Retail has undergone monumental changes in the past few years. From COVID-19 lockdowns to unprecedented supply chain disruptions to staffing shortages and now the influx of generative AI across a number of both internal and customer-facing use cases–retailers have had to adapt faster than at any other time in recent memory. It is no longer enough to rely on gut feelings, hunches, or manual data analysis. Too many variables and complex correlations exist to lean on human experience alone.
In an industry with tight profit margins, businesses must better understand their customers or risk losing out to more technologically progressive competitors. Retail data analytics allow retailers to engage their customers in new ways—through targeted promotions instead of spam, redesigning stores based on where customers spend the most time, and providing an experience that encourages repeat visits.
AI helps retailers mine deeper insights from more data points and sources. Internet of Things (IoT) devices and AI drive numerous innovations, including video analytics, location-based marketing, and advanced inventory tracking and analytics.
Now more than ever, retailers must engage consumers and create personalized experiences that keep them returning.
Why are retail data analytics so important?
Customer demand has reached new heights, while patience is at an all-time low. Consumers expect to be able to engage with retail brands through the channel of their choice–voice, web, app, text, chat–and be presented with a seamless experience.
AI and big data analytics can help retailers aggregate data across channels (up to billions of data points) and drill down to create actionable insights. In a nutshell, AI and various types of retail data analytics remove the guesswork from retail operations.
The types of retail data analytics
Descriptive analytics – This category includes basic business intelligence that provides the “what happened” of analytics, such as sales and inventory reports. All retailers use descriptive analytics to some extent, even if it is just a spreadsheet.
Diagnostic analytics – By aggregating data from various source—POS devices, consumer feedback surveys, financial metrics, and the like—retailers can pinpoint issues and identify potential root causes.
Predictive analytics – This set of analytics involves probabilities and forecasts, such as sales. It allows retailers to “game out” strategy decisions such as promotions, inventory shifts, etc.
AI analytics – This emerging field is changing the game for retailers by utilizing generative AI and big data. AI can recommend best practices after identifying issues and their root causes.
How can retailers develop a deeper understanding of their customers?
Wi-Fi is a great way to collect data. Customers want free Wi-Fi, the data is easy to collect, and they are willing to answer a few questions to gain access.
At a coffee shop, for example, maybe the first time a device logs in, the question is, “What is your favorite pastry?” The next time, the question is, “Do you prefer coffee or tea?” Or, “What is your age range?” Over time, retailers can start to compile all this data to create a customer profile.
Additionally, e-mail and apps are critical. If the customer downloads an app or provides an e-mail for receipts, retailers can now correlate where they are in the store, what they are doing, and how much they spend.
IoT devices, such as cameras and sensors, add a new data layer. Besides providing security, cameras can generate store heat maps to show where customers’ attention lands the most. Retailers can also use location beaconing, which measures how long a device stays in the network, to measure interest.
Various beacons can trigger personalized promotions sent straight to customer devices based on purchase history. For example, a customer with a strong buying history of purchasing ice cream might receive a text message with a 10% coupon when they enter the frozen foods section.
Learn more: Location-based marketing technology in retail: A primer
What do retailers need to do to make use of this data?
Retailers need a platform that correlates customer data, such as an analytics and location engine (ALE), where an artificial intelligence engine analyzes and finds qualified candidates to determine return rates. In other words, AI discovers who opened your messages. The latest generation of AI can create prescriptive messages to marketing teams to suggest potential tweaks to messaging to increase return rates. Predictive analytics use this data to forecast return rates for a possible promotion.
This technology allows retailers to define essential metrics—for example, filtering by the number of visits or the amount spent per visit. These “rule-based” searches might unearth a customer who has visited three times in the past but not in the last six months or someone who spent $500 on a single visit.
Once you have created these custom fields, the technology can do powerful things. Consider the example of a bookstore. From the location data, we see that the users spend most of their time in nonfiction. Now, we can correlate that with increasing the size of the nonfiction display or moving its placement to a more prominent location in the store.
Understanding what attracts customers to your store, what keeps them there, and what they buy is crucial to maximizing data utilization.
What potential pitfalls do retailers need to keep in mind?
Consider going to a Starbucks or Panera while traveling. Those brands understand the importance of the customer experience—people know that the Wi-Fi is excellent, it will be easy to connect, and they will not have to jump through a bunch of hoops and re-authenticate several times. In other words, people are willing to trade snippets of data for a smooth connection experience.
When building a profile, customers are willing to answer a question or two at a time.
One airport asked 12 questions at once, and connection activity fell from 70 percent to 40 percent. Progressively building a profile over time is a much better fit. Additionally, AI can analyze a customer’s purchase history and recommend customized promotions.
Retailers must embrace using AI to better understand their customers, or they risk falling behind competitors with more sophisticated analytics programs.
Learn more: Navigating the future of AI security, emerging threats, and zero trust
How can CBTS help retailers develop solutions for retailers and their customers?
Over the years, CBTS has helped our customers navigate different technology revolutions, and the AI revolution is no different. Our AI-readiness experts can point your team in the right direction and help you prioritize technology investments.
The ultimate goal is to create an experience that drives customer loyalty. Wi-Fi, AI, IoT, and data analytics are all tools that help you get to know your customers more deeply. Wi-Fi and IoT pull in data points from across all your channels. AI data analytics helps your retail establishment aggregate, structure, and visualize those data points and suggests next steps and best practices.
Keep your business from falling behind the AI curve. Talk to a CBTS AI expert today, or get in touch with questions.
