I was therefore pleased to see the Financial Times (FT) publish a series of articles on consumer data. Accompanying this series is an interactive calculator  that allows readers to determine a price for their personal data based on pricing benchmarks supplied by a data broker. After filling in the options for my own profile - in terms of demographics, family and health, property, activities of interest, and consumer behaviors - it appears that the price for my personal profile is about US$0.80.
Pricing information in the FT’s calculator shows how the market is developing in 2013. ZIP code (post code) data for a person in the USA for example can be obtained for a fee of $0.0005. In 2006, this data would have cost $0.50 according to a survey  of the main data brokers of that time.
The rapid price decline, a CAGR of over 60% between 2006 and 2013, is not merely due to lower costs of data collection. It also points to a significant escalation in the use of data for consumer profiling and marketing. This price-volume dynamic is a common feature of the IT and mobile industries.
In practical terms, companies that purchase consumer data sets are obliged to buy in bulk. If we assume, for illustrative purposes, an average price of $0.25 for a single profile then an extract of 10m consumers would cost $2,500,000, an amount that categorizes this as a large-enterprise undertaking. This data would then need to be analysed to identify promising targets to sell one or more offers (for now, let’s put aside the possibility that the data is used for other purposes, such as being part of a customer relationship management activity and hence an operational cost).
Of these targets, only a sub-set would make an actual purchase. A one-in-ten success rate would equate to an expenditure of $2.50 per customer, without accounting for the costs of data analysis, marketing and sales; a one-in-thousand success rate yields a figure of $250 per customer. Now, a one-in-ten success rate is quite optimistic because of the snap-shot nature of the data.
Time-series data would be much more powerful because it would improve the ‘richness’ of an individual consumer’s profile; it would also provide the seeds for contextual marketing. Location information is another dimension that would add to the ‘richness’ quotient of an individual's profile.
An even better approach would involve the use of “interactive-data” which could be used for closed-loop marketing. An example of this is where an offer could be made to a consumer based on his/her profile information. Their reaction would then be gauged ("interactive data" is derived by linking cause and effect data points) and revised offers could then be made. What this means is that if an adventurous traveller made a vacation hotel reservation in Timbuktu last June, that traveller would not be bombarded with hotel offers for Timbuktu in June of this year when he/she declines Timbuktu adverts or where there is credit card evidence of a hotel booking in June for a different destination.
For a rough idea of the value that consumers place on rich forms of data exchanged for services here are the results from a 2010 study carried out by Aricent and Frog Design . This study attempted to quantify the value of personal data that individuals would give up in exchange for a “free” IT service.
The data comes from a sample of 180 individuals and needs to be used with caution. In addition to being a small sample, it is not clear to what extent the survey is representative of the general population. Also, “revealed value” does not necessarily correspond to a willingness to pay.
One opportunity that the FT and Aricent/Frog Design data does point to is the opportunity for service provider companies to engage with consumers. Specifically, there is a much higher perceived value to time-history and location forms of data compared with ‘static’ profile data.
Finally, even at price points of a few tens of cents per profile according to the FT calculator, a simple success-rate analysis of current day consumer data suggests that prices are very much on the high side.
 Connected Life: The Need for New Business Models, GSM Association (2012)
 Companies scramble for consumer data, Financial Times, (June 12 2013)
 How much are your personal details worth? Bankrate.com, (21 February 2006)
 Smart Home Opportunity: Balancing Customer Data and Privacy (3 February 2010)