How Consumer-driven Technologies are Disrupting the State of Retail

The State Of Retail And The State Of Retail Technologies 

In recent years, many have talked about a Retail Apocalypse, looking at the clash of Brick-and-Mortar Retail Titans overcome by E-Commerce and online shopping Giants. People have waited for the Apocalypse to happen, but it never did. To understand the new state of Retail, we should look at what’s driving the Industry. Retail is all about selling products and experiences to everyday consumers. Consumers are driven by social, economical and technological transformations and there’s no other factor that changed consumer behavior in the past 20 years more than its access to digital technologies. Digital technologies are transforming all aspects of daily lives and new retail technologies are raising awareness of everything that’s widely available. But the state of Retail Technologies is still an uncertain territory and The Guardian was spot on that predicting what technologies are to become mainstream is absolutely impossible. To some, this will be viewed as a great learning opportunity. To others, this will be cause for panic. How is this state of mind affecting the state of Retail?

Before the pandemic It was natural for Retail to undergo tremendous pressure and changes, as it tried to help offline and online businesses coexist. To understand where Retail was at the beginning of the year, we looked at the facts. The US National Retail Federation (NRF) told us that Retail was actually growing and that for each company closing one store, 5.2 companies were opening stores. The Apocalyptic expectations most likely took into account Retail as Brick-and-Mortar alone, when the reality is shaped by the consumer. And the consumer paints a very different picture. Statistics showed us that the global retail industry had seen annual sales increases, placing it on an upward growth trend with a 4.1% growth forecast for 2020. And this is even more important when you realize that in 2019, consumer spending encompassed almost 70% out of the total US GDP. 

Navigating through the pandemic, some Brick-and-Mortar Fast Moving Consumer Goods (FMCG) businesses had to impose social distancing rules (and looked at AI technologies to achieve this), some department stores had to move their activities online, some discount stores had to temporarily close shop (due to the lack of digital presence) and some online stores have seen surges in consumer models. Faced with a new normal, businesses are slowly reopening shops or getting back to their old traffic patterns and this McKinsey survey shows that retail executives expect to reopen shops or increase traffic. They are indeed considering changes in operating models and procedures and several months of delay between achieving old traffic models, but the optimistic truth is that expectations are to see things going back to normal eventually.

As things get back to normal, Brick-and-Mortar and E-Commerce retailers will return to research and development. However, the new R&D process might need to become even more agile now, than it was four months ago. To all accounts, retailers should continue to predict customer habits and design models to make the customer happy, continuing to automate the stores of the future. But until the crisis is over, they’ll have to face an important technical problem. Simply put, all demand predictions and strategies are usually designed by comparing data collected in two different time sets: for example, comparing data from Q1-Q2 in 2020 to similar data from Q1-Q2 in 2019. This is easily done when things function within normal parameters. But disruptors such as an economic or social crisis have a direct impact on consumer behaviors, changing the buying habits in ways the models cannot cope with, as they don’t have equal data for comparison. As MIT Review puts it, “Our weird behavior during the pandemic is messing with AI” - and so, it’s messing with retailers’ technical systems. Tuning personalization parameters used in the past will achieve different things in the near future - until the system starts getting enough data to remake proper predictions. 

That will need to be done gradually - and carefully. Retailers should be patient and expect the parameters to be adjusted over time. With the right mindset, things will eventually get on the right track for the Retail sector and when they do, retailers will have to be prepared to seize the primary growth opportunity: customer experience. But customer experience is not easily quantified, as it depends on personal taste, preference, point of view, and shopping micro-moments. From a consumer’s perspective, hyper-personalization is achieved when retailers answer their specific needs. To make sense of the retail technology trends and associate technology matches, we thought it useful to compare different types of retailers based on their offer, target market and goals.

What should retailers consider when implementing hyper-personalization? Well, as technology evolves, retailers get the ability to reinvent themselves and achieve their goals through optimized ways of working. A digital journey may be different for each retailer, yet most leverage technologies to upgrade what they consider key features (some start with inventory management, some with pricing strategies, others with shelf monitoring solutions). Generally speaking, the road to hyper-personalization starts with a customer-centric business model and is mapped through a general knowledge of the customer journey, analyzing its behavior across multiple channels and devices. This is where data and big data come into play. It’s a fact that retailers collect massive amounts of data: from pricing and summary product data to navigational data, direct searches data, transactional data, sensor data, competitor data. Fueled with data, the following consumer-driven technologies are changing the shape of retail, one use case at a time: 

We tried to find use cases for Brick-and-Mortar vs E-Commerce applications. After careful analysis and consideration, like others before us, we’ve reached the conclusion that it’s not a matter of “vs”, but a matter of “&”. In the ‘90s when Amazon and eBay launched,  e-commerce was seen as an alternative to Brick-and-Mortar, promoting diversity, speed, and control. Back then, digitalization and the use of the internet belonged to the few and traditional companies didn’t feel the urge to take advantage of digital technologies in order to get ahead. Things changed over the years, as the use of the internet became widespread. As more and more new businesses emerged from this online space, some traditional companies recognized they needed to double their efforts in order to stay relevant. The same can be said for E-Commerce: in 2015, Amazon opened its first Brick-and-Mortar store, Amazon Books, as a direct response to Brick-and-Mortar going digital. Instead of looking at one another as competition, they recognized one another as new channels to attain new generations of shoppers. In 2020, these companies are equally focused on their physical and digital locations and so, find all the above use cases relevant - and relatable. 

For all the other retailers, priorities shift. Defining these use cases will help you form a clear idea of why.

Top Retail Use Cases Shaping The Customer Experience Journey

For Brick-and-Mortar 

Enhancing customer experiences in stores is particularly challenging in the digital age. Not so long ago, retailers struggled to provide decent customer service. And they invested substantial funds and energy in employee training to achieve that. As if maintaining an exceptional and identical code of conduct throughout their stores isn’t hard enough, now they also need to sell their brand identity. They need to become more personal and empathetic as a service. Let me give you a personal example. A few years back I was meeting multiple clients on a daily basis, as an Account Manager in the HR space. One of my favorites was a Global Electronics Company, recruiting senior technical roles in a highly competitive market. They were very demanding, always expecting custom solutions and I spent the better part of my first meetings asking dozens of questions to understand account habits, preferred experiences, and possible platform loose ends. Analyzing the type of roles they were hiring, their ad patterns, content and keywords, and comparing present to historic habits, I managed to create an account profile and a personalized platform experience, transforming our regular one to one interactions into hands-on team workshops. This engagement turned out to be the type of hyper-personalized experience they needed, as it created the opportunity for them to use more functions, significantly ease their workload, greatly enhance their loyalty and future shopping journey.  It’s the same for customers going into a store to buy shoes. It’s nice when they find the right shoes. But it’s amazing when they create a connection with the brand because the store clerk knows their sizes, preferences, stock match, and even what discounts are applicable to the preferred products. Getting the most out of your customer data means winning, every single time. 

For Brick-and-Mortar retailers, acquiring this data implies capturing everything that happens in-store. This is usually done by installing physical devices and pieces of hardware, such as sensors or cameras, that capture insane amounts of data and send it to systems that later analyze it, interpret it by linking the missing pieces and respond accordingly: with promotions,  helpful advice or invitations to buy personalized products. 

For example, Smart shelves enable Brick-and-Mortar retailers to stay connected with their customers. Placed in strategic points in stores, sensor beacons collect information about how much time customers spend in stores, what products they like, and what they walk by. Analytics applied on top of this data is also connected to inventory management, so that for instance supermarkets can make sure they will never go out of stock and that customers will never miss an item off their grocery list.

Facial recognition goes one step further. It came as a necessity to stop shoplifting and theft prevention is indeed its most common use. And an important one at that. But facial recognition models are also excellent at delivering customer satisfaction. Basic emotion detection can alert employees to help customers that can’t find products on the shelf. It’s also used to detect habits and shopping patterns much needed to design customer loyalty programs and send personalized newsletters, in-app, or by email.

Forward-thinking retailers are also looking at automating many tasks in-store to enhance workflow: either to relieve employees from menial tasks or to speed up some processes. They bring in Robotic Store Assistants, in many shapes and sizes: from robots used to scan shelves, to assistive robots on the aisle or in warehouses, to all-purpose robotics for fashion warehousing, cashier-less checkouts or shopping assistants like Lowebot. This trend can create a genuine Brick-and-Mortar buying journey, more accessible, convenient and supportive.

There are cameras connecting the physical world to machines and then there’s the virtual world. Virtual Reality greatly enhances the shopping experience through smart mirrors and virtual dressing rooms, allowing customers to completely change the appearance of their make-up, hairstyles and virtually try on dozens of clothes and accessories without the hassle of actually waiting in line or applying products. VR also changes the store optimization processes: retailers get to completely redesign their locations based on direct customer preferences, virtually trying on multiple designs to reach the best one.

Technical implementations & common ground: Computer vision plays the biggest role in Brick-and-Mortar technologies

Shelf monitoring solutions, cashier-less checkouts, theft prevention, and facial recognition use cases find a common ground with computer vision implementations. Specific hardware is designed for computer vision analysis: as data is being collected from images through sensors, beacons, thermal scanning or cameras, it is being processed, classified, and interpreted to allow retailers to speed up key operations like shelf management and stock inventory, easy payments or customer care. While Virtual Reality’s basic focus is to simulate the vision by creating an immersive 3D environment, in retail environments VR is closely connected to Computer vision. Cameras and sensors help VR models understand the customer’s environment and make it more responsive and personalized to the user's needs. This way, the environment actually improves customer engagement and satisfaction and promotes a tailored shopping experience. 

As a market, computer vision is growing rapidly and supports a wide range of commercial applications. Thanks to the fast development of technology, devices and sensors are now smaller, more affordable and accessible and due to advances like cloud computing or 4G technologies, Brick-and-Mortar retailers are already capable of moving the data faster than ever before, so it’s actually an exciting time for them to take the reigns of computer vision applications. If you fit the criteria and are looking for advice on project integration and delivery, look no further! Our consultants are highly proficient in computer vision and are always keen to help or train your teams.

Now, all of these technologies help Brick-and-Mortar retailers offer the best in-store experiences and catch-up with the digital revolution. And it’s great that Brick-and-Mortar takes advantage of these implementations because in some ways E-Commerce is one step ahead. 

For E-Commerce

The fact that E-Commerce is by design digital doesn’t necessarily mean that online retailers are more digitally advanced than Brick-and-Mortar ones. Having a basic shopping cart doesn’t make them shopping experience authorities. The road to converting shoppers moves way beyond a shopping cart, to convenience, location, product match, and price tag and starts with the very first interactions with your brand. Consumers judge online shops based on their ability to retain information, likes and dislikes, based on their virtual assistants, on the capacity to change language/currency, to be consistent across channels and devices, to remove out of stock products from the available ones, to offer personalized discounts and recommend other products. They offer their loyalties to the brands capable of delivering end-to-end experiences through integrated solutions and some E-Commerce stores aren’t advanced enough to fit the bill. 

Nonetheless, in a way, it’s not wrong to consider E-Commerce one step ahead. A lack of physical presence means customers are only using day-to-day hardware components: computers, mobile devices, tablets, electronics like Alexa or Echo. This means retailers can shift all their development budgets towards digital technologies and they can take advantage of hardware upgrades to understand what customers want. For example, Siri and Bixby were major upgrades in the mobile space: these assistants disrupted the mobile world and customer behaviors. Some tech-savvy E-Commerce retailers understood the change Siri and Bixby imposed to Retail and developed personalized models to capture their audience in that mobile space. 

As such, Voice Assistants became very popular in the past few years. Other companies picked up the trends and developed their own devices and assistants: see Amazon Echo powered by Amazon Alexa or Google Home. All these are great helpers around the house in our personal time, so it’s only natural for retailers to pick up this trend. The first stop for retailers has been to use Voice assistant brands like Theatro to upgrade their internal communications and team capabilities. And if you think that for now only Apple, Samsung, Amazon or Google reap the benefits of real voice shopping, keep an eye out for Google’s Shopping Actions, which will create a universal shopping cart, making sure that with the right Answer Engine Optimization in place, you’ll never again face shopping basket abandonment in favor of the competition.

If voice assistants are focused on mobile and smart speakers, Customer-care Chatbots capture both the mobile and the web-worlds and they’re more in vogue every day. Retailers that automate manual tasks by deploying chatbots directly in the shopping journey can actually lower cart abandonment rates and solve many customer issues in a timely manner - or at least, expedite the customer service process. They can solve many problems such as page overload dissatisfaction, basket loss, inventory or delivery issues, package tracking or even make product recommendations when paired with predictive analytics. As chatbot responses can be controlled and interpreted, they are actually allowing retailers to improve their brand via fast and high-quality customer service.

Another amazing E-Commerce technology is the ability to track and interpret what your customers are saying about you, everywhere they go online. Sentiment analysis helps you build retention models and customer loyalty, as it creates the ability to understand customer reviews and what they felt about their experience with your brand. A 2016 Customer Loyalty statistic showed that 37% of shoppers not only consider themselves loyal to a brand, but they make repeated purchases from that brand. 37% is an important percentage of customers you don’t want to lose or miss out on because you haven’t done your homework right. If customers are attaching sentimental values to your brand, keeping score of your good vs bad products and whether or not their shopping experiences on your website made them happy, satisfied, or annoyed is critical. You need to gather their feedback fast and generate quick responses and positive experiences. Sentiment analysis is a fast, accurate, and high-value replacement for old customer polls, greatly improving all sales and pricing strategies that lead to better customer loyalty rates and increased profit potential. 

Technical implementations & common ground: NLP (Natural Language Processing) leads the way in these E-Commerce use cases. NLP is the technology that assists computers to understand human language, through text or voice recognition. NLP is implemented through applied machine learning algorithms that identify and apply natural language rules to unstructured language data collected, converting it into a comprehensible form for computers. Though its task is not easy and it doesn’t generate 100% results accuracy, Natural Language Processing is the next step in truly understanding the customer in an online environment and it might even revolutionize retail customer service altogether.

Excellent voice assistants, chatbots, or interpreters are not easy-peasy to design and implement and they require an incredible focus on research & development. However, not having to worry about smart sensors, beacons, and VR on top of digital does help E-Commerce stay with both feet on the digital ground and it can offer a terrific sense of power. 

For both Brick-and-Mortar and E-Commerce

There are some use cases that work for all - some essential (like contactless payments or price optimization), others more circumstantial (like Augmented Shopping), and others just vital (see Omni-Channel Retail below). When you just glaze over them, you might have the misguided impression that they aren’t connected, because implementations don’t find a common ground in a particular technology. The fact is that when you strip Brick-and-Mortar and E-Commerce of their unique use cases, everything that remains converges in the power of data. And #data might just be the Eight wonder of the world. 

We mentioned that Contactless Payments are essential. NFC-enabled payments and mobile wallets are definitely used on a larger scale. Some retailers can’t recall a time they didn’t provide contactless payment methods, because a) 28% of shoppers think contactless is easier and 29% think it’s faster, b) it greatly increases sales, giving that shoppers paying contactless spend 30% more, according to Mastercard and c) it helps create a loyalty channel. For digital payment solutions, you can take a look at Youtap. And contactless payments are a great source of data: the insights collected from purchases are then turned into customer spending habits that are the foundations of customer loyalty programs.

Once retailers understand spending habits, they can perfect pricing strategies. And Price Optimization solutions are more important than ever to all retailers. As this is a concerning factor that affects future purchasing decisions and buying habits, setting the correct price for each product means a thorough analysis on its impact on sales and business performance. This process needs to identify core groups of customers and their purchasing habits and apply analysis on survey and raw data that comes from production costs, customer expectations, and competitors' prices (for thousands of products or more) and produce both basic and discount pricings. Price optimization comes as close as technology gets to personalize pricing models and it’s an important asset, also driving customer loyalty.

We know it seems like everything is extremely important - and we haven’t finished yet. We’re not going to lie. If smart shelves or virtual assistants can wait (not much longer, though!), contactless payments had to be implemented last year. Price optimization is a must and inventory management is a deal-breaker. That’s just reality. Having the correct Inventory Management systems in place means retailers get a great competitive advantage. Understanding product data and knowing what products they have in stock and where, when, and what to reorder helps retailers optimize fulfillment fast, track assets, reduce overstock risk and inventory shortage, improve supplier negotiations for wholesalers and achieve great cost savings. And these cost savings might actually end up paying not only for the Inventory Management systems but also for other of the mentioned solutions. 

Now, Augmented Shopping is a bit more complicated. Same as with VR, it’s killing two birds with one stone, transforming not only the way customers perceive and experience the shopping journey but also resolving some key problems in goods’ returns. Apps like Ikea Place or Sephora Virtual Artist are leading the way with immersive experiences. Just imagine redesigning your room with one tab or trying on dozens of shades of lipstick without living the comfort of your own makeup station. And if you’re worried that Augmented experiences are limited to big brands, don’t be! Different startups in the field are on their way to transforming a once disruptive technology into a sustainable trend: for example, Algoface is developing an AI that allows you to virtually test all your favorite makeup brands, making it the perfect opportunity for lesser-known brands to finally stand out.

Combined with machine learning and behavior analytics, Augmented Reality also acts as a Return Technology, trying to solve one of retailer’s biggest issues: alarming return rates, as it’s estimated that during 2019’s holiday season alone retailers returned approximately $100bil worth in goods). AR should paint an accurate picture of how the product would actually look like, helping customers make very informed buying decisions. There’s no denying that AR could be a significant edge for retailers, however whether or not it’s the right time to implement it is circumstantial - it’s a general impression that trailblazing technologies should be adopted last. It would be a shame if a retailer allowed you to try on 10 products, only to realize that 4 of them were out of stock. Improving inventory management, introducing e-wallets, and setting up the right price strategies should come first. 

One thing that’s not circumstantial however, is Omni-Channel Retail. More than a use case, it’s a technology strategy supposed to integrate Brick-and-Mortar to web and in-app shopping through a centralized data management system. Here’s how a basic Omni-Channel journey looks like: you are shopping in-store and you hear a promo prompting you to download the mobile app and enjoy great products at half-off. You download the app, connect it to your e-wallet, but finish your in-store purchase and leave. The system starts working on your personalization journey, based on key entry-points from your 1st purchase. It then gives you recommendations of what to buy next based on this specific history and what discounts are already available (the above mentioned half-off products), and offers you the possibility to buy in-app and pick-up in store. From that point on, the Omni-Channel journey works around the clock to collect and keep track of all of your data and inspire loyalty. This is what Walmart does. It’s a great example of Omni-Channel done right: once Walmart understood that 20 - 35% of its audience was buying through the mobile cart, it maximized its efforts to connect all online and offline touchpoints. Now, it remembers all customer habits and order histories, offering customers uninterrupted, seamless shopping experience across physical stores and digital devices.

All of the above technologies focus on personalized customer experiences. But if you take a look at industry frontrunners Amazon and Walmart, Best Buy, Sephora, The Home Depot, or Nordstrom, such brands are well known for having customer-centered business models around the best recommendations. This factor is so important that every year companies feel the need to build Retail Personalization Indexes around it. Big brands constantly reinvent themselves and adapt to customer expectations: they are impressive not because they recommend the best products, but the best alternatives. They put their customers first by taking a page out of Netflix, Facebook or Apple’s playbooks: understand personal behavior first.

Recommender Systems are as close as systems can get to learning personal behavior and predicting future preferences. As they turn infinite data into an ideal shopping engine, recommender systems are probably the most powerful machine learning technologies for retailers, as they predict customer preferences, remind them what they need, and recommend matching products and the best alternatives they haven’t yet discovered. These systems analyze historical data to filter and forecast the products most likely to result in purchases: they go a long way to helping customers decide what to buy next. Recommendations are key to improving customer satisfaction, basket value, and customer loyalty. How do others use them? Amazon integrates recommendations in almost all aspects of their purchasing process, converting into a 29% sales increase. Home Depot has played with project-based recommendations to crack the code of “DIY” shoppers specific to the home improvement sector, turning in-store advice into an online assistance model. Nordstrom designed an entire recommendation project called Nordstrom Looks, around an outfitting experience based on their shopper's preferences via their most recent engagements. While Recommender Systems might not work for all retail categories ( see Convenience stores in Cheat Sheet above), these engines can do a fantastic job for Specialty Stores like Nordstrom, Department Stores like Home Depot or Walmart, Supermarkets or E-Commerce, even Discount Stores. To dive deeper into your customer’s imagination-land, I put together some thoughts that make me particularly happy when I think of all my favorite retailers: 

A retailer’s key to success and how to get there faster

With more than 10 thousand businesses competing in the same space, success is determined by a retailer’s ability to have in store what their customers want, to forecast sales, reduce churn and keep expenses in check. It’s obvious that not all retailers are equally digital, nor do they have the same capabilities to offer their customers everything they need, at the right time. Still, agility, resilience, and innovation usually come from customer-centric retailers. Now and in the future, retailers have to become better and better at selling their products, at optimizing their processes, improving their home deliveries, lowering their return rates and finding ways of locking their existing customers. 

Consumer-driven technologies will focus less on physical automation and robot-human replacement, and more on creating the most rewarding experiences. Retailers of the future must prepare for more insights-driven business decisions, as the benefits of Machine Learning in Retail are endless and they’re out for grabs. 

As with all business strategies however, implementing something new is not easy. Your data might not be properly stored or cleaned, your teams might not be trained to design advanced image or voice recognition systems or recommender engines, and your systems might not be generally ready for machine learning. There’s no denying that implementing a data-driven approach in your business strategy and including a machine learning practice will allow you to find a more personal route to your customer, fix your short-term problems and achieve your long-term goals. Our team of experts supports companies in the Retail Space to adopt data-driven models, focusing on advanced Research & Development, organizing workshops, and setting up complete delivery models. We can help you get there faster. Contact us now for a Free Machine Learning Consultation and implement the project in your own timeframe!