Modern Analytics groundbreaking “autonomous predictive analytics” recognized by world’s leading expert on advanced analytics

In early December, Modern Analytics was recognized by the world’s leading expert on analytics, Tom Davenport, in an article he wrote for the Harvard Business Review. The article, titled “Move Your Analytics Operation from Artisanal to Autonomous,” uses Modern Analytics as an example of how automated predictive analytics can significantly increase a company’s propensity modeling output and value. The following excerpt highlights the section about Modern Analytics and Model Factory, but we encourage you to read the article in full.

To illustrate the movement from “artisanal analytics” to “autonomous analytics,” I’ll provide an (anonymous) detailed example. The company involved is a large, well-known technology and services vendor, with over 5 million businesses as customers, 50 major product and service categories, and hundreds of applications. Each of its customer organizations has on average four key buyers. The company needed to target sales and marketing approaches to each company and potential buyer. To do this, it created a score for each customer executive, reflecting their propensity and ability to buy the company’s offerings, so that sales and marketing approaches could be more effective.

This approach is called “propensity modeling,” and it can be done with either traditional or autonomous analytics approaches. Using traditional human-crafted modeling, the company once employed 35 offshore statisticians to generate 150 propensity models a year. Then it hired a company called Modern Analytics that specializes in autonomous analytics, or what it calls the “Model Factory.” Machine learning approaches quickly bumped the number of models up to 350 in the first year, 1500 in the second, and now to about 5000 models. The models use 5 trillion pieces of information to generate over 11 billion scores a month predicting a particular customer executive’s propensity to buy particular products or respond to particular marketing approaches. 80,000 different tactics are recommended to help persuade customers to buy. Using traditional approaches to propensity modeling to yield this level of granularity would require thousands of human analysts if it were possible at all.

Harnessing the power of machine learning and other technologies.
There is still some human labor involved. Modern Analytics uses fewer than 2.5 full-time employees to create the models and scores. 95% of the models are produced without human intervention, but in the remaining cases people need to intervene to fix something. The technology company does have to employ several people to explain and evangelize for the models to sales and marketing people, but far fewer than the 35 statisticians it previously used.