The advent of the Internet, and with it the corporate website, created the first hint of what we call “digital analytics” today. For years, retailers have had the ability to gather a wealth of data regarding customer behavior online. Retailers can record every visit, view and click customers make on their e-commerce sites and they’ve been able to answer previously open-ended questions such as: What did customers buy? How did they arrive at the decision to purchase? When did they lose interest or abandon the shopping cart?

However, e-commerce analytics is only one part of the customer measurement puzzle. Website data, people counting in physical stores, satisfaction surveys and other customer touchpoints create a disconnected archipelago of analytical data. Fortunately, with the introduction and prevalence of the smartphone, retailers now have an expanded ability to understand how customers are interacting with brick-and-mortar stores.

Consumers and Their Smartphones

Over the last 10 years, the evolution of smartphones has been rapid and constant. As smartphones evolved they become more accessible, with most mobile device owners becoming so attached to the technology that they never have it far from them. In a February 21, 2012 blog post by Forrester Research Inc., forecast analyst entitled “Mobile Internet Users Will Soon Surpass PC Internet Users Globally,” forecast analyst Susan Huynh wrote “…about a fifth of the world’s mobile subscribers are currently using their mobile handsets to go online.” Further, “…the global penetration of mobile Internet users will exceed that of PC-based Internet users in 2016.”

What used to be a brief digital window into analytics is now growing rapidly into a window that is always open, where mobile users are always on. As a result, opted-in consumers have the possibility to provide information to brands and receive contextually relevant messages in real time almost 100 percent of the time.

From an analytics standpoint, the most significant piece of new information afforded by mobile technology is the ability to obtain accurate location details of a mobile device. Because shoppers take smartphones wherever they go, the device’s location acts as a proxy for the user’s location and provides clues to the shopper’s context during the purchase decision cycle, opening up a powerful new field for analytics. Combining digital activity with physical world context may be the most important field in digital analytics over the next decade and its importance will be proven as this newfound intelligence improves the shopping experience and impacts sales at the POS.

For retailers, location analytics is a long-awaited dream come true. The detailed insight into how consumers interact and shop with brands is now available in the physical store, where 92 percent of retail sales still occur.

Getting Permission to Analyze Shopping Visits

It is extremely important for companies of all kinds to respect their visitors’ rights to privacy, even if there aren’t laws prohibiting certain surveillance or monitoring techniques. One of the fastest ways to lose customers is to make them feel violated in one way or another — and people can be sensitive about unsolicited analysis of their phones.

The best way to get location data from mobile devices is through mobile apps. Mobile apps can understand a device’s latitude and longitude, speed, attitude and direction.

When a consumer downloads a store’s mobile app, he or she has the opportunity to opt-in to receiving and participating in location-based services, including communication regarding special offers, events and other promotions. If a customer opts-in to these services, a retailer can then safely and responsibly access location data when the customer is near or in the store. A retailer should never access location data in a persistent manner — retailers should only be recording location data that is relevant to the shopper’s engagement with the brand, allowing the brand to improve the customer’s experience at physical locations.

Additionally, retailers should consider the impact that the mobile app’s use of on-device location detection functionality such as Wi-Fi and GPS may have on the device’s battery. Persistent GPS activity creates a more accurate view of location but drains a battery quickly, often resulting in a negative shopping experience and an app uninstall. Retailers should ensure that the location analytics program optimally uses battery and protects privacy when identifying a customer’s proximity to a store location.

The Four Basics of Location Analytics Data

Retailers can begin with four very basic data points that allow them to make significant strides in understanding and improving customer interaction and store performance.

  • Latitude and longitude (or, simply, location). Record which locations each customer visits.
  • Arrival Time. When did the customer arrive?
  • Dwell Time. How long did the customer stay in the store?
  • Behavior. What did the customer do while in the store?

Refer to Figure 1 for an example of a data set with these four basic analytics components.
Figure 1

This data set is not large in terms of the size of the data but is dramatic in terms of the impact it has in the way a business approaches its physical locations. This data allows the retailer to ask:

  • Why did User #1 visit one store for a long period of time and then visit another store for a shorter time? Was something out of stock? How frequently does this inconsistency occur?
  • Why did User #1 visit both stores around 2:30 p.m.? Is it a pattern?
  • User #2 visited store #1867 after 5 p.m., but didn’t stay as long as User #1 did earlier in the day. Is this isolated to personal preferences or does this tell us something about people who shop during the workday versus after the workday?

The Frameworks for Presenting Data

Having access to data is a good thing, but it means nothing unless companies can organize it in a way that makes the data actionable. The best way to organize streaming location data is by using the dimensions of time and perspective.

The first way to approach location insights is by looking at the data through two different lenses: the customer versus the location. When the objective is to gain insight into customer behavior, retailers should adjust the data so that new segmentation profiles based on location behavior can help determine marketing or service approaches that are customized to the needs of an existing customer.

If the objective is to gain insight into a specific location’s performance, retailers can compare and contrast store locations based on their usage performance data in order to fine-tune store operations, such as merchandising and marketing.

The second way to approach location insights is by separating real-time versus long-term goals. Real-time data allows for immediate opportunities to modify marketing or service approaches. Long-term data collection (supported by a large amount of data over time) leads to more structural changes to physical and digital experiences. Both are important but can be used in very different ways.

Take a look at Figure 2, which incorporates customer versus location and real-time versus long-term data points into a quadrant framework.
 
Figure 2

To put the quadrant into an easy-to-understand application, consider the following scenarios:

  • Real-time, customer-focused insights
    • a. Six Loyal customers are currently scanning barcodes of Product 2300088 in Store J.
    • b. Loyal “gold level” customer is using their app’s My Personal Wardrobe feature.

 

  • Real-time, location-focused insights
    • a. Store C currently has nearly twice as many app-holding customers than it typically does during this time period.
    • b. Redemption of coupon 67099 in Store X in its first three hours since release is only 25 percent of what similar coupon 67098 achieved.

 

  • Long-term, customer-focused insights
    • a. Cake decorators are more likely to visit stores in the afternoon whereas Scrapbookers are more likely to visit stores in the evening.
    • b. Local customers tend to visit store locations near concert venues shortly after the concert.

 

  • Long-term, location-focused insights
    • a. Store G, which is in close proximity to Store H, receives twice as many visits as Store H.
    • b. Average store dwell time at Store P is 53 minutes, whereas average store dwell time at Store Q is 22 minutes.

Expand Location Analytics Across the Enterprise

Sales and customer service aren’t the only organizations within an enterprise that benefit from location analytics. Location analytics is most powerful when systems generating location data are connected with other systems within the enterprise, including CRM, loyalty, marketing, automation, POS, customer service, space management, merchandising, and business intelligence systems. When retailers leverage these systems, they get a new intelligence layer in the integrated analysis and response systems that affects every customer-facing aspect of the enterprise.

Figure 3 illustrates what data is currently available across the retail enterprise with traditional, non-location analytics and combines it with the new location layer, along with an explanation of additional insight gained from this new analytics layer.


(Click here for a larger image of Figure 3.)

The addition of location analytics and its highly contextual intelligence capabilities changes everything for retailers, brands and media companies. For example, now retailers can measure the omnichannel experience. Retailers are able to better understand how their online spend and website visits lead to brick-and-mortar location visits and how visits to brick-and-mortar locations lead to the use of online resources during the store shopping experience. From this, marketers are able to create correlations between on and offline events and their impacts on brick-and-mortar stores — a feat that was previous unattainable on such a scale.

Location analytics also allows for comparative measures. Measuring activity at store locations and mapping those events to consumer profile information enables the marketer to know which stores are seeing the highest repeat shopping rates or which campaigns generate the highest conversion rates to in-store foot traffic. By comparing long-term data across locations, retailers can optimize the planning and execution of digital and in-store marketing and operations.

Analytics in Action

Let’s examine how expanding location analytics across the enterprise fits in with our discussion about data frameworks with the four level quadrant.

Real-Time, Customer-Focused Insights:

Insight: Six loyal customers are currently scanning barcodes of product 2300088 in Store J.

Response: Heavy barcode scanning on product 2300088 initiates an action in a pricing system that looks up competitive discounts, which discovers a nearby competitor is discounting this product. A price adjustment is scheduled and an offer on a bundle that includes the item is sent out to all in-store app users at Store J and all other stores that are near that competitor.

Long-Term, Customer-Focused Insights:

Insight: Cake decorators are more likely to visit stores in the afternoon whereas scrapbookers are more likely to visit stores in the evening.

Response: Store signage is biased toward advertising baking products before 5 p.m. and biased toward scrapbook products after 5 p.m. Staff shifts are optimized to provide more expertise in the appropriate categories at the right time.

Real-Time, Location-Focused Insights:

Insight: Store C currently has nearly twice as many app-holding customers than it typically does during a certain time period.

Response: Associates at Store C are notified of the fluctuation and the normal number of mobile POS associates is doubled temporarily to speed up the checkout process. A “Share the App” incentive goes out through the retailer’s app to everyone currently in the store to drive additional downloads through sharing on social media.

Long-Term, Location-Focused Insights:

Insight: Store G, which is in close proximity to Store H, receives twice as many visits as Store H.

Response: A brief inquiry with Store H management reveals that staff turnover is much higher than Store G and general customer satisfaction is lower. Store G management is staffed temporarily with store H to share best practices on staff training, customer service and staff retention.

It’s All About the Customer

Eventually, it all comes back to the consumer. By using location analytics to gather data about consumers and stores, brands can develop a complete, 360-degree view of their consumers. By leveraging this 360-view, brands can finally develop a seamless omnichannel experience between channels and market to consumers in a way that is relevant, timely and personal.

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