Data as a Service Business Model
Organizations have always depended on data — to manage operations, to communicate with customers, to pay employees and suppliers, to plan their futures, and so forth. Those with the best data have enjoyed distinct advantages — in commerce, for example, better understanding the market leads to better products offered at better prices, and so forth. Data has enabled strategy, but, with few exceptions, neither driven strategy nor sat at its heart. That’s changing.
Data is invading every nook and cranny of every sector, every company therein, every department, and every job. As it does, it’s flexing its strategic muscles, and four ways to compete with data are starting to emerge. The first involves cost reduction through improved data quality. Too much data is not up-to-snuff, and it wreaks all sorts of havoc — the drone strike that killed an American and an Italian because no one knew they were there is one spectacular example.
Fortunately, most data quality issues are more mundane, but in aggregate they add enormous cost as people and departments check and correct data that just doesn’t look right before making a decision; reconcile data from sources that employ similar, but not quite-the-same, data definitions; and fix mistakes — rerouting packages that were sent to the wrong address, correcting customer bills, and redoing decisions gone wrong. In a nutshell, improving quality takes those costs out, largely by creating data correctly the first time.The sums can be enormous. AT&T, for example, saved tens of millions yearly in a single department and company-wide, giving it a great advantage. Improved data quality also lies at the root of the second strategy, which I call “content is king.” This strategy aims not to reduce internal cost, but to provide additional, more relevant, or newer data to the customer. There are at least three types of content providers. Pure content providers, which capture new or novel data, or data that simply had not been captured before, and build a business around it.
These businesses include Bloomberg and Morning star, which provide data about financial markets. Uber is another example. It found a way to connect “I need a ride” and “I’m looking for a fare,” and provide a service that people love. Similarly, Fitbit counts steps and provides some simple summaries that help people better manage their fitness. I expect an almost limitless parade of such opportunities as drones, the Internet of Things, and nanotechnologies create data only dreamed about a few years ago. Informationalization of existing products and services, where you build in data that customers need.
Route guidance, based on GPS in cars, and the mountains that turn blue on a can of Coors when the beer is cold are classic examples. The strategy appears quite profitable — some years ago Wired magazine predicted that, by 2010, half the value in the delivery of a shipping container, from half-way around the world, would lie in the data associated with the contents. My friends in the industry tell me that bogey was met by 2005. Infomediation, where you do not to create new data per se, but mediate the exchange, helping people find the data they need. Think Google. There will be lots of mediators; after all, more data means more data to exchange. For example, I view much of the data about myself as mine.
Any number of companies would like that data, and I’d be happy to provide it to some, at a price. But how, and at what price? I’ll love the company that mediates on my behalf! “Building a better data mousetrap” — or data-driven innovation — is the third way to pursue competitive advantage through data. Indeed, discovering a game-changing relationship previously hidden in the data and using big data and advanced analytics inspires data scientists everywhere. More practically, the strategy aims for a series of small discoveries, a few larger insights, and, maybe, just maybe, the occasional big one. The strategy has deep roots in science and is beginning to prove itself across a broad spectrum.
Insurance companies are developing new understandings of risk, retailers are better stocking their shelves, and human resources is finding new sources of talent, just to name a few. Finally, the fourth strategy is to become increasingly data-driven, in everything one does. Academic research suggests that the strategy is very profitable. To simplify just a bit, the idea is that everyone — individually and in groups, up and down the organization chart, across internal silos, and with business partners — makes better decisions by using more and better data.
Since the data can only take the decision-maker so far, they must learn to combine that data with their intuition. Everything is just a little better as result. Of course, competing through lowered cost, providing better products, and innovating are nothing new. Similarly, managers have always strived to make better decisions. Driving these efforts with data, however, is new. And for most, data is an unfamiliar asset, with different sorts of properties. It will take real effort to relearn these strategies. But the benefits could be huge for your business.
Build to Order. Tailored products and services configured to the customer’s specifications—a customized traffic-pattern analysis for a city planning department, for instance, based on location data from multiple GPS devices—may increase customer satisfaction and perceived value, while a high degree of specialization can create barriers to new entrants. On the downside, customers may have to wait longer to obtain customized products or services, which are also often difficult to resell.Service Bundle. In this model, several offerings are combined into a single offering. For example, a retail energy company might combine gas and electricity delivery with a monitoring service to help customers save energy.
Bundling can be profitable, drive rivals from the market, and open up opportunities to cross-sell or up-sell existing products. However, once products and services have been bundled, it can be difficult to separate them and hard for customers to assess the value of each component of the offering. (See “Better Bundling in Technology, Media, and Telecom Markets: Four Simple Rules,” BCG article, March 2013.)Plug and Play. Here the same product is sold to every buyer. An example might be a bank that sells high-level reports based on aggregated and anonymized data about customers’ spending patterns.
Such offerings can be easy to deliver, lend themselves to discounting strategies, and increase margins through economies of scale. But customers may consider them to be of lower value than build-to-order products because of the lack of personalization, and their transactional nature can increase the risk that customers will switch to a competitor.The remaining four business models differ in terms of the duration of the relationship with the customer, from short term to long term.Pay per Use. This option gives customers easy access to a wide selection of offerings, but they only pay for what they actually use—for example, on-the-spot ski insurance based on the user’s location.
While it offers improved product margins compared with subscriptions (discussed below), this business model does not create a stable source of revenues, and the sometimes high cost of customer acquisition must be factored into the profit equation.Commission. A bank that analyzes credit card transactions and offers discounts to stores and restaurants that agree to pay a fee, usually based on the revenue generated, exemplifies this business model. The relationship is generally stronger and more long lasting than the one associated with the pay-per-use model, because of the ongoing nature of the revenue-sharing arrangement.
However, a high degree of variation can creep into the offering. Companies must also consistently add value in order to increase the fees that customers pay.Value Exchange. In this model, a partner standing between the company and the customer offers some kind of rebate, discount, or additional service, depending on the business. For example, a bank could offer a merchant discount brokered by an intermediary, crediting cash back to the customer upon completion of the transaction. Value is generated in the form of a commission paid to the partner by the company and the monetary benefit delivered by the company to the customer.
By targeting only customers of interest, the company improves the return on its marketing investment, but the presence of an intermediary that captures value from the customer may be a long-term disadvantage.Subscription. With subscriptions, the customer pays a periodic fee for unlimited access to a service over a set period. For example, a health care company could analyze electronic medical records and provide an anonymized-information service on patient outcomes.
The subscription model ensures a predictable revenue stream with good potential for up-selling and cross-selling of additional products or services. The downside is lower margins than those typically generated by the pay-per-use model.Of these models, those that involve the delivery of products and services to a mass market predominate. This is not surprising, given the need to quickly get to market with nascent services in the early days of an industry. In the future, we expect that even greater value will be generated by bundling and build-to-order offerings, particularly those that secure a longer-term relationship with the customer and create greater engagement.
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