Personalization Is Back: How to Drive Influence by Crunching Numbers

By: Eric Siegel, Ph.D., Founder, Predictive Analytics World
Originally published at information-management.com


Standard predictive analytics does not directly address what is the greatest challenge faced by marketing and healthcare: Across large numbers of individuals, deciding who to treat in a certain way.

Yes, you heard me correctly. Predictive analytics still needs a certain tweak before it’s designed to optimize organizational activities.

Let’s take a step back. The world is run by organizations, which serve us as individuals by deciding, for each one, the best action to take, i.e., the proper outgoing treatment:

TREATMENTS: Marketing outreach, sales outreach, personalized pricing, political campaign outreach, medication, surgery, etc.

That is, organizations strive to analytically decide whom to investigate, incarcerate, set up on a date, or medicate.

Organizations will be more successful, saving more lives or making more profit—and the world will be a better place—if treatment decisions are driven to maximize the probability of positive outcomes, such as consumer actions or healthcare patient results:

OUTCOMES: Purchase, stay (retained), donate, vote, live/thrive, etc.

In fact, the title of my book itself includes a list of such outcomes: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.

But this book title—and predictive analytics as a field in general—may lead you astray by implying the best way to improve the probability of these actions (or, alternatively, the probability of averting them, in the case of the latter two, lie and die) is to predict them. However, predicting an outcome does not directly help an organization drive that outcome. Instead, treatment decisions are optimized when organizations predict something completely different from outcome or behavior:

WHAT TO PREDICT: Whether a certain treatment will result in the outcome.

Click here to read the full article in Predictive Analytics Times.

 

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