Everyone knows that Michael Dell built a giant computer business from scratch in a college dorm room. Less well known is how he got started: by selling newspaper subscriptions in his hometown of Houston.
You see, most newspaper salespeople took lists of prospects and started cold-calling them. Most weren’t interested. In his biography, Dell describes using a different strategy: he found out who had recently married or purchased a house from public records – both groups that were much more likely to want new newspaper subscriptions – and pitched to them. He was so successful that he eventually surprised his parents by driving off to college in a new BMW.
This is an example of data monetization – the use of data as a revenue source to improve your bottom line. Dell used an example of indirect data monetization, where data makes your sales process or other operations more effective. There is also direct data monetization, where you profit directly from the sale of your data, or the intelligence attached to it.
Data monetization has become big business nowadays. According to PWC consulting firm Strategy&, the market for commercializing data is projected to grow to US $300 billion annually in the financial services sector alone, while business intelligence analyst Jeff Morris predicts a US $5 billion-plus market for retail data analytics by 2020. Even Michael Dell, clearly remembering his newspaper-selling days, is now predicting that data analytics will be the next trillion-dollar market.
This growth market is clearly being driven by massive growth in data sources themselves, ranging from social media to the Internet of Things (IoT) – there is now income and insight to be gained out of everything from Facebook posts to remote sensing devices. But for most businesses, the first and easiest source of data monetization lies in their contact and CRM data.
Understanding the behaviors and preferences of customers, prospects and stakeholders is the key to indirect data monetization (such as targeted offers and better response rates), and sometimes direct data monetization (such as selling contact lists or analytical insight). In both cases, your success lives or dies on data quality. Here’s why:
- Bad data makes your insights worthless. For example, if you are analyzing the purchasing behavior of your prospects, and many of them entered false names or contact information to obtain free information, then what “Donald Duck” does may have little bearing on data from qualified purchasers.
- The reputational cost of inaccurate data goes up substantially when you attempt to monetize it – for example, imagine sending offers of repeat business to new prospects, or vice-versa.
- As big data gets bigger, the human and financial costs of responding to inaccurate information rise proportionately.
Information Builders CIO Rado Kotorov puts it very succinctly: “Data monetization projects can only be successful if the data at hand is cleansed and ready for analysis.” This underscores the importance of using inexpensive, automated data verification and validation tools as part of your system. With the right partner, data monetization can become an important part of both your revenue stream and your brand – as you become known as a business that gives more customers what they want, more often.