Cause Marketing and Big Data
Few topics are hotter in information technology right now than big data. In science big data holds the potential to unlock the secrets of the universe from the sub-atomic level to the entirety of the cosmos. In business big data promises “the end of theory,” to use the phrase of Wired magazine editor Chris Anderson.
“Who knows why people do what they do?,” Anderson wrote in Wired. “The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.”
What application does this have for cause marketing and cause marketers? Well, imagine a paper icon campaign at the Walgreens store near my home for its longtime partner, the Juvenile Diabetes Research Foundation (JDRF).
Paper icons are typically slips of paper, often in the shape of charity logos, sold at retail for $1 or more, and benefiting one or more causes. Paper icons, others call them pinups, are among the most elemental and easiest to understand cause marketing efforts, and among the most successful. Retailers offer them to customers at checkout. I tell my clients that they should offer them to every customer, because it’s impossible to know who will say yes. Once asked, people either buy them or they don’t.
When the retailer does a lot of daily transactions, the law of large numbers means an appealing paper icon campaign can generate terrific ROI for the charity even if the individual response rate is low. It’s usually a different story for retailers with fewer daily transactions.
But imagine all the data points that could be collected into a dataset when someone buys a JDRF paper icon at Walgreens.
- Store #, city, state, etc.
- Clerk name, age and (probably) gender.
- Day, week, month, year.
- Name and address of the customer if they use a credit card.
- Coupon usage.
- Average ticket receipt.
All that kind of information is gathered at every transaction or can be gleaned from the transaction data. That’s not big data, per se. (Unless we’re talking about Wal-Mart, which is said to collect information on 1 million transactions per hour. Nor is it big data in the sense of what’s collected by the large Hadron Collider, which generates 13 petabytes a year. A petabyte is 1,000 terrabytes.
But think of all the other available data that could be overlaid onto that basic dataset when a JDRF paper icon is sold at Walgreens.
- The other items purchased with the paper icon
- The weather and the forecast at the time of the transaction. The Walgreens in question is the closest pharmacy to two major ski resorts, so weather or even snow depth could certainly bear some relationship
- The number of people in line at the time of the transaction
- The kinds of items they purchased. Wouldn’t it be interesting to know if the paper icon purchaser also bought diabetes supplies (or candy bars)?
- Whether the customer purchased other items at full-price or at discounted prices
- The type and placement of paper icon point-of-purchase materials
- Staffing in the store.
Data could get even crazier still. Store data is almost certainly available for things like if the HVAC system was on at the time, whether or not lights in the parking lot were active during the transaction, even what music was playing overhead in the lead-up to the transaction, plus much more.
What implications could any of this hold for cause marketers?
Well, big data might be able to tell us that JDRF’s paper icons in Walgreens sell better on cloudy days to women aged 24-28 with more than 10 items in their shopping cart and including one six-ounce yogurt carton. In such cases, the clerk could be prompted by the point of sale register to propose a JDRF paper icon at a suggested donation of $3.
Nonprofit fundraisers have never known that much about the mass of people who donate to their causes. And so they use demographic and psychographic profiling techniques to guess at who donors are and what motivates them.
But big data, applied to cause marketing, could help causes concentrate on the efforts and campaigns that do work and the people they’re likely to work on. Not what theory suggests should work.