Insight, Analytics and the New Marketing
What should I pay for a piece of data?
People have been buying and selling data about consumers for a long time. Companies like Axciom have been doing this for years in the direct marketing business. But recently a new breed of companies has been popping up who are acquiring and selling data about consumers in the online advertising space. Companies like BlueKai and Datran are the modern day, digital equivalent of the Axcioms. The good old direct marketing techniques from the 80’s and 90’s are now also being used for targeting online advertising ads. Therefore data collected about consumers can now be used for smarter targeting across all direct and digital channels. This broader playing field will dramatically grow the size of the data reselling business in the next few years.
So with so many companies buying and selling data about consumers, what really determines the value (and therefore the price) of a data point? I think there are 3 drivers.
1. Predictive Power
The 1st driver is Predictive Power of the data point. Let’s say for example that I am a manufacturer of drills and that I am trying to purchase data points that will help me identify whether a consumer is interested in buying a drill. And let’s assume that I can choose between the following 2 sets of data points.
|
Set 1 |
Set 2 |
|
Number of hours spent on DIY per week |
Number of vacations taken per year |
|
The number of hammers owned |
Interest in water sports |
|
Size of the house owned |
Age |
Most people would agree that the data points in set 1 are more valuable for a drill manufacturer than those in set 2. This is because of their natural correlation with someone’s likelihood to purchase drills. This example is very straightforward. If you had to determine the Predictive Power of a 100 different data points however, you would have to build statistical models that predict the likelihood of someone buying a drill based on all 100 data points. Those that enter the model have a high Predictive Power which can be quantified by the lift they generate in the models. Whether you build statistical models or not, the principle is that data points with a high Predictive Power will improve our prediction of whether a consumer will be interested in buying a drill and, as a drill manufacturer, I am prepared to pay a higher price for them.
2. Recency
The 2nd driver is Recency. This is really a special case of Predictive Power but I want to call it out separately as it has become an increasingly important driver. In a digital world people often reveal real time what their intentions are. Knowing whether a person has searched for drills on Google, whether they have clicked on a banner for drills or whether they have seen a drill related video online can be very powerful. These data points generally outperform the more traditional data points that are listed in the example above because they are direct indications of a consumer’s interests and needs at a certain point in time. For these self disclosed data points, Recency is very important. When someone searches for a drill on Google then that is very valuable information if I can target that person immediately. However, if I know that someone searched for a drill 3 months ago then that single observation in itself is a lot less valuable. The predictive power of self disclosed data points starts to decline minutes after the observed event. Because of the disproportionately high value of very recent data we anticipate most of the future innovation to focus on capturing multiple events real time and shortening the cycles between observed events and the ability to use that knowledge for targeting. This is already happening on advertising exchanges through the introduction of Real Time Buying.
3. Exclusivity
The final driver is Exclusivity. Let’s use the same example and let’s assume that I can only buy the data points in set 1. Let’s also assume that I have built a statistical model and have determined that the general predictive power of the number of hammers a consumer owns is far more predictive than the other 2 data points. I would be prepared to pay a relatively high price for data on hammer ownership. Now consider an alternative scenario where one additional data point is available: the number of nails a person uses per year. Let’s assume that the general predictive power of nails consumption is almost as high as that of hammer ownership. The availability of nails consumption will have an effect on the price I am prepared to pay for hammer ownership. It’s the basic laws of supply and demand.
In the next few years the buying and selling of data will undoubtedly become a lot more streamlined. When that happens, the market drivers described above will increasingly determine the price companies are willing to pay for information about their consumers. Consumers on the other hand will get a much more transparent view of the value they are generating by allowing companies to collect their data. Who knows, maybe they’ll even be able to claim their share of the pie.
I think you’re right about the drivers of value, but, in the long run, it’s not value, but cost which determines price. Think of the diamond-water paradox. Water is very useful, but very inexpensive. Diamonds aren’t very useful, but expensive to mine.
In the case of data, some data are very useful (e.g., web site visits) and powerful for behavioral targeting. Other data–and I would include most attitudinal survey data in this category–are certainly less reliable and predictive, yet will remain fairly expensive to collect. Unless we come up with a much cheaper way of collecting them, they will remain relatively expensive (compared to the mass of data that can be collected by cookies and web scraping). The result will be that more research will move to the less expensive sources, because of both its cost advantage and usefulness.
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