Predictive Analytics Should Sell Themselves, But May Need an Added Push

Predictive Analytics Should Sell Themselves, But May Need an Added Push

Some say predictive analytics almost sell themselves – business leaders would pay anything for the opportunity to see market and customer trends before they become apparent to anyone else.

However, as with any technology-intensive initiative, doing predictive analytics right means investment and resources from the business. Ironically, it takes predictive analytics to determine where and how the technology should be best pitched to the business.

Tony Cosentino, VP and research director for Ventana Research, outlined the finer points of selling and ensuring the success of a predictive analytics project in a recent post that draws from Ventana’s research into the topic. Here is his advice:

Get the business involved from the beginning. There may be just one department working with predictive analytics, but insights gained may affect other parts of the enterprise as well. “Predictive analytics can be transformational in nature and therefore the audience potentially is broad, including many disciplines within the organization,” says Cosentino.

Tie predictive analytics to business pain points. Executives need to understand “how specifically the deployment will impact their areas of responsibility and what the return on investment will be.” It’s essential to identify the specific requirements for each department and how predictive analytics will positively impact those requirements. Be specific. Ventana finds the most often-identified tangible business cases for predictive analytics include achieving competitive advantage (57%), creating new revenue opportunities (50%), and increasing profitability (46%).

Tie predictive analytics to other analytics efforts within the enterprise. There’s a lot of money being spent on various big data projects, tying a predictive analytics effort to these projects will help cost-justify them as well. “The business case can be made even stronger by noting that predictive analytics can have added value when it is used to leverage other current technology investments,” says Cosentino.

Incremental steps are best. As with many technology-intensive projects, things are much simpler when broken down into bite-sized, digestible pieces. “For complex initiatives, break the overall project into a series of shorter projects,” Cosentino advises. “If the business case is for a project that will involve substantial work, consider providing separate timelines and deliverables for each phase. Doing so will keep stakeholders both informed and engaged during the time it takes to complete the full project.”

Go beyond traditional measures. It may be more compelling to go beyond the traditional measures of competitive advantage and revenue benefits – though, as noted above, these are the leading benefits seen so far. Predictive analytics means new ways of thinking, so think about the hidden and not-so-hidden advantages it may also deliver. “A software deployment also can yield benefits related to people (productivity, insight, fewer errors), management (creativity, speed of response), process (shorter time on task or time to complete) and information (easier access, more timely, accurate and consistent),” says Cosentino. “Create a comprehensive list of the major benefits the software will provide compared to the existing approach, quantifying the impact wherever possible.”