Predictive Behavior Modeling: How to Find and Win Future VIP Customers

Insights Sep 04, 2019 Robert Heger 6 min read

Every business discovers that some customers are more valuable than others. This can be due to a wide range of reasons, including purchase size, price sensitivity or ease of post-purchase management.

Whatever the reason, successful businesses are those that identify high-value customers early, foster loyalty and work to bring lookalike customers with a similar profile.

According to Forrester, customer-focused businesses expect to be 7x more relevant to customers, 5x more likely a top provider of products, and 4x more profitable.

At high-growth retail companies, predicting customer value isn’t just a luxury, but an essential revenue driver deserving thoughtful investment.

I’m game, but what tool can do this for me?

The well-organized capabilities of a Customer Data Platform (CDP) with a predictive behavior modeling add-on is all it takes.

This article outlines what you’ll need to discover who your highest value customers will be, how to foster loyalty with them, and how to recruit similar customers in the future.

Let’s dive in.

Key Takeaways

For Senior/BI Analysts and CMOs:
  • The Customer Data Platform (CDP) identifies, reorganizes, and merges customer data, directing communication channels to a central, un-siloed data hub. 
  • Tapping into the now well-organized historical data, predictive behavioral models uncover future trends that can identify risks and opportunities, guiding company-wide decision-making.
  • Predictive behavioral analytics can even be used to predict Customer Lifetime Value (CLV), discovering future most profitable customers, determining how to allocate resources to foster loyalty, and identifying traits that can be used to attract like-minded individuals.

What is Predictive Behavior Modeling?

Predictive behavior modeling utilizes whatever historical customer data you have to forecast future trends and behavioral patterns. This can be done individually on a one-on-one level, in segmented groups, or for your entire customer base. In turn, this added information aids in the decision making process when assessing next best marketing actions, such as:

  • Predicting if and at what time the customer will open your email campaigns, ensuring emails are sent when they’re most likely to be seen.
  • Highlighting those who are most likely to churn, to take care of their needs early.
  • Identifying customers ready to buy, to nudge them towards the purchase.

See all the use-cases in action in our dedicated article, The Latest Predictive Marketing Techniques to Boost Your E-Commerce

Whatever the intent, the process is typically similar, where variables are taken from collected customer data, analyzed by machine learning algorithms and results are produced in the form of a simple score, ranking each customer according to their likelihood of a future, otherwise unknown event.

So what can a brand hope to achieve with this new information?

The Benefits of Predictive Behavior Modeling

As long as data is readily available, and un-siloed in form of a Single Customer View (SCV), predictive analytics can really take off. Since the more thorough the data collection, storage and organization, the more accurate the resulting predictions can be. 

Author's Note

Don’t cheat yourself when you can treat yourself: Think of core company decisions built off sturdy analytical foundations.

Once predictive behavioral analytics is introduced, retailers unlock a deeper understanding of purchasing patterns which allow them to better meet and prepare for customer demands. It encompasses machine learning algorithms to process copious amounts of data.

The resulting customer score helps differentiate one customer from the next, identifying for example, more profitable leads, preferred channels or those most likely to churn. 

With a combination of a few predictions one could locate a highly profitable potential customer that has a high likelihood to churn, prompting an investment in retention that suddenly puts the worth in worthwhile. So by employing fewer resources to target less yet higher-valued customers, marketers can essentially boost campaign effectiveness, while maintaining a lower spend on campaigns. Any cash flow that this frees up can be spent on other pressing projects.

Predictive Behavior Modeling Techniques

Predictive behavioral analytics can also be leveraged to estimate the future lifetime value of a given customer. Yes, historical customer lifetime value has been around for ages, but predictive CLV allows marketers to obtain several new key insights, including who your highest value customers will be, how to foster loyalty with them and how to recruit similar ones in the future.

So how is this achieved with predictive behavior modeling?

Customer Lifetime Value Forecasting

How much will a customer be worth to your business?

The answer lies in Customer Lifetime Value (CLV) forecasting, where we use powerful predictive models to quantify the future value of a customer. In turn, marketers can use the results to identify, retain and target high-value customers.

According to data scientist Jean-René Gauthier, CLV is the total profit of the entire relationship with a customer, which comprises:

  • Cost to attract, service and maintain a customer
  • Customer transactions (number and value)
  • Customer network effects (e.g. word-of-mouth)

The result is a breakdown of each and every customer and their potential value.
Customer Lifetime Value Forecasting

By identifying the traits and features shared by high-CLV customers, retailers can determine how to allocate resources among them, including the evaluation of cost to acquire the customer relationship.

Fostering Customer Loyalty with Predictive Behavior Modeling

Customers stick around for a reason. By understanding their needs, it’s easier to identify tailored opportunities to target them with appropriate offers and make their overall experience with your brand a pleasant one.

This can be accomplished by calculating a historical customer lifetime value, however the backward-looking analysis can cause misleading results since it does not incorporate real-time company/market changes.

Aided by predictive behavioral analytics, predictive CLV is a step up from historical CLV, where it uncovers future preferences by tapping into the log of historical customer transactions, to average past transactions, and infer what purchase behavior will occur in the future.

Predictive CLV

Where some customers may look more valuable at first glance—due to their high number of purchases in the past—others may in-fact be more valuable in a predictive CLV setting, since they’re more likely to make more purchases in the future than those who’ve already ceased to make continuous purchases.

Predictive CLV is great at capturing the diverse aspects of customer behavior, as opposed to simple methods that could be unintentionally leaving out important factors in the equation. Thanks to the single customer view provided by CDPs, more advanced analytical models can be created that go beyond just historical purchase data and RFM to include sessions, email open rates, etc.

By paying close attention to the recency of purchases (time between the first purchase and their last one), frequency (number of purchases a customer has made beyond the first one) and monetary value of each customer purchase, we can ideally identify prosperous behavior, support them and recognize trends that we’d like to see from others.

Frequency Purchase

Besides, building a more robust relationship with your customers (with tailored promotions, surprise perks, personalized experiences, etc.) ultimately helps build customer loyalty. Customers who had a very good experience are 3.5x more likely to repurchase and 5x more likely to recommend the company to friends and relatives than if they had a very poor experience.

And selling more to an existing customer can be far more cost-effective and profitable than continuously finding new ones. In fact, instead of blindly acquiring just any customer in sight, what if we targeted those similar to our top-performing ones? That’s where predictive behavioral models can help.

Using Predictive Behavioral Models to Find Lookalike Audiences

Outfitted with the ability to profile your customers, you also unlock easy access to find new ones. We’re creatures of habit, which makes it straightforward to look for similar prospects, then market and sell to them in a similar manner.

The Pareto Principle states that, generally speaking, 20% of the input creates 80% of the results. Or to apply it for our purposes, 20% of the customers create 80% of the revenue.

Predictive Behavioral Models

Once our customers are segmented into their various value tiers, we can easily identify our VIPs in the top 20% to discover what attributes got them there. These attributes can then be used to compare with new customers to ensure any future VIPs continue their path from their current tier to the one above.

Conclusion

Effectively managing customer data is vital to ensure accurate insight that provides the information you need to handle customers appropriately. There are tools available to help achieve this which businesses use on a day-to-day basis.

 

The Customer Data Platform (CDP) identifies, reorganizes, and merges customer data directing communication channels to a central, un-siloed data hub. Tapping into the now well-organized historical data, predictive behavioral analytics is able to uncover future trends that can identify risks and opportunities, guiding company-wide decision-making.

A recent Accenture study discovered that 78% of companies recognize AI as a competitive advantage, fearing that more advanced competitors will overtake them. With the right martech software you too can obtain that much needed boost, simplifying your data infrastructure, running predictive algorithms and atomizing steps along the traditional customer flow in order to save time and money.

How Can Exponea Help?

What would your company achieve if they had access to timely, actionable intelligence? That’s the promise we aim to deliver with our actionable CDP.

The Exponea Core integrates with your company’s data to un-silo, identify, merge and reorganize customer intelligence into one central communication hub—operating so effectively that it unlocks real-time capabilities.

Partnered with a Predictive Layer, Exponea offers real-time predictions powered by a live predictive model, which ensures that the predictive data (purchase prediction, email open, ideal email time, predictive CLV, etc.) contained in the single customer view are always up to date.

Want to see us in action? We’d love to show you how Exponea can solve even your toughest problems. Let’s discuss more over a demo.

 

meet the author
Robert Heger
Inbound Content Specialist
Robert is the Inbound Content Specialist at Exponea, where he spends much of his time researching and writing to create Exponea’s articles and e-books. Robert’s previous experience revolved around project management, business strategy and innovation. With Exponea, Robert has been leveraging his talents for the world of e-commerce.

Frequently Asked Questions

What is predictive behavior?

We’re creatures of habit when it comes to purchasing products. This helps artificial intelligence and machine learning to discover trends among our buying patterns. By understanding predictive behavior, retailers can better prepare when considering next best marketing actions.

What is predictive technology?

Predictive technology is software that taps into customer data to identify trends and patterns. 

How do predictive models work?

Variables are taken from collected customer data, analyzed by machine learning algorithms and results are produced in the form of a simple score, ranking each customer according to their likelihood of a future, otherwise unknown event (such as predicting future purchases, optimal email times, likelihood to churn, etc.).

What is a prediction engine?

Predictive behavior modeling utilizes machine learning and artificial intelligence algorithms to uncover trends in otherwise unimaginable amounts of customer data. The processing of tasks in data organization, identifying patterns found in historical or transactional data, is typically called an engine.

Why is predictive modeling important?

Predictive modeling produces a predictive score for each individual customer in order to determine, inform or influence an organization’s decision making process.

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