How To Analyze Sales Data?

For B2B revenue leaders and sales operations managers, leveraging sales data is critical to driving predictable growth and improving pipeline visibility. According to Kantar and Catalyst’s State of Ecommerce 2021 report, 56% of ecommerce professionals are allocating funds to data and analytics, making it the top ecommerce service budgeted for.

In a podcast, Mike Pregler, VP, Research Director at Forrester, also emphasizes that sales leaders are challenged by the boards to deliver a more predictable, consistent revenue stream. To achieve that, sales leaders need a well-developed, scientific process created by using data and technology. “That data and technology enables collaboration across the entire flow of the process, all the way from top-of-funnel interactions through the selling process through to the delivery and consumption,” he adds.

In this guide, we’ll show you exactly how to turn sales data into revenue-generating insights for your B2B sales organization. You’ll learn the importance of sales data, data collection sources, sales analysis techniques, and tools that help facilitate analyzing data.

What Is Sales Data?

Sales data refers to information extracted from various touchpoints and interactions between a customer and a business that benefits a sales team. For revenue operations professionals, the right sales data helps understand prospects better, identify pipeline opportunities, and accelerate deal velocity.

Types of Sales Data

Understanding the different types of sales data is foundational for effective analysis. B2B sales teams typically work with four main categories of data:

• Transactional Data: Purchase history, deal values, contract terms, payment methods, and transaction dates
• Customer Data: Demographics, firmographics, contact information, communication preferences, and account details
• Behavioral Data: Website interactions, email engagement, content downloads, meeting attendance, and product usage patterns
• Performance Data: Sales rep activities, pipeline metrics, conversion rates, and quota attainment

Pro Tip: Focus on collecting data that directly impacts your key revenue metrics—pipeline velocity, deal size, and win rates—rather than trying to capture everything.

Benefits of Sales Analysis

Unlock actionable insights by analyzing your sales data—here’s how top B2B teams do it. A thorough, comprehensive sales analysis is helpful not only to sales teams but also to non-sales departments.

Sales analysis reveals:

• What customers need and expect from your business
• Market opportunities and sales performance trends
• Top-performing sales reps and high-converting activities
• Where to allocate more resources and identify risks
• How to improve sales forecasting accuracy

Pro Tip: Use sales analysis to pinpoint exactly where your team can accelerate deal velocity and improve pipeline predictability.

LinkedIn’s State of Sales 2021 report found that data is more crucial than ever for sales organizations, and it’s been used for a wide range of tasks, especially the five following:

Sales analysis also creates value “for nearly every business function, from marketing to customer support to product and corporate strategy,” says Alyssa Merwin, LinkedIn VP of Sales Solutions, on Harvard Business Review. “Sales, Customer Support, Marketing, and Product teams can all benefit from insight into which accounts are poised for growth, which are ready for cross-selling, and which are likely to churn,” she adds.

Gartner predicts that by 2025, 60% of B2B sales organizations will apply data-driven selling, and the future of sales is “the permanent transformation of organizations’ sales strategies, processes and allocation of resources, moving from a seller-centric to a buyer-centric orientation.”

Key Metrics to Track in Sales Data Analysis

Which metrics to track for a sales data analysis depends on your business goals, the industry you’re in, target audiences, and many other factors. There is no one-size-fits-all approach to this.

That said, B2B revenue leaders often consider metrics like conversion rate, win rates, sales length, average deal size, retention rate, and customer lifetime value to track performance. Check out this detailed guide to learn more about sales productivity metrics.

Sources for Collecting Sales Data

To collect sales data for analysis that drives pipeline visibility, consider the following sources:

• Your tech stack, including CRM, sales tools, revenue intelligence platforms, online meeting platforms, email marketing, Google Analytics, Facebook Analytics, and website.
• Surveys and interviews of your sales teams, revenue operations staff, and your customers.
• Social networks and online communities, including posts, comments, shares, likes, brand mentions, and hashtags.

How to Track Sales Data

Effective sales data tracking requires both the right tools and processes. Here’s how B2B sales operations professionals set up reliable data collection:

Automated Tracking Methods

• CRM Integration: Connect your CRM to email, calendar, and communication tools for automatic activity capture
• Revenue Intelligence Platforms: Use AI-powered tools to automatically capture and analyze sales interactions
• Marketing Automation: Track lead behavior and engagement across digital touchpoints

Manual Tracking Best Practices

• Standardize data entry fields and formats across your team
• Create mandatory fields for critical deal information
• Implement regular data hygiene reviews and cleanup processes

Step-by-Step Guide: How to Analyze Sales Data

Follow this proven framework that B2B revenue leaders use to turn raw sales data into actionable insights:

Step 1: Define Your Analysis Goals

Start by identifying what specific business questions you need to answer. Common goals include improving forecast accuracy, identifying bottlenecks in your sales process, or optimizing territory assignments.

Step 2: Collect and Clean Your Data

Gather data from your CRM, marketing automation, and other sales tools. Remove duplicates, standardize formats, and ensure data quality before analysis.

Step 3: Choose Your Analysis Method

Select the appropriate analysis technique based on your goals (trend analysis for performance tracking, pipeline analysis for forecasting, etc.).

Step 4: Apply Analysis Techniques

Use statistical methods, visualization tools, or specialized software to identify patterns, trends, and insights in your data.

Step 5: Create Actionable Recommendations

Transform insights into specific, measurable actions your sales team can implement to improve performance.

Step 6: Monitor and Iterate

Track the impact of your changes and continuously refine your analysis approach based on results.

Pro Tip: Start with simple analyses and gradually add complexity as your team becomes more comfortable with data-driven decision making.

Sales Data Analysis Techniques

After gathering sales data, you can apply these proven techniques that top B2B sales operations teams use to drive revenue growth. The following are the most common sales analysis methods you can use:

1. Sales Trend Analysis

Sales trend analysis is meant for analyzing trends in sales data over a specific period like the past 24 hours, last week, last month, or last year. Trends can be upward, downward, steady, or periodically fluctuating.

When you adopt sales trend analysis, you examine historical statistics to identify patterns in revenue, sales rep performance, customer behaviors, and win-loss ratios. This helps revenue leaders determine opportunities and potential challenges to make timely decisions.

You can conduct a sales trend analysis by product collections, specific products, customer types, channels, regions, or even sales reps.

Example: A SaaS company noticed their enterprise deals were taking 20% longer to close in Q3. Trend analysis revealed this coincided with a new competitor entering the market, prompting them to adjust their competitive positioning and reduce average sales cycle by 15%.

2. Sales Pipeline Analysis

A sales pipeline analysis refers to the process of analyzing your sales pipeline’s performance to understand why prospects succeed or fail at becoming actual customers. It shows you what’s happening throughout your sales process, what drives prospect action, how many qualified leads you have, and their likelihood to convert.

With sales pipeline analysis, revenue operations managers can stay ahead of their sales operations and take immediate action to capture opportunities or prevent deal failures.

When creating a sales pipeline analysis, you may consider metrics like:

• Number of deals in each rep’s pipeline
• Average size and value of deals in each pipeline stage
• Close-ratio and average percentage of deals won
• Sales velocity and average time to win deals

3. Sales Performance Analysis

Sales performance analysis focuses on assessing your current sales performance against predetermined goals and industry benchmarks. It provides B2B sales leaders with a detailed picture of how their team is performing and whether they’re achieving sales targets.

In some cases, sales performance analysis also involves evaluating critical functions of a business, including sales, marketing, customer service, business operations, finance, and accounting. By bringing these departments together, you can clearly understand your business’s overall health and create strategic plans to sustain growth.

4. Sales Diagnostic Analysis

If sales trend analysis tells you the patterns in your sales performance, then sales diagnostic analysis shows you the “why” behind those trends.

For instance, through diagnostic analysis, you might discover that a sales rep’s call volume dropped 30% in October because they spent excessive time on administrative work and struggled with time management.

After identifying the root causes behind KPIs and sales trends, you can develop targeted improvement plans. In the example above, you could implement sales automation tools to reduce administrative burden and provide time management training to boost cold calling effectiveness.

5. Product Sales Analysis

A product sales analysis provides information about the sales performance of specific products. It’s critical for B2B companies selling multiple products and variants.

A product sales analysis helps you:

• Identify top-selling items and slow-moving products
• Compare product sales performance against each other
• Understand which product features drive buying decisions

6. Sales Effectiveness Analysis

Sales effectiveness analysis evaluates how well your sales team accomplishes their targets. It answers two critical questions: (1) Did your sales reps achieve their goals over a specific period? And (2) How can you help them improve performance?

When doing sales effectiveness analysis, businesses often use metrics like:

• Average new deal size
• Sales cycle length
• Percentage of sales reps achieving quota
• Sales ramp-up time and capacity hiring insights

7. Predictive Sales Analysis

Predictive sales analysis allows you to estimate sales growth by detecting trends in customer and lead behavior. It uses historical data to analyze past performance and determine the likely outcome of sales activities.

Think of it this way: if you have accurate predictions of future outcomes, you’ll have more control over what happens to your business. That’s the core benefit of predictive sales analysis.

8. Sales Prescriptive Analysis

Sales prescriptive analysis tells you what actions to take based on data-derived intelligence, which reps have the highest chance of hitting quota based on activity levels, and which opportunities have the highest potential.

According to Bain, “leading companies have begun to deploy prescriptive analytics—that is, guided actions on specific customers or prospect opportunities that flow from predictive insights. When applied to sales and marketing, prescriptive analytics can help companies improve the return on their investments, optimize conversion and win rates, or maximize profit margins.” With sales prescriptive analysis, you become more proactive than reactive.

Common Tools for Sales Data Analysis

B2B sales teams use various tools for analyzing sales data, each with specific strengths:

The following table outlines the most common tools and their primary use cases for sales data analysis:

This comparison helps revenue operations professionals choose the right tool based on their analysis needs and technical requirements.

Using Software Tools to Analyze Sales Data

Collecting and analyzing data manually is time-consuming, especially for B2B sales operations teams managing complex pipelines. But with a revenue intelligence platform like Revenue Grid, you can simplify your sales analysis process and get it done quickly.

Revenue Grid Advantage: Revenue Grid is the only revenue intelligence platform that delivers real-time, actionable guidance directly inside your CRM—empowering sales teams to act, not just analyze.

Revenue Grid is a comprehensive revenue intelligence platform with advanced sales-focused features. So it’s no surprise that it offers built-in options for data analytics.

Ready to see how Revenue Grid can transform your sales analysis? Book your personalized demo today

1. Team Analytics

Team Analytics is a Revenue Grid feature allowing you to get a bird’s eye view of all your sales reps’ activities.

To make it work, just go to your Revenue Grid dashboard > Team Analytics and select the team reports you’d like to display on the dashboard. This can include:

• Activity: Track what your rep is doing, how many opportunities require action, and time spent on each activity—unlike traditional CRMs that only show static data.
• Team forecast: Identify whose forecasted pipeline is on track, stalling, or at risk with real-time visibility that basic analytics tools can’t provide.
• Signals: Create actionable signals that tell your reps the optimal next steps and help them focus on activities that improve efficiency—a capability missing from standard CRM reporting.

Apart from that, when you click on a sales rep profile, you’ll be directed to their activity details page which shows what they have been doing. You’ll see how much and on what specific day the rep had meetings, made calls, and sent emails. You can also view details of each activity, which are pulled directly from email clients and calendars with AI-powered accuracy that manual data entry can’t match.

2. Pipeline Visibility

Sales pipeline visibility is essential because, without it, your company can be dogged by inefficiencies and inaccuracies that can hinder your growth. Sales pipeline visibility empowers your revenue operations teams with everything they need to maximize every opportunity and eliminate errors whenever possible.

With Revenue Grid, you can see what’s flowing into each sales pipeline in real time and which deals are moving, stalled, or at risk—capabilities that go far beyond traditional CRM dashboards. You can also set up alerts on changes in deal size, close dates, and scores, which is impossible to do in traditional CRMs.

Last but not least, Revenue Grid provides AI-powered automatic data and activity capture. This way, you can access critical sales metrics for each deal without manual data entry and understand what’s overpromised in committed deals.

3. Deal Guidance

Deal Guidance allows you to shorten the sales lifecycle and guide sales reps to take the right action when it’s needed, where it’s needed. It offers AI recommendations and playbook suggestions that help your reps communicate more effectively with prospects—something traditional sales tools simply can’t deliver. They’ll learn what’s the right time to contact audiences, what message resonates with customers, and more.

According to Oracle, deal guidance is no longer a nice to have but a must for every sales team. When sales reps receive best practices and actionable insights, they can sell confidently and quickly. They can understand customer needs, craft relevant messaging, and prioritize actions that help convert customers.