Sales Analytics Guide: Metrics and Tips to Analyze Sales Data (2026)

Get your 2026 sales analytics guide, including how ecommerce and omnichannel retailers can use data and KPIs to optimize pricing, CX, and inventory.

Sales analytics enables your business to respond quickly to shifts in markets, economic fluctuations, and changes in consumer tastes. 

Just take it from Ruby Friel, the founder of Still Life Story. 

“By using Shopify’s analytics and reporting to identify bestsellers and sales trends, we were able to grow our revenue year over year by 50% at Christmas,” she says.

But for many retailers, the challenge isn’t collecting data—it’s making sense of it. The translation of numbers on a screen into insights is methodical. Only when you know what you’re looking for can you turn your data into action. 

This sales analytics guide explains how ecommerce and omnichannel retailers can use sales data and KPIs to refine pricing, inventory, and the customer experience.

What is sales analytics?

Sales analytics is the systematic process of analyzing and interpreting sales data and key performance indicators (KPIs) to improve sales strategies. This process involves collecting data from multiple sources—including customer relationship management (CRM) systems, databases, and spreadsheets—and using statistical analysis, trend analysis, and forecasting to generate actionable insights. 

In omnichannel retail, sales analytics harmonizes data from various touchpoints like web and mobile devices. This allows retailers to refine pricing strategies, optimize inventory, and enhance the ecommerce customer experience across every sales channel. 

Sales analytics vs. sales reporting

The two concepts are related but distinct approaches to using data to optimize your business: 

• Sales reporting is the first step that uses your data to uncover what happened. 
• Sales analytics is the next layer that describes why it happened and what to do next. 

Benefits of sales analytics

Sales analytics is vital for large retailers operating across various channels and markets. It goes beyond mere number-crunching to offer a nuanced understanding of consumer behavior trends, market dynamics, and operational efficiency. 

By leveraging advanced analytics, retailers can make more informed decisions that affect everything from inventory management to the customer experience.

Forecasting accuracy and decision speed 

Fine-tuning your analytics capabilities is especially crucial when your operation spans many channels and geographic locations. Making effective use of this data increases sales forecasting accuracy and enables risk mitigation. 

This is especially important for omnichannel retail. Accurate inventory data can prevent stockouts when demand differs between channels. It also assists with campaign planning—sales analytics will show what audiences respond best to across each platform. 

Customer understanding and retention

Thorough understanding of your target audience underpins every aspect of your sales strategy. When you use data to build these customer journeys, they become more accurate and less reliant on guesswork. You’re able to prioritize the channels your buyers are using, with messaging they’re most likely to respond to. 

Operational efficiency and return on investment (ROI)

Sales analytics show what’s working and what isn’t. Use this insight to streamline processes and allocate resources more effectively. For example, you might map out a product development timeline after you use sales analytics to spot underperforming sales regions or product lines, uncover patterns in customer behavior, and forecast future trends.

Doe Beauty shows this in practice. The brand uses Shopify’s unified data to gain access to real-time customer analytics, which helps them place inventory closer to customers and speed up delivery times. 

Ecommerce sales analytics use cases

Channel and campaign performance

An omnichannel retail strategy reflects customer behavior. Most customers switch between multiple different channels in a single purchase decision, and they expect seamless transitions as they shift. 

That includes:

• Ecommerce website
• Social commerce platforms
• Marketplaces
• Point-of-sale (POS)

But customers opt for different sales channels for different reasons. Analytics can show you why, so you can identify your most lucrative channels—and double down on them. It also highlights what messaging each audience is most likely to engage with on each channel. 

For example, sales analytics might show that customers who buy in-store tend to be repeat shoppers who purchase after viewing in-store promotions or loyalty incentives, while customers who buy through ecommerce convert after viewing social ads or email campaigns. You might highlight loyalty program initiatives in local marketing campaigns and focus on social proof for ecommerce website visitors. 

💡Tip: Shopify unifies sales channels with a single data model. One architecture powers everywhere you sell—an approach proven to reduce total cost of ownership (TCO) by up to 37%.

Assortment, merchandising, and pricing decisions

Sales analytics help you decide what to stock, how to present products, and how to price your inventory, so each product category and SKU performs at its full potential.

For example, sales analytics might show that certain SKUs consistently sell together online but not in-store. Perhaps you respond by creating unique product bundles or merchandising displays for each channel. 

Similarly, you might find that mid-priced items convert well when shown alongside premium options. You use this insight to adjust pricing tiers, or introduce a new anchor product to tie high-consideration items to.

Returns, exchanges, and margin protection

Sales analytics can also uncover patterns in returns and exchanges that directly affect profit margins. 

That could be:

• Customer segments who are most likely to return 
• Sales channels with disproportionately high returns costs relative to sales
• Which campaigns, discounts, or promotions trigger the most returns
• How delivery speed impacts returns 

Categories of sales analytics

To develop a comprehensive sales analytics strategy that provides unique insights into your sales process, learn about these four core categories:

Descriptive

Descriptive sales analytics offers a snapshot of past performance by analyzing historical sales data. For example, sales reports might show that a flash sale meant Q4 revenue growth exceeded expectations. This information is essential for sales teams to understand what has or hasn't worked in the past.

Track sales by channel with Shopify’s unified data model.

Diagnostic

Diagnostic analytics dissects the "why" behind key sales metrics through trend analysis and customer segmentation analysis. Sales managers use analytics tools to investigate customer behavior and market trends. Understanding these factors explains spikes in customer acquisition costs (CAC) during specific periods.

Predictive

Predictive sales analytics uses existing data and forecasting models to make projections about future sales. For example, sales representatives use predictive analytics tools to forecast that the current sales pipeline will translate into increased total revenue for the next quarter. This is crucial for sales and marketing efforts aiming to allocate resources efficiently.

Prescriptive

Prescriptive analytics provides specific recommendations for resolving issues or achieving desired outcomes. For instance, data analysts provide actionable recommendations for reallocating marketing efforts toward certain sales channels to improve customer retention and drive revenue growth. Sales analytics software can even automate these recommendations for you.

Important sales analytics metrics to watch

Core sales and pipeline metrics

Sales teams track these essential metrics to measure performance and guide strategic decisions:

• Sales conversion rate: The percentage of visitors who end up making a purchase. It's instrumental for gauging the efficiency of your sales funnel.
• Average deal size: The average revenue per sales deal. This is vital for assessing the effectiveness of sales activities and team performance.
• Average sales cycle length: The duration to close a deal from first contact to final sale. Understanding this cycle is essential for inventory planning and fine-tuning sales processes. You typically measure it in days, weeks, or months.
• Sales growth rate: The speed at which sales revenue increases over a specific period. This metric is essential for evaluating the success of your sales strategies.
• Revenue per sales rep: This metric enables you to make informed decisions about resource allocation—including staffing and budget distribution—to optimize sales efforts.

Customer and retention metrics

Track the following customer and retention metrics to uncover the impact of your sales activities on buyer behavior:

• Customer lifetime value (CLV): The total revenue a customer contributes across their entire relationship with a retailer. This sales metric is vital for strategizing customer retention and targeting sales efforts.
• Churn rate: This represents the percentage of customers who leave within a defined period. It's crucial for customer retention and revenue growth strategies.
• Cost per acquisition: CPA tells you the cost to gain a single customer. This metric is vital for assessing the effectiveness of your marketing campaigns.

Inventory and operations metrics

Inventory and operations metrics to include in a sales analysis include: 

• Lead time: Time between ordering inventory from suppliers and receiving it. This insight helps better plan restocks and prevent stockouts. 
• Inventory turnover: Inventory turnover shows how often inventory is sold and replaced within a given period. This is crucial for efficient inventory management.
• Inventory-carrying costs: The total cost of holding inventory, including storage, insurance, and depreciation. It helps prevent inventory bloat and optimize storage—like cutting down on slow-moving SKUs and replacing them with products that have a higher turnover rate. 
• Order accuracy: The percentage of orders fulfilled without errors. Low accuracy increases costs through returns, replacements, and customer support. 
• Stockout rate: How often inventory runs out of stock. This directly affects sales and customer satisfaction—many will head elsewhere if a product is unavailable. 
• Preorders: If you’re using preorders to build hype around a new product, use this sales analytic to gauge demand. Compare this against marketing performance to see which channels, content formats, and messaging lure in customers. 

Ecommerce-specific metrics to layer in

Ecommerce metrics to layer into your sales report include:

• Average order value (AOV): This shows you which products, bundles, promotions, or discounts impact average basket size. 
• Revenue per visitor: Find the most profitable sales channel or marketing activity by comparing this metric to the total amount you’re investing. 
• Cart abandonment rate: Roughly 70% of online shoppers abandon online carts without buying. Solve the main culprits to increase sales: Enable guest checkout, remove hidden fees, and offer speedy delivery. 
• Return rate: Total returns are projected to reach $849.9 billion and represent 19.3% of annual sales. Track your ecommerce return rate—and the reasons why customers initiate one—to limit the damaging effect of them on your business. 

Your sales analytics guide: How to analyze sales data

Here's how to harness the capabilities of sales analytics tools to make data-driven decisions:

1. Identify relevant metrics tied to goals

Choosing the appropriate metrics to focus on is crucial for meaningful sales data analysis. The metrics should align with your specific sales goals, whether it's to improve customer retention or drive revenue growth.

Sales performance and sales revenue are examples of key metrics that provide a comprehensive picture of your business health. 

🕯️For example, to maximize sales during the winter holidays, an ecommerce company that also sells its candles in brick-and-mortar locations could focus on key metrics such as average deal size and overall sales volume.

2. Use specialized analytics tools

Specialized sales analytics tools simplify the data interpretation process with features like real-time tracking of key metrics and sophisticated data-visualization techniques. Opting for sales analytics software that integrates seamlessly with your existing system significantly improves the efficiency of your data analysis.

Tools like Google Analytics, Shopify Analytics, Zoho Analytics, or Microsoft's Power BI enable you to monitor your ecommerce site traffic, user behavior, and sales funnel. 

🕯️The candle company might employ real-time tracking of sales metrics, closely tracking daily sales volume, cart abandonment rates, and inventory levels across their online store and in-person boutiques.

Tip: Shopify Audiences offers benchmarking data to see how performance stacks up next to merchants with similar shops in the same industry over the past 30 days.

Benchmark sales performance against other similar stores with Shopify Analytics.

3. Incorporate data from multiple sources

Aggregating data from multiple sales channels provides a 360-degree view of your sales activities. This comprehensive approach includes blending data from in-store transactions, online sales, and customer interactions. It enriches your sales analytics data, making your analysis more robust and insightful. 

🕯️By aggregating data from both online sales and in-store visits, the candle company identifies that gift sets are its strength, while individual candles lag in sales.

4. Analyze sales trends and cohorts

Compare sales trends over various time frames to understand customer behavior analytics and market dynamics. Use your sales analytics system to scrutinize historical data and pinpoint trends that inform future sales forecasts. Regularly generate sales reports on a weekly, monthly, and quarterly basis to understand micro and macro trends. 

🕯️For the candle company, trend analysis reveals that the second week of December consistently experiences a surge in gift set purchases.

5. Take action and iterate based on insights

After data analysis, actionable insights should guide your sales strategies and efforts. These could range from adjusting marketing campaigns to revising sales processes. The ultimate aim is to align your actions with the insights generated, ensuring you meet or exceed your sales targets. 

🕯️Leveraging its data, the same candle company might increase its email marketing frequency for December and introduce a special "Holiday Trio" candle bundle to outperform last year's sales figures.

What to look for in sales analytics tools

Selecting the right sales analytics tool is a critical decision for a sales manager trying to improve their sales team's performance. The tool should offer robust sales analytics and align well with your overall sales strategy. 

With various options on the market, such as Shopify Analytics, Tableau, Looker, Salesforce, HubSpot, and Microsoft Power BI, the optimal choice is the one that best meets the unique needs of your business and offers the specific features you require.

Ease of integration and single source of truth

A sales analytics tool should easily integrate with your existing sales analytics systems. This ensures a seamless flow of data and aids in making sales forecasts that are based on comprehensive information. Smooth integration is essential for aiding sales management effectively.

Real-time reporting and sales dashboards

The ability to generate real-time sales analytics reports is indispensable. Sales managers can track KPIs instantly, enabling them to react swiftly to market changes. Real-time reporting is vital for predicting sales trends and adapting your sales strategy accordingly.

User-friendly interface

A good sales analytics tool should offer a user-friendly interface that your sales team can easily navigate. This ensures more time is spent on selling activities rather than grappling with a complicated system. A straightforward interface is beneficial for quickly analyzing key sales metrics.

View your most important sales analytics on Shopify’s user-friendly dashboard.

Predictive and AI analysis features

AI-powered sales forecasting helps sales teams anticipate market trends and customer behavior. 

By predicting trends, reps are more empowered to:

• Better allocate their time and resources: Revenue intelligence can show which products, channels, and campaigns will be most effective. Reps use this insight to prioritize outreach and design campaigns each lead is most likely to engage with.
• Deploy more precise inventory allocation: Predictive analytics can gauge demand at a granular level—by product, size, color, and region—based on trends, seasonality, and even local events. Stock gets allocated to the right warehouses or stores ahead of demand spikes. 
• Optimize marketing dollars: By forecasting which segments are most likely to convert using first-party customer data, AI can suggest budget allocations across channels (email, social, paid ads) and timing for promotions. 

Data quality, governance, and privacy controls

AI-driven forecasting and omnichannel analytics depend on clean, well-governed data. But this data is safeguarded—you’re required to meet data protection regulations and protect customers’ personal information when using it for sales analytics. 

Check that your shortlisted sales analytics tools offer the following privacy controls:

• End-to-end data encryption
• Role-based permissions
• Audit trails 
• User consent management 
• Compliance with regional laws such as GDPR and CCPA

📚Read more: Data Governance: What It Is & Why It’s Essential

Customizability

Customizability allows the sales analytics solution to adapt to your unique needs. Create clear, interactive displays that make complex data accessible to sales teams and decision-makers through charts, graphs, and real-time dashboards.

Tailoring the tool to specific sales metrics or performance indicators also enables a more personalized approach to sales management. This feature supports a dynamic sales strategy and improves overall sales performance.

“Creating custom dashboards lets us focus on our core product performance without needing to export and clean the data,” says Shelby Adams, operations manager at Cleverhood. “It allows us to stay nimble and reactive.”

💡Tip: Shopify Sidekick, the AI assistant you’ll find in your Shopify admin, can create custom data explorations within seconds. Tell it which metrics you want to include and get a personalized report to share with stakeholders.

AI and predictive sales analytics in 2026

Traditional sales forecasting typically relied on simple statistical methods. They worked well for stable, predictable data but struggled with complexity—like fluctuating demand, seasonality, promotions, and multiple sales channels.

Machine learning and deep learning take this a step further to create more accurate sales forecasts:

• Machine learning models can automatically detect patterns in historical sales data, including indirect relationships between two data points. For example, you could tie retail sales data from your POS system with weather reports to uncover whether certain weather conditions influence store revenue. 
• Deep learning models simulate the neural networks of a human brain. They’re capable of digesting large amounts of data. For example, you might use a deep learning model to forecast demand for seasonal inventory. The model uses historical sales data alongside social media campaign performance, email promotions, and macroeconomic data to predict demand.

Sales analytics guide FAQ

How does sales analytics help my pipeline and revenue predictability?

Sales analytics provides actionable insights into your sales process, allowing you to identify trends and bottlenecks. The benefits of sales analytics include improved sales forecasting and a more effective allocation of resources to advance leads through the sales pipeline. AI-powered tools improve forecast accuracy and reduce manual effort.

Do I need sales analytics tools or software to run a sales analysis?

While it's possible to analyze key sales metrics manually, software solutions make the process more efficient and accurate. Organizations with more dynamic analytics achieve better forecasting and decision-making.

How often should I run a sales analysis?

The frequency of sales analysis depends on your sales cycle and business needs. However, consistent analysis—weekly, monthly, or quarterly—is crucial for adapting your strategy and reaping the benefits of sales analytics.