Past Analytics Excellence Awards
Author: Jeffrey Jones (@JeffreyJones), Chief Analytics Officer
Company: Bristlecone Holdings
Awards Category: Name Your Own - Most Entertaining (but Super-Practical) Use of Alteryx
Describe the problem you needed to solve
Our marketing department needed a working Sex Machine, but that sort of thing was strictly prohibited in our technology stack.
Describe the working solution
Analytics built a functional Sex Machine! Let me explain...
Because our business involves consumer lending, we absolutely cannot -- no way no how -- make any kind of decisioning based on sex or gender. Regulators don't want you discriminating based on that and so we don't even bother to ask about it in our online application nor do we store anything related to sex in our database. Sex is taboo when it comes to the Equal Opportunity Credit Act. But the problem was that the marketing department needed better insight into our customer demographics so that they could adjust their campaigns and the messaging on our website, videos, etc., based on actual data instead of gut instinct.
Well, it turns out the Census Bureau publishes awesome (and clean) data on baby names and their sex. So we made a quick little workflow to import and join 134 years of births in the U.S. resulting in over 1.8 million different name/sex/year combinations. We counted the occurrences, looked at the ratio of M to F births for each and made some (fairly good) generalizations about whether a name was more likely a "Male" name or "Female" name. Some were pretty obvious, like "John." Others were less obvious, like "Jo." And some were totally indeterminate, like "Jahni."
Then we joined this brand new data set to an export of our 200k customer applications and were able to determine the sex of around 90% our applicants fairly reliably, another 7% with less reliability, and only 3% as completely unknown. The best thing about it is that we were able to answer these questions completely outside our lending technology stack in a manner disconnected from our decisioning engine so as to maintain legal compliance. We also didn't have to waste any money or time on conducting additional customer surveys.
This was literally something that was conceived in the middle of the night and had been born into production before lunch on the following day. (bow-chicka-bow-bow) Doing this wouldn't have been just impossible before Alteryx, it would have been LAUGHABLY IMPOSSIBLE. Especially given the size of the third-party data we needed to leverage and the nature of our tech stack and the way regulation works in consumer lending.
Describe the benefits you have achieved
It sounds silly, but our organization did realize tangible benefit from doing this. Before, we had no idea about a critical demographic component for our customers. It's impossible to look a bank of nearly 200k names across four totally unrelated industry verticals and conclude with any kind of confidence sex-related trends. Now we can understand sex-related trends in the furniture, bridal, pet, and auto industries. We can link it to the products they're actually getting and tweak the messaging on our website accordingly. And what's more, we're able to do all this in real-time going forward without wasting any of our DBAs' time or distracting our legal department. This probably saved us a hundred man-hours or more given all the parties that would have needed to get involved to answer this simple demographic question.
We should probably tidy up this workflow and the .yxdb because it might be useful for other companies who want to get a full demographic breakdown but don't have any pre-existing information on customer sex. If anybody wants to know the total number of people born with every name for the last 134 years and needs the M:F occurrence ratio for each, holler at me.
