Just about every business is awash in data these days. From data about your customers and their buying habits to data about the market in general, you have an enormous amount of information right at your fingertips. But the question then becomes: how can you monetize that data?
I want to share a three tiered pyramidal approach to the monetization of data that will allow you to think about where you are and what the next steps might be. We will look at the credit industry which follows the framework of Aggregation, Analytics and Actionable Predictions.
Aggregation: At the bottom level of the pyramid, which is the most readily available and least valuable way to monetize data, is aggregation. What I mean by this is when you take data from different sources, including your own business, and merge it all together to create an integrated picture. While any of the data sources by themselves might be interesting, it's when you combine them that they become valuable. For example, consider your credit report. This is information the credit bureaus aggregate like what credit cards you have, whether you have a mortgage, and if you pay your bills on time. By pulling all this data together into a single report, the credit bureaus can sell that information to interested parties in some volume for about $1 a piece. That's not a lot of money, but they sell an awful lot of them.
Analytics: The middle level of the data pyramid involves applying analytics to your data, meaning that you process the data in a way that produces insights and helps paint a clearer picture. An example would be the creation of FICO scores, which give lenders a very concise picture of your credit score--which can range form 200 to 800. By analyzing your entire credit history and then assigning your credit worthiness a score, FICO becomes a far more valuable measure than just the raw or aggregated data involved. That's why FICO scores can cost around $5 each to purchase.
Actionable Predictions: That then leads us to the top of the pyramid--which is where you create actionable predictions out of your data. This might involve a process where you both aggregate and apply analytics to a particular market which allows you to predict the outcome of a situation and take action. You can use your data to give guidance and help make better decisions.
Consider the example of how you receive offers for credit cards in the mail where you are pre-approved for a certain dollar amount. That's not a random number. What's going on behind the scenes there is that a company ran the data they had on you and sold it to the credit card company, where they learned that, based on your history, there was a 99% chance you would be able to pay back the credit limit they were willing to approve you for. They might also be able to predict the likelihood that you will accept the offer. That's incredibly valuable information for a credit card company to have, something they would be pay from $25 to $50 for. Because they are able to take action on the predictive data - it is 25 to 50 times more valuable than simple aggregated data!
The example we explored shows how you can find ways to take your raw data and find different ways to monetize it. The more you can apply analytics and predictive analysis, the more valuable your data becomes.
So ask yourself what kind of data you have in your business and how you might be able to turn it into a profitable revenue stream using the model of Aggregation, Analytics and Actionable Predictions.