The Data Daily

Best Practices for Analytics Profiles

Last updated: 05-14-2018

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Best Practices for Analytics Profiles

In our Big Data engagements, we talk about the importance of building detailed “profiles” of our most important entities, such as customers, products, devices, machines, employees, partners, stores, wind turbines, cars, ATMs, etc. As part of our data science process, we build a profile on each individual entity that:

1)    Captures that entity’s tendencies, propensities, patterns, trends, behaviors, relationships, associations, affiliations (plus, in the case of humans, interests and passions)

2)    Compares that entity’s current state and recent transactions, activities, and interactions to their individual profile in order to flag “unusual” activities and behaviors that might be indicative of a problem or monetization opportunity

But what do we mean by the word “profile,” and what elements might comprise a profile?

A profile is a combination of metrics, key performance indicators, scores, business rules, and analytic insights that combine to make up the tendencies, behaviors, and propensities of an individual entity (customer, device, partner, machine). The profile could include:

A profile could be made up of hundreds, if not thousands of different metrics and scores that—when used in combination against a specific business initiative like customer retention/up-sell/reference, predictive maintenance, supplier quality, or on-time shipments—can improve the predictive capabilities of the model.

Let’s review in the table below what a profile might look like for a particular customer. Note that I have grossly oversimplified the profile to facilitate the explanation and because I can’t process anything more complex myself. My data science team is probably rolling over laughing in their Python, R, Mahout and SAS toolsets as they read this.

Some metrics and scores are more important than others, depending upon the business initiative being addressed. For a financial services firm focused on customer acquisition, certain data (disposable income, retirement readiness, life stage, age, education level, and number of family members) may be the most important predictive metrics. For customer retention, however, metrics such as advocacy, customer satisfaction, risk comfort score, social network associations, and select social media relationships may be the most important predictive metrics.

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