Let's say Team A is your favorite basketball team. Over the past few seasons, it has absolutely owned its rival Team B on the court, to the tune of 18 wins in the last 20 games against Team B. Going into the upcoming season, you feel pretty confident about Team A's chances to get off to a fast start since its opening game is against Team B. Predictive analytics would tell us that you're good for a win 90% of the time. So, trust the numbers, right?
But hold on a moment...
In the off-season, Team B shocked the sporting world and signed LeBron James, Kevin Durant, and Stephen Curry. No one is quite sure how this new "super team" will play together, but one thing is for certain: it'll play a lot differently than last year.
Trusting the numbers suddenly becomes a little trickier.
Looking at the Problem
For marketers, the trouble with predictive analytics starts when an unknown variable (like a trio of significant newcomers to your audience) enters the equation or when you don't have enough data (or standardized data) to analyze. Both those situations are difficult to plan for. Even if you've anticipated the unknown, you've still never seen it before, making it hard to adjust for what its true effect will be.
Without the right data in place, what are you really predicting?
A new Gartner research report concluded that B2B technology marketers must have some form of predictive lead scoring to prioritize leads from inbound channels.