In its simplest form, predictive analytics helps to identify the likelihood of future outcomes based on historical data. In other words, you take the data you do have to predict the data you don’t have.
Of all the tricks in the predictive analytics bag, purchase intent can be one of the most exciting, mostly because in the digital world, it’s extremely actionable. If you know what signals are predictive of real, tangible value, then you’ve got some very clear targets you can optimize against, which can not only give the business a significant lift, but also can do so in a very quantifiable way.
Lots of organizations are still using gut instinct to make business-critical decisions and missing the real opportunities. By measuring the real-dollar value of specific measurable actions across the digital ecosystem, you’ll understand exactly which events you should be driving more of and exactly what the impact is on your most important business objectives.
Most importantly, purchase intent analysis allows you to truly optimize and prioritize marketing budgets and strategies accordingly.
It might be tempting to limit your view of the digital world to that final sale that makes it through your e-commerce platform. But the reality is (and we all know it) that there’s a tremendous amount of value in all of the touch points and actions leading up to that final transaction — whether it’s happening online or in-store.
Think of the research-online-purchase-offline (ROPO) effect, for example. This extremely prevalent behavior can make it tricky to understand the true value of digital channels, and this is exactly the kind of example of where purchase intent can come into play.
Joe Nunziante, group director, client services at Cardinal Path (my employer), has seen this all too many times, both in his current role advising enterprises on analytics and marketing optimization and in his former role in an agency responsible for media buying.
“It’s weekly report time, and you’re the manager of the digital channel. You’re becoming masterful in the art of interpreting and re-interpreting your data in a way that shines a positive light on the digital channels you’re using, leveraging all the metrics available to you,” Nunziante said.
With vague and often misunderstood metrics, not to mention zero mention of actual revenues, it’s hard to showcase digital performance on an equal footing with other channels. How did we get here, when digital held the promise of being the most measurable channel of them all?
Even if we move beyond the click and focus on things like website actions that are seemingly closer to an ultimate purchase (Think “Store Locators” or “Make an Appointment” functionality), we’re making optimization decisions based on logical, but still fairly broad, assumptions. Purchase intent aims to connect these online-to-offline dots and give us a clear picture of the true value of these online actions.
Nunziante was involved in using purchase intent to solve these problems for a large organization where the digital team was experiencing the familiar pain of weekly reporting meetings. They recognized the need to break the cycle of “torturing the data until it said what they needed it to” week after week and instead, truly understand the role (and measured value) that digital was playing in driving in-store, offline sales.
This is, of course, important to justify budgets and strategies. But more importantly, said Nunziante, “just imagine how incredibly you could optimize performance if you had this kind of information!”
The first step was exactly where many organizations start, attempting to assign some ballpark values to digital actions and behaviors that they thought were important. This approach is based primarily on some good old logic and common sense.