Eating Groupon’s LunchJune 19th, 2012 | Posted by in Uncategorized
Over the past few months, we’ve seen several companies put effort into unlocking the vast insights of transactional data. Using this type of data, American Express has launched targeted, merchant-funded offers, and Bank of America is piloting a program to provide tailored deals inside online account statements. Both Google and PayPal are exploring ways to use this data to open new advertising channels. Furthermore, PayPal has also begun offering Point of Sale terminals to major US retailers such as Home Depot, in an effort to improve loyalty and expand the breadth of their transaction log data.
The transaction log – commonly referred to as “t-log” – details every transaction conducted by a card, account, or customer. For many, using this data isn’t a new concept. Retailers have had the ability to look at their sales data for several decades and have aggregated it to understand the general behavior of their customers. The richness and size of t-log data has the potential for companies to increase loyalty, expand customer relationships, and increase profits.
There are a couple of recent trends that have brought t-log back into the spotlight. Retailer and financial services companies now sit on aggregated t-log’s for millions of customers. With the rise of computing power, it is extremely valuable to be able to leverage this detailed data in near real-time.
Imagine the possibilities that could arise if a company could look at not only their t-log, but also the transactions of nearby competitors. This ability has the potential to improve promotional targeting far beyond the capabilities of the Groupon-type sites. For instance, instead of making a public daily deal, which captures many “cherry-pickers” and existing customers, one could target it to only those consumers who are already spending on the same industry and but not at that store/restaurant/hotel. In this way, deals can be designed to avoid the risks of not generating incremental recurring customers.
T-log provides additional insights in comparison to the data that’s currently offered by social networks and daily deals sites. Financial services companies are in a unique position as they have up-to-date identifiable, location-based, and demographic information. Many of the other sources for this data may be inaccurate (e.g. social networks) or simply unavailable (e.g. spending patterns). See Figure 1 for more details about the unique advantages of data captured by financial services firms.
While these trends show an exciting frontier, it remains to be seen how financial services will use this data and how successful they will be at it. Innovative and successful businesses will take advantage of all of these trends, gaining deep insights in an efficient way. We’ve only scratched the surface so far.
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