Retail Trends in the Use of Big Data Analytics
As of January 2018, over twenty traditional retail chains, including Sears and Radio Shack, were either liquidated or have filed for bankruptcy. These failures are due in large part to these retailers’ slow adaptation to the digital retail marketplace. In contrast, today’s most successful retailers have seamlessly integrated digital storefronts with brick and mortar stores and are leveraging huge volumes of data gathered on suppliers, competitors, store locations, social media, customer purchasing and lifestyle behaviors to create value. These retailers are embracing retail analytics powered by big data to develop and execute strategies for improving profits. In this report, we’ll cover the most common trends and topics we’re seeing with big data in retail.
Analyze Customer Behavior
Retailing success in 2018 and beyond is all about personalization and connecting directly with each consumer. To achieve this, a retailer must understand what motivates and appeals to its different customers. The first step to achieving this is to gather and critically analyze all types of customer-related data. While in the past, retailers had to make educated guesses about their customers, successful retailers now use big data analytics to ascertain useful data based on measurable statistics. They collect, identify and measure patterns found in customer behavior in specific stores based on point of sale systems, virtual dressing room technology, reservation of products, in store sensors, online interactions, loyalty programs and a variety of other internal and external data sources. They then use the results and metrics to enact a variety of strategies and techniques.
Improve In-Store Efficiency and Performance
Retailers are outfitting stores with beacons, smart sensors and Wi-Fi analytics platforms to generate location-based traffic data and to track how many users enter stores, how long they stay, where they spend the most time and the number of times they return to each store. A major use of such in-store data is to help optimize marketing, merchandising and product mix strategies as well as store layouts with a view toward providing greater incentives for customers to purchase in-store and on the spot. For example, if a retailer notices that many customers are repeatedly purchasing just a handful of items, they might install self-checkouts to make the shopping experience more enjoyable and convenient. Additionally, a centralized repository of store data can be used to optimize product and promotion placements, localize product assortments and to help keep in step with consumer volume demands, thereby reducing out-of-stock incidents and the need for markdowns. Finally, retailers can save on costs and reduce wait times by better planning of staff levels based on store traffic analysis.
Personalize In-Store Experiences
Shoppers now make a habit of doing in-store research to decide if they really want a product, then many go online to find the lowest price for it. This results in significantly lower sales for brick and mortar retailers. The key to combating this trend is to use technology and analytics to create personalized in-store experiences, which incentivizes on-the-spot purchases. A single customer may generate thousands of data points per visit in a large store. Retailers are using technology to track individual customers, what they are looking at and what actions they take both off and online. They also use data to identify their most important customers. With this information, they personalize loyalty rewards and in-store customer services, offer timely customized incentives and do whatever they can to make the customers’ in-store experience personally engaging. Data also provides an opportunity to recognize the best customers each time they enter the store. Recognition can be as simple as knowing customer names and greeting them in the store or adapting customer service to their preferences.
Increase Customer Retention
As mentioned previously, analytics enables retailers to target customers with more personalized experiences and rewards. Retention of the most valuable customers will flow more naturally by using data to focus on specific customer needs. Retailers can analyze the current data of the most valuable customers and incorporate that into retention strategies, such as personalized loyalty programs and tailored promotions that meet individual needs. Data can also inform how staff and team members approach and interact with specific customers. All of this in turn helps to create a longer-term relationship with those customers. On the flip side, retailers can mine data to identify customers at risk of defecting to competitors. By understanding behavioral patterns of former customers who stopped shopping and by identifying trends such as when a certain habitual item is no longer showing up in a customer’s basket, retailers can identify at-risk customers and take action to retain them. At StrategyWise, we have seen a significant uptick in the request for support in integration and leveraging of data generated from loyalty rewards programs over the past two years.
Leveraging customer purchase data can help retailers create and execute more targeted and relevant advertising campaigns across all channels. Data from loyalty programs, multi-channel interactions including mobile app and social media, browsing activities and purchase history generates information that helps retailers understand what products are trending for specific customers and groups and what an individual customer’s preferences are in terms of products, aesthetics and more. The result is less overall spending on advertising and better conversion rates. Data can also help to measure the effectiveness of advertisements with respect to conversion. Finally, newsletters, essentially a type of mass advertising, can be customized for groups of customers with similar needs and spending habits.
Improve Supply Chain Efficiency
Supply chain optimization through data analytics is perhaps the most fruitful use of data for retailers. Retail supply chain logistics create massive overhead expenses due to innumerable and invisible inefficiencies along the chain, both from manufacturers to retailers and back up the chain to producers for damaged and returned products. Aggregated data from logs, sensors, machines and other highly repetitive details can highlight outliers in performance and detect weaknesses and complications in a retailer’s supply chain. Action can then be taken to improve the supply chain. In addition, by combining real-time shelf data with advanced ordering algorithms, retailers can more precisely control their supply chain and help improve inventory management and the quality of products and services.
Vendors such as SAS and Oracle now produce tools and software solutions that analyze and predict how changes in price affect demand for a given product. With information on consumer responses to prices made available instantaneously, retailers located anywhere in the world can quickly assess what the sales impact is for every dollar change in price. By determining the percentage of sales lost or gained in correlation to a new price, retailers can efficiently tweak prices to maximize the revenue of any given product. Retailers can then use this information to optimize the value of pricing promotions. In addition, by analyzing data to find frequently price-checked items that were subsequently not purchased, retailers can identify products with mislabeled pricing. Other ways to use data modeling to optimize pricing include using data to best price promotions, timing mark downs to maximize margins and inventory sell through, and pricing differently to different customers at different times to maximize revenue.
Omnichannel analytics provides a much more sophisticated view of customer demand and the expanded customer buying journey. Retailers are hiring data scientists to deploy such newly developed retail analytics software in order to consolidate information from multiple channels and obtain a holistic overview of performance. With this information, retailers can drive efficiency in all aspects of the business, shorten reporting cycles for more timely insights and decision making, create a culture of analytics with self-service tools and manage cyber activity risks.
Forecast Trends with Predictive Analytics
Retailers are increasingly using predictive analytics for a variety of reasons. Knowing what will be in demand tomorrow and the ability to predict what retail trends will be hot ahead of the competition will put a retailer at an advantage. Retailers can apply predictive algorithms to social media posts, web browsing habits and other purchase data to figure out what will sell in the future, which in turn assists in determining how much of a product to place in stock. In addition, retailers can use predictive analytics to prevent inventory shortages from occurring in the first place. By enabling retailers to understand how specific customers shopping at specific locations choose from an assortment of products, they can then predict what demand will be in different geographies. Finally, predictive merchandising can also help merchandisers, retail planners and inventory analysts better determine which products to put where.
Create Strategies Using Prescriptive Analytics
The most progressive retailers will be turning to prescriptive analytics in 2018. Prescriptive analytics can offer retailers the best course of action for a given prediction. For example, prescriptive analytics can take a vast set of data, including product availability, customer trends, geolocation and time, and then run a set of algorithms to find mistakes among retailers’ disparate data sets, such as store analytics, merchandising systems, CRM and ERP systems. It can then provide recommendations on what the retailer must address specifically. Prescriptive analytics can also be used to run more effective promotions, offer dynamic pricing and optimize product offerings. For example, by offering promotions on the right products at specific times of the day or week, a retailer can optimize sales opportunities and maximize overall performance.
Incorporate Artificial Intelligence
This year, 2018, is the year in which retailers will greatly increase their use of artificial intelligence to influence various parts of the retail experience. More retailers will adapt machine learning to figure out which items an individual customer actually wants or needs to purchase based on historical and real-time behavioral data. Then, a bot or app will engage the consumer in discussion and make product recommendations. Another new practice in AI is sentiment analysis, which is the process of using machine learning-based algorithms to process and determine the context in which a product is being discussed in social media. The analysis shows whether the product is being seen in a good or bad light and whether associated feelings are mild or strong. In addition, a plethora of new retail AI solutions has appeared in areas such as conversational commerce, sizing and styling, natural language search, real-time product targeting, visual search, multichannel marketing, cart abandonment reduction and even using robots equipped with visuals to restock shelves autonomously.
Big data has changed the retail landscape. Old retail models cannot survive without making changes and adopting new retail analytics tools that provide a holistic view of every customer across every channel and help to generate profit-maximizing insights. Once a retailer possesses a deep understanding of what its customers are buying and why, there are numerous benefits to reap in the areas of marketing, merchandising, sales, operations and supply chain management. If your retail company is not using data-based analytics to improve its performance, customer engagement, productivity and supply chain management operations, StrategyWise can help you get started on the road to survival.
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