Retailers can survive the ever-evolving waves of a dynamic market by building a loyal customer base. It is the stark reality of the current retail industry and thus brands must be customer-centric which is key to delivering personalized experiences for enhanced customer satisfaction. Online giants like Amazon and Netflix adopted this approach early on – no wonder they enjoy a global customer base!
According to McKinsey, a positive customer experience is immensely important for a retailer’s success as it accounts for 20% higher customer-satisfaction rates, a 10 to 15% boost in sales conversion rates, and an increase in employee engagement of 20 to 30%.
Retailers are waking up to this realization of using disruptive technologies like AI, big data, and customer analytics to expand their customer base, target the right customers, and thus improve retail customer experience (CX). These nifty technologies enhance visibility by deep-diving into data, allowing brands to understand customer behaviors, preferences, and buying patterns better.
Customer Analytics at the Core
Predictive analytics forms the foundation for customer analytics. It aims to make accurate predictions on customer behavior using AI and ML models and statistical tools.
Retailers, Brands, and CX experts collect data from disparate sources, including in-app interactions, social media, websites, customer support, point-of-service software during checkout, etc. Customer Analytics processes, analyzes, and interprets the collected data to identify customer behaviors for accurate buyer personas based on factors like preferences, purchase history, requirements, needs, age, location, gender, etc. Marketing executives can develop highly customized strategies and campaigns according to individual personas. This immensely helps in generating and nurturing leads and converting them into loyal customers.
Essentially, Customer Analytics is an efficient way for retailers to understand their customers through curated data insights. Additionally, it helps them identify the gaps, anomalies, and trends in the market.
The basic steps of Customer Analytics are –
- Collecting data about customers at all touchpoints
- Running advanced analytics models on the data
- Deriving actionable insights
- Using the insights to engage customers better
Key challenges faced by Retailers
Tough competition
Increased technology penetration and adoption have created a highly dynamic market with fast churning trends. In this competitive space, retailers must constantly compete with pure play eCommerce companies to maintain their market foothold. While this is the biggest challenge retailers face today, gaps in demand and production forecast, fulfilment & logistics delays, and decoding customer sentiments create a volatile environment.
Retailers can further use analytics for market and competitor research to see what their competition is offering at what prices. It helps a retailer to maintain competitive pricing and lure more customers.
Supply chain & inventory management issues
According to a survey, 88% of retailers faced delays in the delivery of goods and services, while almost half of them faced stock-out issues. Inventory management is another pressing issue for retailers. It involves adequate stock planning for all retail outlets to ensure that products (particularly high-selling ones) aren’t out of stock, by anticipating demand accurately.
Advanced analytics models can process data in real-time using AI/ML algorithms at various points in the organization’s supply chain process. And retailers can use the insights to accurately track shipments and inventory to take necessary and timely measures.
Fast-changing consumer expectations
Consumers today are a tech-savvy bunch. They know where to find the right information and how to research value-for-money deals. Keeping up with their evolving expectations is a tough game.
Customer Analytics enables retailers to promote their products to potential customers at the right time and place. It can identify the most searched or in-demand products/services in a geographical area. Hence, it becomes easier for retailers to attract customers through targeted advertisements and promotions.
Customer Analytics brings the following benefits to retailers:
- Identify unsatisfied customers
- Attract and retain high-value customers
- Personalized engagement with customers
- Promote products/services through unique interaction points
- Boost sales revenues, margins, and ROI
Advanced Analytics enhancing customer experience
Retail businesses using Customer Analytics can deliver a better customer experience by –
Decoding buying patterns
Customers often tend to display predictable buying patterns. The better you understand the pattern, the more revenue you can generate by providing curated services. As highlighted earlier, Customer Analytics aims to extract meaningful patterns in customer data. These insights help retailers see customers’ preferences, interests, and needs. Hence, retailers can push the most relevant products to each customer segment and increase their brand loyalty.
For example, Nordstrom, one of the best-known fashion retailers in America, uses customer analytics to monitor their customers’ buying behaviour. They keep track of almost everything, starting from which section of the store the customers visit the most, how much time they spend on each section, and other similar behavioural patterns.
Promoting personalization
According to experts, personalized marketing and tailored strategies can increase a firm’s revenue by 15%. Customer Analytics enables retailers to tailor-make marketing and sales plans for target segments.
Sephora is one of the best examples of personalized marketing. The beauty store’s app allows consumers to book appointments and virtually experiment with store products. It also offers customized recommendations to mobile app users, encouraging them to return to the brand repeatedly.
Improving in stock accuracy
Customer Analytics examines sales data to identify the primary reasons why some products/services are more popular than others. Thus, retailers can focus more on frequent and fast-selling products. It also identifies seasonal trends to understand spikes in customer demands for specific products, helping retailers maintain adequate inventory levels. Retailers can further use data analytics to boost the efficiency of checkout and returns/exchange processes. These measures certainly improve a brand’s CX.
Shiseido, a Japanese beauty retailer, has revolutionized digital shopping. With the help of ‘cosmetic mirrors’, customers are now able to virtually apply products on their faces, without the hassle of removing the sampled makeup numerous times. This enables them to explore the various options available and select the best one accordingly. Retailers, in turn, can now keep track of the fast-selling items and maintain their inventory levels as per the demands.
Increasing multichannel visibility
Automation software and analytical tools increase visibility through all customer-brand interaction touchpoints, from browsing the store to checkout and customer support calls/emails. It also analyzes social media posts, customer feedback, chats, and phone calls to assess customer satisfaction and brand reputation. Such transparency and visibility across all touchpoints can highlight the problem areas, allowing retailers to enhance their product placement, marketing/selling, and customer support strategies.
Kate Spade is one such example of a successful retail brand that has been able to harness the power of customer analytics to connect better with its customers. They have already garnered a huge following base across various social media platforms like Facebook, Instagram and Youtube. They are constantly updating their accounts with various kinds of fun and inspirational posts to retain their customers’ interest.
Conclusion
Like many other industries, retail industry too is embracing advanced analytics to increase in-store efficiency, improve competitiveness, and expand their customer base. For instance, retail outlets can integrate Hyperautomation to speed up their checkout process, eliminating long queues and reducing wait time. Using AI and ML, they can create simulation environments where customers can visualize or try products before making the purchase.
Data analytics can scourge social media and websites to identify which products will sell best under specific conditions. For instance, Walgreens and Pantene collaborated with a weather channel to create personalized product recommendations. During the humid season, the brands increasingly promoted anti-frizz products in store. While Pantene’s sales at Walgreens increased by 10%, the retail giant witnessed a 4% increase in the hair care segment during that period.
JK Tech’s Smart Analytics team can generate actionable insights by scrutinizing past data. Using these business-ready insights, retailers can drive in-store sales, operations efficiency, seamless inventory management, and much more!
