by Anh H. Nguyen
If there is one thing most brick-and-mortar business owners agree on, it is that getting customers in the door is already half the battle. In the increasingly cutthroat retail world, customers are spoilt for choice, therefore the mere fact that they choose to come inside your stores instead of others means you are doing something right. Despite the rising popularity of online shopping, people still like to conduct their retail therapy in person. You just need to give them a good reason to do so.
Managing to bring customers and products together is a momentous feat for retailers, since even the smallest amount of traffic means opportunities. For sales, obviously, but also for staff engagement, for brand exposure, for feedback, and for data. Once in, customers are able to witness and experience the goods first hand, interact with the staff, and leave multiple clues about their shopping habits in the process. Even when no sales occurs, retailers might learn a great deal about their customer base' preferences and possibly utilize that knowledge to better their business.
As a result, "how to drive more traffic" is the challenge that occupies the mind of most retailers. The Internet rises to the occasion as usual, offering numerous ideas and tips to boost retail traffic, from the basic ("Put up a sidewalk board!") to the sophisticated ("Engage audience with Wifi Ads"). However, store traffic contains more layers than meets the eyes. It is a power to be harnessed, and as such, could either empower retailers or overpower them. In this article, I outline the various ways retailers could take control of store traffic with the help of AI solutions.
1. Measuring traffic accurately
Weeky traffic graph of a store by Palexy
First thing first, retailers need to get a precise gasp of foot traffic before even thinking about analyzing it. There are many ways to measure foot fall data, all of which are valid. There are pros and cons to every method, though. For example, retailers may employ the staff themselves to count the number of visitors, observe their behaviors, and mark when the stores are the busiest. Is it effective? Depends. Is it scalable? Probably not. This process may only work for mom-and-pop stores, if at all.
A more forward-thinking business may use a digitized process of people counting to do the job, the most common being thermal sensors placed near the entrance of stores. This technology works by registering the heat emitted by people as they walk through the doorway. There are several caveats, though. While this system can detect independent heat imprints, it could not tell staff and customers apart. Neither could it ascertain whether the customers have been there before, or discern their activities in any meaningful way. The only thing it perceives is the number of passages going in. This is similar to managing a website and knowing only the total sum of visits, not which sections people linger at, nor for how long.
A more comprehensive approach is the use of people tracking technology which follows customers individually. Based on in-store camera footage, it allows for customization so that retailers could differentiate between staff and customers with ease. Not only that, this technology also sorts customers into separate demographics, and captures their entire in-store journeys. This is state-of-the-art, almost futuristic innovation, and while still uncommon, it is slowly gaining a competitive edge over the more outdated modes of people counting, trumping them at both accuracy and thoroughness.
Palexy's detailed report of 20 to 30 years-old female customers' journey for a fashion retailer
2. Analyzing traffic with digital solutions
Once retailers get traffic down to the most minute details, the real fun begins. Far too many retailers only have eyes for POS data, discarding the rest of shoppers' information. Wrong strategy! Most consumers are unaware that every time they walk in a store, they leave behind clues about their favorite shopping times of the day or days of the weeks, their preferred route to get around the store, their average dwell time, their picks of products. These "breadcrumbs" of likes and dislikes, when accumulated, form a gold mine of insights. It is up to the retailers to interpret and leverage them, to squeeze meaning out of them, so to speak.
Even the failed sales are not failures: they offer suggestions on how to improve. From reports gathered over incremental periods of time, retailers could, among other things, improve store design and layout, optimize staff deployment, monitor operations such as product listings, and manage customer experience. They could use A/B testing to examine the efficiency of their online marketing campaigns and fine-tune their cross-selling attempts. The implications are endless.
On the other hand, successful sales may not be the best indicator of a store's performance. Retailers often set numbers of transactions made as the chief KPI. While this perception is not inherently wrong, it can create a sort of restricted vision and limit the store's potential. The concept "sales funnel" was created to help retailers overcome this shortsightedness.
By calculating the conversion rate between each step, retailers could compare this figure to the industry's average, their own average, or a target to be reached. Then, "problems areas" could be pinpointed, addressed, and improved. This practice is simple, intuitive and useful, but it is virtually impossible without the aforementioned people tracking technology. Palexy's Store Optimizer, for instance, visualizes the customers' journey, breaks it down into clear cut sections, and helps retailers maximize their success in all areas.
3. Maintaining profit when traffic decreases
Wouldn't it be nice if store traffic stays on a perpetually upward trajectory? Unfortunately, sometimes life just happens. Despite retailers' best efforts to drive more in-store foot traffic, unforeseen setbacks completely outside of people's control is part of doing business. The most notable recent example is the Covid-19 pandemic which has swept across the global retail landscape like a tsunami, leaving death and destruction in its wake. Even the best-laid plan is helpless in the face of such a severe catastrophe. There are only two choices for retailers in such a grim situation: either lay down their weapons and be wreaked, or stand up and fight with all they've got.
Fight with what, you may ask? This is akin to a movie trope when the protagonist finds out the strength is within him all along. Even when traffic (and revenue) slows from a stream to a trickle, retailers could still mobilize their existing forces to wring what they could out of it. Take a look at Vua Nem, the top retailer of mattresses and bedding in Vietnam. Covid-19 presented three negative ramifications for Vua Nem: the stores were forced to close by government's mandate, people were reluctant to go out even when the stores opened up, they also tightened their purse strings, especially since a quality mattress was a substantial investment for middle-class earners.
Luckily, Vua Nem had employed the help of Palexy's Layout Optimization Solution. The data, collected and analyzed by Palexy's flagship AI software, showed that within each store, there was a prized "hot zones" that attracted the biggest number of visitors. After locating these areas, Palexy advised Vua Nem to shake up the product arrangement. Instead of grouping similar mattresses close together, which was the default mode, Vua Nem started selecting products with the highest margins and placing them at the "hot zones".
Palexy's heat map for Vua Nem showed the "hot zones" that buyers frequented the most
Furthermore, Palexy developed a custom feature for Vua Nem to determine the correlation between customers' behaviors (how they interacted with mattresses, e.g. lying on them, how often, for how long) and the resulted sales. From that basis, Vua Nem put together new training scripts for the staff. These scripts served a dual purpose: they allowed the staff to guide customers on a few structured, predefined journeys so as to increase both customers' satisfaction and the average basket value. Throughout the implementation of this Layout Optimization Solution, Palexy kept close tabs on the daily sales results and communicated the fluctuations to the store managers.
In the end, the Profit per Unit for Vua Nem increased by 20%, thanks to a mixture of higher buyers per shoppers rate and higher basket value. Despite the smaller traffic volume, this growth helped Vua Nem retain their bottom line. It also breathed a new life into the stores and kept the staff in relatively high spirit, which was thin on the ground during Covid-19.
4. Adapting to high volume of traffic
High traffic is a positive thing, but it is not without hidden pitfalls. It can induce retailers to grow complacent and rest on their laurels, and it is a slippery slope from there. Although a busy store is any retailer's dream, if the stores cannot provide consistent services to accommodate everyone, it could quickly turn into a nightmare. Unsatisfied customers may choose to flock to other stores, leave negative reviews, or turn away from the brand for good.
As a clothing company with a customer-centric mindset, Couple TX has ruled the casual wear segment in Vietnam for a long time. Its signature items such as the matching his-and her-apparel and sun-blocking garments have been in constant demand since their inception. Lately, Couple TX has embarked on a new mission to create better in-store experience for customers with the use of Palexy' AI solutions. That was when they discovered some previously unnoticed missed sales opportunities.
How, exactly, did this come to light? The sales funnel that Palexy generated for Couple TX revealed an uncomfortable fact: when the number of visitors per hour increased over 10%, the conversion rate dropped drastically, sometimes below 30%. This was obscured by the relatively stable sales made, however, it showed that the current staff arrangement at those times was imperfect. The staff was ill-equipped to handle high influx of traffic through none of their fault; nonetheless, customers were clearly unsatisfied. These under-the-radar lost sales were bad enough, but if this went on for too long, the displeased customers might shun the brand for good.
The answer, according to Palexy, could be found in staff optimization. Using the Daymap created from traffic data, Palexy was quick to establish the "power hours" at each Couple TX's store. Those were the times with the highest amount of traffic, which coincided with conversion rate plummeting. Couple TX responded by scheduling more staff at these periods, meanwhile, they also utilized new and part-time staff to chime in with non-sales duties (replenishing stock, tidying up the store) so that the core, more experienced staff could focus on catering to customers.
Couple TX used insights from Palexy to keep customers happy during peak hours as well as sustain the conversion rate.
The result spoke for itself. At 10 visitors/store, conversion rate reached 33%. At 20 visitors/store, it stayed at 30%, and only dropped slightly to 28-29% at 30 visitors/store. The staff was not overworked, the customers were happy, and Couple TX saw 3258 extra transactions after 10 days of implementing Palexy's solution. That was an impressive 35% increase in transactions.
The more successful a business becomes, the more challenging it gets for the leaders to stay on top of every detail. Adopting a data-driven approach to business operation makes it more manageable, and puts the retailers in a position of control instead of being at the mercy of unknown factors. Store traffic, in particular, may appear disorienting and unpredictable at first glance, but AI-powered digital transformation might just be the reliable vessel for retailers to safely navigate that volatile sea. For more solutions that may benefit your business, please visit https://www.palexy.com/solutions