As a retail business owner, you’ve got multiple decisions spinning around in your head each day. Which products are you running low on and require restocking? Do your prices reflect customer demand? How many employees do you need to cover the store?
With decision fatigue a clear problem for store owners, it’s no wonder that the market for business intelligence software will hit $36.46 billion by 2029.
Business intelligence is a type of technology that allows you to collect data and turn information into insights, ultimately enabling you to make more informed decisions. This guide shares how to do it even as a non-data whiz, with examples and use cases to get you started.
What is retail business intelligence?
Business intelligence is the process of using retail data to learn about your business and make smarter, more informed decisions. It helps you uncover trends and changes in customer behavior, sales trends, and inventory management.
You can use these insights to pivot your business—whether that’s introducing new products, changing your marketing angle, or altering a product’s price. In either case, you have data to indicate a positive outcome (as opposed to relying on guesswork and being surprised when a decision turns out to be the wrong one).
Retail BI involves three stages:
- Data collection, which means recruiting data from various sources like your ecommerce platform, point of sale (POS) system, social media profiles, or inventory management solution.
- Integration, which compiles this data in a central repository for you to benchmark and cross-reference.
- Data analysis, whereby you’ll examine your data, pull insights, and plan further action.
Tools like Shopify Analytics make this easy to do, even if you don’t have an extensive budget for reporting tools. Shopify collects data from multiple channels and presents it in the Analytics report, helping you to benchmark business performance and make smarter retail decisions.
Use cases of BI in retail
Optimize inventory levels
Business intelligence tools like Stocky can analyze sales patterns and inventory levels, so you find the right balance for each product. It can uncover SKUs that are:
- Best sellers
- Slow moving
- In demand
For example, Stocky might show that you have 10 units of your vanilla candle out of stock. Around 16 units of the cherry scented version are also unavailable.
At first glance, you might think that the cherry scent should be prioritized for a reorder. Stock levels for that SKU are the lowest. However, further analysis shows that vanilla scented candles are trending on TikTok right now and competitors are retailing similar products for $2.99 more.
Based on this retail BI, it would make sense to restock the vanilla candle as a priority. You’ll make more profit on these items since you can bump up the retail price, and they’re easier to market because they’re in-demand.
Personalize the customer experience
A positive retail experience goes beyond buying a product. Modern shoppers expect some type of personalization from the brands they’re shopping with.
Marketing intelligence is what helps you do this at scale. It collects customer data like:
- What they’ve browsed (either offline or online)
- Items they’ve bought
- Products they’ve added to an online shopping but later abandoned
- Whether they’re a member of your customer loyalty program
We can see this business intelligence use case in practice with a retailer using standard countertop POS systems or POS terminals. When a customer approaches the checkout, the sales associate can quickly access the customer's data through the POS system.
For example, you already know that they’ve bought hiking equipment and is a member of your POS loyalty program. Now, you can personalize the shopping experience by showing them products related to those they’ve already bought, ask where their next hiking trip is, and remind them of the rewards they can redeem as a VIP customer.
Set dynamic pricing
Dynamic pricing is a smart merchandising strategy that uses data analysis to adjust a product’s retail price in real-time.
Instead of doing manual market research to uncover competitor’s retail pricing and applicable promotions, it uses BI to adjust prices based on factors like:
- Customer demand
- The weather
- Inventory levels
- Previous promotion data
- Market trends
Say you’re selling a wooden dining table that would ordinarily retail for $449. Retail BI solutions might show that demand tends to fluctuate in the first few months of the year when people are redecorating their homes. Wooden tables are also becoming more popular and consumers are spending more on them, so your BI can automatically update the price on January 1 to $499.
Improve retail store layout
The layout of your retail store has a major impact on how likely people are to buy from you. A clear path to navigate the store helps new shoppers discover your products and guide them towards the checkout desk with their new items in tow.
Business intelligence apps like Dor use thermal sensors to gather data on how people currently interact with your store, helping you to make better layout and retail marketing decisions.
For example, if Dor shows that your store’s foot traffic drops but conversion rate increases by 0.5% when it’s raining outside, you can run a “rainy day” promotion—like a flash sale on seasonal items such as umbrellas or waterproof coats. You already know that the rain has a positive impact on sales. Now you’ve doubled down and given shoppers an incentive to brave the rain and visit your store.
Benefits of BI for retailers
Personalized marketing and promotions
Modern customers don’t just want personalized experiences—they expect them. Whether it’s a personalized coupon code or a product recommendation for an item related to something they’ve already bought, personalization has become the norm.
Business intelligence helps you offer these personalized retail experiences at scale. Instead of wading through pages of data (or worse, trying to remember every small detail about your customers individually), business intelligence tools can do it for you.
Efficient inventory management
Inventory management is one of the biggest challenges that retailers face. Overstocking means you’re paying for storage space and holding money in unsold goods, yet understocking leaves you at risk of stockouts which can drive potential customers towards competitors.
Business intelligence tools can assist with inventory decision making, helping you to answer questions like:
- Which products are selling the fastest?
- Which SKUs will be out of stock if high demand continues?
- What’s the optimal reorder point for each item?
- Which products will soon expire or become out of date (and therefore unsellable)?
- Which items have the highest inventory carrying costs?
Competitive advantage in the market
Business intelligence software has the potential to surface insights before your retail team notices them. This lets you stay one step ahead of the competition. You can act on consumer trends, introduce new products, or avoid upcoming risks (like looming supply chain disruption) before competitors realize, giving you the first-mover advantage.
Better financial management
A retail BI solution gives you the information you need to make financial decisions without relying on notoriously unreliable gut instincts. It can track operational costs, supply chain expenses, and monitor profitability—all of which help with cash flow.
Financial threats like theft or fraud also wreak havoc on your business. Business intelligence tools use financial data to anticipate these risks before they impact your business, like flagging high-risk orders before accepting them.
Improved supplier and vendor relationships
Strong supplier relationships are a retailer’s secret weapon. Open lines of communication, and vendors that are willing to help you out, could open the door to preferential treatment. You could be first in line to experiment with new products, get expedited shipping in a rush, or negotiate better bulk discounts.
Business intelligence assists here because you’ll be prepared and know what you need in advance. Vendors won’t become accustomed to your last minute requests or overdue payments. You’ll be pleasant to work with, which goes a long way in building mutually beneficial supplier relationships.
Challenges of business intelligence in retail
While business intelligence does have its place in retail, some business owners struggle to implement it because of the following challenges:
- Data quality: Business intelligence is only effective if you’re basing your decisions on accurate data. Make sure your data is clean by checking for duplicate entries and merging information from multiple sources into a central repository (such as POS and ecommerce sales data within Shopify).
- Integration issues: An ecommerce platform, customer relationship management tool, POS system, and marketing automation tools each collect unique data. Use automation or artificial intelligence to pull data from each source into a central repository and reduce human error associated with manual copy and pasting.
- User adoption: You’ll need a 360-view of your data to form decisions from it. That’s only possible if your retail team regularly logs their activity and references the information when decision-making. Provide retail staff training that shows employees how to do this.
Best practices for implementing BI in retail
Choose the right BI tools
Your ability to use retail business intelligence is only as good as the platforms you’re using to collect data.
Granted, you might have a best-in-class ecommerce platform, point-of-sale system, inventory management tool, and supply chain management software. But only when these data sources come together can you slice and dice the data to reveal retail insights.
Shopify Analytics has the ability to pull data from each sales channel into one reporting dashboard. It offers tools to optimize your retail strategy, including:
- Detailed Reports: Sales, customers, and financial reports to analyze performance.
- Live View: Real-time data on visitor behavior and sales.
- Dashboards: Customizable for monitoring key metrics.
- Product Analytics: Actionable insights on product performance and inventory needs.
- Customer Segmentation: Identify trends and behaviors for targeted marketing campaigns.
Ensure data quality
Poor quality data can point you towards inaccurate conclusions and skew your decision-making process.
If your retail BI tool tells you that 45% of in-store sales happen at the checkout counter, for example, you might incentivize sales associates to make people queue in the checkout line and place items either side to encourage impulse purchases.
While this isn’t a bad strategy, in reality: people don’t like queuing. The data should’ve shown that the majority of those sales actually happen on a mobile POS system. Your focus should therefore be on engaging customers in all areas of the store—not just the checkout desk.
Simple tips to make your data ecosystem more accurate include:
- Conducting random spot checks on your data to ensure that whatever’s coming into your reporting tool is accurate
- Establishing data governance policies that outline how data should be stored, and who should be taking ownership of it
- Creating standardized processes for manual data entry, like checklists for inventory spot checks
Build a data-driven culture
Data management isn’t the most fun activity for your retail team, especially if they’re creative spirits who like to make decisions based on how they feel. A data-driven culture helps these team members buy into your business intelligence strategy. Collecting, uploading, and relying on data becomes the norm.
The easiest way to do this is by showing team members how data benefits them. Can they earn more commission by referencing customer data to personalize the retail experience? Spend less time doing boring inventory counts when the software does it for you? Avoid working during slow hours when they’re bored since the BI data helps make smarter staffing decisions?
Monitor and evaluate performance
Monitor how well your business intelligence toolstack is working by referencing the new data against your initial goals.
If you invested in retail BI software to optimize inventory, for example, compare whether stockouts have reduced or product sales have increased. If it was to optimize pricing, benchmark key performance indicators like profitability and sell-through rate for pre- and post-implementation.
Incorporate retail BI into your strategy
Retail BI has the potential to turn masses of data into insights you’ll actually understand. From inventory and staffing decisions to marketing campaigns and personalization, Shopify analytics has all the data you need to get started with BI.
Retail business intelligence FAQ
What is business intelligence in the retail industry?
Brands in the retail industry use business intelligence to collect data on their customers, inventory, or sales performance. They turn this into insights that help them make data-backed decisions related to promotions, pricing, or marketing campaigns.
How does Walmart use business intelligence?
Walmart uses business intelligence to collect data about its customers. It uses this information to understand their shopping patterns and product preferences, which the department store uses to personalize in-app and online marketing campaigns.
How is business analytics used in retail?
Business analytics helps retailers understand how people interact with their brand, product performance, and customer expectations. One example of this is a retail store consulting inventory data to find best-selling products during their assortment planning process.
How is AI used in retail stores?
Artificial intelligence can help retailers adjust a product’s price, prioritize stock replenishment, and adjust the layout of their store. It uses predictive analytics to anticipate what will happen in the future, and therefore, which actions a brand should take to future-proof their retail strategy.
