Potential: Revealed

Strategic Thinking, Innovative Ideas, Growth Marketing, and Revealing of Potential

Predictive Analytics: How it Works (1)

Sorry for the delay between posts. For past month or so we’ve been working on a very interesting project dealing with product ideas based on financial transaction data and powered by predictive analytics. While we are working to develop some early prototypes we have also been talking about challenges that need to be addressed when taking such products to market.

One issue over and over has been risk of market launch failure due to lack understanding of how analytics work (often lacking even rudimentary let alone deep understanding). A majority of key stakeholders – potential customers and internal business unit and functional area team – have heard of and are relatively convinced of the potential for analytics to optimize decision making. Whether that be to improve marketing effectiveness or precision of sales forecasts. Yet the basis for belief is often what they’ve read about or been led to believe by others. Analytics are not perfect and an important approach to achieving long term benefits from analytics is experimentation, challenging current results, and continual tuning of analytical models. We can foresee a gap forming where confidence in what is being developed and sold to clients falters due to lack of basic understanding of predictive analytics.

So, I thought I’d put together a brief series of posts (sort of like I did on “Practical Strategy” a little while ago) to explain predictive analytics.

The essential building block of predictive analytics is the predictor. It is a value calculated for each entity to be predicted – for instance the recency, in months, since a customer’s last purchase. Typically, the higher the calculated recency the more recent was the last purchase. As you’d expect, a good predictor is usually a reliable variable that consistently improves accuracy of some decision or action. Such as “customers with a high recency value typically have a higher response rate to marketing programs.”

There are other predictors that might work better with certain actions or decisions. For example, if you have an online subscription-based service, customers who spend less time logged on are less likely to renew annually. Tuning attrition or churn reduction campaigns by targeting customers who have low usage predictor values can boost effectiveness.

To make prediction even more precise you can use more than one predictor at a time. In doing so you are creating a model. Models are the heart of predictive analytics. Some simple models that might predict likelihood of a customer to renew their subscription:

– Linear – adding predictors together. For example: Recency + Household Income.

– Behavioral Rules – joining two or more behaviors with rules defining predictions of another behavior. For example: Usage (high or low) and Responded to Offer in Past 3 Months.

The best predictors will be predictive models that combine multiple aspects of a customer (e.g., demographics) and their behavior. A predictive model characteristically must be deeper and more complex than the above examples – uniting sometimes dozens of predictors. More on determining the best predictive model and harnessing rich sources of data to create powerfully predictive analytics in the next post. Thanks for reading and let me know if you have comments or can share your own experiences.

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4 Comments»

  marketingagent99 wrote @

Are you saying that simpler predictive analytics are not enough? Aren’t there places where simple is good enough or even best? I like this post though because even though my company uses some strong modeling to improve our marketing we still don’t always speak the same language on analytics. The basic definitions are important for every to understand.

  Randy wrote @

Good question 99. I might paraphrase Jim Collins from Good to Great, “good enough is the enemy of great.” But in practical terms good enough at any given point in time can be great. It is usually only in hindsight that you recognize clearly that what was great before is not only good enough or possibly less. So I’d say don’t get hung up on whether current models are great but whether the current ones are better than what you were using before. And that the next day you are actively looking to make current “champion” models the loser in a contest against a “challenger” model you’ve developed. The simple example is where all that is being done today is targeting your customers based on a profile, say, of how what zip code they are in. If you only added predictor that targeted based on recency of last purchase you would likely get an improvement in results. That’s a very simple model yet it is possibly a marked improvement for your business. Hope that helps. R

  adamgrizzly wrote @

I know you love that Jim Collins book. You quote it all the time to me! I continue to like this angle you’ve been taking lately on “practical” ways to approach complex things like strategy and now analytics. I’m adding it to my vocabulary to use in our management team meetings. Grizz.

[…] analytics, data-driven, decision-making, experimentation, insights, marketing, Practical In the first post about predictive analtyics we learned about the essential building block of predictive analytics: the predictor. This is a […]


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