Reprinted by permission of Harvard Business Review Press. Excerpted from Competing on Analytics: The New Science of Winning – Updated, with a New Introduction. Copyright 2017 Thomas H. Davenport and Jeanne G. Harris. All rights reserved
At a time when companies in many industries offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation. Many of the previous bases for competition are no longer available. Unique geographical advantage doesn’t matter in global competition, and protective regulation is largely gone. Proprietary technologies are rapidly copied, and breakthrough innovation in products or services seems increasingly difficult to achieve.
What’s left as a basis for competition? To execute your business with maximum efficiency and effectiveness, and to make the smartest business decisions possible: to make your organization an analytical competitor, wringing every last drop of value from business processes and key decisions. As the case of Netflix shows, competing on the basis of analytical capabilities, and being highly successful in your industry, are not unrelated attributes.
That dent in his wallet got him thinking: why didn’t video stores work like health clubs, where you paid a flat monthly fee to use the gym as much as you wanted?
In 1997, a thirty-something man whose resume included software geek, education reformer, and movie buff rented Apollo 13 from the biggest video-rental chain on the block— Blockbuster— and got hit with $40 in late fees. That dent in his wallet got him thinking: why didn’t video stores work like health clubs, where you paid a flat monthly fee to use the gym as much as you wanted? Because of this experience— and armed with the $750 million he received for selling his software company— Reed Hastings jumped into the frothy sea of the “new economy” and started Netflix, Inc.
Pure folly, right? After all, Blockbuster was already drawing in revenues of more than $3 billion per year from its thousands of stores across America and in many other countries— and it wasn’t the only competitor in this space. Would people really order their movies online, wait for the US Postal Service (increasingly being referred to as “snail mail” by the late 1990s) to deliver them, and then go back to the mailbox to return the films? Surely Netflix would go the route of the many internet startups that had a “business model” and a marketing pitch but no customers.
Netflix employs analytics in two important ways, both driven by customer behavior and buying patterns.
And yet we know that the story turned out differently, and a significant reason for Netflix’s success today is that it is an analytical competitor. The online content creation and distribution company, which has grown from $5 million in revenues in 1999 to $8.3 billion in 2016, is a prominent example of a firm that competes on the basis of its mathematical, statistical, and data management prowess.
Netflix employs analytics in two important ways, both driven by customer behavior and buying patterns. The first is a movie- recommendation “engine” called Cinematch that’s based on proprietary, algorithmically driven software. Netflix hired mathematicians with programming experience to write the algorithms and code to define clusters of movies, connect customer movie rankings to the clusters, evaluate thousands of ratings per second, and factor in current website behavior— all to ensure a personalized web page for each visiting customer.
Netflix also created a $1 million prize for quantitative analysts outside the company who could improve the Cinematch algorithm by at least 10 percent.
Netflix also created a $1 million prize for quantitative analysts outside the company who could improve the Cinematch algorithm by at least 10 percent. It was an innovative approach to crowdsourcing analytics, even if the winning algorithm was too complex to fully adopt. But no doubt Netflix’s data scientists learned from the work and improved the company’s own algorithms. CEO Reed Hastings notes, “If the Starbucks secret is a smile when you get your latte, ours is that the website adapts to the individual’s taste.”
Netflix analyzes customers’ choices and customer feedback on the movies they have viewed— over 1 billion reviews of movies they liked, loved, hated, and so forth— and recommends movies in a way that optimizes the customer’s taste. Netflix will often recommend movies that fit the customer’s preference profile but that aren’t in high demand. In other words, its primary territory is in “the long tail— the outer limits of the normal curve where the most popular products and offerings don’t reside.”
Now that Netflix is solidly in the business of creating new entertainment, the company has used analytics to predict whether a TV show will be a hit with audiences before it is produced. Netflix has employed analytics to increase the likelihood of its success. It has used attribute analysis, which it developed for its movie recommendation system, to predict whether customers would like a series, and has identified as many as seventy thousand attributes of movies and TV shows, some of which it drew on for the decision whether to create it.
Analytics can support almost any business process.
Netflix may seem unique, but in many ways it is typical of the companies and organizations— a small but rapidly growing number of them— that have recognized the potential of business analytics and have aggressively moved to realize it.
Analytics can support almost any business process. Maybe you strive to make money by being better at identifying profitable and loyal customers than your competition, and charging them the optimal price for your product or service. If so, analytics are probably the answer to being the best at it. Perhaps you sell commodity products and need to have the lowest possible level of inventory while preventing your customer from being unable to find your product on the shelf; if so, analytics are often the key to supply chain optimization. Maybe you have differentiated your products and services by incorporating some unique data and proprietary algorithms. Perhaps you compete in a people- intensive business and are seeking to hire, retain, and promote the best people in the industry. There too, analytics can be the key.
Analytics themselves don’t constitute a strategy, but using them to optimize a distinctive business capability certainly does. Whatever the capabilities emphasized in a strategy, analytics can propel them to their highest level.