E-tailers are challenging the relevance of brick-and-mortar stores by using data to hyper-personalize shopper experiences as well as their ability to offer same-day delivery. Despite this very real threat, the good news is that the physical store continues to be the preferred destination amongst everyday shoppers. A recent PwC study found that compared with PC, tablet and mobile channels, in-store shopping is still most popular with weekly and daily shoppers. The usage of the in-store channel hasn’t changed significantly since 2013, with a significant percentage of global consumers shop in-store at least once a week.
Out of Stock, Out of Luck
When shoppers make the decision to visit a physical store, they trust that the store will not just provide a quick way to accomplish their shopping mission, but also provide a shopping experience more personal than shopping online. It’s critical at this point that retailers avoid disappointing this shopper by making sure that items are available for purchase in the store and on the shelf.
Despite much investment over the past decade, product availability remains a critical issue. Out of stocks (OOS) account for more than 4% of lost revenue, a number that increases in the case of fast-moving items. When customers cannot find the product they want, around one in eight of them ends up buying from a competitor. In some cases, an out-of-stock is also the “final straw,” encouraging a shopper to defect to a competitor.
Retailers today monitor stock availability using perpetual inventory systems which are often incorrect and don’t account for multiple locations in the store. The other method is inefficient manual “store walks” that may involve store associates executing up to 700 million intricate tasks. This not only adds to costs but could also cause inventory mismatches, misplaced SKUs and long periods where products remain out of stock.
Computer vision (CV) solutions are helping retailers address this challenge by providing them with “eyes in the store” and automating store monitoring. A large supermarket operator in Israel, for instance, managed to reduce average OOS duration from 40 hours to just 8 hours and increase on-shelf availability by 14 percent by using Trax’s retail solution.
The Challenge of Staying Planogram-Compliant
As consumer preferences change and competition for shelf space intensifies, retailers and manufacturers need to provide more segmented, targeted shopper experiences to drive greater revenues. But lack of sufficient data about store-level execution and an inability to continuously track planogram compliance make this challenging.
What is planned is often not implemented on the shelves, due to poor store mapping, planogram decay, high costs of surveys and incomplete sample-based audits. A 2018 Trax survey of 300 senior industry professionals found that 53% of respondents were not satisfied with the quality of their compliance tracking methods.
Computer vision technology allows retailers and manufacturers to compare images of the actual shelf or a “realogram” with the planogram for each category, and thus measure compliance.
Pressure on Promotions
Promotions promise higher store traffic and topline sales, even for non-promoted items. But between 20% and 50% of promotions see no noticeable lift in sales, sometimes due to flawed promotion execution. In fact, the OOS rate for promoted items is 10% ― more than the industry average for normal out of stocks.
Machine learning algorithms are fueling advanced IoT and augmented reality applications which enable robust promotion execution by:
- Ensuring that products on promotion and in display locations are always on-shelf
- Recognizing shelf tags and unique product codes to help store managers ensure that labels reflect the featured price
- Helping retailers improve supplier relations through consistent compliance
A Three-Step Framework to Getting Store Execution Fundamentals Right
Providing today’s consumers with a delightful shopping experience requires retailers to go back to basics and execute the fundamentals around shelf effectiveness well. This involves three key steps:
#1. Focus on the Right Execution. With store associates and sales staff representing more than half of an average retailer’s operating expenses, there’s a strong need to empower them with the right tools to perform retail activities more efficiently. A combination of technologies like CV, Internet of Things (IoT) and integrated mobile applications can capture store conditions and provide real-time mobile alerts to help retailers avoid low value, traditional “store walks” and long hours of manual audits.
#2. Drive Targeted Actions. Rethinking store labor also means looking at ways to improve execution performance. Adopting the right technology can give retailers granular insight on shelf gaps and how each associate is managing them. By monitoring execution quality to drive targeted actions, retailers not only motivate staff to do better, but more importantly, reduce shopper frustration arising from stockouts and other shelf gaps.
#3 Accelerate Sales Outcomes. An ideal execution strategy also requires headquarter-based teams (category management, merchandizing, supply chain and marketing teams) to be able to assess how shoppers are reacting to in-store conditions. This is achieved by juxtaposing store execution indices like out of stock, planogram and display compliance with transactional data like sales and inventory records. For example, by using CV to know exactly what’s in the store, the retailer can test and learn which shelf sets drive the most sales and prepare for the next reset.
Towards the Next-Generation Store
The arrival of Amazon Go in 2016 provided us a glimpse of what one version of the store of the future could look like. Now, by offering shelf and product-level visibility, technologies like CV and IoT are empowering retailers to explore numerous new ways in which to realize their digital transformation agenda.
The industry is already talking about the future store that unifies physical and digital, and offers assisted, interactive shopping experiences: alerting shoppers to the best deals on the shelves around them or recommending the best product in stock based on their respective buying profiles. Before these new applications can become reality, retailers first need a line of sight into what is currently on the shelf. Computer vision and its ability to provide continuous real-time store monitoring gets us there.