Customers expect personalization. Unless they have a seamless experience on your online channels, they’ll leave for a different retailer. Retail analytics can solve these problems for merchants looking to increase customer satisfaction and sales. It provides insights into inventory, sales, customers, and other essential aspects crucial for decision-making. Retail analytics also encompasses several granular fields to create a broad picture of a retail business’s health and sales, along with improvement areas.
Big data analytics in the retail market is expected to reach USD 13.26 billion by the end of 2026, registering a CAGR of 21.20% during the forecast period (2021-2026). The growth of analytics in retail depicts how it can help companies run businesses more efficiently, make data-backed choices, and deliver improved customer service.
Assortment planning allows retailers to choose the right breadth (product categories) and depth (product variation within each category) for their retail or online stores. Assortment management has grown beyond simple performance metrics like total sales or rotation numbers. Instead, retail analytics offers a comprehensive analysis of product merchandise and an estimated number of units at the push of a button. Retailers that effectively apply assortment analytics can enjoy increased gross margins and prevent significant losses from overstocks sold at discounted prices or out-of-stock inventory leading their customers to buy from competitors.
It also helps retailers gain insights into the trendy and discoverable brands and products on all e-commerce websites across the globe. They can boost sales by making sure they have an in-demand product assortment. They can also track pricing information and attributes common across popular products to drive their pricing and promotion strategies.
An inadequately maintained inventory is every retailer’s worst nightmare. It represents a poor indicator of inadequate demand for a product and leads to a loss in sales. Data can help companies answer issues like what to store and what to discard. It’s beneficial to discard or increase offers on products that are not generating sales and keep replenished stocks of popular items.
In 2020, the estimated value for out-of-stock items ($1.14 trillion) was double that of overstock items ($626 billion). A similar trend was especially prominent in grocery stores, where out-of-stock items were worth five times more than overstock items.
Unavailability of high-selling products can lead to reduced sales, ultimately generating incorrect data for future forecasting and producing skewed demand and supply insights. Retailers can now use analytics to identify which products are in demand, which are moving slowly, and which ones contribute to dead stock. They can know in real-time if a high-demand product is unavailable at a specific location and take action to increase the stock. Retailers can use this historical data to predict what to stock, at what place, time, and cost to maintain and optimize revenue. It helps satisfy consumer needs, prevents loss of sales, reduces inventory cost, and streamlines the complete supply chain.
The ability to accurately predict trends after the global pandemic and with an unknown economic future is becoming the cornerstone for successful retailers. Smart retailers know how important it is to Pandemic-Proof their retail strategy with Market Intelligence & Competitive Insights
With 90% of Fortune 500 companies using competitive intelligence, it’s an essential tool to gain an advantage over industry competitors. Competitive Intelligence allows you to gather and analyze information about your competitors and understand the market–providing valuable insights that you can apply to your own business. A more strategic competitor analysis will explain brand affinities and provide insights on what to keep in stock and when to start promotions. Customer movement data will also give you access to where your customers are shopping.
Retailers have been in a constant struggle with fraud detection and prevention since time immemorial. Fraudulent products lead to substantial financial losses and damage the reputation of both brands and retailers. Every $1 of fraud now costs U.S. retail and eCommerce merchants $3.60, a 15% growth since the pre-Covid study in 2019, which was $3.13. Retail Analytics acts as a guardian against fraudsters by constantly monitoring, identifying, and flagging fraud products and sellers.
Some of the challenges of the retail industry are that it’s seasonal, promotion-based, highly competitive, and fast-moving. In today’s competitive marketplace, consumers compare prices and expect personalized shopping experiences. Campaign management allows marketing teams to plan, track, and analyze marketing strategies for promoting products and attracting audiences. Retail analytics can help businesses predict consumer behavior, improve decision-making across the company, and determine the ROI of their marketing efforts.
According to Invesp, 64% of marketing executives “strongly agree” that data-driven marketing is crucial in the economy. Retail analytics can help businesses analyze their data to learn about their customers with target precision. With predictive analysis, retailers can design campaigns that encourage consumers to interact with the brand, move down the sales funnel, and ultimately convert.
Retail firms often look to improve customer conversion rates, personalize marketing campaigns to increase revenue, predict and avoid customer churn, and lower customer acquisition costs. Data-driven insights on customer shopping behaviors can help companies tackle these challenges. However, several interaction points like social media, mobile, e-commerce sites, stores, and more, cause a substantial increase in the complexity and diversity of data to accumulate and analyze.
Insider Intelligence forecasts that m-eCommerce volume will rise at 25.5% (CAGR) until 2024, hitting $488 billion in sales, or 44% of all e-commerce transactions.
Data can provide valuable insights, for example, recognizing your high-value customers, their motives behind the purchase, their buying patterns, behaviors, and which are the best channels to market to them and when. Having these detailed insights increases the probability of customer acquisition and perhaps drives their loyalty towards you.
Market trends fluctuate at an unprecedented pace, and pricing has become as competitive as it’s ever been. The only way to keep up with competitive pricing in retail is to use retail analytics that enables retailers to drive more revenue & margin by pricing products competitively.
A report from Inside Big Data found companies experience anywhere from 0.5% up to 17.1% in margin loss purely because of pricing errors. Pricing analytics provides companies with the tools and methods to perceive better, interpret and predict pricing that matches consumer behavior. Appropriate pricing power comes from understanding what your consumers want, which offers they respond to, how and where they shop, and how much they will pay for your products.
In 2021, the price optimization segment is anticipated to own the largest share of the overall retail analytics market. Retailers can identify gaps and set alerts to track changes across crucial SKUs or products with pricing analytics. Knowing your customer’s price perception will increase sales and also allow you to design promotions that’ll attract customers. Pricing analytics also accounts for factors like demographics, weather forecasting, inventory levels, real-time sales data, product movement, purchase history, and much more to arrive at an excellent price.
Sales and demand forecasting allow retailers to plan for levels of granularity—monthly, weekly, daily, or even hourly—and use the insights in their marketing campaigns and business decisions. The benefits of a granular forecast are apparent since retailers don’t have to bank on historical data of previous clients and customers to predict revenues. Retailers can plan their strategies and promotions that suit their customer’s demands.
With sales and demand forecasting, retailers can also consider the most recent, historical, and real-time data to predict potential future revenue. Sales and demand analytics can predict buying patterns and market trends based on socioeconomic and demographic conditions.
With the development of eCommerce, more and more customers prefer to browse and interact with the product before purchasing online. They look for better deals and discounts across stores and platforms. 3 out of 5 consumers say retail’s investment in technology is improving their online and in-store shopping experiences. To enhance merchandising and marketing strategies, retailers can gather data on customer buying journeys to understand their in-store and online experiences.
Retailers can run test campaigns to know the impact on sales and use historical data to predict consumers’ needs based on their demographics, buying patterns, and interests. Retail analytics help retailers to bring more efficiency in promotions and drive impulsive purchases and cross-selling.
Promotions are potent sales drivers and need to be cleverly targeted towards specific customers with precise deals to generate outstanding sales. Retail analytics allows companies to study their customers and competitors to a vastly elevated level.
To be an industry leader, retail companies not only have to understand their customers, but they must also analyze competitors’ promotions to improve their marketing strategies. Analyzing your competitor’s promotional banners, ads, and marketing campaigns are no more associated with imitation.
With data analytics and AI, retailers can watch their competitors’ commercialization strategies. It can uncover vital information about their target audience, sales volume fluctuations, popular seasonal product types, product attributes of popular items, and significant industry trends. Knowing exactly which products and brands are popular among your competitor’s campaigns can help retailers improve their promotional strategies.
The benefits of retail analytics are spread across various verticals, from merchandising, assortment, inventory management, and marketing to reducing losses. The need for analytics has become even more apparent considering the growing eCommerce platforms, changing customer buying journeys, and the complexity of the industry. Understanding which products sell best among which customers will help retailers to deliver an optimized shopping experience.
Want to drive profitable growth by making smarter pricing, promotions, and product merchandising decisions using real-time retail insights? DataWeave’s AI-powered Competitive Intelligence can help! Reach out to our Retail Analytics experts to know more.
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