contemplating business outcomes
contemplating business outcomes
contemplating business outcomes
ML Fundamentals

Why machine learning isn’t delivering for your business

As a leader in your organization, you know how important it is to have the right information before making critical decisions. That’s why your data science team is so important. The work they do to develop machine learning models capable of taking large amounts of consumer data and turning it into accurate customer insights is essential. Done right, it’s what allows your company to make real-time decisions at the customer level, so that you can optimize your organizations’ profitability.

Great expectations, disappointing results

As wonderful as that is in theory, in practice you may not be completely satisfied with the results you’re getting. After all, although you’ve probably made a pretty substantial investment in consumer data and data infrastructure, that doesn’t necessarily mean you’re seeing the improvement in business outcomes you’re looking for. For example, your conversion models may not be all that useful in helping you determine how likely a customer is to make a purchase or what you might have to offer someone on the fence to get them to convert. Or maybe your fraud models aren’t accurate enough at detecting fraudulent activity and some customers and transactions are falling through the cracks.

The reason for this underperformance is that the models your data science teams are building probably aren’t powerful enough to make accurate predictions about customer behavior. That, of course, makes it much harder for you and your colleagues to make the best decisions. Not only that, their models can take several weeks or more to produce, which delays business decisions and means that your company is always making those decisions using old data. Plus, those models are often built with data that’s difficult to explain and that may not hold up to scrutiny.

The problems with traditional approaches to data

So why exactly is this happening? If you’re only using internal data, your organization is likely starting to see diminishing returns from it. Moreover, if you’re like most companies, you probably only collect a limited number of data points on each of your customers that are relevant to your specific industry, products, and services. And while that’s a start, it’s not enough. With a limited dataset, there’s only so much model optimization your data scientists can do to improve model performance. Over time, they’ll hit a wall unless they acquire external data to help give their models a boost.

The problem with that approach is that it means that your data scientists wind up spending far too much of their time on prepping second- and third-party data, and not enough on actual data modeling. The reason for this is that any second- or third-party data your company acquires isn’t going to be optimized for use in your machine learning models. As a result, your data scientists will have to devote weeks to understanding the data, identifying which data has value and which is irrelevant, and then matching it with your existing internal data. That leaves little time for model optimization as they rush to get their models into production.

Yet another issue is the fact that your data probably isn’t updated frequently enough. That’s because by the time your data science team has finished spending weeks prepping the data they’ve got and building their model, the data they’re using has already become stale. That ultimately leads to less effective predictions and less informed business decisions. Unfortunately, updating the model with fresh data can take another few weeks, delaying the process further and resulting in the data being stale once again by the time the model is ready to go into production.

Signals offer a solution

To move beyond these challenges, here at IAI we’ve developed fit for purpose, machine learning-ready Signals for your data science team. If you’re not familiar with Signals, they’re rich, machine-learning-derived insights about consumers’ needs that are generated from real consumer behavior. They’re easy to work with, which reduces the time data scientists are spending on low value activities like data prep, so they can devote more of their attention to model optimization. Best of all, Signals provide cross-industry insights built on internal data that are relevant to the model your team is working on. And, since they are updated continuously, the data is never stale.

Ultimately, that leads to much better model performance, which in turn gives you the best information possible to help guide your company’s decisions. That, in turn, leads to better business outcomes for everyone.


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