Move Your Analytics Operation from Artisanal to Autonomous

Move Your Analytics Operation from Artisanal to Autonomous

Move Your Analytics Operation from Artisanal to Autonomous

Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation.

There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t.

Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

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.

Read Also:
Predictive analytics in healthcare helps manage high-risk patients

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.

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.

Read Also:
Open data as a game

 



Read Also:
Data science is easy; making it work is hard
Read Also:
The Benefits of Centralized BI in Healthcare
Read Also:
Predictive analytics in healthcare helps manage high-risk patients
Read Also:
LinkedIn Knowledge Graph – KDnuggets Interview

Leave a Reply

Your email address will not be published. Required fields are marked *