How Data Analytics Can Relieve These Retail Pain Points
Retail pain points are the challenges that retailers and shoppers face and want to address. Fortunately, data analytics can address many of them, leading to better experiences for everyone concerned. Here are some of the top retail pain points that data analysis could solve.
1. Long Checkout Lines
A 2018 study from Forrester Research and Digimarc found that customers were least satisfied with the checkout line length when they went shopping. Only 23% said they were happy with the amount of time they had to wait to complete their shopping trips. It’s understandable, then, why people may decide they’d rather shop online than go to a brick-and-mortar store.
Then, they can get the checkout process done in just a few clicks, especially if they’ve shopped somewhere before. But, data analytics can assist retailers with cutting down the checkout times at physical stores.
For example, data analytics can look at historical information and determine which days of the week are the busiest. Or, analytics tools connected to cameras in the stores could measure foot traffic and adjust checkout staff members accordingly.
2. Store Is Out of Touch With Shoppers’ Needs
Have you ever been to a store and left thinking, “This brand doesn’t understand my life and needs at ALL!”? If so, you probably haven’t had much patience for the retailer. Macy’s is among the companies that are digging into data and using it to deliver extreme personalization to customers. For example, it can provide 500,000 unique versions of a direct mail piece.
Macy’s utilizes data analytics for both its offline and online customers. This has reportedly increased its sales, plus enabled better internal decision-making since company executives can review data before making any changes. When using data for personalization reasons, it’s especially important to make sure you don’t do it in intrusive ways that make customers feel uneasy.
3. Products Are Often Sold Out
Shoppers get frustrated if the stores they visit don’t have the items they want. Retailers don’t like that outcome either because it often means they miss out on sales. Big data can help, especially when applied at the logistics level. In that case, it can predict spikes in demand, optimize delivery routes and more.
Some stores even use automated replenishment systems that alert them to when products will likely become depleted from shelves. Then, store workers can receive updates to bring more items from the stockroom when possible.
Data analysis can also help retailers get to the bottom of what makes some things more in demand than others. Things like weather, holidays and media coverage can all increase consumers’ desire for products.
4. Insufficient Merchandise Placement
Problems can also arise in the retail world when people have trouble finding the stuff they need, even when it’s available to buy. Imagine the last time that you went shopping and stared at a shelf that was jam-packed with stuff. You may have felt so overwhelmed by the shelf layout that you either walked away without buying something or settled for a product that wasn’t the one you wanted.
One of the ways that Walmart uses big data is to optimize its product assortment choices across various departments. The company looks at information that reflects shopping preferences, then uses it to tweak how it shows customers the available items. For example, the top-selling items may be closest to the average customer’s eye level, making them easier to see and grab from the shelf.
5. Stolen Items
Although retailers usually have dedicated loss prevention programs in place, merchandise thefts happen. Sometimes, customers are the ones to blame. But, in other cases, the theft happens internally, such as when employees walk out of a back entrance with items or use their employee discount privileges fraudulently.
However, more stores are using data analytics to ease this pain point. A 2019 survey from the National Retail Federation showed that point-of-service data mining is one of the top five strategies in use, with 65.1% of respondents saying they depend on it. That’s a 7.9% increase from the previous year’s survey.
Store associates can also get insights about the items that people steal most often and when the thefts typically occur. Then, they can take preventative action by increasing the staffing numbers in those high-theft areas or installing better theft-deterrent systems on the items that get stolen most frequently.
Data Analytics Can Reduce Retail Headaches
The five issues mentioned here are probably familiar to anyone who’s worked in retail for any length of time. Thanks to data analytics, there’s no need for people in the retail sector to tolerate them at the same levels anymore. Data analysis tools don’t completely remove these problems, but they make them substantially easier to deal with and learn how to fix.