Larger retailers spend significant amounts of their marketing budgets on "non-store" marketing programs, specifically coupons and free-standing inserts (FSIs). Yet the vast majority of these programs are not customized in any way to reflect regional or local differences in shopping behavior.
The lack of variability in traditional marketing programs has been driven largely by the desire to maximize the reach of media buys; media companies and media buyers have promoted the idea of uniformity as the most cost-effective way to achieve reach. By having one national FSI, for example, production costs could be kept as low as possible.
But, with the current economic pressures facing media properties, advertisers have the leverage to push for a more targeted approach that can boost traffic based on more localized conditions. In order to execute on this, retailers must be able to develop a data-driven strategy for their non-store marketing. Retailers' own customer data combined with third-party industry data on category sales can help tailor the content of coupons and FSIs and increase in-store traffic.
The place to start is with the categories that represent a significant amount of current budget for these forms of advertising. Perform a geo-demographic analysis of customers' spending patterns across these categories, based on a defined geographic trading area for each store or cluster of stores. Then use third-party data to identify total household spending for these categories across the same geographic areas for member stores.
The result will be a store-by-store analysis of where the amount spent by your customers for that category is either over- or under-indexed for that specific geographic market. For example, if a retailer offers personal-care products, it could create a category-based, store-by-store analysis that would look something like this:
Personal Care Products |
||
| Store 1 | Store 7 | |
Percent of Store Market that Purchase Weekly |
25% |
25% |
Percent of your Customers that Purchase Weekly |
20% |
25% |
Index |
0.8 |
1.0 |
Percent of Store Market that Purchase Monthly |
50% |
50% |
Percent of your Customers that Purchase Monthly |
45% |
55% |
Index |
0.9 |
1.1 |
Such an analysis would identify what geographic markets show higher levels of spending for these products per household than the retailer is currently getting from its own customers at each of its stores. Armed with that information, the retailer can really get started making changes to coupon, flyer or FSI distribution and target more personal-care product offers to these markets.
Discussion Questions: Are more localized approaches required for FSIs? What are the challenges of using customer-data analysis in implementing more localized FSI approaches? How does a retailer weigh localized benefits versus the cost efficiencies gained from broader FSI approaches?