Extensive data collection by e-commerce platforms has immensely helped to boost sales by acquiring customers over omnichannel touchpoints. The dynamic personalization that these platforms attempt over these touchpoints is highly contextual. Traditional brick and mortar retailers have grappled here, where-in they could not utilize their data repositories to engage with and acquire new customers. Also, traditional retailers have been more concerned with profitability and optimization of their operations rather than with personalization and the in-store customer experience.
Today, in this era of customer-oriented retail, retailers need to focus on customer interactions and in-store experience to improve the sales conversion rate.
According to BCG's study, levels of personalization maturity differ greatly between organizations and retail categories. It also demonstrates that organizations who execute personalization strategies and become best in class in offering tailored experiences may treble their revenue boost. Personalization is being used to make the buying experience as simple, quick, intuitive, and seamless as possible across all touchpoints.
In another study according to McKinsey, personalization results in a 20% rise in customer satisfaction, a 10% to 15% rise in sales conversion rates, and a 20% to 30% rise in staff engagement. Retailers must proactively sift through massive volumes of customer data to get meaningful insights about them and tailor product suggestions and end-to-end shopping experiences appropriately. Retailers can make use of predictive analytics which will help them in tailoring personalized messages by examining data from marketing campaigns, previous sales, website interactions, and customer service undertaken. This increases the buying affinity of the customers towards the products that are marketed keeping in mind their needs.
Following are a few attempts in diverse areas that are being made across the retail industry for a personalized customer experience:
Proximity Marketing
Brands like Red Bull, McDonald’s, Walmart etc. are using beacons for proximity marketing leveraging the locations of their customers around the stores. Different campaigns are defined to promote their products, services, and special offers. However, this requires the customers to have the retailer’s application to be installed on their devices. This automates the content delivery and also collects the in-store behavior of the customers.
With retailers struggling from app fatigue as a major problem, other technologies like wi-fi can also enable retailers with app-less proximity marketing. There are solutions like the Next Generation Hotspot is a solution that helps brands with location-based marketing and engagement with the customers on-premise.
This provides the marketing teams of retailers an additional engagement channel to reach out to the in-store customers for real-time engagements. Retailers can also enable different services like "click and collect service" to complete orders of the customers that come within the proximity with their orders placed already.
Customer-centric recommendations
With the onset of AI-based recommendations, Retailers can improve their conversion rate by recommending the right product to the right person at the right time considering customer’s past buying behavior, current store journey, and lifestyle choices. Since customer expectations are evolving and expanding, customer-centric recommendations let customers know that retailers care about their preferences and desires at the moment.
Sephora, a French multinational personal care and beauty products retailer, in the USA, is providing its employees with hand-held devices that can enable them to recommend products based on the customers' skin tone by scanning and capturing their skin details. This AI-enabled device creates a four-digit code called the customer's Color IQ, which is then securely stored to personalize future shopping experiences.
Uniqlo, a Japanese clothing store, is working on the idea of tailoring their recommendations based on the customer’s mood. Customers are given, an AI-powered wearable gadget named UMood, that was implanted on their brows. They were then shown a series of photos and videos when they come to the store. The customer's psychological reactions are then used to generate a brainwave reading, which helps in selecting a t-shirt from the retailer's collection that matches the customer's mood. These various mental states are categorized and labeled as "adventurous," "calm," or "stressed".
Automated shops
Facing heavy crowds and long queues have been an age-old problem for customers in retail stores. Retailers today are trying to involve minimize the manual effort required for handling customers and automate their shops. This is being done with the use of cameras, sensors, and mobile apps for easy checkouts and improving the shopping experience.
Amazon has revolutionized online purchasing, but with its Go shops, the company is evolving in physical stores as well with an employee-less delivery of services. To automate the payment and checkout process, they employ a mixture of computer vision, deep learning, and sensor fusion technologies. So, ideally, customers can simply collect their items into the cart, like they do on their website or app, from the store and checkout hassle-free.
Jack & Jones and Veromoda, fashion retailers, have opened smart stores in China with facial recognition technology. Shoppers must first register for facial recognition in-store, which connects their face to WeChat Pay. A computerized kiosk at the exit reads their face and approves payment. Such an automated experience reduces the hassle that customers face with queuing for checkouts.
Nike’s flagship store in New York is making an effort to smoothen the consumer experience with the help of smartphone applications. Nike customers are provided an option to reserve a pair of shoes online for a trial in-store. To provide a seamless experience to their loyal customers, Nike reserves a labeled locker for them which the customers can open with their smartphones for accessing the reserved shoes or any billed items online. Mobile checkout in-store over the app makes it even easier.
Virtual Try-on & Smart Mirrors:
One of the classic examples of how innovation can help in creating a fully customized shopping experience is the smart mirrors/virtual try-on leveraging the VR-AR technology. These mirrors also work alongside the mobile apps to engage customers contextually through the data that is being collected through customers’ on-premise browsing once they are connected to Wi-Fi in the store. This digitally-driven experience is said to have resulted in a 30% increase in sales, with buyers requesting extra things because of the smart mirror recommendations.
Brands like Kohl’s have established interactive fitting rooms/changing rooms where customers may try on clothing and accessories. If the fit of a garment is not quite perfect, customers may request different sizes or designs from fitting rooms themselves. In addition to such fitting rooms, Rebecca Minkoff, a global fashion brand, has smart mirrors connected to the wall across its stores that broadcasts films and product-related material constantly. Customers may engage with the mirror by touching it and giving directions on how to prepare dressing rooms for them. They can even order beverages and make changes to the menu. All these choices are saved in profiles, ensuring that when customers return to the store, they get a personalized experience basis their history.
Smart Vending Machines
& Other Stories released a vending machine that doubles as an interactive center, with a huge touchscreen that allows visitors to learn more about the offered items and watch in-depth visual storytelling. Customers will be able to locate and order their favorite cosmetics and fragrance products, as well as something new, all in one location, rather than dispersed throughout the store's enormous clothing categories.
Such technologies are successfully leveraging personalization. It majorly requires a proactive attempt in understanding customer behavior and buying patterns in a way that collects, analyzes, and streamlines data. For this, it is essential to track the movement and location of customers in-store, up-sell, and cross-sell products basis the customer sales data, send personalized discounts and offers to customers based on their shopping needs and preferences with the help of various technologies at disposal. This will boost the conversion rates of customers by making physical shopping more targeted and personalized. The more the retailers obsess about a customer the more a customer will obsess about shopping.
