Think about the last time you made a big decision for your retail store. Did you rely on guesswork? If so, you shouldn’t have. Even if your gut instinct proved to be accurate, it’s a risky way to run a business.
Most retailers don’t. The global retail analytics industry, an industry worth $8.64 billion, helps retailers by providing them with the data they need to make smarter decisions. From deciding where to open your next location to prioritizing inventory restocks, relying on data takes the guesswork out of running a profitable retail business.
Unsure where to start? This guide reviews the types of retail analytics you should consult when decision-making, with tools and examples to show each metric in practice.
What is retail analytics?
Retail analytics the process of collecting information related to your store and using it to fine-tune your strategy. It’s a type of business intelligence retailers use to learn more about customer behavior, inventory levels, and store performance.
The importance of retail analytics
While some retailers rely on gut instinct to make business decisions, as noted, it’s not the best way to run a profitable store. Here’s why it’s important to base decisions on retail analytics.
Inventory management
Inventory is the largest business expense for many store owners. It’s costly to get wrong, too—both under- and over-stocking can have disastrous consequences. Poor inventory management costs retailers $300 billion each year.
Inventory analytics helps prevent inventory errors from wreaking havoc with cash flow and customer experiences. Using inventory level data, you’ll spot SKUs that should be prioritized for a restock—before out of stock messages force potential customers into a competitor’s store.
Merchants need to be able to understand what's working for them and what's not working for them. So continued support in terms of analytics, understanding the business, understanding their marketing investment, making that easy and understandable is a big one.
Understanding consumers
The best marketing campaigns solve the challenges your target customer is facing, using their natural language, on the channels they use to communicate with brands. It’s impossible to do all three of these things if you don’t understand your audience.
So use retail analytics to understand your consumers and plan marketing campaigns that reach them. For example, if 55% of people who open an email that contains parking instructions later visit a store, it makes sense to feature this information across other local marketing channels—including your Google Business Profile and Instagram bio—for other parking-conscious customers.
If we can get more meaningful and more valuable data, like understanding customers’ attributes, understanding their sizing, and understanding their preferences—these are the things that I think present an opportunity for us here.
This isn’t just for customer acquisition. “One of the most important metrics that we track at our retail store is customer retention,” says Ian Kelly, CEO of NuLeaf Naturals. “Our business strategy is heavily based on turning one-time customers into repeat customers for long-term growth.
“We aim to have at least a 60% customer retention rate, and anything over 50% can be considered above the industry average for us. Customer retention rate is an important metric for many reasons, and it's really easy to track both in retail and online stores,” says Kelly.
Competitive advantage
The more data you have about your store, the better decisions you can make. This is a competitive advantage, considering that just 15% of retail and CPG brands use artificial intelligence and smart analytics.
“Any project that your business embarks on should be really based in a performance-based framework,” says Kate Collinson, managing director at Kate Collinson Digital Consulting.
“So, asking: what do we expect to see from this action in terms of metrics like traffic, average order value, and conversion rate? Are there any personal or industry benchmarks that we can leverage to understand that performance? And, when will we assess the performance? How long do we need to wait to make a confident conclusion about the results, and then actually go through?”
Collinson says, “I think that's going to be really integral for businesses to really use the data available at their disposal in 2022.”
Types of retail analytics
- Predictive analytics
- Prescriptive analytics
- Descriptive analytics
- Diagnostic analytics
Now that we know the importance of analytics, let’s take a look at the four main types of data your retail store can use to make better decisions.
Predictive analytics
Predictive analytics uses historical data to predict the future.
For example, if you know that half of the customers who buy your hair straighteners later purchase your heat protection spray, use predictive retail analytics to bundle the two products together and increase average basket size.
We can change the inflection of marketing from being purely focused on driving attention to purchase, to actually balancing that out with customer attention and focusing on metrics like customer lifetime value.
Prescriptive analytics
Prescriptive analytics helps retailers build a strategy based on available real-time data.
An example is seasonal shifts in staff rotations. If you know that the last quarter of the year sees a major increase in foot traffic, these prescriptive analytics remind you to hire seasonal staff and adjust open-to-buy budgets to serve an increase in customer demand.
💡 PRO TIP: Only Shopify POS unifies your online and retail store data into one back office–customer data, inventory, sales, and more. View easy to understand reports to spot trends faster, capitalize on opportunities, and jumpstart your brand’s growth.
Descriptive analytics
Descriptive analytics is the opposite of prescriptive. It examines data after something has happened, such as a launch, promotion, or marketing campaign. The first few months of the year are peak times to use descriptive analytics to analyze what went well in the year just past and where you need to improve next year.
Diagnostic analytics
Diagnostic analytics investigate why something happened. In the case of an uplift in conversions, for example, diagnostic analytics rely on other data sources, such as email open rates or public holidays, to understand why it happened.
💡 PRO TIP: Analyze your POS data in tandem with your ecommerce data to be more cost effective with your inventory, measure your store’s impact on online sales, repeat purchases, lifetime value, and more.
Retail analytics tools
- Point of sale
- Foot traffic counter
- Inventory analytics
- Email analytics platform
Ready to start making the most out of your data? Use retail analytics technology to extract data and fine tune your retail strategy.
Point of sale
Your point of sale (POS) system contains a wealth of data you can use to understand consumer behavior and grow your retail business. Inside Shopify POS, for example, you’ll see:
- Product data, such as top- selling products and gross profit margins on each SKU
- Customer data, such as how much each individual customer has spent, their last purchase, and how often they return
- Staff data, such as your top-performing retail staff or those who need more training
- Sales data broken down by each sales channel—including stores and your ecommerce website—to find your most profitable channels
We use Shopify because we control the data. We get to keep it.
Foot traffic counter
The more foot traffic you have in your store, the better your chances of generating revenue.
Use a foot traffic counter like Dor to track how many people visit your store. Its thermal sensor attaches to the entrance of your store and allows you to compare foot traffic against Shopify POS data. You’ll uncover in-store sales trends that help you:
- Plan staffing around peak days and hours
- Understand how weather impacts in-store conversion rates
- Discover which marketing strategies drive the most people to your location
Inventory analytics
Inventory management is a major thorn in any retailer’s side, so make it easier to optimize your stock levels using apps like Stocky and Shopventory. Both tools provide insight into key inventory analytics such as:
- Shrinkage to spot theft, stock count errors, or supplier fraud
- Sell-through rate to adjust open-to-buy budgets and buy more bestselling products
- Inventory turnover ratio to forecast consumer demand as you approach busy seasons
- Inventory holding costs to determine when the stockroom is too bloated and needs trimming
- Gross margin return on investment (GMROI) to understand how much money the inventory generates for each store
💡 PRO TIP: Shopify POS comes with tools to help you control and manage your inventory across multiple store locations, your online store, and warehouse. Forecast demand, set low-stock alerts, create purchase orders, know which items are selling or sitting on shelves, count inventory, and more.
Email analytics platform
Retailers use email marketing to communicate with both new and existing customers. With an average ROI of $42 for every dollar spent, it’s one of the only direct lines you have with people who’ve opted in to hear from your store.
Use Shopify Email reports to analyze retail analytics like:
- Open and click-through rates. Do subscribers respond best to emojis, questions, or discount codes in the email subject line? Double down on the format that comes out on top.
- Order rate. What percentage of people open the email and make a purchase through your ecommerce store? Replicate formats with the highest order rate.
- Products purchased. Which products sell best when promoted through email? Update your email sequences to highlight these SKUs for new subscribers.
💡 PRO TIP: Want to create beautifully branded emails to promote your next product launch? Use Shopify Email to create, send, and track campaigns, all from within Shopify—no coding experience required.
Examples of retail analytics
“If you are able to determine how many of your customers are converting, you will be better able to pinpoint areas for improvement,” says Dan Lee, head of marketing at Sealions. “These could include new techniques for client retention, new inventory items, or a new point of sale system for improved service.”
Let’s put that into practice and say your POS system shows that 60% of loyal customers return to the store within a month of their first purchase. To encourage more first-time customers to become brand ambassadors, create an email marketing campaign staggered over 30 days post-purchase to drive them back in-store.
Similarly, if your inventory analytics show that in-store traffic falls dramatically on rainy days, run a bad weather promotion. Give nearby shoppers an extra reason to visit the store when it’s raining, such as 20% off their purchase. It’ll make in-store sales more consistent and improve profit margins on slow days when you’re still paying retail staff to work.
The future of retail analytics
Companies are investing heavily in their data and analytics strategy. Let’s take a look at where the industry is heading, and how you can use technological advancements to optimize your store.
Tracking omnichannel customer journeys
Customers interact with retailers across various touch points prior to making a purchase. From social media to in-store and back again, our research shows that 53% of brands are investing in tools that allow them to sell anywhere.
Omnichannel retail analytics allow you to dig deeper into popular routes to purchase. For example, if you discover that half of in-store shoppers visit to collect an online order, promote buy online, pick up in-store options to those visiting your ecommerce website. Simply making this offer more visible will drive local foot traffic.
Similarly, if someone is visiting your store but not ready to purchase, send an email cart. Analytics might uncover that 10% of people receiving this email go on to make a purchase via your ecommerce website. It makes sense to train retail associates on how to use this POS feature across all retail locations.
Proof in the pudding: Our Omnichannel customers (folks who shopped in both our retail store and website) have a 2.5x higher LTV than both our repeat web-only customers, and repeat retail-only customers. Retail experience and trust + online convenience = loyalty & advocacy boost.
— Ben Sehl (@benjaminsehl) July 26, 2021
💡 PRO TIP: Use Shopify POS email carts to recover abandoned store sales and increase store conversions. Store staff can add items to a customer’s cart, send it to them by email, and get credit for making the sale—even if it happens online.
Artificial intelligence
Artificial intelligence (AI) is a technology that uses machine learning to make better decisions. Here are some examples of how retailers can use AI to make more money in their stores:
- Dynamically price products using a machine learning algorithm that takes seasonality, demand, and competitor prices into account.
- Optimize local delivery routes to reduce pressure on your supply chain while limiting carbon emissions and fuel consumption.
- Create a chatbot that answers frequently asked questions on autopilot
Big data
Big data is a term used to describe datasets too large to be processed as they are. Platforms like Soleadify, Talend, and Datumize condense big data into easy-to-understand insights.
Office Depot, for example, uses big data to improve its European stores. It syncs data from its offline catalog, call centers, fulfillment systems, and enterprise resource planning (ERP) software to segment and personalize its communication with customers. The result? Office Depot Europe outcompetes its competitors and runs more efficiently.
Leverage retail analytics at your store
Retail analytics give actionable insight into how your store is performing, what customers best engage with, and how external factors impact conversion rates. Even if you’re not a natural with numbers, use the data sources we’ve shared here to figure out—and double down on—what works.
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Retail analytics FAQ
What are examples of analytics used in retail sales?
- Customer segmentation – dividing customers into different groups based on their buying habits and preferences.
- Cross-sell and upsell analytics – analyzing customer buying patterns to detect opportunities for encouraging customers to purchase additional items.
- Sales forecasting – predicting future sales volumes and trends.
- Inventory optimization – analyzing historical sales data to determine optimal inventory levels and product mix.
- Location analytics – analyzing customer data to determine which stores should be opened or closed.
- Pricing analytics – analyzing customer data to determine the optimal pricing levels for products.
- Campaign analysis – evaluating the effectiveness of marketing campaigns.
- Customer lifetime value – analyzing customer data to determine the value of a customer over their lifetime.
How to learn retail analytics?
- Take a Retail Analytics Course: Many universities and colleges now offer courses specifically focused on retail analytics. These courses can provide a comprehensive introduction to the field and teach you how to use data and analytics to identify trends and make better business decisions.
- Read Retail Analytics Books: There are a number of excellent books on the subject of retail analytics. These books can help you learn the basics of retail analytics, as well as provide tips and strategies for applying analytics in the real world.
- Attend Retail Analytics Conferences: Attending conferences and workshops dedicated to retail analytics can be a great way to learn from industry experts, network with other professionals, and stay up-to-date on the latest trends and technologies.
- Follow Retail Analytics Blogs and Websites: There are a number of great blogs and websites dedicated to retail analytics. Following these sites can help you stay informed on the latest news, trends, and developments in the field.
- Practice: The best way to learn retail analytics is to actually practice using the tools and techniques. Try working with real data sets and analyzing them to gain hands-on experience.
