In this day and age, customers expect personalization. They expect seamless experiences between online channels and brick and mortar stores. If they can’t easily make a purchase, they’ll leave for a different retailer. Retail analytics and merchandising analytics can solve these problems for retail merchants looking to increase sales and customer satisfaction.
What is Retail Data Analytics?
Retail analytics is the process of using big data to optimize pricing, supply chain movement, and improve customer loyalty. Big data describes a large volume of data that is used to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Historically, it has been defined by three key factors: volume, velocity, and variety. For the retail industry, big data means a greater understanding of consumer shopping habits and how to attract new customers. Big data analytics in retail enables companies to create customer recommendations based on their purchase history, resulting in personalized shopping experiences and improved customer service. These super-sized data sets also help with forecasting trends and making strategic decisions based on market analysis.
How Big Data is Transforming the Retail Industry
How Do Retailers Collect Data?
One of the most common ways that big data is collected in the retail industry is through loyalty programs. These days, it’s also collected through credit card transactions, IP addresses, user log-ins and more. As more information is collected, retail businesses can use market insights to analyze the ebb and flow of shopping and spending by consumers historically to predict future spending and make personalized recommendations.
Predicting Spending
Amazon uses customer data to recommend items for you based on your past searches and purchases. They generated 29 percent of sales through their recommendations engine which analyzes more than 150 million accounts. This has led to big profits for the ecommerce giant.
Personalizing Customer Experience
For retailers, big data can create opportunities to provide better customer experiences. Costco uses their transaction data collection to keep customers healthy. When a California fruit packing company warned Costco about the possibility of listeria contamination in fruits like peaches and plums, Costco was able to email specific customers who had purchased the items affected by the contamination instead of a blanket email to their lists. Leveraging the power of big data in retail? AimReply's AI email writer and generator can transform your data analytics into actionable customer engagement strategies. Ensure every email communication is personalized and effective, driving sales and customer satisfaction to new heights with AimReply's cutting-edge technology.
Forecasting Demand in Retail
In addition to big data, some algorithms analyze social media and web browsing trends to predict the next big thing in the retail market. Perhaps one of the most interesting data points for forecasting demand is the weather. Brands like Walgreens and Pantene worked with the Weather Channel to account for weather patterns in order to customize product recommendations for consumers. Walgreens and Pantene anticipated increases in humidity--a time when women would be seeking anti-frizz products--and served up ads and in-store promotions to drive sales. The purchase of Pantene products at Walgreens increased by 10 percent over two months and Walgreens saw a 4 percent sales lift across the hair care category during that same period. Retail forecasting and retail projections are used to properly allocate their resources the most effectively throughout different parts of the year.
Customer Journey Analytics
The customer journey is not a straight line. It’s a zig-zag across channels from research to purchase. The only way to get a handle on the customer journey and create better experiences is to use big data. Analytics solutions can help retailers answer questions such as: Where are customers actually looking for product information? Where are we losing them? What are the most effective ways to reach them and compel them to purchase?
How Do Data Analytics Help Manage Multiple Locations of Retail Chains?
Retail chains and retail businesses can use analytics to understand the differences in demand for their product across various geographic locations. Using consumer spending analytics, retailers can use this data to better service customers in specific regions and also stock products more efficiency.
How Envestnet | Yodlee Brings Big Data to the Retail Industry
Retailers are constantly looking for the competitive edge--better ways to reach customers, more efficient customer journeys and opportunities to proactively meet customer needs. With the Envestnet | Yodlee Retail Analytics for Market Research, your business can access easy-to-use dashboards that display customer affinity profiling, share of wallet metrics, and market shares. As a web-based consumer spending analytics tool using billions of de-identified transactions to answer competitive analysis questions for retailers, it is easy to use. Our platform does not require technical knowledge and helps find meaningful insights in large datasets. It gives you access to near real-time shopping measurements to determine the impact of advertising campaigns and allows companies to increase and maintain market share in key regions by discovering geographical areas with high or low market share. Unlike survey and traditional data sets, the Envestnet | Yodlee Retail Analytics for Market Research solution is powered by a de-identified and dynamic data panel that can be segmented in countless ways to reveal consumer spending data patterns for a variety of market categories and services. Drawing from 16 million de-identified active consumers, the panel is consistent with U.S. Census data in terms of geographical location and income distribution. Stand out against your competitors in 2019 by tapping into our market research solution and using our economic trends data analytics software.
