Core Concepts of Predictive Marketing: Predict Likelihood To Buy

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How to use predictive marketing to predict the likelihood of customers to buy a product or service.

We believe that anyone can learn to do predictive marketing with the right foundation. In our series, “Core Concepts of Predictive Marketing”, Acquia’s Chief Science Officer, Omer Artun shares excerpts from his book: “Predictive Marketing: Easy Ways Every Marketer Can Use Customer Analytics and Big Data.” 

This series will be a guide to everything you need to know about relationship marketing and predictive analytics in marketing. Dive in to learn how to activate your customer data and tap into unlimited opportunity.   

Propensity models, also called likelihood to buy or response models, are what most people think about with predictive analytics. These models help predict the likelihood of a certain type of customer behavior, like whether a customer that is browsing your website is likely to buy something. We’ll examine how marketers can optimize anything from email send frequency to salesperson time when armed with information about likelihood to buy or likelihood to engage.

One online pharmacy company sells many products that customers have to reorder at varying times from 3 months to 12 months. Like most retailers, the pharmacy took a one-size-fits-all marketing approach, offering a set calendar of discounts and promotions to all customers. But not all customers are alike and many are looking to buy at different times of the year. Using predictive analytics, the brand was able to differentiate discounts across customers, leading to higher sales and retention without increasing costs. Customers were ranked according to their likelihood to buy. 

Based on that ranking, the business was able to determine which discounts would obtain the optimal response from each customer and offer minimal discounts through email or mailed postcards to customers who were already deemed likely to buy and offer larger discounts for customers who were less likely to buy. The surgical promotions drove incremental margin from customers who were already motivated to buy and incremental revenue from customers who previously felt no incentive to buy. Thanks to this and other predictive marketing campaigns, quarterly sales increased by 38 percent from the year before, profit rose by 24 percent, and customer retention increased by 14 percent. Plus, the changes allowed the pharmacy to more than double its campaign response rates without increasing the marketing or promotional budget by a single dollar.


To predict which prospects are ready to make their first purchase, a likelihood to buy model evaluates non-transaction customer data, such as how many times a customer clicked on an email or how the customer interacts with your website. These models can also take into account certain demographic data. For example, in consumer marketing they may compare gender, age and zip code to other likely buyers. In business marketing, relevant demographics may include industry, job title, and geography.

Here’s how it works: the models compare the pre-purchase behavior of prospective buyers to the pre-purchase behavior of thousands or millions of previous customers who ended up buying, comparing attributes like what emails they opened and what products they spent the most time looking at. The prospects that behave most like the previous buyers are tagged as “high-likelihood buyers” and marketers can then alter the way they interact with them to increase the likelihood of closing a sale. Once you’re armed with this data, you can prioritize your investment in each prospective customer.


For consumer marketers, likelihood to buy predictions allow you to decide how much of a discount you might allocate to a certain customer because people who are already more likely to buy won’t need as aggressive of a discount as customers who are less likely to buy. The models then get better over time, as companies collect more data and automatically test whether predictions actually become reality.

For instance, the large European household appliance manufacturer Arcelik maintains a call center where employees are given a list of customers who are likely to be ready to buy a new washing machine within the next few months. Agents then make calls to these customers with offers such as a year of free detergent with the purchase of a washing machine. The tactic works well for considered purchases, such as refrigerators or cars, and larger-ticket items such as high-end fashion apparel. A high-end shoe brand provides store associates with lists of customers to call too. The store associates have already developed strong relationships with their customers, but they can be even more successful when armed with predictive analytics. Employees can now see which customers are likely to be interested in a certain style when a new season’s shoe comes out, based on customers’ past behavior or how similar their purchase habits are to other customers. 

Employees can then reach out to customers with that information. A call could go something like this: “Hi Joe, it has been a while since we’ve spoken. I just wanted to let you know that there is a new cross-country running shoe I think you might like. It’s similar to the shoes you bought two years ago, but in a new material. I have put a pair aside for you in your size. If you have time, perhaps you could stop by on your way home from work to have a look?” Who would not want to receive a call or an email like that from their personal shopper?

As reported by the New York Times and others, President Barack Obama used propensity models, specifically propensity to vote for the Democratic Party, to help him win reelection in 2012. His staff of volunteers could not possibly meet with every voter in the country so the challenge was to find the undecided voters. There was no point spending time or money trying to woo diehard Republicans who would not change their minds anyway, or diehard Democrats who were already likely to vote for Obama. Rather, using propensity models, Obama’s team of data scientists found those voters who were undecided but could still be persuaded. They then focused on finding already strong Obama supporters in the undecided voter’s social circle and asked them to spend time with the undecided voter to explain their views.


What good is spending money to acquire new customers if they only buy once and do not return? Therefore, it is not only important to predict likelihood to buy for first time buyers, but it is equally important to predict likelihood to buy for repeat buyers. Your goal is to keep customers coming back time and time again. It is happy and loyal customers who have a large lifetime value and many customers with a large lifetime value make for large revenues and profits for your company.

Predicting likelihood to buy for repeat buyers is a lot easier than predicting likelihood to buy for first-time buyers because there is a lot more information to go on. The likelihood to buy model for repeat purchases evaluates earlier transactions as well as other interactions similar to the model for prospects. However, the added information derived from the first purchase can significantly improve the accuracy of the likelihood to buy model for repeat purchases, as compared to a similar model for prospects. Unlike the first purchase predictions, repeat purchase predictions utilize all interactions of the customer, such as past purchases, returned purchases and phone calls to customer service.

In our next Core Concepts of Predictive Marketing blog series, we’ll be looking deeper into how marketers use machine learning to offer individualized recommendations for customers and create more personalized experiences.