We all have unbreakable habits and rituals that we follow. Some people enjoy going for an early exercise, while others prefer to stay up late watching their favorite show.

Our daily lives are full of repetitions, as depressing as it may seem. When it comes to commuting, most individuals take the same route every day, sip the same flavor of coffee on their way to work, and work in the same monotony throughout the day.

Similarly, considering the shopping habits, some people have a designated shopping day and a list of products that they purchase on a regular basis. Others go with the flow, looking for the best discounts whenever they have free time to shop, without considering brands or previous purchases.

So, why not can’t we train a machine learning model to forecast a customer’s future purchase and estimate when that transaction will take place?

Machine learning has struck once more.

The difficulty of developing a next-best offer recommendation based on a customer’s purchasing history can be approached in two ways:

Customers’ overall (macro) shopping preferences are sought, and global best-selling items are effectively returned as recommendations.

Analyzing sequential client actions (micro) – perhaps they’ve only lately begun to buy certain products — and avoiding any universal bestsellers?

The beauty of AI/ML is that it allows you to combine various approaches and have the solution determine the optimal recommendation based on model results and configurable parameters.

Deep neural networks, a popular AI option, are the way to go in this scenario.

In a word, they are algorithms that seek to replicate how the human brain processes and remembers information. It can find patterns in how specific customers like to buy by providing it with a vast number of past baskets as an input. It is possible to produce a high-accuracy prediction of what each consumer is most likely to buy in their future transaction if built and refined properly.

Without going into technical details, a model like that can provide each consumer with a tailored ranking of past products. The ranking is determined by the products’ “score.” The model training is based on the fact that a consumer likes a product that they bought over a product that they didn’t buy. Thousands of such pairs are processed during the training phase. This kind of recommendation creation is known as a Bayesian recommendation system, which is currently one of the most used recommendation systems.

But how do you put it to use?

If you can forecast the most likely contents of the next transaction and when it will occur, you can engage in a wide range of marketing or loyalty activities.

Marketers can use this information in a variety of ways, including:

• Making a special offer for members who are planning to buy within the next three days.
• Customers who have other products from the same category or brand identified as a potential purchase in the next transaction are targeted for a cross/up-selling campaign for promoted products.
• Members who purchase before their expected next transaction date will receive an additional incentive or reward.
• Differentiating campaign messages based on the member’s predicted next basket.
• Members who have not purchased anything in the ten days following their next forecasted purchase date are eligible for a “come-back” offer.
• Adding gamification aspects to members who “follow the projected pattern” of purchases – possibly more loyalty points, progress bars, achievements, badges, etc.?

These are just some of the common instances of how transaction time and content prediction can help you build a really engaging loyalty program. The possibilities are practically unlimited when you combine other information about a consumer – segment assignments, anticipated lifetime value, demographic data, and the list goes on….and on…

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