How real-time data is improving the retail experience

Real-time data is making big data even more valuable


Real-time data is data which is available for evaluation immediately after collection. It’s particularly advantageous in the retail sector since conditions such as stock shortages, customer traffic and sales can all determine the success or shortcomings of a brand or location.

Retailers depend on real-time data in several ways to make the shopping experience more pleasant and streamlined for everyone involved.

1. providing personalized recommendations or perks to customers

Research shows that as many as many as eight out of 10 customers will come back when stores give them targeted offers based on their actions. Some retailers, such as Amazon, serve up suggestions when people place things in their virtual shopping carts.

In other cases, a retailer might depend on real-time data to track how a customer moves around a website and provide offers based on that behavior. For example, sometimes just before a person attempts to close an online store tab in a browser, they get a pop-up window that says “Wait! Before you go …” and offers them a substantial discount for making an immediate purchase.

While shopping in a physical store, a barcode scanner at a checkout counter might trigger the receipt printer to include a discount on a person’s favorite brand.

2. Preventing empty shelves and giving customers more ways to buy

Retailers know that if people see empty shelves, they might quickly leave and try other stores without checking with associates to see if there’s more merchandise available in the stockroom.

Real-time data gives store locations up-to-the-minute insights when products run low or attract more interest than expected. This allows managers and other sales floor team members to respond proactively before the shelves become bare.

Sometimes, such information empowers customers, too. Gant, a fashion brand with stores around the world, tapped into real-time data when it offered a feature that allowed shoppers to perform stock checks at stores in their areas after discovering specific merchandise was sold out online.

Then, instead of deciding they are no longer interested in buying merely because the products they want aren’t available online, shoppers can explore other possibilities. This increases the chances they’ll make stores profit even when online stock levels aren’t sufficient.

3. Enhancing telephone-based customer service experiences

Even though many people buy items online, some customers prefer the older method of picking up the phone and talking to a sales representative. In other cases, people have to speak to company representatives if they have questions about or problems with the products they purchase.

A business’s ability to retain customers can increase up to 40 percent if it delivers an excellent quality of service. That’s why some companies that hire representatives to sell things over the phone use real-time monitoring software that gauges things like the tone of voice or other factors that indicate a customer might be getting upset.

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While evaluating the characteristics of a phone call, the software intervenes when necessary and displays tips to call center employees, reminding them to remain empathetic and get their supervisors if things escalate.

Due to that kind of updated information, supervisors can step in before it’s too late. This increases the chances that customers come away from telephone-based interactions feeling like the representatives at the companies genuinely cared about them.

4. Reducing guesswork for staffing professionals

Before the availability of real-time data, the people responsible for keeping retail stores adequately staffed from week to week had to make educated guesses during the task. They usually relied on historical data, such as by typically staffing a higher-than-average amount of people for shifts on Saturday afternoons because they’re often the busiest periods.

However, analyzing retail data in the moment might involve using sensors and cameras to show the most highly trafficked areas in a store at any time.

If a store had a list of employees who agreed to be available with a limited amount of notice, the managers at that location might realize the jewelry counter is getting more business than usual and it’s time to call in another team member for support.

Retailers often engage in demand-based rostering by using predictive analytics, but adding real-time data into the equation can result in managers noticing a store is understaffed and immediately calling upon other workers to do something about it.

If that happens, the people who are already on shift feel less stressed, and supervisors know precisely where to send the other workers who arrive to provide relief.

Moreover, the time customers spend waiting in lines goes down, and they should be able to readily find an adequate number of employees to help them regardless of where they are in a store.

Real-time data contributes to content customers

Many things determine how people feel about their retail experiences, including prices, the ease of finding help when they needed it and seeing plenty of merchandise on the shelves.

Real-time data can improve all those things and others, boosting the chances that customers feel satisfied enough to plan future visits to retailers.

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