The Definitive Guide to Retail Analytics: Why It's Critical in 2021
Retail analytics is a relatively new field that has been growing in popularity over the past few years.
What started as a niche industry for major players (like Walmart and Target) has grown into something that many small and large retailers are now investing in.
This post will discuss what retail analytics is, why it's important, and how retailers can use it to improve store operations.
What is retail analytics?
Retail analytics is an emerging discipline that uses data and statistical analysis techniques to understand a retailer's business, market and customer behavior in retail stores.
Retailers often employ procurement specialists who are responsible for ensuring that items come into the company inventory at the appropriate time.
Searches for “procurement specialist” are up 81% over the last 5 years.
Procurement specialists have to manage how much merchandise they order from vendors and make sure it's what consumers want when they're in the store.
When these people are informed by detailed data about consumer demand, this enables them to find new products faster, keep inventories leaner and ensure that stores provide a better customer experience.
Why is retail analytics important?
Retail has been hit hard by the pandemic.
This is why retailers are investing in data-driven approaches to maximize the ROI of each store.
Unlike eCommerce, brick-and-mortar stores often struggle to find customer data because it's not easy to collect.
Many stores rely on guesswork and external data points from other companies.
But they often know very little about the customer's actual experience in their store.
Retail analytics is what empowers retailers to gain a deep understanding of customers' needs by leveraging internal data collection that goes beyond credit card transactions or loyalty programs.
It provides insights into customers' behaviors and preferences, which allows retailers to make more informed decisions about merchandising, marketing efforts, store layout, and staffing.
The 4 main components of retail analytics
Retail analytics is made up of four major components:
- Data management software platforms.
- Customer relationship management tools like loyalty programs or social media channels
- Point-of-sale data that tracks what's being sold and to whom
- Analytics tools for analyzing sales, inventory and consumer behavior
Data management platforms are the backbone of every retail operation because they help retailers keep their store inventories lean by connecting them with manufacturers and suppliers.
Google search growth for “data management platform” since 2016.
Customer relationship management tools help retailers better understand their customers and create customized experiences based on what those consumers want. These are loyalty programs like Starbucks, customer service teams that communicate with customers on social media channels like Facebook, and even the layout of each store.
Shot of the Starbucks rewards page. Logged in users see their order history and points totals.
Point-of-sale data is a critical tool for retail analytics because it tracks which products are being purchased in stores by whom, which can then be analyzed to help inform future decisions.
Finally, there are analytics tools that allow retailers to make sense of all the data they're processing.
Direct benefits to retailers that use retail analytics
Retail analytics can help retailers:
- Increase profitability by reducing inventory costs, increasing sales and improving customer service
- Stay ahead of competitors who may not have as much access to data about consumers' needs or how they're using your store
- Deliver a better customer experience
- Avoid the time and expense of going to market with products or in-store layouts that don't resonate with customers
Examples of ways retailers can use retail analytics to support growth
1) Improve Customer Engagement: Analyzing the entire shopping journey allows retailers to learn exactly how customers interact with their stores and online sites. This enables them to optimize in-store experiences, such as increasing engagement through interactive displays or improving the placement of impulse buys at checkout areas. Understanding customer journeys also helps retailers anticipate shopping trends.
2) Increase Online Conversion Rates: Learning the path each shopper takes from the site's homepage to checkout will identify critical components of online shopping experiences that can make or break conversions. Retailers can optimize these paths for more conversions through appropriate site architecture, UX improvements and social proof. They can also learn when shoppers are using the site, and optimize when they send emails to increase their engagement with email marketing and keep them coming back for more.
Using analytics tools like Google Analytics can help retailers find elements of their order flow where users drop off.
3) Improve Store Operations: Analytics allows retailers to predict which products will be most popular with customers on any given day or during promotional periods. This enables them to forecast inventory levels and make better decisions about what items to bring in over which ones to be replenished. Analytics also reveals the best time to schedule staff for busy shifts, allowing retailers to allocate labor efficiently and appropriately.
4) Increase Efficiency by Reducing Waste: Analytics allows retailers to monitor how willing customers are to purchase surplus stock, allowing them to reduce waste and offer products only when they're needed. It can also reveal the highest-risk time periods for returning merchandise, enabling higher stock levels before these peak times occur. Especially important for retailers that sell perishable goods (like supermarkets).
Examples of retail analytics being used
1) Aldo’s Analytics-Based Black Friday Management: Each year, Black Friday is one of the busiest days on the retail calendar. Canadian shoe brand Aldo utilizes service-orientated big data architecture to maintain efficient e-commerce throughout this hectic period. This architecture supports cloud-based analytics and decision-making during the most turbulent of times.
2) Video Footage Analysis in Supermarkets: In-store video footage is being used to inform supermarket layouts. Major retailers analyze the frequency at which consumers occupy areas of the store. Items are then strategically placed in these hotspots to promote sales. These insights are only made possible through the use of retail analysis.
3) Kidiliz Group Leveraging Trend Insights: Kidiliz Group is a major fashion retailer with several high-profile brands in its portfolio. In order to maintain its standing within the industry, the French company must stay ahead of the trend by using retail analytics. Specifically, Kidiliz Group collects and analyzes data from two significant enterprise resource planners (ERPs). This provides insights into purchasing patterns that influence retail and inventory choices.
4) Rakuten’s Flexible and Reactive Approach: The retail group Rakuten observes and tracks buyer and seller activity on a large scale. Rakuten runs a big data platform featuring over 100 million of its own products. Updates on their 3rd party buying and selling information occur instantly, allowing the company to be responsive and flexible.
Retail analytics is a critical tool for retailers to use in order to stay competitive, but all three key foundations of a successful retailer are important. Retailers need an understanding of customer needs, fast decision-making capabilities and the ability to adapt quickly to changing marketplaces.