DDM - Retail Trade

Retail Store Planning Use Case

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use case For example, a retailer can use CRM data from key buying periods, such as Christmas or other holidays, as the basis for analysis. Retailers will typically append their CRM records with Economic Cohorts data and match customer households with Economic Cohorts clusters in each market. Top-performing clusters are identified and further comparison can be made against the national distribution of these clusters to see how the customer base compares. Profiles are typically generated for the top performing clusters in the customer base as a whole, and also for discrete store locations or markets. For a luxury retailer, a profile might include the following data points: ■ Income: Top clusters generally fall within select estimated total income ranges (e.g. $50-$100K, $100K-$200K), whereby households in the clusters are often likely to have the income needed to purchase the retailer's products. ■ Spending: Households in top clusters typically have higher-than-average spending and often comprise a significant percentage of the overall spend at the retailer. ■ Distance to Location: The distance that the highest-spending customers traveled to shop at current stores can be easily identified. ■ Age and Lifestage: Households' age ranges can be identified as well as which clusters have children in the household. Outcome: With the addition of economic insights, retailers can likely reduce their target audience and still reach a high-potential sweet spot of buyers Retailers can now combine economic data, insights, and prospecting lists from Equifax to enhance the efficiency of marketing efforts promoting new store openings, as well as on-going store campaigns. Economic Cohorts cluster information helps retailers paint a better-rounded picture of their high-value customers, which can then be applied to prospect lists. Using the Economic Scorecard of their top clusters, marketers can better define their preferred target audience(s) of customers and prospects for marketing campaigns in terms of their likely income levels, estimated discretionary spending capacity, and affinity to purchase comparable goods. To further narrow the target audience, a specific radius can be generated around each store that matches the estimated distance that existing customers travel to other locations. Detailed maps around potential new store locations can be generated using our data to show estimated household income ranges for potential buyers. Looking at the trade area for new or proposed stores, retailers are better able to identify households that fall within top-performing Economic Cohorts clusters. Prospect lists can be matched to clusters and, based on prior analysis, we have seen retailers reduce their prospect lists anywhere from 10-40%.* By focusing marketing efforts on likely high-potential customers and prospects, retailers can possibly reduce their marketing campaign spend, sometimes as much as 60-80%,* while still reaching a high-potential sweet spot of buyers. Retailers can better optimize their marketing spend by focusing on a sweet-spot of likely high-potential customers and prospects.

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