3 Helpful Tips From NRF, Retail’s Biggest Expo

Retail is changing fast.

Point-of-sale data, the weather, local events, social media and other data sources are creating unique windows into a customer’s life, transforming the industry. They are creating snapshots into who they are. What clothes they need and when, why they purchased that particular type of baseball bat, and what kind of mattress they may need next.

By analyzing aggregated views of users, data scientists at retail companies can derive insights about how customers arrive at certain store locations.


Learn About IBM MetroPulse


The explosion of user location data presents a new opportunity for retailers – the ability to gain insights into who is visiting their stores and when. While traditional point-of-sale data provides information about what products are being sold, geo-location data adds the additional layer of who is making those purchases, giving retailers a deeper view into who their customers are.

Tip #1: Understand Your Customers

While point-of-sale data can tell retailers when customers are coming to their stores, geo-location data can tell them where those customers came from, and where they went next. This allows companies to build demographic profiles around customers based on both their shopping patterns and on their neighborhood’s characteristics.

Do customers visit a store on their commute to work, or in the middle of a long day of shopping? Are there a list of similar stores that customers will shop at before or after visiting? Do more of a store’s customers buy their coffee at Starbucks, or at Dunkin Donuts?

Being able to correctly identify those customers has a variety of applications, from improving targeted marketing at the store level, to alternating product mix to align with the shifting demographics of customers.

By creating profiles of customers around each store, merchants can begin to understand the characteristics of neighborhoods and users that draw in the most shoppers.

Tip #2: Identify New Store Locations

Profiles can be valuable for merchants considering an expansion into a new market, or who are trying to optimize their store locations.

Analyzing correlations between existing store performance and neighborhood characteristics, companies can rapidly pick out new cities and blocks within cities full of similar target customers, automating the market research necessary for expansion.

Not only does this speed up the market research process, but the use of high-quality location targeting can provide new insights about types of shoppers that go beyond traditional demographics, while identifying other hyper-local characteristics conducive to sales.

Tip #3: Research Customers Going to Competitors

Finally, the same analysis we apply to a store’s customers can be easily applied to competitors’ customers.

This allows merchants to ask questions about the difference between two brands.

What are the demographic differences between a store’s shoppers and their competitors? Which brand is preferred by customers coming from higher-income neighborhoods? Do certain marketing segments prefer one brand over the other?

Answering these questions can help merchants better serve the shoppers that prefer their store, or capture market share from their competitors. Since these comparisons use the same methodology and user-base, they provide the narrowest comparison between two brands.

Adapt to Your Environment and Personalize Each Store

Retailers have an increasing number of data flows and technologies at their disposal. Data from e-commerce, point-of-sale, and membership programs are all improving retailers’ abilities to understand and target their customers.

High-quality location data can supplement these sources and help merchants improve their targeted marketing, identify new areas of opportunity, and understand trends among their competitors.


Watch the Video


North American Leader, Advanced Analytics Practice Global Business Services

More C-Suite stories

Transforming Talent Acquisition in Life Sciences, Part 2

Succeeding as a talent leader has never been more important.

Continue reading

Nurture Candidate Relationships Through Lead Manager Automation

One of the most efficient ways you can nurture relationships with talent is by using the automation capabilities of a candidate relationship management (CRM) tool. IBM Kenexa® Lead Manager, a marketing and relationship-building CRM that integrates with the IBM Kenexa BrassRing applicant tracking system, offers a number of automation settings, such as automatically creating a […]

Continue reading

Transforming Talent Acquisition in Life Sciences, Part 1

Pricing pressures and increased public scrutiny are compelling life sciences organizations to demonstrate value through the effectiveness of patient outcomes. The more targeted treatments these companies develop for patients, the more specialized talent they require. As global life sciences companies have discovered, demand for this talent often outpaces supply. A demand for talent transformation IBM […]

Continue reading