Retail shopping in a post COVID-19 world is undergoing a sweeping transformation. Prior to the pandemic, supply chains were highly stressed as they were prioritizing cost competitiveness and relying on lower-cost labor locations, often compromising on pivotal supply chain determinants like flexibility and agility. In the aftermath of COVID-19, challenges to supply chain continuity and cost have “laid bare vulnerabilities and fragility brought about by this strategy and approach."
Today, retailers are struggling to predict consumer behavior and ensure that supply meets demand. Post COVID-19 however, social distancing and contactless shopping/delivery are the primary focal points in the ‘new normal,’. Consider this,
87% US shoppers prefer to shop in stores with contactless or robust self-checkout options
29% shop online
About two-thirds prefer to pay using self-checkout, contactless self-checkout or frictionless micro-markets
Also, because of the pandemic,
60% shoppers are afraid of shopping in a grocery store
45% disinfect groceries bought once home
60% experience a sense of panic or anxiety when shopping
Clearly, physical shopping is a cause of concern for many customers in the current scenario. Does this dip in consumer confidence signal the end of brick and mortar stores?
Before COVID-19, the world was anyway moving towards online/e-commerce mode of shopping. Online retail was disrupting brick and mortar retail. More than 9,300 stores were closed in the United States in 2019 alone. However, that has not rung the death knell for physical stores yet. Customers continue to want the myriad of experiences that physical stores offer – the ability to feel the product, try it out or gauge which product proves to be the best fit, interact with sales agents, and so on.
In a post-COVID world however, retailers need to address specific in-store inconveniences, if they wish to regain customer confidence. Having to search an entire store for an item, product unavailability, store congestions, long queues, inconvenient checkouts, etc. - all these factors result in poor shopping experiences.
In fact, according to an NCR survey,
73% customers think checking out is their biggest pain point
1 in 5 customers will abandon carts if line is too long.
And another survey revealed that over a period of 12 months,
86% of consumers left a store due to long lines — resulting in approximately $37.7 billion lost in potential sales.
Retail stores are now aiming for in-store experiences that are ‘frictionless' and 'delightful' to the customer, and can retain the essence of physical shopping that consumers crave sans any inconveniences.
This article aims to delve deeper into the factors that go into implementing Frictionless Retail, how best to leverage data science and analytics to offer the best shopping experience for customers, and the challenges that retailers face when making the transition from a traditional retail shopping set-up
Creating an in-store experience where customers can swiftly get the products they are looking for and conclude their shopping experience in the most efficient manner, is a vital part of Frictionless Retail. A Frictionless Retail set-up ensures that customers find his/her product easily with the help of in-store digital maps, app-based directions, and access additional information like product comparisons, reviews, alternatives etc., (a feature that is already available on multiple e-commerce platforms), and make payment/checkout a seamless experience such as shop physically, and pay digitally, without the hassle of long queues.
Many retailers are already in the process of building or have already built digital presence to enable shopping experiences from the comforts of the customer’s homes. Omni-channel strategies need to tap into the best of the digital world (ease/speed/access to information) and physical store settings (the interactive, hands on/feel-for-the-product experience). To drive this, the new omni-channel is one that transcends mediums, i.e., digital to physical and vice-versa, to provide the customer a near continuous shopping experience.
Walmart’s curbside pick-up is an example of a near continuous digital to physical shopping experience where customers can pick up pre-ordered items. Such methods are also relatively easy for stores to implement as they can enable a hassle-free shopping experience for the customer, using existing resources and workforce.
On the other hand, virtual shelves that have tablets or kiosks that allow customers to browse products in the store itself, are an example of near continuous physical to digital shopping experiences. For instance, in the off-chance that a product is unavailable at the store, using the virtual shelf, customers can purchase the product on the store’s e-commerce platform, or figure out if the product is available in the warehouse stock, and so on and so forth.
Personalized customer experiences led to approximately 40% of consumers in the United States purchasing something significantly more expensive than their intended spend amount. Therefore, if a shopping experience is aligned to the expectations of each consumer, it would not only improve the store’s revenue, but also give way for improved consumer relations.
a) Personalized product recommendations: 31% of e-commerce site revenue arises from personalized product recommendations, and recommendation engines contribute to almost 35% of Amazon’s revenue. Therefore, digital presence provides insights into consumer behavior, which can then be leveraged to drive better in-store shopping experiences.
Recommendation Systems should evolve to provide need-based product recommendations from the current, activity-based product recommendations.
b) Experience Personalization: A survey by Infosys suggested that close to one-third of customers longed for a personalized shopping experience that was custom-made for their needs. Recognizing the need for the same, the Amazon 4 star store concept drives its customers who only prefer products 4 stars and above in ratings, to visit the physical store for a more complete shopping experience. The store has a robust digital system in place, which provides product recommendations, product insights & reviews, while the customer also gets a feel for the product – in an attempt to combine the best of both digital and physical shopping experiences.
c) Personalized Product Design: Many stores are creating opportunities for customers to personalize their products. This ensures that customers would be more emotionally attached to the products, given that the design is curated to their liking. This is beneficial to the retailer too because it improves customer relationships, creates niche markets, without too much of a hike in production costs. The kitchen and bath retail chain Bed Bath & Beyond, for instance, offer a design center where customers buy the product that best fits their home decoration or home improvement plans, as opposed to picking up products based on its primary features.
d) Promotions and Offers: Rather than having a one-scheme-for-all approach, each loyalty program ought to be tailored for the individual to foster loyalty and increase repeat transactions. According to a research by Accenture Interactive, customers who are part of loyalty programs, generate 12%-18% more than non-members. Makeup giant Sephora is a stellar example of a brand that has near-perfected this personalization tactic. 80% of Sephora’s transactions are driven because of their robust loyalty program, Beauty Insiders - which consists of free beauty classes, perks/incentives to loyal customers, birthday gifts, seasonal savings, among other attractive features.
This includes ensuring in-store product availability and fast turnaround times for home deliveries. And the core to fulfillment is a fast and agile supply chain. The products need to be replenished in the shelf, just in time. Additionally, an extensive last mile delivery network needs to be set up so that products can be swiftly delivered to customer. Target, for instance, recently acquired last mile delivery technology from Deliv. This enabled them to provide home deliveries, with little impact, even during the ongoing pandemic.
One of the key factors is to identify aspects that delight customers, and also create seamless shopping experiences.
Today, data can provide insights into customer demographics, psychographics, lifestyle choices, purchase and transactional behavior (both online and offline) andinsights from loyalty programs, which help build behavioral segmentation models that define the general needs and preferences of a customer base. This is a starting point as well as an endless cycle. The more we enrich the data we have about customer behavior, the more it enriches our understanding of customers.
Leverage new technology:
Leveraging new technology helps to improve customer interaction, ensures that stores keep up with times and do not have to shut shop due to competition from tech-savvy, new-gen operations of their contemporaries.
Here are some upcoming technologies that are changing the course of shopping experiences by enabling the creation of new sources of data, and signals that can drive advanced analytics and machine learning algorithms:
a. Beacons Technology: Stores can improve sales using accurate context-based marketing with the help of Beacons technology, i.e., wireless Bluetooth transmitters that enable proximity marketing to improve customer's interactions with the products, and better understand customer preferences.
b. Location tracking: Tracking the location of customer through WiFi Signals, GPS, mobile networks, can help triangulate whether the customer is at their home/workplace or even close to any of the retail stores when they are looking at purchasing products. These insights can help to understand where the customer is shopping and when (office hours/recreational hours) and drive more context to promotional offers and product recommendations.
c. In-Store tracking: Setting up in-store GPS and tracking systems can enable track customer movements and dig up a gold-mine of insights on how a customer shops at a store, the aisles they linger at, the aisles they move past, and so on. This data can be leveraged for customer path analysis and help define ideal product placement strategies, store layout strategies, modular placement strategy, etc. Not just that, these systems can help to locate points of congestion across the store, and de-congest to create seamless shopping experiences.
d. In-store digital kiosks: These can provide on-ground product insights and recommendations to customers – a benefit that is usually restricted to e-commerce platforms. By leveraging technologies such as virtual shelves and virtual assistants, the kiosks can offer an insight into the extended range of products on offer and curated recommendations for each customer.
e. IoT sensors and cameras: Setting up cameras can enable real-time tracking and replenishment of stock. The tracking and forecasting processes would ensure consistent product availability for customers. This technology would always enable fulfilment of customer needs, ensure adequate inventory levels and also enable seamless shopping experiences. At Hema supermarkets run by Alibaba, there are digital checkout options where a kiosk embedded with a camera, uses facial recognition for swift payment options.
f. Immersive technology: Implementing AR tools can help to provide near-real experiences enabling customers to try products from the comfort of their homes. Furthermore, the products customers choose after their trials, should be accessible in physical stores in the vicinity, or be available for same-day delivery. Measures such as these will ensure a near-continuous, delightful experience for customers.
g. Retail Mobile Apps: Close to 80% consumers in the US admitted to having at least 1 retailer app downloaded on to their smart phone. Custom-made retail apps help to create an easily accessible platform for shopping, and drive near continuous experiences.
The Walmart mobile app, for instance, enables customers to pre-plan shopping trips, check in-store pricing, order essentials, link their payment options to the app, etc.
Digital transformation requires the setup of systems and technologies that can support and process advanced machine learning algorithms in real-time and at scale. Therefore, getting the basics right is pivotal for generating real-time analytics that can keep up with the constantly changing nature of shopping experiences in today’s world. Setting up cloud-based infrastructure that can process vast volumes of current and in-motion data, are some of the factors that need to be in place if firms wish to efficiently implement real-time analytics.
Cloud-based infrastructure is key to drive the nature of innovation that frictionless retail entails. Cloud transforms archaic and legacy processes into flexible, intelligent and automated systems. Adoption of cloud-based computing systems enables retailers to behave like tech companies, and cloud computing allows for massive amounts of data storage, in which retailers can generate insights, trends, and solutions to better run their business.
Cloud also supports with unlimited storage, processing capacity to run advanced AI and ML applications that drive frictionless retail and enables scaling at lower costs. Cloud-based tech lowers the transaction costs of making these services available in a highly configurable, pay-as-you-go manner, thereby, allowing for on-demand scale up or scale down based on business need.
These are only surface-level indicators of the value this technology can have, and a full understanding of how this tech can be leveraged efficiently requires a separate deep dive altogether.
AI, ML use cases that drive Frictionless Retail experience:
AI and ML are among the key technologies that ought to be leveraged to drive an efficient Frictionless Retail experience. Here are some use cases of how these can help implement such a set-up:
a. Hyper Personalization: By analyzing customers’ purchasing behavior, product recommendations can be accurately generated. Factors that are considered are purchase history, shopping path analysis, product dwell time, etc. Once customer behavior is analyzed, the right offers, product recommendations can be curated, and this would drive customer retention as well. For instance, if a customer has purchased a particular cookware, this data can be stored and leveraged to give targeted discount coupons that can be redeemed the next time they purchase from the same brand. This, in turn, will drive customers to explore more products and enable the creation of repeat customers.
b. Computer Vision: Computer Vision enables you to monitor your shelf real time, eliminating the need for physical associates. Continuous monitoring is essential in supply planning, stock replenishment in the store and in the aisle. Amazon Go leverages this technology to create a seamless shopping experience where customers need only have their phones with the Amazon app installed, and they can then proceed into the store, pick the products they like, and walk out, without the hassle of lines, delayed checkouts, etc. The computer vision technology used by Amazon, performs facial and image recognition to automatically detect purchases, tracks them in a virtual cart, for which the customer can pay through the app.
c. AI Chatbots and Voice assistants: Setting up a 24X7 customer service helps toaddress the needs, questions and issues commonly faced by customers. Lowe’s LoweBot, is one such AI robot assistant that can converse in multiple languages, help customers to navigate through the store, and provide additional support to track inventory management.
d. Advanced Customer Segmentation: Customer segmentation helps in identifying general behavioral traits of various customer segments. This will help in developing contextualized offers for newer customers, next best product recommendations etc. For instance, customers that are high spenders, can be categorized into a VIP segment that gets priority access during seasonal sales, new product launches, specialized offers, etc.
e. ML-driven Demand Forecasting: ML-driven Demand Forecasting can accurately predict demand based on high volumes of historical data and a variety of data elements, rather than relying on manual methods that are based on intuition. During events of significance, like the Super Bowl in the US, revenue worth millions of dollars is at stake. Relying solely on human intuition runs the risk of underestimating or overestimating demand. In such a scenario, data-driven forecasting, can make the most of the opportunity, and ensure a smooth shopping experience for customers, by maintaining optimal inventory levels.
Challenges of implementing Frictionless Retail
Frictionless Retail, like other technologies, takes a while to implement, and there are certain set-ups that retailers need to have in place when looking to make the transition. Firstly, retailers still have Legacy IT ecosystems without the capacity to adopt latest tech. However, this is something they might want to consider investing in, otherwise their contemporaries would be better prepared for future retailing practices, and retailers with legacy systems might get left behind. Secondly, there is no one-size-fits-all approach to going frictionless. Therefore, retailers need to assess their customers and tailor a roadmap to cater to their requirements. Thirdly, with tech evolving at a fast pace, how do we figure out what to invest in? This is a difficult question and there is no answer that is set in stone.
However, one thing is clear. Tech is here to stay. Therefore, retailers might want to plan towards creating set-ups conducive to tech implementation, and pick tools that can evolve as more advancements take place. Lastly, low risk appetite can also result in lack of investment on data platforms and technology. And without a robust technological foundation, Frictionless Retail cannot be optimally effective. Now is the time for retailers to evaluate their future aspirations and ensure that having a low-risk appetite does not leave them in a flux when facing competitors who have heavily invested in data and tech tools.
Frictionless Retail has always been touted as a defining factor for retail shopping experiences for the future. However, given that COVID-19 has created the need to be socially distant and shop contactless, a collaborative set-up merging the features of digital and physical stores, becomes more relevant than ever before. This might just be the era where Frictionless Retail witnesses an accelerated growth. The numerous technological advancements only make it more and more possible to implement the same.
Now is the time to plan for leveraging technology and data science to truly know your customer and build relationships that enable delightful, fulfilling, and near continuous experiences.
Raghuveer has over 8 years of experience in analytics and data science consulting, and through the years, he has helped Fortune 500 Retailers drive large initiatives through data and analytics-driven problem solving. He is ever-curious about solving complex problems using data and technology. Outside of work, he loves backpacking and trekking, is a dedicated football fan, and always takes the time out to read.