by Anh H. Nguyen
If the retail apocalypse touched upon in the last article has not rung the alarm bell for retailers, then the swift avalanche of bad news brought about by the coronavirus pandemic has surely made them reconsider their stance on Artificial Intelligence. Case in point: even as retailers worldwide are in dire straits because of shrinking buyer confidence and disruption in the supply chain, the Artificial Intelligence (AI) in retail market is expected to grow at a CAGR of 35.9% from 2019 to 2025, with a projection to reach $15.3 billion by 2025. During crises, the need for traditional retailers to connect with customers via new technologies and avenues is even more pressing. It is no longer a question of if retailers need AI technology, it is a question of how.
Industry growth forecast for AI in retail market (Global Market Insights)
If a picture paints a thousand words, then footage from a retail store must contain the equivalent volume of a novel. Yet for the most part, retailers only install cameras for security reasons. What a pity that such a treasure trove of information goes largely unnoticed when it could have helped propel business towards great success and self-sustaining longevity. In-store visual monitoring and surveillance have enormous potential to offer high growth opportunities, especially for retailers in emerging economies such as Asia-Pacific and Latin America. For example, softwares like Palexy In-store Analytics have had the most remarkable results with end-users in the fashion & clothing, food & groceries, and home goods segments. With such a wide range of artificial intelligence tools on the market, retailers could customize software packages according to their specific needs and requirements. The solutions could be grouped into a few main divisions as follows:
1. Staff-based approach:
Anyone who has ever worked in retail could elucidate the numerous stressful aspects of it, but one thing is clear: many retail workers are not well-equipped enough to deal with unexpectedly high traffic. During peak hours, weekends, or the holidays when themed promotions are run, stores are often filled to their full capacity. This should be a dream for retailers, but in many cases, they find that a rise in traffic does not equal a rise in transactions. This paradox could be explained away when one looks at the camera footage: throngs of customers arrive, many in groups or at the same time, which results in the stores being short-staffed. Customers are frustrated with the lukewarm services, the sales assistants are flustered, and conversion rate drops. All this could be avoided with the aid of a suitable AI service. By analyzing the staff interactions, retailers could allocate personnel as needed to serve customers better and keep the sales staff from being burnt out. Additional training, some timely encouragement and empathy also go a long way towards the sales team's emotional well-being and improve customer satisfaction. This is a reliable way to boost sales and staff morale at the same time.
Happy sales staff, happier customers.
2. Customer-based approach:
How well do retailers know their customers? The answer is not very much, if all they have to go by is the POS data. Without computer vision AI, retailers simply have no way of measuring the interest of different demographics. For example, fashion retailer X may learn through video streaming that their stores attract a large following of females aged 30-40, but their conversion rate is below the industry average. With this information, they attempt to revamp the in-store selling efforts for this particular group through sales staff coaching, and target them via online marketing campaigns. Customer interactions for this demographic increase, positive feedbacks and profit ensue. The same retailer may struggle with the decision to close down one low-performing store in their chain despite its high conversion rate. The shop assistants are enthusiastic and close sales with ease, but there are just not a lot of visitors. After reviewing the camera footage, it becomes clear that due to its location's proximity to many universities, it would be productive to put up more prominent displays catering to young customers' taste. Traffic at the store rises and so does revenue.
3. Product-based approach:
Visual merchandising - the practice of displaying products to highlight their most appealing features and benefits, is an art. However, many retailers follow a one-size-fits-all model when it comes to their store layouts. Only when an AI analytics software reveals the customer journey do they see the fault in their way. The customer journey here is defined as the literal walk customers take from the moment they walk in the store. By visualizing the store traffic distribution, retailers could identify "leaks" (i.e. when customers cease their visit without buying) and patch them. For example, when customers take dresses with them to the fitting room but leave empty handed, the likelihood strongly lies in fitting problems. Or maybe the majority of customers are fixated on a single range of products while ignoring the rest. The solution would be to identify an optimal layout plan to entice customers to see the entire store, which could be achieved via A/B testing. AI also allows retailers to better prepare their inventory, a crucial task during temperamental times such as now.
Store traffic visualized with heat map.
The body's strong immunity is key to fighting off viruses; likewise, a smartly invested AI solution has far-reaching advantages for retailers beyond the usual day-to-day. A reluctance to adopt new technologies is understandable, due to the perceived high cost of deployment and complex infrastructure. Fret not, since Palexy offers various solutions at flexible price points, and a fully cloud-based solution. There is no need to install additional hardwares or softwares, and informative reports may be easily accessed via our web/mobile apps. Through good times and bad, AI tech is here to stay by the side of retailers.