Predictive analytics FAQ #1: What does it take for predictive analytics to deliver business value – what are the prerequisites for success?
Predictive analytics can only succeed with the right ingredients in place:
- One or more experts in-house or deeply engaged
- A business case for predictive analytics deployment, such as one of the business applications listed in this article (i.e., a way a predictive model can and will be used, rather than just being a nifty model that may not provide business value); management buy-in for the integration and deployment of predictive scores
- Sufficient data to train a predictive model for the prediction goal at hand
- General understanding and buy-in of a predictive analytics initiative by stakeholders across business functions
- Implementation of organizational process best practices. For analytics, this means CRISP-DM (Cross-Industry Standard Process for Data Mining — www.crisp-dm.org) or equivalent. An iterative process that ensures comprehension, feedback and buy-in is attained across a group of relevant managers at key phases of a predictive analytics project
- When initial deployment success is achieved, sufficient executive buy-in to facilitate long-term maintenance that keeps the deployment alive and effective
Some of these are elusive; if one goes astray, adoption or longevity is not attained.
The good news is that in fact these ingredients usually do exist for mid-tier to large companies – and often for smaller companies, if they have data pertaining to enough customers or prospects. And, with these ingredients place, predictive analytics delivers high returns – significantly higher than analytics that are not predictive in nature. An IDC study showed that predictive analytics initiatives show an average ROI of 145%, in comparison to 89% for non-predictive analytics (“Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study,” September, 2003).
What makes the difference is widespread understanding and buy-in, executive buy-in (and perhaps a bit of executive understanding :), and adoption of best practice business processes (on top of killer core analytical methods). This is where Predictive Analytics World comes in. There’s no better way for non-experts to learn what predictive analytics does and how it works – and to become convinced of its effectiveness – than named case studies, which is why PAW’s program is built primary of such success stories, across verticals. See the full program, at https://www.predictiveanalyticsworld.com/agenda_overview.php