Location-enabled data and the insights to understand customer touch points and...

Location-enabled data and the insights to understand customer touch points and expectations

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The proverb “birds of a feather flock together” describes how those of similar taste congregate in groups. We live in neighborhoods in which we share many similarities with our neighbors. Yet, when we look further at the level retailers want for mass personalization, people can be more different than they are similar. The same is true for groups of customers, markets, trade areas and neighborhoods. These variations, individual activity, lifestyle, neighbors and place, are the key to unlocking a better understanding of shopping patterns and observed behaviors.

But how do we find out what’s really important to customer activity and store performance? It will require new ways of thinking about the way large enterprises provision location infrastructure and whom they empower with it. Retailers are increasingly realizing that to move from the technological and knowledge silos of cross-channel and multi-channel to omni-channel they need to be able to more effectively apply context and activity at scale. Location-enabled data and the insights gained by analyzing it with dedicated spatial models and what-if scenarios brings deeper understanding of customer touch points and expectations. This is especially true in making sense of purchasing, home delivery and product assortment.

Figure 1 shows the customer relationship management, who have been mapped with their corresponding attributes such as preferred store, total annual spend, delivery location, etc

 

Consumer behavior continues to evolve and change. Today's rate of change is an order of magnitude faster than three years ago driven by new technologies, affluence, availability and expectations. Business models and operations must change but often retailers struggle to identity how to change and the infamous failures which make the headlines in the press make retailers more nervous and cautious. They know they need a more sophisticated response if they truly want to impact the customer experience. I believe it’s time retailers started using real location insight to deliver on all those expectations — from boardroom and investors to customers. 

Integrating Location as a Service (LaaS) enables retailers to work more collaboratively and turn location-based data into answers to business questions. Let’s look at an example in the San Francisco Bay area using LaaS. Location data is far more prevalent and powerful than most retailers realize. It’s not common to think of the buying and selling process as happening because of where, so location data management and analysis are not at the center of most retail business activities. To compound things, none of the enterprise systems that most retailers use have not been designed to truly create, manage, and share location based insight.

 

Where are my customers?

In Figure 1, a store has over 14,000 customers in the customer relationship management (CRM) who have been mapped with their corresponding attributes such as preferred store, total annual spend, preferred method of payment, purchase channels, number of items purchased and delivery location. Every one of these attributes can be used to form a better picture of who customers are, where they shop, what they and their neighbors look like, and how goods and customer service can be improved to deliver higher loyalty and profitability from every customer.

Customer density

We can create a heat map of where customers are located to show concentrations. Any attribute can be used to calculate these density surfaces which are the basis of retail demographic analysis. Understanding the characteristics of customers, who they are, what they want and what else they might need allows retailers to better plan their products and services.  
Different variables create different surfaces of spend, supply, demand and market opportunity that make up the basis of retail models. Individual customer points can be aggregated to heat maps, ZIP Codes, sales territories, Census areas, States, MSAs or any unit of geography a retailer desires. 

Aggregation creates scores and summaries that can be used as inputs to other models and analysis. In Figure 2, thousands of individual customer transactions have been used to create a map of total sales from low (around $15,000) in blue to high ($135,000 and more) in red.

The nearest store

Customers could visit their nearest store, creating beautiful patterns of roads with the store in the center.  In the real world, people travel and shop for many different reasons. They may shop near where they work, or make a special trip to a flagship store. Regular mall trips may be part of the weekly or monthly shopping. There are any number of reasons based on habits, activities and preferences. 

In Figure 3, each customer has been assigned to the nearest store based on driving conditions. Each customer journey, between home base and the store, is broken into the individual roads used to get there. Each time a segment of road is used in a trip, it is counted so every road has the number of customers who would potentially use that road to get to the store. This creates a beautiful, organic pattern of dendritic roads as many different journeys feed into the same locations as customers get nearer to the store.

 

Purpose dictates purchase

People shop at stores for many different reasons. The store may be convenient from work, close to a point of recreation or as part of another activity. How, why and where people shop is complex.
In Figure 4, the Downtown store has customers coming from all over the region; many traveling for over an hour. Notice how major roads build on each other and the time taken to get to the store also varies by road type and potential maximum speed.

Connecting stores to customers’ homes

The top 5 stores in Figure 5 paint a complex pattern of interconnection between the customers’ home base and where they shop. Here we are looking at the third, fourth and fifth most popular stores. 
How do these stores differ in the people they serve, where those people come from and how far they are willing to travel? Can factors like demographics, spending behavior and liveability explain customer and store interactions?

 

Getting to know you

Esri Tapestry Lifestyle data helps retailers understand their customers’ lifestyle choices — what they buy, and how they spend their free time. Tapestry (Figure 6) classifies US residential neighborhoods into 67 unique segments based on demographic and socioeconomic characteristics. It enables retailers to get more insights on their best customers and underserved markets, conduct better marketing campaigns with higher response rates and turn less profitable areas into success stories.

Customer spending for each ZIP Code can be aggregated and analyzed against the dominant Tapestry segmentation group. Laptop and lattes are concentrated downtown, while Pacific Heights residents can be found in Daly City and enterprising professionals tend to be found near Sunnyvale. There are other distinct pockets — can you see where trendsetters like to hang out at home or the urban chic neighborhoods?

Educational attainment is often assumed to correlate with high disposable incomes and high spending but is this always true? In Figure 7, customer spend is shown using increasingly large circles and the circles are colored by the proportion of residents having a Bachelors’ Degree or higher. The distribution ranges from 8% (red) to 88% (green). 

Neighborhoods around Store 19 in San Jose show low levels of degrees and low store spending. Downtown areas have high concentrations of degrees but only average spending — perhaps they are servicing expensive apartments — while Sunnyvale has highly educated people and above average spending.

Spending and population density

Urban areas have much higher population densities than the suburbs. Downtown San Francisco has densely packed pockets and its famous Queen Anne homes around Alamo Square with beautiful bay windows, turrets, and decorated rooflines. Housing density impacts house size and affordability. Here average spend per customer (size) is mapped against population density (color). Generally spending increases away from the denser multi-story and multi-family downtown areas towards the more spacious suburbs.

Oakland and San Francisco highlight different patterns in lifestyle and spending behaviors. Oakland is dominated by trendsetters and city lights compared to the laptop and latte segment in San Francisco. All segments live in high-density areas but trendsetters tend to spend more on living life to the full and city lights will commute long distances to support their lifestyle. Each segment is summarized in Figure 6.

 

Wendy’s: Putting Lifestyle data to the test

Wendy's, the world's third-largest quick-service hamburger chain, integrates the Esri ArcGIS platform and Tapestry Lifestyle data with the restaurant's corporate IT systems. The Web-based business GIS solution is part of the company’s reporting system for new locations, assisting in site selection and market analysis.

The company feels demographic data and location analytics are critical components when making investment decisions to build new restaurants. Everything they need — including mapping, analytics, and modeling — can be done on one platform that is scalable across their organization. And their organization is significant — the Wendy’s chain includes more than,500 franchise and company-operated restaurants in the United States and 27 countries and US territories worldwide.

ArcGIS replaces a current system in use at Wendy’s. Implementation was completed by Esri business partner GIS, Inc. located in Birmingham, Alabama. The new solution includes server GIS applications, Esri demographics data and customized analytics developed specifically by GIS, Inc. to streamline and enhance Wendy’s site screening and market assessment process. Staff can easily view sales records, customized demographics and other business reports on existing restaurants through an intuitive mapping interface. The system also enables Wendy’s to perform predictive modeling and assess potential restaurant cannibalization for new and existing sites by simply clicking on the map.

 

Drive your own strategy with location as a service

As we have seen, retail is a location-centric business. Every customer can be analyzed and connected to every action by location. Use Location as a Service to connect your products and services with your customers. You will see new patterns emerge from oceans of data and show the underlying journey to purchase