When buffer management goes wrong: Avoiding bias in AI decisioning models

Marketers turn to AI decisioning to get the most possible lift for their KPIs, whether they’re focused on driving revenue, profit, customer lifetime value, or any other critical metric. But if certain parameters aren’t set up properly, the AI model won’t work as designed and it won’t realize its full potential in terms of helping brands maximize their bottom-line impact.

One of the factors that makes a big difference when building out AI decisioning is the historical data your team provides (or doesn’t provide) to train the model.

Feeding AI the right data and you will likely end up with an impactful model; if you give it the wrong data, chances are that you end up including a bunch of information that’s irrelevant, out of date, or computationally infeasible—and the AI model doesn’t deliver results.

In other words, it all comes down to buffer management.

What is buffer management, anyway?

Buffer management has a lot of different applications, but in the context of AI decisioning, buffer management governs which information is—and isn’t—used to train AI models.

AI decisioning is powered by a type of machine learning called reinforcement learning, which learns from the actions that the model takes and the results of those actions.

Buffer management defines the relevant learning windows—or buffers, thus the name buffer management. It specifies when AI marketing agents should learn from past decisions taken, whether that’s over the past week, the past month, or the past year.

Sounds good. How do I pick the right buffer window for my business?

Depending on the type of marketing use case that’s being considered for AI decisioning and how long it typically takes to develop meaningful insights for that type of campaign, a shorter or longer learning window or buffer might be more appropriate.

For instance, AI models may need weeks to learn about longer-term or complex processes, such as what factors drive customers to make bigger-ticket purchases or what’s involved with getting approved for a credit card. They need much less time to gather insights about what goes on behind the scenes with impulse buys.

Seasonality and other external factors can also impact how long or short a buffer period should be.

Take a retailer that has predictable seasons in which different product types are released. If that brand wants the AI to learn enough information about each season, they would need a buffer period of at least one to two years to gather the appropriate amount of historical data to guide the model in the right direction.

Meanwhile for brands where predictable seasonality isn’t a factor, a shorter buffer might make more sense.

What happens when you pick the wrong buffer? There’s the potential to introduce bias into AI decisioning.

It would be nice if reinforcement learning agents could figure out what the best window is on their own. One day they may, but at the moment the buffer has to be predefined and configured into the engine. Over time, businesses can learn through the model’s constant experimentation whether the window that’s been set is the appropriate amount of time.

When the buffer or learning window isn’t set for the ideal time period (either it’s too short or too long), that can introduce bias and influence the conclusions the artificial intelligence model draws. This can lead to unintended consequences in how the AI decisioning technology works.

Bias in AI is a phrase that gets thrown around a lot, but in this context it means that the AI model is learning the wrong thing. As a result, it will drive less impact or uplift on a brand’s desired marketing KPIs than it otherwise would.

As an example of what can go wrong, imagine a streaming service sets their window to 30 days. The model will learn what it can from customers engaging with the content within that thirty day window. Agents cannot take into consideration releases outside that window and thus could miss pivotal data, skew the results, and build incorrect assumptions.

Self-service AI decisioning products have a hard time understanding the nuances of buffer times. For brands to see real-world, tangible benefits out of 1:1 decisioning, it takes white-glove expertise.

Okay, buffer management is a big deal. How do I make sure my AI decisioning platform offers the right buffer management capabilities?

For marketers evaluating different AI decisioning platforms, here are some important considerations as it relates to buffer management.

1.Can you set the buffer to the window of time that makes the most sense for your business and your specific use case?

Many AI decisioning solutions often offer standard, one-size-fits-all 365-day rolling windows across the board. Our platform, the BrazeAI Decisioning Studio™, on the other hand, is highly configurable, not just for setting the right buffer window but for every aspect of AI decisioning.

That level of customization can make a huge difference when it comes to marketing performance. For instance, it could lead to very different results on the same KPIs. It’s an order of magnitude because if you get the use case design wrong then the AI decisioning isn’t going to perform.

2. Can you A/B test different buffer strategies to see which window of time delivers better results?

Within the Braze AI decisioning platform, brands can A/B test their buffer strategies and see which window of time ends up driving more uplift.

3. Can you retrain the model using historical data?

For example, the BrazeAI Decisioning Studio™ can be retrained based on historical data, which is important for organizations operating in a dynamic environment, such as financial services brands where external factors can dramatically alter the nature of the business. Based on rates currently available, it might be a great time to drive checking account sign-ups, but in another quarter checking accounts could quickly become a loss leader. The ability to retain the AI on historical data becomes all the more important.

If a brand ends up working with an AI decisioning platforms that doesn’t offer this capability, they might have to restart the model’s learning period from scratch or be locked into what the model has learned about what drives clicks or conversions, even if the learnings are no longer relevant.

If you’re ready to let AI decisioning guide smarter marketing decisions for your brand, check out the BrazeAI Decisioning Studio™ and see how we can help you get your AI decisioning buffer right to achieve your most important marketing KPIs.