What if you could better understand the characteristics of individuals likely to commit fraud or customers at risk of attrition?
What if you could personalize your marketing outreach and website content for each individual customer?
What if you could target only the customers you knew were most likely to respond to a particular offer?
If your organization is like most, you dream of being able to answer these questions. And, if you are one of the few with access to predictive analytics, you can turn those dreams into reality — and dollars.
More likely, however, you face some significant challenges to using predictive analytics in order to make a measurable impact on your organization’s bottom line.
The most pervasive challenge is the age-old problem of resource constraints.
With ever-tightening IT budgets and data specialists skilled in predictive analytics overburdened with other work, you probably spend a lot of time tapping your feet and sending “gentle reminder” emails while waiting for the answers you need, because you’re dependent on others within the organization to get them to you. Frustration mounts and business opportunities pass you by
What’s more, even when you have a responsive IT team, preparing the right dataset for predictive analytics can be a Herculean task for even the most skilled data scientist: it is simply an extremely time-consuming process.
While solutions are becoming available that make data preparation faster, easier and in many cases automated, they don’t address the related issue of needing to know and understand the specific types of data — and how much of it — is required to answer the business issue at hand.
Third, you likely don’t know exactly when to use predictive analytics, let alone understand the intricacies of specific analytic techniques.
This is not a knock on you — it’s a knock against the tools available today: they’re too complex for business users.