using predictive analytics

What if you could predict the future? What if you knew what a customer was going to want, even before they did?

If you’re in business to make money, this is the kind of information that can keep you ahead of your competitors. 

Whether or not you understand Big Data, AI, and other related buzzwords, just know this…

Predictive analytics can give you deep insights into what your customers are thinking and feeling. Which can help you make better decisions about how to reach them. 

You see, predictive analytics is the use of data, statistical algorithms, and machine learning to find out the future outcomes based on historical data. 

As a result, predictive analysis has become the norm with the rise of Big Data and Machine Learning. This technology is useful to any industry where repeat customers are an important part of the business strategy.

Therefore, you can use predictive analytics to predict customer churn, identify upsell opportunities, and influence cross-sells. But the potential for predictive analytics goes beyond these tried-and-true applications.

Perhaps the most significant advantage of predictive analytics is the ability to improve customer experience.  

How Do Historical Data and Predictive Analytics Work? 

Predictive analytics uses historical data to predict future outcomes.

Algorithms are used to analyze current and past data — identify trends, and predict events that have not occurred yet.

What is historical data?

Historical data is the past data of a company or a company’s past performance. 

All companies keep records of their previous performances to learn from their mistakes and improve upon them. These records are also used as reference points for future decisions.

How does historical data help in predictive analysis?

Patterns in historical data help identify how an event happened in the past and how it might happen in the future too. It helps us make more informed decisions by analyzing what has happened before so that we can avoid mistakes and make better decisions in the future.

9 Ways Your Organizations Can Use Predictive Analytics to Improve Customer Experience

1. Customer Needs Forecasting

The value of predictive analytics and data mining is that they allow businesses to anticipate customer needs. And proactively offer their products and services before customers ask for them.

Customer needs forecasting enables businesses to predict (not guess) — customer needs so that they can offer the correct products at the right time. 

Using historical data and machine learning, companies can build models that forecast future customer behavior across a variety of metrics, including product purchases, lifetime value, churn, and more.

Predicting the future allows businesses to be proactive. They can develop an accurate picture of what their customers are likely to buy next by using past purchase history. This way they are able to put the right products in front of the right customers before they even know they need them.

AT&T is a great example of this. They have set up a machine learning system for customer service so that they can get into customers’ minds. 

During the life of a customer project, the technology consumes hundreds of different types of data. It looks at things like customer effort, cycle time, retry rates, and so on to figure out if a customer will stay a promoter or start to move toward neutral or detractor territory. 

The best part is — the system is always learning and improving its algorithms as it gets more and more experience.

2. Reduced Customer Churn 

Customer churn is a critical ratio for any business, especially for subscription-based organizations. However, it is important to note that the impact of churn may not be felt immediately. 

For example, if you’ve 5% monthly churn and 10% revenue growth, your customer base will grow by 5% each month. This doesn’t seem too bad until the end of the year when you look back and realize that half of your customers have churned away.

Predictive analytics can help you identify customers who are more likely to leave you, thus giving you a chance to intervene and retain those valued clients. Or even achieve customer loyalty

Anyway, when you know what factors lead to customers leaving, you can take steps to address them before they happen. 

For example, they might be leaving because they’re not fully aware of all the features available in their paid plan. Or because they’re not using the product or service as often as they’d like. 

Basically, you can use historical customer data to understand which customers are likely to churn. And then use predictive models to calculate the probability of churn for each individual. 

3. Hyperpersonalized Marketing

Hyperpersonalization is a technique that uses predictive analytics to deliver highly personalized marketing messages based on a customer’s digital footprint. 

Marketers use data, such as purchase and browsing history, location information, and IP address, to analyze a customer’s behavior and anticipate their future actions.

By analyzing behavioral patterns, marketers can identify high-value customers and create relevant and timely marketing campaigns that feel customized for each individual.

This type of marketing is not just about getting the timing right. But also about delivering personalized messages that speak directly to the individual at the right time. It’s about making the customer feel like you know them personally.

4. Virtual Concierges

The virtual concierge is a great example of how predictive analytics can be used to enhance the CX (customer experience). Virtual concierges, such as Lexus’ Enform, predict what you might want or need based on your behaviors and preferences. 

For example, if it’s time to eat and you have a favorite restaurant in the area, the virtual concierge will send you a text message with directions. If you have an appointment at 10:00 am on Thursday, it will send you a reminder on Wednesday evening. Every interaction is personalized to meet your needs.

Amazon’s Alexa has similar capabilities. Unlike most virtual assistants, which are programmed to respond to specific commands, Alexa learns from your responses. 

It learns from past interactions and makes suggestions that are relevant to you. For example, if it plays music and you don’t like the song picked, it’ll remember that for next time and play something else. 

If her default wake-up time doesn’t work for you, she’ll adapt to your schedule. She learns and evolves based on your interactions with her.

Therefore, it’s now possible to create both an immersive experience & instant gratification and ultimately retaining customers using AI-driven analytics

For example, Netflix and Spotify change their recommendations based on what you’re watching or listening to right now. Similar to Alexa. And we have so many examples like that. 

5. Management of Resources

Predictive analytics can help you to be more efficient with your resources. Analyzing the data about your customer’s behavior allows you to allocate resources in the best possible way.

The customer’s preferences and habits can be analyzed, so you can send out personalized offers or stock up on items that are popular among your customer base. 

This will ensure that products your customers want are always in stock and that they’re not being overwhelmed with irrelevant offers.

You can also use predictive analytics to improve your supply chain management by using it to predict future demand for your products & services. 

Simply allowing you to have the right amount of inventory at any given time – translates into lower costs for you.

6. Shipping Efficiency 

It’s little wonder that the shipping experience has become a key consideration in eCommerce.

According to a survey by Dimensional Research, 88% of online shoppers said free shipping was most important for a positive delivery experience, with 61% saying fast deliveries were their priority.

In other words, customers expect their packages to be delivered quickly and at low cost — or even no cost at all.

With such high expectations from consumers, it’s no longer optional for retailers to provide excellent shipping. The question is: How can you realistically achieve this?

Predictive analytics enables businesses to improve the customer experience all the way up to the day of delivery. 

As more customers demand next-day and same-day deliveries, predictive analytics assists retailers and their shipping partners in ensuring consistent, on-time delivery.

Predictive analytics is now used to ensure that deliveries reach on time by predicting potential maintenance issues & pinpointing optimal transport routes.

7. Internal Team Support 

Use your predictive analytics data to help your team members understand what’s important to customers. This knowledge can be shared with your customer service department, so agents know which customers are most likely to make a purchase.

For example, if a high-value customer calls in with a problem with their most recent order. This information could be shared with the customer service representative prior to speaking with the customer. 

To improve the customer experience and reduce churn, the rep could then offer the customer a discount on their next purchase.

Train your team members on your company’s value proposition — what makes it unique and different from other brands — and how to communicate it effectively. 

This will help employees engage customers more successfully, which should increase brand loyalty and retention rates.

8. Real-time Marketing Bets 

Real-time marketing bets are particularly useful for eCommerce businesses. Because they help you determine which products are likely to sell the most profitably at any given moment. These bets can be taken by using a variety of different approaches.

The simplest thing is to look at how sales were on a particular product in the past. That gives you some idea of which products will be most profitable at any given time. 

To do this, you need access to historical data that goes back far enough that you can see sales figures over time.

You can also look at the sentiment of your customers and use algorithms to determine what people like and dislike about your products. By learning exactly what people are saying, you can discover which products are likely to sell well and when.

Another great example of real-time marketing bets is hotels and casinos. Predictive analytics can help marketers forecast customer retention at casinos. Repeat customers’ data can reveal what it takes to attract them to remain overnight, such as a free room, meal, or chips. 

The same data can be used to develop demographic analytic models that forecast what new consumers with comparable backgrounds will do, when, and why.

9. Track Unhappy Customers

When it comes to managing customer relationships, it’s easier to keep an unhappy customer happy than it is to win back a former one. As such, it’s wise to identify unhappy customers early, so you can save them before they’re lost for good.

Collecting large amounts of historical data can help you create a model that can predict which customers are likely to leave your company. So you can take action before they do. 

With this information on hand, you’ll be better equipped to predict when a customer is likely to churn, who exactly is at risk of leaving. And what the most effective strategies are for saving those customers.

Predictive analytics provides you with a comprehensive list of dissatisfied customers, allowing you to reach out to them using specific methods. 

You can then offer special discounts and offers while attempting to resolve their issue in order to meet their needs. 

Summing Up 

Yes, all this sounds like a dream. But it’s 100% true. You can use data analytics to know what customers want before they make any decisions and then serve them exactly what they wanted. 

In fact, According to a Forbes Insights survey of 357 executives, the benefits of moving to data-driven customer experiences are extensive — including increased revenue generation and cost reduction. 

Data-driven customer experience is also critical for accelerating process efficiencies and quality improvements. It also allows firms to better target and optimize for specific customers across numerous channels.

Moreover, when you invest in Big Data tools powered by AI, machine learning, and advanced analytics, you offer a personalized CX to your customers. Which is the key as we all know.


It’s Time To Eliminate The Guesswork. Do The Right Thing To Retain And Win The Loyalty Of Your Customers. Leverage The Power Of Predictive Analytics Now. Contact Us!