How Do We Predict Customer Behavior in Seconds with Artificial Intelligence?

How Do We Predict Customer Behavior in Seconds with Artificial Intelligence?

In the fast-paced world of e-commerce, every company has a magical question on its mind: "Which of my customers will shop again, and when?" The answer to this question can fundamentally change everything from personalized discounts and perfectly timed notifications to inventory management and marketing strategies. So, do we need a crystal ball to answer this question? No. All we need is a smart artificial intelligence that can listen to what the data is whispering.

This is precisely where we are pulling back the curtain on our project, which predicts our users' purchase intent in the next 1, 3, 5, 7, and even 10 days with surprising accuracy.

Every Click Tells a Story

It all starts with the digital footprints our users leave behind. Their browsing on the homepage, their a-product inspections, and their eventual purchases... All of these are more than just data points for us; they are behavioral stories.

Before training our model, we created a "digital diary" for the past 8 weeks for each of our users. This diary includes not only "what" the user did but also "when" and "how often." But we went deeper and asked some critical questions to better understand the current situation:

• When was the last time the user visited our site?


• How many days have passed since their last purchase?


• How many times did they shop in the last month?


• What day is it today? Is it the weekend? What time of day?

Thanks to this approach, we not only had a film of the past, but also a clear snapshot of the "moment" we would predict the future from.

An AI with Memory and the Ability to Focus

We needed a special brain to make sense of these rich "behavioral stories." So, we built one of the most advanced deep learning architectures.

1. Powerful Memory (LSTM Layers)

At the heart of our model lies a technology called LSTM (Long Short-Term Memory), which mimics the memory ability of the human brain. With this technology, our model can remember a user's 8-week behavioral sequence from start to finish. It understands how a browsing session from last week can trigger a purchase today.

2. Attention Mechanism

But just remembering is not enough. The important thing is to know which memories are more valuable. This is where our model's "superpower" comes into play: the Attention Mechanism.

Think of it this way: When studying for an exam, you don't memorize every word in the book; you underline the parts you think are important. The Attention Mechanism does exactly that! It focuses on the most critical moments that signal a purchase among the hundreds of events in the user's 8-week history and concentrates its attention there. Perhaps a user browsing the same category for three consecutive days is a much more important signal than a purchase from a week ago. Our model can discover this on its own.

3. The Master Chef Who Finds the Best Recipe (KerasTuner)

To ensure we found the best model, we left nothing to chance. We used a "master chef" named KerasTuner, which automatically tries hundreds of different model architectures and settings. This system worked tirelessly for us until it found the most delicious, or in this case, the most accurate "recipe," and presented us with the most performant model possible.

How Do We Make Sense of the Results?

Our model doesn't give us a simple "will buy" or "will not buy" answer. It offers something much more valuable: probability scores. For example, "User A has an 87% chance of making a purchase in the next 3 days."

This gives us incredible flexibility. For instance:

• High-Value Campaigns: We can use our marketing budget most efficiently by sending a special discount coupon only to users the model is 90% or more confident about.


• General Announcements: We can make sure we don't miss anyone by announcing a new product to a wider audience that the model gives a 60% or higher probability.

So, by adjusting the model's "confidence level" according to our business goal, we can strike a perfect balance between accuracy and the reach of our target audience.

A Data-Driven Future

This project is an exciting testament to what's possible when we combine big data and modern artificial intelligence. We no longer rely on fortune tellers to predict our customers' next move but on the power of our own data and the algorithms that interpret it intelligently. This is just the beginning; our future goal is to strengthen the bond between us and each user by offering them a personalized experience exactly when they need it.