- Good analytics (I'll combine the three terms into 'analytics' for the sake of simplicity) requires an understanding of the tools, as well as a significant understanding of statistics so that you know which analysis to pick. But in addition, it requires a lot of creativity (see my examples below) and a significant amount of time to analyze/slice/dice data in a zillion different ways.
- This is a huge opportunity. Much, much bigger than people realize and much bigger than past trends of new technologies like client-server in early nineties or web apps of 4-5 years back. Why? Because it has the power to affect business processes very powerfully.
- Example 1: I spent 10 months working for a $5B shipping company analyzing data from their Marketing department. I combined it with several hundred global data sources. I worked on over 100 hypotheses. At the end of it, I came up three specific actions that their existing customers take about 6 months before going to a competitor. The Marketing department was thrilled. They spent $17 Million coming with a plan to tackle this. It has been a few months since then; and they have not lost a single customer. This is a powerful proprietary competitive weapon for them now.
- Example 2: I analyzed 10 years of power meter reading data for a large utility company. I combined it publicly available data sources of power consumption of major appliances and census data on family composition/wealth for various neighborhoods. I was able to reliably predict the lifestyle of every family, down to whether the person living in the house streamed a movie on Friday evenings and a whole lot more. So the company decided to use this analysis to change their Direct Mailers with very specific, personalized offerings. Their response to the first test mailer sent to 10,000 people? Twenty seven percent!!! They predict that a significant portion of their profits would come from DM's.