AutoML’s ability to detect patterns and predict can out-perform algebraic formulas and Boolean logic in common tasks.

Anyone who has written a formula in Excel or adjusted parameters in an online application knows how even the smallest change can produce dramatically different results. The power of mass calculation is exactly what puts algebraic and Boolean logic at the core of nearly every productivity tool you use. But there are costs and risks.

“I just blew up my whole workbook.” “That one little setting changed the whole forecast?” “You should have told me that was an important criterion when we wrote the code.” Sound familiar? Programmed logic is limited by our ability to understand the problem and represent it in reliable formulas. Precision and timeliness are critical, so hard-coded logic requires careful maintenance too.

AutoML works by letting data speak for itself. By finding patterns and applying them to new data, machine learning can obviate coded logic and go straight to the answers. Here is just one example, lead scoring:

CRM and marketing automation systems have features to rank leads by creating a “score.” Sales teams use lead score to prioritize activities. But scores are generated by applying weights that users set manually. How many points for a whitepaper download? How do page visits and time-on-page bump the number? Do three clicks on email calls to action mean I have a hot prospect? In practice, it is nearly impossible to fine-tune scoring models well and quickly enough for them to be useful. (More on this scoring example in this blog post.)

By learning from patterns of known outcomes in existing data, AutoML can make predictions on new data. Lead tables contain tens or hundreds of columns of data, and AutoML discovers which are most predictive automatically. That means a prioritized list of leads can be generated in minutes based on the very latest trends with no coding. Squark AutoML even reports which variables were most important, so you learn buyer persona as a nice side benefit.

The takeaway: Find opportunities where AutoML can free you from the shackles of programming logic.

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