The book has two parts - one on the nature of analytical competition and another on building an analytic competency. The first part describes how analytics are used in both internal and external processes. The second part lays out a roadmap that describes how an organization can become an analytical competitor and how to manage analytical people. This section also provides a quick overview of a business intelligence architecture and offers some predictions for what the future of analytics holds.
The authors argue that organizations that use analytics extensively and systematically are able to out-think and out execute their competition. They also argue that organizations must have a strategic distinctive competency. Without one, they cannot be an analytic competitor. Their experience suggests, however, that analytical competitors may start with a primary focus, but the culture of test-analyze-learn spreads quickly and widely. To be successful, analysis has to be an overarching company skill and not just the province of a few rocket scientists.
While I generally agree with their perspective, I think that organizations sometimes confuse having a company-wide analytic mind-set with teaching everyone analytic skills. Many of the people in your organization do not need analytic skills or software so much as an understanding of when analytics and other decision automation help and how to use them effectively.
In addition, the book outlines what they call four pillars of analytical competition - a distinctive capability, enterprise-wide analytics, senior management commitment and large-scale ambition - and also lays out five stages of analytic competition from "analytically impaired" to" analytic competitor". The importance of experimentation is made clear, and the book repeatedly emphasizes the need for companies and executives to be willing to run their business "by the numbers."
The book is full of stories about how companies compete analytically, for example:
- Capital One's focus on identifying and serving new market segments before its peers can. They have a lovely concept of "deaveraging" - breaking a segment into small segments for better targeting.
- Marriott's total hotel optimization using a new measure called "revenue opportunity" - what percentage of the theoretical maximum revenue they actually made. They got this to rise an amazing 8 percent.
- Progressive Insurance is so certain that if another company offers you a better rate then you would not be profitable and so are willing to disclose what their competitors' rates are.
- The Veteran Administration's use of evidence-based medicine and predictive analytics, along with automated decisions for treatment protocols, is noted, as is the fact that perhaps only 25 to 30 percent of medical decisions are scientifically based.
- Honda makes good use of text analytics to flag early problems in cars by analyzing warranty claims calls by customers or dealers to headquarters.
- Vertex, a pharmaceutical company, starts by identifying the right metric to measure success and then drives into the data needed to measure that.
- Harrah's focuses on real-time analytics at the point of sale so that action can be taken as it is being collected.
- DnB NOR bank uses event triggers to prompt customer relationship offers, using analytics to trigger the right events.
- O2, a mobile phone company, uses personalized menus to maximize value of limited phone real estate and uses predictive analytics to add personalization.
- CEMEX used analytics to move focus from the sale of a commodity (cement) to the delivery window using analytics and GPS. They went from three hours for a change to 20 minutes.
- Netflix focuses on giving each customer a personalized website experience based on recommendations, ratings and segmentation.
The stories illustrate many factors, from creating new measures to tracking the right measures and from the need to change your perspective to the power of executive sponsorship. The book also has a great list of questions regarding new initiatives - how will it make you more competitive, what data do you need, does the technology work and what complementary changes need to be made in order to take full advantage of these new capabilities.
They outline a number of ways to get a competitive advantage from data - by collecting unique data, manipulating data better, using a unique algorithm or embedding it in unique process. Regardless of the competitive approach, the need for analytical executives' willingness to act on the results of analysis was clear: segmentation of customers is not enough...you must differentiate their treatment to make a difference.
There is a lot in the book about data quality - a major focus on getting a single version of the truth and clean, accurate data. Clearly, an analytic competitor will spend more time and effort on data quality than others, but is this cause or effect? My sense is that focusing on getting the data right first, without a view of the kind of analysis you are attempting, will get you into trouble as well as delay the benefits. Indeed, I don't think you should try and collect and clean all of your data before doing analytics, but rather, focus on what analytics are needed and see if the necessary data is accessible in order to fix the problem. The authors seemed to imply that consistent, quality data across the board was essential for analytic competition - something I don't completely agree with.
Take one of their examples - a bank refusing to waive a $35 bounced check fee for a customer who had a $100 million trust fund. Does the data need to be integrated to fix this problem? Well, integrating the customer data would be one way. But what about sharing the insight? The fact that a $100 million trust fund could lead the private banking group to identify the person as an excellent customer could be shared. There is still some integration - you must be able to identify that the customer is the same person in each case, but you don't necessarily have to integrate all the data.
