Prescriptive Analytics at All Stages

I don’t know who or which analytics company started it, but we often hear that “prescriptive analytics” is at the top of the analytical food chain. I’m sure anyone who is even remotely involved in marketing analytics has seen that pyramid chart, where BI and dashboard reports

5. Recommend Next Steps. This is “the” hardest part of it all, and it requires teamwork.

    • Ask ‘So what?’ If you identified a problem, is there a way to fix it? Or is it even a problem? Let’s not over-prescribe medicine, as that is how analysts lose credibility.
    • Form a Hypothesis. “Why” is it happening? Look beyond obvious places. If it is hard to make connections between the dots, ask the stakeholders. Again, is there anything that analysts should have known? End-users, like bad patients who often forget to provide doctors all relevant information, make bad assumptions, too.
    • Always Make Multiple Suggestions, as decision-makers may reject the first idea. Be budget-conscious, as every division deals with limited resources. Do not take an easy way out or make habitual suggestions. Not every situation calls for complex statistical modeling as the next step.
    • Involve All Stakeholders in the Solutioning Stage. Analysts often try to be the hero, but they shouldn’t forget that this prescriptive analytics is a team sport.

This is just a high-level summary of the five-step process to provide insights out of large reports or even raw data. Prescriptive analytics is a lot like playing jazz music where no note is pre-written, and it is something that should be taught through countless case reviews via a master-apprentice relationship. But I hope this guideline helps kickstart insights-driven analytical practices. Even if one doesn’t get to go all the way through this list, each step will be a bragging point for the analyst and a step toward better insights.

The analytical landscape is changing rapidly, and analysts – and marketing organizations to some extent — who are unable to provide insights will be replaced by machines in the near future. To remain in this analytics game, analysts must be able to raise complex questions and answer them in forms of stories and recommendations. Or, to put it differently and at the risk of sounding like a cliché, to provide insights through prescriptive analytics.

Author: Stephen H. Yu

Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at

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