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. 

What's Getting in the Way of Predictive Analytics?

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. 

Learning Opportunities

While you probably know some standard techniques and in which situations to apply them, such as linear regression or decision trees to identify which customers will likely respond to a campaign or are at risk of churning, what about more complex techniques, such as optimization or Monte Carlo simulations? Traditionally statisticians or data scientists apply techniques such as these. 

And the work doesn't end when you apply the techniques, it’s about interpreting the results as well. Do you have the tools you need to do that?

Make it Simple, Vendors

So, how can organizations overcome these challenges and give you, the marketing professional, and other business users the power to run your own predictive analytics? 

It begins with a determination and a commitment to giving business users the power to run predictive analytics themselves — without depending on IT or other resources to support them.

Organizations must make data preparation a priority. The old saying, “garbage in, garbage out” prevails here: Your predictive analytic model is only as good as the data that goes into it and if your data isn’t right, building a predictive model can be a long and frustrating process.

And finally, organizations must make sure that the technology they choose supports your and other business users’ skillsets. It may be great that the product utilizes R, or can support SAS code, but if you don’t know these languages, you’re not going to use them — and you don’t have the time to learn them.

Ultimately, in order to put the power of predictive analytics in the hands of business users, analytics software vendors must make it easier not only to build predictive models but also to understand and consume the outcomes. 

By eliminating the need for coding, simplifying modeling techniques based on users’ various skillsets, implementing automated modeling, and even creating wizard-based systems or applications that walk users through a process of data preparation and predictive modeling, vendors will give you the tools you need to positively impact your organization’s bottom line.