Landing on the right price for a product is a daunting task.
If the price is too high, you could deter some shoppers from buying; if the price is too low, you could alter the product’s perceived value or attract a customer base of deal-hungry shoppers. Pricing analytics is the methodical approach that retailers use to find the middle ground.
But to master pricing analytics and land on the optimal pricing strategy, you’ll need to pull data from all aspects of your business to get the full picture of how (and why) customers buy. McKinsey calls this end-to-end excellence, a mindset that “can transform retailers’ operations and retain margins in a challenging economic environment.”
This guide shares how retail pricing analytics works, with practical use cases on using the insights you uncover.
What are retail pricing analytics?
Retail pricing analytics is a process retailers use to determine how pricing affects sales, profit margins, and consumer behavior. The goal is to land on the right product price that maximizes revenue and attracts customers, while remaining competitive in the market.
A unified data model is the foundation upon which retail analytics is built. With one single source of truth powered by cross-channel data integration and real-time synchronization, you can assemble data to help you make informed pricing decisions—without a disjoined technical infrastructure that inflates operational costs and contributes to tech debt.
How retail pricing analytics works
There are two components of retail pricing analytics: data collection and visualization.
Data collection
Pricing analytics requires customer, order, and inventory data to truly understand how customers buy.
Traditionally, retailers would merge data from separate POS systems, an ecommerce website, and marketplace dashboards to gather data. This approach is fraught with challenges: data doesn’t flow in real-time. Any insights you pull might be outdated when you come around to analysis.
There’s also the financial drain of managing disparate systems. Each platform has its own maintenance costs, and team members are tasked with manually pulling together data—time that could be better spent elsewhere.
Shopify's unified data model solves these problems by bringing three main data components together on one platform:
- Customer data: Shopify automatically creates a customer profile whenever someone shares their email address or phone number. Any supplementary data you collect—including their purchase history across channels, behavioral data, demographics, and preferences—feeds back to this profile for a real-time view of your buyers.
- Product data: Sales channel integrations assist with SKU rationalization, category hierarchy, and product attributes standardization, no matter where you sell.
- Transaction data: Transaction data from every sales channel feeds back to one central operating system to standardize data collection. This includes POS, ecommerce, marketplace sites, mobile transactions, and those made through social media storefronts.
A recent study from an independent research firm found that, when retailers combined Shopify POS with their Shopify ecommerce operations, they experienced an 8.9% uplift in GMV on average. Merchants also experience a 22% lower total cost of ownership, and benefit from a 20% faster implementation time relative to the market set surveyed.
And because both POS and ecommerce are built natively on the same platform, you’ll eliminate patchy middleware and data fragmentation that wreak havoc on data quality.
Data visualization
Now that you’ve got your data into a centralized repository, it’s time to make sense of it. Shopify’s data visualization turns endless pages of numbers into something more legible, so you can digest data and communicate reports with stakeholders.
Work from a premade reporting template to quickly access detailed retail sales reports. These include:
- Retail sales by product
- Retail sales by product variant SKU
- Retail sales by product vendor
- Retail sales by product type
- Retail sales by Point of Sale location
- Retail sales by staff at register
Alternatively, create custom reports by either building them from scratch, modifying existing default reports, or duplicating and editing existing reports. Select metrics, dimensions, and visualizations that best represent your data. Or use the ShopifyQL Query Editor to create queries that extract specific data points and visualize them in different formats, such as line or bar graphs.
Plus, with the new Shopify Analytics, you can create a custom data exploration, which allows you to analyze specific metrics or dimensions in detail. Start from an empty state or modify an existing report to surface the pricing analytics you need.
How to use pricing analytics in retail
Price optimization
Pricing analytics might show that customers are willing to pay a higher price during certain times of the year, or avoid purchasing entirely if the figure goes above a certain price.
For example, you might learn that conversion rates skyrocket during your summer sale. Customer feedback suggests that first-time customers who bought during this period were incentivized through a discount code.
Future attempts to inspire repeat purchases, such as targeted emails and “back in stock” notifications, were unsuccessful. Subscribers opened your campaigns, but they didn’t buy unless a discount was on offer. This insight supports a hypothesis that your product pricing is too high.
The simplest fix is to reconsider your pricing strategy. Analyze competitor pricing—what do they charge for comparable products? Can you remain competitive? Offer a discount on repeat orders (such as subscriptions) to encourage retention and increase customer lifetime value?
If you don’t want to slash profit margins, increase the product’s perceived value. Reevaluate your target market; perhaps you need to target more affluent customers with more disposable income. Position your product as a premium option that’s worth the investment because of its unique selling proposition.
Dynamic pricing
Dynamic pricing uses retail analytics to adjust a product’s price in real time. Instead of scheduled analysis (such as every quarter), it uses business intelligence (BI) to adjust each SKU’s price based on factors such as:
- Customer demand
- Inventory levels—including products with high return rates
- The weather
- Promotion data
- Fulfillment channel
- Market trends
We can see this in action with an apparel brand that sells raincoats. Customers don’t have an urgent need for a raincoat when the sun’s shining—perhaps your retail BI tool suggests a price of 90% of the manufacturer’s suggested retail price. When it’s raining, however, waterproof jackets are in high demand. The tool might suggest a 10% markup on rainy days to capitalize on extra demand.
Price segmentation
Not all customers are willing to pay the same price for the same product. Students, for example, are likely to be on a much tighter budget than professionals established in their careers—even if both fit the mold of your target market.
Price segmentation aims to find the highest possible amount that different customers are willing to pay based on factors like their willingness to pay, location, time of purchase, or demographic characteristics. You might offer different product prices for:
- Loyalty program members
- New customers
- Wholesale buyers
- Event attendees
- Customers in a certain location (e.g., pricing analytics might show consumers in New York are willing to spend more)
Demand forecasting
The prices you set today likely won’t be the optimal ones in six months' time. Demand forecasting uses machine learning to digest large data sets—like historical buying patterns, market regulation changes, and supply chain volatility—to accurately predict the optimal price for a product over the coming months.
Pricing analytics can also uncover patterns and seasonality. Customers might be willing to pay less during shopping events like Black Friday and Cyber Monday when they’re conditioned to search for discounts. However, they might be more open to paying a premium for stationery during the back-to-school season. Bump up prices for this product category in early August to capitalize on demand.
Price elasticity
Elastic demand occurs when a product’s price affects customers' willingness to buy. Elasticity indicates that customers are highly sensitive to price changes—just a small increase in the retail price can cause sales to disappear.
Retail pricing analytics gives you this insight before sales dry up. You’ll be able to determine how much you can raise or lower the price of a product without losing customers.
Customer lifetime value (LTV)
Customer lifetime value (LTV) totals how much the average customer will spend throughout their relationship with your brand. Pricing analytics lets you determine the ideal price that customers will pay, therefore increasing their LTV and encouraging retention.
Say, for example, that your current CLV is $85. The average customer makes three purchases during their lifetime. Using this data, you could:
- Segment customers who exceed the average CLV and thank them for their loyalty—perhaps by inviting them to your customer loyalty program (if they’re not already enrolled).
- Use the average basket size of $28.33 to create a bundle of bestselling products around this figure, and promote them to new customers.
- Identify how often customers spread out their purchases, and automate email marketing campaigns that encourage them to restock their products when you estimate they’d run out.
Customer acquisition costs
Retailers in all industries are facing increasing customer acquisition costs. Browsers are limiting cookie tracking, and the sheer number of options consumers have means it’s more expensive than ever to acquire new customers.
Retail pricing analytics helps you define the optimal pricing strategy that maximizes sales while remaining competitive. It also considers any discounts that might lure customers in. That might mean launching a new product using penetration pricing to capture attention in a new market, and increasing prices once you have an established customer base.
Omnichannel promotions
The modern shopping journey is anything but linear. Customers might find you on social media, subscribe to your email list, browse your online store, and visit your retail location—all through a single purchasing decision. Pricing analytics lets you build promotions that convince customers to convert, depending on their preferred sales channel.
For example, pricing analytics might show that shoppers visiting your store after opening an email campaign are likelier to use a buy online, pick up in-store (BOPIS) service. Encourage more people to use the service by offering an incentive for future BOPIS orders that’s exclusive email subscribers, such as an additional 10% off their purchase or a free gift on collection.
Benefits of retail pricing analytics
Increase profit margins
Retail pricing analytics tells you the maximum price you can sell a product for. Instead of relying on a manufacturer's recommendation, you might learn that customers—such as those in locations where it’s difficult to find a retailer that sells those products—are willing to pay more. This lets you net more profit on every product sale.
Better inventory turnover
It costs money to hold unsold inventory. Retail pricing analytics helps improve performance for products with low inventory turnover. You can identify slow-selling stock and determine the retail price that encourages customers to buy, instead of having it clutter your shelves.
💡Tip: Get a handle on your inventory with the Stocky app for Shopify. Use it to conduct stock checks with barcode scanners, set safety stock levels, and be alerted when you’re at risk of a stockout—so you can proactively prevent stockouts and optimize carrying costs.
Personalized offers
Modern customers expect a personalized shopping experience wherever they engage with brands. Retail pricing analytics lets you do this at scale. Analyze historical sales data, market trends, and customer insights to determine what price point a customer is most likely to buy at—even if this sits below your original sale price.
For example, you could offer a 10% discount for new customers, while participants in your loyalty program get free shipping on every order. Both are attractive promotions that are tailored to each customer segment.
Higher customer satisfaction
Nobody wants to feel like they’re paying over the odds. On the contrary, getting a good deal can feel like an accomplishment. Retail pricing analytics lets you find the sweet spot, so you can improve the experience and help shoppers leave your store feeling satisfied.
Improve your retail operations with Shopify
Making smart pricing decisions requires complete, accurate data about your business. Shopify gives you this foundation by building POS and ecommerce on the same platform, providing real-time insights about your customers, products, and sales performance.
With Shopify Analytics, you can:
- Make confident pricing decisions based on complete data
- Understand how pricing affects customer behavior across channels
- Identify opportunities to improve margins and inventory turnover
- Create custom reports that surface your most important metrics
Leading retailers are discovering that success comes from having one platform where everything naturally works together. Join the brands using Shopify to make smarter pricing decisions and drive profitable growth.
Retail pricing analytics FAQ
What are the four types of retail analytics?
- Descriptive analytics: Analyze historical performance.
- Diagnostic analytics: Determine the cause of a problem.
- Predictive analytics: Model future performance.
- Prescriptive analytics: Guide retailers toward the next steps.
What are retail pricing insights?
Retail pricing insights are the approach retailers use to find the ideal price for a product, including any discounts or promotions. The goal is to find the maximum amount a customer will pay while remaining profitable and competitive.
What are analytical skills in retail?
Retail staff with analytical skills know how to gather, interpret, and analyze data. This can include sales reports, market trends, customer feedback, product performance, and pricing data.
