Doing the Data Science That Drives Predictive Personalization

Doing the Data Science That Drives Predictive Personalization

Doing the Data Science That Drives Predictive Personalization

Agile collaboration within data science teams is essential to the vision of customer analytics and personalization. Attend IBM DataFirst Launch Event on Sep 27 in New York City to engage with open-source community leaders and practitioners.

Customer analytics is a prime proving ground for many business data scientists. One of their primary tasks is the building, testing, and revising of customer segmentation models.

Segmentation analysis can be a process of seemingly endless iteration. Typically, it involves testing and tweaking feature-engineering models in order to find the specific independent variables that are most predictive of some future scenario of interest. Data scientists identify segmentations through statistical exploration of relationships among variables such as gender, age, ethnicity, nationality, education level, prior purchases, and personality types.

Microsegmentation is the art of finding narrower customer segments—-in other words, smaller groups who share fine-grained behavioral affinities. Being able to drill into the entire aggregated population of customer data, including rich real-time behavioral data, enables you to do more fine-grained target marketing, nuanced customer experience optimization, and context-sensitive next best action. Also, if you have ample detail on all the inventory you carry and everything that customers have requested, no matter how seemingly unpopular, you can do powerful long-tail analysis on overlooked product niches of keen interest to specific customer segments.

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Pushed to the logical extreme in today’s business environment, microsegmentation can culminate in the vaunted “category of one,” also known as “predictive personalization.

 



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