What is your store showing you? Understand it with retail analytics

Santiago Guaqueta

Video surveillance has historically been used to monitor for loss prevention, recording footage to later be reviewed. Of course, that is a basic and prevalent function that is still necessary; thorough coverage of your store is crucial. But what if a surveillance solution could do more—to not only prevent losses but increase efficiency and ultimately boost the bottom line? You can with the use of intelligent solutions that take advantage of the latest in analytics technology.

How stores are losing their edge

There are several factors causing retailers to lose in-store foot traffic and sales, including online shopping, lack of merchandise and poor customer service. Retailers invest heavily in surveillance for loss prevention in an effort to protect their stores and assets. While this can dramatically reduce losses resulting from theft, a lack of consideration still remains for the most important asset: the customer. The secret to a successful, healthy store is in the customer’s experience. How can providing first class service to the customer benefit the store?

I will answer that question with another question:

Why should a customer go to your store if they can just shop online?

Shopping online is extremely convenient. Customers can browse at their leisure and have goods delivered directly to their front door. By going into a store, the customer is sacrificing the convenience of shopping online. Of course, there are downsides to online shopping that could make an in-store visit preferable. In a 2017 TimeTrade State of Retail survey, 72 percent of buyers said that what they like most about in-store shopping is they have the ability to touch and feel products before buying them. However, sometimes visiting a store can be a burden, especially when there are long wait times at checkouts and poor customer service. As technology evolves, brick-and-mortar stores are losing their edge, and to survive they need to catch-up with their online counterparts.

The customer needs a reason to choose going into a store over shopping online. And that reason is the in-store experience. The majority of a store’s—even a brand’s—relationship with the customer is dictated by what the latter experiences within the store’s walls, which is something that is often in the retailer’s control.

Therefore, stores must find ways of adapting to accommodate customer preferences. According to research from the Harvard Business Review, the best way to improve a customer’s experience is by simply making it easier to shop. This comes down to two things: prompt service and a personalized in-store experience. Essentially, to have the convenience and ease of online shopping while maintaining the benefits of an in-person experience are the most ideal conditions in a customer’s shopping journey.

The same TimeTrade retail survey proved this point when it found that 47 percent of shoppers said “prompt service” is the most valuable aspect of shopping in a retail store, while 26 percent said personalized experience and 17 percent smart recommendations.

Although only 26 percent of people in the survey prioritized a personalized experience, 49 percent of buyers would be willing to pay more for products or services if they had a highly personalized in-store experience. The research show that when pitted against one another, prompt service is the most important aspect of shopping in a retail store. But they also show that when the two choices are detached from one another, a personalized experience holds great weight in the eyes of the customer in terms of what they are willing to pay for products. Even still, when almost one-fifth of customers value smart recommendations, it is a factor to be reckoned with. Ultimately, the customer’s experience will largely dictate revenue.

Retail intelligence from a single smart solution enhances customer experience, optimizes staff productivity and boosts in-store campaigns.

To provide the best service possible, you need to start “listening” to your store because there is a lot it can tell you. In addressing some of the following questions, you’ll gain insight into how to better optimize your store:

  • How many people walked into the store?
  • Once they are in the store, where do they go?
  • How can I proactively manage queue lengths and response times to enhance customer experience?
  • What is my in-store customer conversion rate?

The answers to these questions can help you eliminate inefficiencies while tailoring your store to each visiting individual.

The subtle presence of retail intelligence

To look at retail intelligence at play, let’s take a look at a customer’s typical journey while shopping in a retail store:

Our fictional customer, Tammy, is a 38-year-old woman. She enters a store, which is relatively busy, looking for shoes to complete her outfit for a wedding she is attending a few days later. On her way to the shoe section, Tammy sees endcap displays with boys T-shirts for sale. When she sees this advertisement, she remembers that she needed to buy T-shirts for her son, so she decides to pick some up. Tammy then walks to the shoe section and is greeted by an employee. Immediately, she sees an ad playing in the background displaying a pair of high end heels, reminding her of the shoes her friend bought for the wedding. She approaches a nearby employee and asks if she could get the heels in her size. While she waits, she enjoys the background music.

Intelligent solutions can help stores accurately influence buying decisions.

The ad did exactly what marketing is supposed to do: capture the attention of the intended audience—in this case, Tammy.

Although the store didn’t know that her friend owned the shoes, they did know that a woman is more likely to buy a pair of heels than a man is.  Demographics analytics were able to estimate Tammy’s general age and that she is  woman. As a result, it changed the digital signage display from a man’s dress shoe ad to an ad displayed specifically for a woman in Tammy’s age range. Ultimately, this influenced her purchase.

Heat maps provide data which indicate traffic hot spots within the store. This allowed the retailer to place items at specific end caps which ultimately grabbed Tammy’s attention. With nearby employees in every section, the sales floor was appropriately staffed to handle the number of customers that were in the store at that time, allowing Tammy to receive immediate assistance from one of the employees. The music playing throughout the store was selected to match the customer demographic, which has an impact on the customer experience and ultimately sales. In fact, in a field study by HUI Research and Soundtrack Your Brand, researchers found there was a 9.1 percent difference in overall sales when playing music that matched the brand compared with playing randomly selected popular songs.

Toward the end of Tammy’s journey through the store, she heads for the checkout area. As she approaches an open line that already contains four customers, additional staff arrive and open another cash register. Tammy completes her purchase and leaves the store with her shoes in hand and a smile on her face.

Analytics can help staff properly so they can purchase their items quicker.

The same technology that influenced Tammy’s purchase also works to optimize other areas of the store to ultimately enhance customers’ experiences. Tammy was satisfied with her experience, and as a result, will likely visit again.

Not only was Tammy’s purchase an influenced decision, but the technology in place helped ensure Tammy’s visit was quick and effortless.

One solution, multiple purposes

Let’s take a closer look at the technology that allowed for Tammy’s successful in-store experience:

People counter: counts in real time the number of people passing under the camera and in what direction.

Queue monitor: tracks how many people are standing in a predefined area (e.g. a queue) and the level of activity within that area. It also triggers real-time push alerts if the queue is too long.

Demographic identifier: counts male and female visitors and provides an age estimate. It can also be used to trigger digital signage messages based on age/gender.

Occupancy estimator: provides data about store occupancy, such as average visit time and number of visitors, which allows for appropriate staffing levels. It’s also capable of sending alerts based on predefined occupancy parameters.

Network audio solutions: provides in-store background music and enables live and scheduled announcements.

Heat maps: enables quick identification of hot spots, dead areas and bottlenecks to visualize traffic patterns over time as well as in real-time.

Digital signage: enables targeted marketing messages to visitors based on age and gender when integrated, for example, with AXIS Demographic Identifier.

The individual components of this intelligent solution coalesce into one platform to enhance customers’ in-store experience. Fundamental statistics that most stores have may provide information on current sales and transactions, however they do not provide insight into efficiencies and inefficiencies.

Below is an example of real statistics in action (from a retailer that is using one of our solutions), which have been used to benefit store operations. The statistics compare two stores in a similar geographical location with similar customer demographics. Naturally, sales are a driving factor in determining store performance, but that may be an issue when you aren’t collecting additional customer insights.

When simply comparing sales figures, Store A is clearly the outstanding performer, earning nearly 30 percent more in sales than Store B over the course of the last week. Store A had more transactions and a higher average purchase value and slightly less items per transactions. By looking at these four KPIs (key performance indicators), Store A is the better performing store. When analyzing the data (in the red box) provided by the retail video analytics deployed at both stores, Store A had three times as much foot traffic than Store B which had 15,953 visitors compared to 5,276.

But what is this data actually telling us?

Surprisingly, it is telling us that the conversion rate, the number of visitors that came into the store and made a purchase is significantly lower at Store A than it is at Store B. Store B did a much better job turning their visitors into paying customers.

As management, we can attempt to find out what the employees or managers at Store B are doing right and apply these same strategies to Store A to boost sales and take advantage of opportunities to optimize the customer experience for every visitor. When analyzing other metrics, we also discover that Store B’s visitors are staying in the store longer than Store A, while also standing in shorter lines, which raises further questions to be analyzed, such as: What is happening at each of these stores and what is the best model to apply to the organization to boost the bottom line? Average queue lengths make a substantial difference when it comes to the customer experience.

Understanding the customer through analytics

Visiting a store can be overwhelming and stressful, especially if it has poor customer service and long service queues. To avoid this, stores should always keep the customers’ needs and preferences top of mind in part by making it easier for customers to find needed items, enhancing their in-store experience with friendly, knowledgeable staff and decreasing overall customer service wait times.

Otherwise retailers may continue to see customers leave for competing stores or, worse, convert to online shopping.