With all of the coverage these days around analytics and big data, it makes you wonder why every business -- large and small -- isn't embracing predictive analytics?
Two recent conversations reminded me of the difference between how laggards and leaders approach their peers and business leaders when it comes to adopting predictive analytics.
The Laggards and the Leaders
In the first example, the head of analytics for a Fortune® 500 entertainment company was frustrated by his Chief Marketing Officer's desire to "understand" his models. The analytics leader lamented that his boss will never “get" his model, since he wasn't a trained expert in modeling and statistics.
To him, predictive modeling was a complex and sophisticated pursuit that only those with the highest training and experience could grasp. What he failed to realize was that the CMO wanted to believe the model, not understand it. His failure to gain the CMO’s trust has led to his models sitting on the shelf to this day and has limited his organization's adoption of predictive modeling.
In contrast, a global-integrated supply-chain company was being challenged by its client on the accuracy of its forecasts. The client, an international fast-food retailer, was relying on its supply-chain partner for daily and weekly demand forecasts to optimize inventory at each restaurant. Instead of addressing the forecast error statistically, the modeler took a different approach. He took different items, such as hamburger patties, potatoes, etc., and translated the average error rates into actual orders or units of inventory. In other words, if patties can only be purchased in packages of 10 with 10 packages per case, he calculated the percentage of time when the forecast was off enough to order more or fewer cases.
By defining the model's performance in the language of the restaurant manager, rather than the language of the statistician, the modeler was able to show that the error was rarely large enough to change the number of cases ordered. The restaurant managers have now become advocates for the modeling solution in the field.
The Art of Predictive Analytics Persuasion
As these two examples illustrate, while there is much science behind what we do as analysts, there is an art to getting your organization to change its decision-making process. Whether you’re using traditional regression techniques or the latest machine-learning algorithms, there are some simple rules to keep in mind when adopting predictive analytics.
I call these rules of thumb the three “R-s” of predictive analytics -- Reliable, Repeatable, Relatable.
Reliable refers to the accuracy of your predictive model. A predictive model doesn't have to be perfect, but needs to be accurate enough to have a business impact. The art of analytics is knowing when a model is “good enough,” so you don’t burn cycles searching for perfection.
This is especially true when it comes to marketing or customer analytics where most modeling is comparative in nature and outcomes are measured by their likelihood or rate relative to the average or random outcome, for example:
- A wireless customer is 50% MORE likely to attrite than average based on her demographics, usage and payment history;
- A retail customer is more likely to purchase a particular product because his shopping pattern is similar to others who purchase that product.
Repeatable refers to repeatable results and repeatable process. Models need to be able to replicate their results across customers, time periods, and markets to be most useful to the business. Poorly built models may look good when they’re built, but are vulnerable to small changes in data and won’t hold up over future applications.
Repeatable process means building a predictive analytics framework that can be applied to different business problems. At a high level, we use a Define -- Diagnose -- Predict framework where you define your objectives through the right data and KPI’s, mine the data to explore and diagnose the problem and then build a predictive model that can be deployed going forward. Such a process turns analytics from a one-off project to a meaningful, ongoing business function.
Finally, predictive analytics must be relatable to the business users. Simply put, this means being able to present and explain analytics in business NOT statistical terms. As my opening examples show, this is critical to convincing your users to trust and believe in your models enough to put them into practice. Unfortunately, while this might be the most important of the three R’s, it’s often the least appreciated when it comes to analytics. The bottom line is that no matter how good a model is, if the user can't relate to it, the model will be either ignored or underutilized.
The challenge with this third “R” is that it is rarely taught as part of any analytics curriculum. Modelers typically learn this the hard way as they watch perfectly good models go unused because the business "didn't get it."
While the world of big data and predictive analytics seems to be ever changing, it's important to keep these “R's” in mind when building an analytic enterprise. By focusing on making predictive analytics reliable, repeatable and relatable, you’ll be able to turn your business insight into foresight that creates real business impact.
Title image courtesy of koya979 (Shutterstock)
Editor's Note: Hungry for more data? Read Darren Guarnaccia's 2013: The Year of the Customer and the Rise of the Data Driven Marketer