In a traditional brick-and-mortar store, there are clear operating hours. Most stores are closed for a significant number of hours each day, during which the staff can perform backend tasks such as counting inventory, restocking, and redesigning displays.
However, in today’s digital landscape, there is no such downtime. The digital ecosystem is continually evolving, and users are constantly engaging—viewing and clicking on ads, browsing different products, adding items to their shopping carts, navigating away, and making purchases.
The good news is that digital behavior is the most valuable signal in your marketing technology stack. The bad news is that finding this signal within behavioral data poses several challenges, particularly for data engineers who rely on it to build predictive models. Essentially, when engineers sample a dataset, ensuring that each individual has a similar amount of data is non-trivial, as new data is continuously generated and added.
So, how do you build effective models in a constantly evolving ecosystem?
Let’s say a data science team within an organization is trying to build a model that predicts whether a visitor is likely to make a purchase. The first instinct might be to simply query all visitor activity within a set period—say, a month—then use that data to train the model and deploy it to production.
This is a common approach among data scientists: sample a period of data, feed it into multiple contender models, select the highest-performing model, and send it to production.
However, this method often results in models that fail to perform as expected in real-world scenarios. The issue? Many models unknowingly incorporate post-purchase data during training, which distorts the predictive capabilities once deployed in production.
For example, imagine a model trained on a dataset that includes visitors who viewed a “thank you for your purchase” page. This page may strongly correlate with purchasing behavior, but it is only seen after a purchase. Including post-purchase data leads to inaccurate predictions because a production model won’t have access to this data for users who haven’t purchased yet.
The Solution
At Syntasa, we have spent years developing solutions to challenges like these. Our Sliding Window Framework is designed to address the problem of continuously incoming data and ensure predictive models are trained effectively.
Rather than using a generalized time frame for all visitors, our approach considers each visitor’s journey individually. The key issue is that visitors can make a purchase at any point—Day 3, Day 15, Day 23, etc. A standard fixed-time dataset may contain post-purchase activity, which skews model training.
Syntasa’s framework resolves this by filtering behavioral data to include only pre-purchase actions within a specified lookback window. This approach ensures that models are trained on a dataset that accurately reflects real-world user behavior and excludes data that won’t be available in production.
A sliding window framework more accurately approximates production environments. As we’ve established, in the real world, your model will only have access to data generated before the purchase. Therefore, training your model using a sliding window approach ensures that its results do not rely on data that won’t be available in a production environment.
This more personalized approach also allows for a uniform time period when building user histories. Instead of the length of a user’s history varying based on when their purchase occurred within your timeframe, every user history will contain the same number of days of activity. For example, rather than having 17 days of activity for a user who purchased on day 17 and 26 days for a user who purchased on day 26, both would have the same number of days recorded. The optimal number of days required for an accurate model will vary by use case.
By applying this approach, a data science team building a propensity-to-purchase model can ensure they’re training on the right data—behavioral signals that are truly predictive rather than misleading. Syntasa’s Sliding Window Framework filters out post-purchase activity, aligns user histories to a consistent pre-purchase window, and more closely mirrors the conditions under which the model will operate in production.
The result? Models that are more reliable, more accurate, and better aligned with real-world user behavior—so you can predict who’s most likely to buy before they do, not after.
FAQs
Why does a lookback window matter in AI modeling?
The lookback window determines how much historical data an AI model considers, shaping its ability to recognize trends and make accurate predictions. Selecting the right timeframe ensures the model is neither too short-sighted nor bogged down by outdated patterns.
What challenges arise from using the wrong lookback window?
A short lookback window may miss critical long-term behavioral patterns, leading to underfitting. A long window, on the other hand, can introduce outdated trends, causing overfitting and inaccurate predictions.
Can AI models automatically optimize their lookback windows?
Yes, advanced AI models can analyze real-time data and adjust their lookback windows automatically. This ensures that predictions remain relevant without requiring constant manual fine-tuning.
Final Thoughts
Beyond ensuring clean training data, selecting the best model from a set of contenders is equally critical. Syntasa’s expertise in behavioral data processing and predictive modeling allows businesses to maximize the value of their digital signals. Our Sliding Window Framework provides a robust foundation for building predictive models that remain effective in a dynamic digital environment. By ensuring that models are trained on clean, structured, and real-world-relevant data, we help organizations make data-driven decisions with confidence.Ready to optimize your AI models and want expert guidance? Contact us to see how we can help optimize your predictive modeling strategy.