Imagine tying SKU data to an individual customer, adding in that person’s loyalty purchasing and mixing it all up with the magic of market-basket analysis.
The result may be every retailer’s dream: a concoction of product, message and reward that encourages a higher spend or more trips to that store.
These days, loyalty programs are becoming more sophisticated, not just in their ability to customize rewards but also in fulfilling the larger picture of letting people do more in a mobile environment—everything from getting coupons on their birthdays to paying for stuff.
Recently, Brentwood, Tenn.-based MAPCO added mobile payment via PayPal as a way to reach out to millennials. But Howard Curtis, the brand and marketing leader who focuses on loyalty, digital and mobile innovation for MAPCO, sees loyalty in a holistic way, saying the company’s app needed payment on top of other loyalty-driving features such as digital rewards, promotional messaging and store locating.
“We understood that to win their loyalty, we needed to speak to them in a way that they’re listening,” Curtis says. “We needed to be more appealing and relevant.”
Both Jenny Bullard of Flash Foods, Waycross, Ga., and Scott Hartman of Rutter’s Farm Stores, York, Pa., say finding digital strategies will be a big trend in 2015, with mobile being the common thread. (See story on p. 110.)
To help retailers make the most of mobile and other evolving technologies, providers are morphing. Kickback Rewards,
Twin Falls, Idaho, traditionally a coalition-based loyalty firm, recently brought to market a data-analysis solution that can help marketers create promotional offers relevant to specific segments of customers.
CONTINUED: Using Big Data
According to Pat Lewis, Kickback’s president, these people would have common characteristics, needs and perceptions of value that retailers can leverage to drive new, more profitable behavior.
The solution uses multiple computers in a “big data” environment to do the kind of complex analytics once reserved for major corporations. “Our biggest enabler has been advances in computing and database technologies that have come around in the last two or three years,” Lewis says. “It has brought down costs and created an ecosystem of big-data elements.”
One investigative scenario involves fuel. A retailer would start by creating what Lewis calls a “value chain,” which places consumers in value buckets based in order of importance to the business. If that value is amount of purchase, then the lowest percent might be the No. 10 group in the value chain, with the highest bucket No. 1.
“The interesting thing you find is that it’s easier to move people in the lower value [rank] up through targeted promotions than people at the top end because of the level of consumption, especially on fuel, that they’re already spending,” Lewis says.
Using additional databases involving census data, ZIP codes and demographic information, the consumer segmentation gets more specific. At the end of the analysis, a retailer can develop appropriate promotions for each of the segmented groups.
The data also gives retailers parameters for building visuals for ads, point-of-purchase materials and billboards, selecting things such as age and gender of the people in the ads, the price points that the segment may consider of value and the types of products that are a draw.
“The day where data drives decisions is here to stay,” Lewis says. “It’s something that’s going to be the bedrock of a lot of different technologies, allowing things that were never reasonable before.”
He envisions multiple databases, “whether relational or not, whether data-structured or not structured, to find correlation, insight and lead to better business decisions or new solutions.”
How effective are these apps or technologies at driving sales?
| Loyalty or rewards programs | 46% |
| Retailer app | 28% |
| Limited-time or “flash” sale | 26% |
| Multi-retailer coupon apps | 18% |
| In-store “quick response” or QRcode promotions | 14% |
| Bluetooth or proximity-based promotions | 6% |
| Multi-retailer apps | 6% |
Note: Based on general population of 50 retail decision makers, responding “significant effect”
Source: A commissioned study by Forrester Consulting on behalf of SPS Commerce, July 2014
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