Marketing Machines — Possible or Pipedream?
5. Run The Model: We generate predictions against a new or holdout sample of the same format of the same source of data. You can’t run this predictive model on the same sample you used to build the model. This begins the iterative process
6. Iterate ... Then Do It Again: As is any process where you're engineering new outcomes for the first time, this process is generally iterative. It’s usually not realistic to expect a killer result on the first pass. You’ll likely massage inputs and training methods a number of times before the output starts looking good. More on what a good output looks like in a future column, though. For now, you need to know that the first product won’t likely be the final product.
The Bottom Line — 'Easier' Still Isn’t Quite 'Easy' for the Average Marketing Organization
While Amazon and Google may be among the easiest websites to use, and have made tremendous contributions to the proliferation of data science by providing structure and programming tools with which organizations can develop new capabilities, using Amazon AWS for Machine Language and Prediction is not for the creative marketer or even the “traditional” Web marketer.
There is also a rising category of upstarts in data-driven and database marketing apps that add intelligence to the process and can provide marketers with a significant head-start in advancing their marketing intelligence.
Data Science requires a combination of technical, mathematics/statistics and marketing/business skills. This combination is in great demand the world over, and so it’s not easy to hire top contributors to implement all of this. But for organizations with the programming bench, or external experienced business partners, tools like AWS and Google Cloud Platform can provide a substantial leap forward in using data to make superior decisions.
Remember, the outputs of the predictive process don’t have to be “right” 100 percent of the time — and they won’t be. They only need to make the numbers break in your favor enough to have a material impact on your revenue and profit now — and over time.