Sales Data

What is Sales Data?

Sales data is information businesses collect about their business performance, internal operations, and customers as it relates to sales activities. It includes customer segments and characteristics, purchasing behaviors, KPIs, and efficiency metrics. Essentially, anything that measures the sales process or contributes to a sales analysis could be considered sales data.

B2B and B2C companies gather sales data on an ongoing basis, though they use it slightly differently.

B2B companies typically have large sales infrastructure and dedicated sales reps (SDRs and AEs) to manage outbound efforts, nurture leads, and close deals. B2B sales take a lot longer to close, usually several months.

B2B vendors rely heavily on sales data to inform their go-to-market strategy, optimize their internal sales process, and forecast performance. They use it to identify trends, such as which customers are more likely to buy or how long it takes for an opportunity to convert.

Since they rely mostly on ad hoc purchases, B2C companies have entirely different objectives when it comes to sales data. They use sales data to test different campaigns or promotions, track the performance of marketing channels, and understand what makes certain types of customers buy.

Both B2B and B2C sales companies do share a few similarities in how they use sales data, however. They both prioritize personalization and segmentation, and they use sales data to help them better tailor their messaging and products to the needs of their customers.


Importance of Collecting and Analyzing Sales Data

If businesses didn’t collect information about how their customers (or potential customers) respond to different messaging and selling techniques, they’d leave a lot of potential revenue on the table.

Gut feelings and assumptions don’t yield consistent performance results — a data-driven sales team is an absolute requirement for any company that wants to grow its revenue without “getting lucky.”

Identify Leads and Opportunities

Which leads have converted to customers in the past? And which customers have been the most valuable over time?

If a sales org doesn’t focus its time on leads and opportunities that have the highest chances of converting to paying customers, they’re wasting valuable time.

Sales reps already spend less than 30% of their day selling. If they spend it with the wrong kinds of leads, they won’t hit their quotas and company revenue growth will suffer.

Meanwhile, competitors are actively finding and closing these target customers, encroaching on the organization’s market share.

Customer Segmentation

Customer segmentation is the practice of breaking down customers into groups based on their behaviors, preferences, and other characteristics (i.e., segmenting them). It helps businesses target their audiences more effectively with marketing campaigns and personalize product offerings to match customer needs.

Sales reps also use segmentation when prioritizing prospects — they can quickly zero in on leads that are most likely to buy by filtering through customer segments or demographics.

Sales data is critical to the segmentation process because it tells sales leaders which segments are profitable, large enough to justify resources, and show the most growth and expansion potential.

Improve the Sales Process

Internal selling procedures play a huge role in how accurately reps score and qualify leads, move them through the sales funnel, and eventually convert them to customers.

Inefficient or outdated (yet habitual) processes are commonplace in sales orgs. Sales data is the best way to identify and eliminate these roadblocks and implement changes.

If sales data shows prospects consistently dropping out of the process at one certain stage in the funnel, management can look into why and what they can do to fix it.

Data also helps sales teams understand how reps spend their time (or where they’re struggling). Leadership can find reps in trouble and provide personalized training that gets them back on track.

Track KPIs

Sales departments are ultimately responsible for (and judged on) how much revenue they bring in at the end of every month.

Businesses track key sales metrics to understand their individual reps’ performance, the output of each sales team or business unit within the company, and how they contribute to (or detract from) the company’s financial health.

  • Knowing your conversion rates (e.g., lead-to-conversion, lead-to-opportunity) tells you how high-quality your leads are and how well your sales team moves them through the funnel.
  • Calculating average deal size helps leaders understand how many sales are needed to hit revenue targets and which customers are worth targeting for larger sales.
  • Tracking customer lifetime value (CLV) reveals their customer base’s most profitable and least profitable segments. It also tells leadership where to place extra resources during times when revenues dip.
  • Looking at CAC payback helps businesses understand how long they need to retain their customers to break even from their sales efforts.
  • Measuring sales velocity tells sales leaders if their reps are closing deals faster or slower than average, which they can use to improve sales cycle times.

KPIs measure the progress of a sales team toward their goals, and tracking them with data makes it easier to identify high-performing reps, spot trends, diagnose problems, and optimize strategies.

Accurate Sales Forecasts

Execs use sales forecasts to make decisions and plan for the future. Sales data helps them build trust in their predictions.

Historical data from sales efforts create clear visual trends. Based on the past revenue growth rate, execs can anticipate future performance (e.g., exponential growth, plateauing, seasonal drop-off).

With abundant sales data, predictive analytics models (which are continuously trained on real-time market dynamics) can help companies set realistic targets, allocate resources more effectively, and improve the accuracy of their forecasts.

Better Decision-Making

When leadership and exec teams, board members, and investors have data they can trust, their decision-making prowess plays out in reality.

For instance:

  • Partner managers are in a better position to evaluate potential resale vendors and decide if they’re capable of tapping into a large enough new market.
  • Sales leaders focus resources on opportunities that competitors have yet to discover and markets with untapped potential (e.g., international expansion).
  • Individual reps take a data-driven sales approach by focusing outbound efforts on a few specific segments they know to be valuable.

Demonstrate Investment Return (or Return Potential)

Chances are, sales data is among the first things a prospective investor asks for when considering a company for investment. And that’s precisely because it facilitates better decision-making.

When a company grows its revenue and customer base, retains it, and shows signs of a healthy internal sales process, investors feel more confident about their ROI potential.

In the context of sales, are several reasons a company might need investment capital:

  • Expanding into a new market segment
  • Adding a new location or business unit
  • Internationally scaling the company
  • Hiring additional sales reps or staff to accelerate growth
  • Developing a new product line

Since highlighting past performance and showing revenue potential based on forecasts is the only way to make an investment case communicable, high-quality sales data paints the truest picture of a business and gives outsiders an understanding of where their money is going.

Types of Sales Data and How They’re Used

“Sales data” is a bit of an umbrella term. There are several types of data for sales efforts and different kinds serve different purposes.

Demographic and Geographical Data

Demographics and geographics show business leaders who buys their products and where they’re buying them from. It helps them understand their target market and the best way to reach them.

Businesses use demographics (like age, gender, income level, marital status, etc.) to create buyer personas and tailor their messaging for different customer segments.

Geographic data (like population density, income level of area, urban/rural mix) helps companies identify the best locations for new stores or outlets. It also shows where there’s potential to add more resources to existing stores or expand into other areas that may have been overlooked.


Firmographics are essentially demographic and geographic data points for businesses selling to other businesses.

Firmographics include:

  • Company vertical
  • Headcount
  • Monthly/annual revenue
  • Industry
  • Business model
  • Location(s)
  • Growth stage
  • Tech stack

B2B sellers use firmographics for many of the same purposes B2C companies use demographic and geographical data. Without it, they’d waste their time selling to everybody (which, of course, includes the wrong areas).

Organizational Identifiers

Organizational identifiers such as NACIS codes, SIC Codes, and NAICS codes help B2B sales reps target promising prospects more accurately.

Sales organizations typically use them to group companies into industries and identify specific verticals, making it easier for sales teams to target and route leads.

Behavioral Data

Behavioral data shows businesses how their target customers interact with their products, services, and platforms (e.g., websites and customer portals)

For instance, businesses can track which pages customers visit on their site, how long they stay on each page, and how often they convert after viewing those pages.

Using this, they’ll know which content piques customer interest, what the best leads do before engaging with the sales team, and what makes them decide to buy.

Analyzing customer activity can help sellers create a personalized buying experience, increase customer engagement, score leads, and where to double down on their sales/marketing collateral.

Sales Metrics

Sales data is based on abstract patterns like buying behavior, website engagement, and everyday sales activities that move buyers through the purchase funnel.

To communicate it on a sales dashboard, it needs to be extrapolated, centralized, and numerically represented using sales metrics.

Total Revenue From Sales

Total revenue is the most basic sales metric — all it tells you is how much your company has made from sales efforts.

Total sales help executives understand a business’s overall performance and anticipate future growth or decline.

If a business already knows the amount of sales revenue it needs to be profitable and how much of that should come from each customer segment, total revenue also demonstrates growth sustainability and financial health.

Sales by Lead Source

Every company has an omnichannel strategy. Not all sales channels are created equal, so omnichannel growth requires continuous refinement.

Lead sources include:

  • Cold calling
  • Cold emailing
  • Social media
  • Website pages (e.g., pricing documents, blog posts, product pages)
  • Paid marketing (e.g., SEM, paid social, Google AdSense)
  • Existing accounts (revenue expansion)
  • Referrals

Going a step further and looking at the firmographics and organizational identifiers for each of those leads helps sellers understand the characteristics of leads coming from each source and what kind of ROI each one brings to the table.

Average Deal Size

Average deal size measures the amount of revenue generated by each individual sale. It’s calculated by taking the total sales revenue and dividing it by the number of deals closed (or attempted).

A larger average deal size doesn’t indicate overall profitability (e.g., if acquiring those customers costs too much), but it shows sales teams how much they can expect to make off of each deal.

Every segment will have a different average deal size — enterprise sales will certainly outpace mid-market or emerging market sales — which is why it’s important to break down sales by lead sources.

Companies can gain insights into their customer base and understand their willingness to invest in offerings by tracking the average deal size. Moreover, they can use this information to compare performance across months, quarters, or YoY, set revenue targets, forecast earnings, and make strategic investments in new areas.

Customer Lifetime Value (CLV)

CLV measures a customer’s worth over the course of their relationship with the company. It’s calculated by extrapolating the total revenue a given customer has generated, subtracting related costs, and dividing it by the number of months from initial contact to when they churn (or renew).

CLV helps companies identify high-value customers, which aren’t always the ones with the highest average deal size or conversion rates.

Percentage of Revenue From New and Existing Business

Customer retention is favorable to acquisition for several reasons:

  • If a customer stays, it means they’re happy with the product.
  • If most customers stay, it indicates the company has achieved product-market fit
  • It costs a lot more to acquire a new customer than to retain an existing one.
  • A business that is constantly losing and replacing customers isn’t sustainable

For early-stage companies or those entering a new market segment, a high percentage of revenue coming from new business isn’t necessarily a bad thing. It also isn’t worrisome if the company retains its existing customer base.

But when a year’s worth of sales data indicates a high churn rate and low revenue from existing customers, it’s time to start examining why that’s happening and how the company can fix it.

In general, the percentage of revenue from new business should taper off as a business reaches maturity.

Sales Rep Performance Data

Sales managers measure the individual performance of their SDRs and AEs by looking at the amount of revenue their reps bring in, the number of deals closed, and other sales-specific metrics.

When analyzed over a longer time frame, sales rep performance data also takes into consideration subjective measures like customer experience, compliance with company policies, communication skills with customers, and engagement with peers.

Data points like these aren’t quantitative but can help managers understand how to optimize sales team performance and get the most out of their team.

Sources of Sales Data

Data from sales efforts primarily comes from sales software. An integrated tech stack is required for optimal accuracy and a continuous data flow from one system to the next.

Marketing and Sales Automation

Marketing automation isn’t a sales function, but these tools create lots of leads for sales reps. Tools like HubSpot, MailChimp, and Marketo automatically track lead engagement and generate reports that shed light on what gets a lead interested in the product/service.

Sales data from other parts of the funnel will tell marketers how effective they are at generating qualified leads.

Sales automation platforms collect a wide range of sales data — from individual sales rep performance to total revenue generated. Since they’re central to sales activities, they help teams calculate their overall ROI, understand their conversion rates and sales cycle times, and look into sales metrics by segment.

Customer Relationship Management (CRM)

CRM systems often have sales automation integrated into their backend. They’re the heart of any sales operation, and they house all customer data, including:

  • Contact information (name, company name, email address)
  • Company information (size, headcount, revenue, etc.)
  • Lead source
  • Sales conversations
  • Proposals and contracts sent/signed
  • Payment history
  • Purchase history
  • Communication logs

Thanks to their breadth and depth, CRMs can provide companies with insights into how each customer segment interacts with the company.

For predictive analyses and advanced data mining, companies often use CRM as the primary data source.

Configure, Price, Quote (CPQ)

CPQ software automatically generates pricing estimates, quotes, and proposals based on customer product configurations.

Since its backend contains all product variables and pricing rules, it’s a valuable source of sales data related to products, pricing strategies, and customer preferences.

CPQ data helps companies identify product-specific market trends, competitive pricing strategies, and the most profitable sales leads.

It also shows which leads move through the first stages of the purchase funnel most quickly and how they might respond to price negotiations.

Subscription Management

Subscription management software centralizes billing, payment processing, usage tracking, and contracting for subscription businesses. It’s directly tied to revenue, as it records all customer payments and renewals.

Subscription management data can help subscription businesses identify their most loyal customers (based on renewal rate) and understand how different plans are doing in terms of revenue. It also helps them track product usage, a key indicator of customer satisfaction.

Proposal/Contract Management Software

A contract management tool stores sales contracts, customer agreements, and other paperwork companies need to close deals. It tracks the progress of each document in the sales process and collects data on who signed when, what payment terms were agreed upon, and more.

Proposal/contract management data is invaluable for understanding how customers interact with documents during the buying journey, how long it takes them to move from one stage to another, and whether any barriers affect conversion.

People Also Ask

What is the purpose of sales data?

Sales data mainly aims to provide insights into business performance and drive decision-making. Companies use sales data to track revenue, understand customer behavior, measure sales rep performance, analyze product pricing, calculate ROI from marketing campaigns, and optimize sales processes.

What can you learn from sales data?

Sales data can tell you a lot about customer behavior, sales rep performance, pricing strategies, and marketing ROI. You can use these insights to refine your approach to sales and maximize revenue. You can also optimize your marketing campaigns and pricing strategies based on how changes in these areas reflect sales performance.

What is an example of sales data?

An example of sales data would be the total revenue generated from a strategy targeting a new market segment. If the strategy successfully generates new business, the total revenue generated (a sales data point) would be high enough to be considered a success. Other sales data points include the number of new customers acquired, the average order size for each customer, and how quickly deals are closed.