Guest post from Laura Cahill, Product Marketing Manager, IBM Business Analytics
Follow Laura on Twitter @LA_Cahill
I live in the Northeast region of the US and though it is now officially spring, the weather and forecasts are not accurately reflecting the change in season. Freezing temperatures, snow, ice, and wind… enough already!
And, what makes me even more frustrated is that Mr. Punxsutawney Phil (the famous groundhog) promised us an early spring. Hog wash! I’ve had it with his inaccurate predictions, so I went to visit the oversized rodent to see if he could explain himself.
I have to say that his methods of prediction are pretty antiquated (printed Excel documents in three ring binders, a tattered Farmer’s Almanac from 1985, tarot cards and an abacus) and he still didn’t provide any suitable answers.
While there are almost unlimited amounts of historical data available, and predictive analytics technology that he could integrate into his decision process, he’s simply relying on…his shadow! Essentially, he’s guessing and crossing his sharp little fingers that he is accurate. Are you kidding me? He said that sometimes his shadow is right, but that is not something I would sleep well on at night – in any business scenario.
He explained that he was just too overwhelmed with big data and didn’t know how to begin deploying a business analytics solution. I talked to him about some great alternatives and their benefits, but all he could say was that he came from a small town, had small town funding and small town skills. Did he not know that small town ground hogs could have access to more accurate predictions without the fancy skills and resources? Has he been hibernating or something?
One easy way for Phil to get quick analysis and accurate predictions is to try IBM Analytic Answers – a portfolio of cloud-hosted apps that deliver predictive and prescriptive information for business users who don’t have extensive technical skills.
Unfortunately for Phil, the current set of apps, including Insurance Renewals, Retail Purchase Analysis and Offer Targeting, Student Retention, and Prioritized Collections, does not include forecasting the start of spring. But, it just so happens that predicting spring is Phil’s February gig. For his real career, he runs a small insurance firm and he explained his frustration of losing customers to competition when they don’t renew at the end of their term.
He’s tried to improve his decision making by applying the “shadow method” to predict if a customer would stay loyal: if he saw his shadow while making the decision then they were at risk, and if he didn’t see his shadow they would renew.
What’s crazy is he works in an office with overhead lighting, so he saw his shadow for all customers and was therefore predicting they may all be at risk, and was thereby offering everyone juicy incentives to renew, which was getting way too expensive and resulting in poor ROI on his retention marketing spending. What he needed was to segment his customers and target his resources more effectively. He just didn’t have the tools or methods to do so.
After a short time thinking in his hole, he appeared and explained that he was going to try IBM Analytic Answers for Insurance Renewals, and in a few weeks will be up and running.
Here’s how it works: after sending his customer data to IBM, the IBM’s analytics team customizes his subscription based on his customer data, and after the initial setup (for which there is no charge), he will simply pay monthly on his annual subscription.
Phil decided that every two weeks he’ll upload the current list of policies due for renewal and Analytic Answers will predict which ones are likely not to renew, what the odds of renewal are, and what action will most likely convince the customers to stay with his firm, such as a policy discount or additional VIP benefits. His marketing and communications department can’t wait to get their hands on this information so they no longer waste their time – and Phil’s hard-earned money – on folks that are not at risk.
Phil still wishes there was an app for predicting the start of spring, but I explained to him that the current Analytic Answers portfolio is just the starting point and to watch this space for future apps.
In the meantime, he’s looking forward to the quick ROI for his first implementation at his insurance company. He promises his shadow will never force him into making the same bad decisions over and over and over and over and over and over... (you get the point) at least in the insurance business.
As for predicting spring, well… it’s still his shadow for now.
For more information:
· Read more about IBM Analytic Answers and the current portfolio of apps