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Core Concepts of Predictive Marketing: Predicting Individual Recommendations for Each Customer

How to use machine learning to create personalized recommendations to recommend products and experiences to individual customers.

A North American beauty and cosmetics company with hundreds of stores was looking to personalize its communications to hundreds of thousands of customers and ensure that every online interaction between the brand and its customers was consistent with its messaging. It wanted to shift the mind-set from being discount-driven to improving customer service and satisfaction. The company chose to combine cluster-based targeting with personalized recommendations to send customers more strategic, personalized offers. The company first used predictive analytics to organize its customers in product-based clusters such as “bath and beauty” and “face cream”. Then it emailed each of these customers content and recommendations that were based on their cluster. Clearly customers liked the emails and the company was able to increase revenues per email by six times.

Recommender systems have been around for almost 20 years, Amazon being the primary example that started using this early on. There are three parts to making personalized recommendations: sending recommendations to customers at the right time, understanding the context and sending the right content.

First-generation recommender systems used simple rules configured by human beings based on things like keywords or titles. In order words, a merchandiser or content marketer, setup a rule so that everybody who bought shoes, would get a recommendation for protective spray shortly thereafter: “if browsing for shoes, also recommend spray.” These first-generation systems used people, rather than predictive algorithms, to make recommendations.

Especially in categories with large selections, such as books, videos and content, recommender systems using the so-called wisdom of the crowd are more effective. Actual usage data or review data from users has more information than metadata like title, description, and keywords that describe the content. When we are trying to find a restaurant, book or a movie, we tend not to trust canned descriptions of the product we are looking to buy. Instead, we ask trusted friends and colleagues what they think. We use the same logic with recommender systems, which can figure out which customers are most similar to an individual user and use behavioral data (usage, reviews, purchases, views, downloads) to recommend other content for that person. This strategy will allow for more relevant recommendations, rather than just trying to recommend products based on certain labels or content. In mathematical terms, these user-based recommendations are called collaborative filtering.

 Choosing the Right Customer or Segment

The first question to answer is who to make a recommendation to and when. Good times to make recommendations are either during a purchase, or after a purchase — and at certain times during a customer’s lifecycle, such as when you have not heard from a customer for a while. These recommendations are referred to as upsell, cross-sell and next-sell recommendations respectively.

Recommendations Made at the Time of Purchase

Upsell and cross-sell recommendations can be made to customers during a purchase, served on a website’s product page, or during checkout.

A basic example of upselling is asking a McDonald’s customer if she wants to supersize her meal, but similar instances can be found in all industries. You could suggest a high-end version or a multipack of the same product, perhaps at a better price. Upsell recommendations are typically tied to a specific product: each product has other suggested products that can be used as upsells.

Cross-sell recommendations are also made at the time of purchase. Rather than recommending buying a larger or better version of a specific product, cross-sell recommendations are made to suggest other products that are typically bought alongside this specific item. The recommendation could read: “customers who bought a printer also tend to buy printer ink…” and you could offer a modest discount if the customer decides to buy your cross-sell bundle. Like upsell recommendations, cross-sell recommendations also tend to be tied to specific products: every product has suggested products that can be used as cross-sells.

Upsell and cross-sell recommendations are a great way to increase average order value. Most upsell and cross-sell recommendations are tied to the product, rather than to a specific customer.

Of course recommendations do not have to be product recommendations. The online fare comparison engine Kayak developed a price prediction tool to advise the shopper on whether they should buy or wait, depending on the confidence of a price drop. Kayak uses this competitive advantage to improve customer experience and retain customers: “We want [travelers] to get to the best decision for their needs as easily as possible,” said Robert Birge, Kayak’s Chief Marketing Officer in an interview with USA Today

Recommendations Made After a Purchase

Next-sell recommendations are typically made after a customer has already made a purchase. This type of recommendation could be included in a thank you page or in the confirmation email.

The best next-sell recommendations are specific for each customer and take into account more customer data than just the most recent transaction. By the time somebody has completed a transaction, you know who this person is and should be able to make a more personalized recommendation. The more you know about a person, the better the recommendation. So if you can analyze all the purchases that a person has made, both online as well as in-store, your recommendations will be more accurate than if you are only looking at online transactions. 

Recommendations Made During the Customer Lifecycle

You can try to use recommendations to reengage or reactivate lapsed customers. In this case, first you use predictive analytics to identify a group of customers at risk of leaving. Then you can reengage these customers with a personalized email. The recommendation can be a product, content or relevant person. The power of recommendations is that they can be dynamically inserted into a web page or email and create an entirely personalized experience without having to redo the creative for each customer. The web page or email design is the same for every customer. Even the text in the page or email could be the same, telling lapsed customers “we miss you, please come back soon, we have these products waiting for you” but include person-specific recommendations.

Be careful using product recommendations if the customer has not bought for a long time. The product recommendations might be obsolete and the context of the customer might have changed completely: it is a new season, the customer might have picked up new hobbies or life events might have occurred. Make sure you base your dynamic content on the latest information you have on the customer, and sometimes it might be better to use recent content, rather than products, to entice the customer.

To learn more about how machine learning helps marketers predict customer behavior, understand customer personas and personalize experiences, download our e-book: Machine Learning Models in Action: Making AI Easy for Marketers

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