The secret sauce of retailers: Using data to compete in the new digital world

retail market penetration

Retail is in a period of transition. In the past, when retailers wanted places to sell their products, they would open stores that would serve as distribution points. This required significant capital investment, as well as promotion to drive consumers into the stores, but retailers could at least control where consumers purchased their products, and develop promotional strategies centered around a consumer visiting their store location to purchase their products. As long as the store generated enough sales to make its store management costs profitable, it was fine because the consumer had no other channels from which to purchase the product.

Then came Amazon. And everything changed.

While before, retailers could leverage their stores to communicate the benefits and differentiation of their products, consumers can now gather information about the retailer’s product and its competitors and make a decision without ever visiting and hearing a direct pitch from the retailer. In the past, retailers could promote a price cut to help drive sales. Now consumers can visit e-tailers and competitive websites directly and make immediate decisions on price. And perhaps most critically, retailers incur all costs for maintaining a brick and mortar location because it could promote its brand, products and services directly to the consumer. Now, it must compete against Amazon and other e-tailers, which do not incur typical brick and mortar costs, putting themselves at a strategic disadvantage.

The results have been devastating. Retailers closed 102 million square feet of store space in 2017, and another 155 million square feet in 2018, according to the commercial real-estate firm CoStar Group (Peterson, 2019). This year does not appear to be trending any differently, as 7100 retail locations were closed through the first six months.

In this environment, it becomes more critical than ever for store-based retailers to understand and get to know their target customer, find lookalike prospects, and determine the growth opportunity for each store. Below are three best practices from Experian Marketing Services’ Custom Analytics team:

1. Understand and group current customers into actionable, strategic segments.

In today’s world where a significant amount of information is available, retailers can learn a lot about their customers. Leveraging transactional data allows a retailer to identify its most profitable customers by looking at the length of the customer relationship, and the amount of sales customers have generated for the retailer. Knowing the recency and frequency of a customer provides insight into the loyalty of the customer, in whether it was a one-time event versus an ongoing relationship, and whether the customer was still actively shopping with retailer. Knowing information such as product purchased, even at a category versus an SKU level, can help the retailer understand which of its products drive profitability, and which can be used as loss leaders. Collecting the channel used by the customer to purchase is also important because it gives insight into the methods the customer used to purchase the product, and how to promote them to purchase again.

Third-party data about this customer, such as data from Experian Marketing Services, is also critical. Knowing the demographics, interests and activities, behaviors, attitudes and financial situation of current and potential consumers enables retailers to tailor its advertising to channels that are more relevant to its target customer. For example, an expensive direct mail campaign to a target customer that primarily gets its information through electronic means like email, digital display, online videos or television may be fruitless. Similarly, advertising through digital means may also not reach the target customer that primarily uses direct channels for gathering information and prefers to make its purchasing directly at a store location.

Having this information also enables retailers to identify different groups of customers and adjust their marketing strategy. Retailers often have a variety of different customers where one overall marketing strategy like a direct mail campaign or television campaign may be effective for one segment, while a digital campaign may be more effective for the other. Developing these strategic segments and profiling each of these segments using transactional and third-party data gives retailers insight that can be used to more effectively measure and determine who its best customer segments are, and how much a store location factors into the customer’s purchase decision.

2. Develop predictive models that identify targeted prospects across all active markets.

Once a retailer understands its target customer, the next step is to determine where those targets are located with respect to its current stores and determine its market penetration and saturation around its existing stores. Here, a retailer can develop look-alike prospect models of its target consumers by strategic segments using a data scientist with traditional or artificial intelligence methods. The model will determine what sort of characteristics define the target customer from the general population and enable the retailer to mathematically generate a list of consumers that look much more like the target customer versus the general population. This is critical for retailers looking to measure market penetration because focuses on prospects that are generally in the market for the retailer’s product, as opposed to the overall population, which may include many consumers that would not realistically be targets, such as products targeting a specific gender, age, or income level.

3. Measure the market penetration and opportunity of targeted prospects by store location.

Once the prospect models are developed, the resulting algorithm can be applied to a file of U.S. consumers and ranked from high to low based on the likelihood of looking like the target customer. The retailer’s current customers are matched and tagged, so that the retailer can understand where its current customers are related to targeted prospects, or those that have a higher probability of looking like the target customer. Then, the retailer’s stores are matched so that they can compare the location of current customers to targeted prospects, along with its current store locations. Retailers may also want to apply distance to store filter, whether it be in miles or time, but this helps to further focus targeted prospects to a set of consumers that are more likely to be a customer at the store.

Once this is done, reports are created that measure:

  • Market penetration – The percentage of targeted prospects that are currently customers for each store’s market area. This is used to determine whether a store has captured a significant number of targeted prospects already and determine the store’s prospects for growth.
  • Market opportunity –The number of targeted prospects available in the footprint of the store. This determines the prospects for growth for that store.

These measures are computed for all of the retailer’s stores, then indexed so the retailer can identify stores that have significant or minimal capture of the existing market. Market penetration gives insight into whether the store is overperforming or underperforming and gives the retailer insight into what promotional strategies are working well for stores, and whether individual store management changes are needed to improve performance. Market opportunity enables the retailer to understand whether there is potential for growth of an existing store, and whether there are markets with a significant number of targeted prospects available that would represent an opportunity for expansion.

Leveraging these data and analytics best practices, retailers have a powerful tool for managing store locations and promoting products. Knowledge is power, and knowledge about prospective and existing customers can yield powerful results, including:

  • Who your target customer is, how long they have been a customer, what products they buy, the frequency and recency of purchases and how much the retailer has sold.
  • The customer’s attitudes related to the product, how they process marketing information, and where they make their buying decision.
  • Which prospects to target, where they are located, and how close they live to the retailer’s stores.

In the end, data and analytics can help retailers understand, at the store level, its market penetration, growth opportunity and identify new markets to which to expand so that it can make strategic decisions related to store locations.

Want to learn more about Experian’s retail solutions? Visit https://www.experian.com/marketing-services/markets.html or email us at experianmarketingsolutions@experian.com.