Creating Competitive Advantage Through Data

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Creating Competitive Advantage Through Data

Many executives and investors assume that it’s possible to use customer-data capabilities to gain an unbeatable competitive edge.  The more customers you have, the more data you can gather, and that data, when analyzed with machine-learning tools, will allow you to offer a better product offering that attracts more customers.  You can then collect even more data and eventually marginalize your competitors in the same way that businesses with sizable “network effects” do.

In certain cases, this scenario proves to be true.  But, in most instances, managers tend to grossly overestimate the advantage that data confers.

It all depends on the specific features of the market, the data, and the business.  As Andrei Hagiu and Julian Wright recently explained in Harvard Business Review, under the right conditions, customer-generated data can build competitive defenses, even when network effects aren’t present.

Obviously, companies built on data have been around for a long time.  Consider credit bureaus as well as information aggregators such as LexisNexis, Thomson Reuters, and Bloomberg, to name just a few.  Those companies are protected by significant barriers to entry because of the economies-of-scale involved in acquiring and structuring huge amounts of data, but their business models don’t rely on gleaning data from customers and mining it to understand how to improve offerings.

Gathering customer information and using it to make better products and services is an age-old strategy but until recently, the process was slow, limited in scope, and difficult to scale up.  For automakers, consumer-packaged-goods companies, and many other traditional manufacturers, it required crunching sales data, conducting customer surveys, and holding focus groups.  And the sales data often wasn’t linked to individual customers, and since surveys and focus groups were expensive and time-consuming, only data from a relatively small number of customers were collected.

That has changed dramatically with the advent of the cloud and new technologies that allow firms to quickly process and make sense of vast amounts of data.  Internet-connected products and services can now directly collect information on customers, including their personal details, search behavior, choices of content, communications, social media posts, GPS location, and usage patterns.  After machine-learning algorithms analyze this “digital exhaust,” a company’s offerings can be automatically adjusted to reflect the findings and even tailored to individuals.

These developments make data-enabled learning much more powerful than the customer insights companies produced in the past...

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