Learn how Temporal AI helps increase brand revenue by providing greater customer personalization experiences.

Many customer personalization tools using classic regression and correlation models can learn about your customers and recommend other products they might like based upon their purchase history, preferences, and potential needs. But what happens when those preferences or needs change three, six, or even 12 months from now — as, for example, would be the case for the owner of a growing puppy? Can a regression/correlation model adapt and evolve with your customer over time without additional manual data intervention? Most can’t.

In this post, we’ll discuss the critical personalization element of time, and how its incorporation into an AI engine leads to a greater, more personalized experience for customers, and increased revenues for brands.

The Role of Time in AI 

“Temporal AI is an engine and AI model that takes in the time nuances associated with a purchase in order to do prediction that moves forward with you and your customers.”                                                                           Matt Champion
Former CMO, BevMo!

• Items that trend based on different timing patterns and seasonalities
• Products that are frequently added to carts on Friday nights
• Evolving product recommendations along with customer age
• Food recipes that trend leading up to Thanksgiving
• Predicting when items will peak based on time and other external factors
• Displaying items that are top-selling when it’s raining
• Surfacing articles read largely during a specific time of the day (e.g., morning, lunchtime, evening)
• Recommendations based on projected skill progression (e.g., recommending a 500-piece   puzzle a few months after buying a 100-piece puzzle)
• Highlighting product use case imagery based on the time of year (e.g., showing a tent being used at a sporting event during the summer, and out in the woods during the fall)
• Coupons that drive sales especially well on a particular holiday (e.g., Christmas)

Time is also crucial in an AI engine for optimal speed and adaptability, allowing the AI to learn, adjust, and respond faster than traditional models. The ability to dynamically adapt to customer changes in real-time provides customers with quicker, more relevant recommendations.

How Does Temporal AI Impact the Customer Lifecycle? 

To demonstrate why Temporal AI is such a crucial component for creating customer delight, it might be helpful to look at an example.

Imagine you’re a retailer that sells pet supplies, and a customer buys puppy food on your website today. How might future product recommendations be handled for this customer using a standard regression/correlation model versus Temporal AI?
It’ll likely look something like this:
In the above example, the regression/correlation model correctly learns that your customer has a puppy, and serves relevant puppy-related product recommendations to the customer for months (even years) into the future. The Temporal AI engine starts out in much the same fashion by recommending other puppy-related products. But then things quickly begin to look different.

By intelligently incorporating the element of time, the Temporal AI engine knows that it won’t be long before that puppy grows and its needs evolve. So, it soon starts recommending products better-suited for adult dogs. With Temporal AI, age and health-related foods and supplies are selected and presented to the customer as their pet ages, without any manual work from your brand.

While traditional personalization approaches require manual management for optimal performance, Temporal AI automates this process, making it much easier for brands to stay on top of evolving customer needs. We refer to this as Dynamic Profile Personalization. At Breinify, our Temporal AI grows with your customers over time, no matter how their lives evolve.

How Does Temporal AI Impact Brand Revenue?

• Personalization based on preferences: Consumers who like wine get a different set of product recommendations and content than those who like whiskey.
• Personalization based on seasonality: Consumers who like wine get a different set of product recommendations in warmer months vs. around the holidays.
• Personalization based on individuality: Consumers who like wine get a different set of product recommendations based on the grape varieties they tend to prefer.
• Personalization based on relevant offers: Consumers who like wine get a different set of promotional offers based on what drives results at a given time of year for people with like preferences.