The recent Covid-19 pandemic seems to indicate that some traditional long-standing business models are no longer working - or that they cannot guarantee as smooth a ride as they did before Covid-19. Under these new and quite extraordinary conditions, firms may need to start innovating new business, and as one alternative, many firms might consider selling their data.
Indeed, executives have for some time complained that their companies have managed to collect vast amounts of unique, high-quality data, but at the same time they have not yet been capable of reaping maximum value out of the good data assets they have. The current Covid-19 might just be the needed spark for companies to finally start looking at their data as a sellable item, or as a tradeable asset as well.
The benefits of selling data are plentiful. In a world of unrelenting turbulence, selling data might help companies to build new scalable, recurring, and stable revenue streams, thus building resilience to their business and reducing dependence on legacy businesses and assets. Further, with data sales, companies may increase their relevance to their customers, open new avenues for growth, differentiate from the competition, and build an ability to bounce back from any adversity there might be in the marketplace.
In practice, selling data often means that raw data from the systems, processes, or operations of the company is sold to some other party, e.g. a customer, vendor, or partner, and ownership to data is typically given from the seller to the buyer. The data is often parsed, cleaned, aggregated and/or cataloged but not much more is done before sales. Hence, sales of data in its simplest form might be a rather rudimentary and straightforward way to quickly generate new revenue streams.
Examples of selling data
One can find examples of selling data from both B2B and B2C businesses:
- In the former, in the fast-moving consumer business, retailers such as Walmart, for instance, can sell their point-of-sales data to various brands (i.e. their suppliers) such as Procter & Gamble who can then keep the adequate stock of products in their warehouses and deliver just-in-time resupply of products to the shelves of the store.
- In the latter, a good example is Fitbit, the wearables and activity tracker products provider that offers sports-minded consumers a premium digital monthly/annual subscription offering, in which the consumer gets sleep quality and duration data, as well as personalized health and fit guidance.
It is quite common that sales of data takes place in the value chain of the seller: in other words, the buyer of data typically is a customer or vendor of the seller and thus someone that the seller has a relationship with already. On the other hand, there are many opportunities for companies to explore selling data outside their value chain or industry, too. As examples of this:
- iRobot, the company behind Roomba robotic vacuum cleaners is looking to start collecting mapping information of homes (e.g. distances between sofas, tables, lamps, and other home furnishings) and with its customers’ consent sell it to tech companies such as Apple to help improve their smart home information, thereby their future smartphone products and accessories such as artificially intelligent voice assistants as smart home interfaces.
- Sports clothes designing and marketing companies like Nike have started to collect data generated by smart sports clothes. In the future, they may start to offer that data to insurance companies, who can use the data to predict a customer’s health insurance needs.
In this way, two players that might otherwise have very little (if anything) in common might find joint business opportunities through data. Thus, the point is to think creatively: Who might be the parties that could benefit from the data that we receive from our operations? And, in which format would they prefer to buy our data so that it would create value?
The added value of the data to the buyer can be particularly high in case the data sets are scarce, unique, rich, complete, even real-time. But in practice, the price of data and the fees received by the seller might differ a lot, depending on the data assets in question.
There are several revenue models that could be considered for selling data. If there is a data dump, e.g. a certain static set of information, that is sold to someone only once, then it could be sold like any ‘product’, with a fixed one-time fee. Also, the fee could be charged per each device that is connected to the data supply chain of the seller. On the other hand, if there is constantly data available that is being sold to someone, then a monthly or yearly licensing fee might work very well. Also, various freemium models, project revenue models, and brokerage fees could come into question. Exploring options together with the buyers is often a good idea.
Selling data is a controversial topic
It is, however, good to keep in mind that while selling data might make commercially perfect sense, data sales is still a rather controversial topic that might spark various opinions, including resistance. Some people might fear losing the authority of their personal (or their company’s) data or lose transparency over how that data is being used. If a company chooses not to sell data (or sell data only to selected trusted closest partners) that choice might help it to reduce risks related to e.g. privacy, consent, ethics, and inappropriate data usage. Sometimes it also might make sense to avoid selling raw data, but turn it into reports or data platforms, in which the buyer gains only limited access to view some subset of aggregated and anonymized data.
In sum, given the Covid-19 induced headwinds in business, sales of data can be a nice opportunity for companies to build new scalable, recurring and stable revenue streams, find completely new customers, open new avenues for growth and overall differentiate from the competition. Yet, companies need also to be wary of the issues that are related to data sales; the potential negative consequences need to be recognized rather than glossed over. Most successful data commercialization examples typically incorporated both these two important angles: the business opportunity and the need to act responsively.
Business Director, Futurice Oy
+358 41 447 5652, firstname.lastname@example.org
The writer is a business professional with broad experience in digital transformation, digital media, eCommerce, digital marketing, and software solutions. His new book on data and AI-enabled business models can be found here: Growth Reinvented