Table of Contents
What is Sales Analytics?
Sales analytics describes the processes used to gather insights, gauge sales performance, and measure customer behavior. It is used to gain insights into individual sales reps’ performance, identify areas where sales processes can be improved, and set key performance indicators (KPIs) for a business or team.
The primary goal of sales analytics is to simplify and analyze sales data to improve the accuracy of forecasts, anticipate customer needs, identify areas of improvement in the sales process, and ultimately help organizations make better decisions.
There are four main ways to categorize sales analytics:
- Descriptive (What’s going on?): Sales and marketing teams use historical sales data (e.g., sales reps’ performance, customer buying patterns) to identify past events and understand the current sales process and overarching market trends.
- Diagnostic (Why did it happen?): Diagnostic analytics pinpoint correlations between sales performance and other variables, such as market segments or customer preferences. This helps organizations understand why certain things have happened, such as customer churn or low sales quota attainment.
- Predictive (What will happen next?): Sales leaders and executives use predictive analytics to make sales projections based on their descriptive data. This is generally accomplished with the help of software, such as AI-driven sales forecasting tools.
- Prescriptive (How should we act?): Prescriptive analytics assess all the available data and determine the best course of action for a sales team. They’re used when deciding to enter a new market, test a new selling strategy, or launch a new product.
Sales analytics provides valuable insights into customer behavior, sales performance, and areas where processes can be improved. It helps organizations make more informed decisions about their strategies while ensuring that resources are allocated most efficiently.
- Sales data analysis
- Sales metrics analysis
- Sales performance analysis
Importance of Sales Analysis
Sales analysis gives businesses insights into their customers, internal sales processes, and potential future performance. It helps company stakeholders decide which products to focus on, when and where to sell them, and which sales channels to use to reach customers.
There are several areas where sales analysis is critical to present and future success:
- Looking at sales volume. Understanding the total sales made over a specific time frame tells businesses whether their organization is growing or shrinking. Sales volume on a per-product basis also reveals underlying trends in customer behavior, such as which products are most popular and which markets are booming.
- Quantifying revenue growth. Analyzing the percentage change in sales volume from one period to the next gives businesses a clearer picture of their company’s health.
- Measuring customer engagement. Tracking customer interactions with different channels — such as email, social media, or web visits — and measuring conversion rates provides organizations with valuable insights into how customers engage with their brand and products.
- Pinpointing areas of improvement. Examining the performance of sales reps, marketing campaigns, and product launches shows organizations where they need to invest more resources for better results.
- Evaluating employee performance. Sales analytics can measure individual sales reps’ performance, providing valuable feedback and helping sales managers identify top performers and areas where additional training or support is needed.
Types of Sales Analytics
Several types of sales analytics exist, including product sales, performance, pipeline, predictive sales, churn, and research analytics.
Product Sales Analytics
Product sales analytics describe a product’s overall performance in its respective market. To analyze product sales, companies need to look at:
- Total sales volume
- Sales velocity (how quickly products sell)
- Average deal size
- Product mix
Companies need to look at how well different products perform on a macro and individual level.
For instance, a company may assess the performance of its entire product portfolio compared to industry benchmarks and internal sales goals, then evaluate complementary products sold in bundles.
Sales Performance Analytics
Sales performance analytics relate directly to individual sales reps and their performance. This includes tracking key metrics such as:
- Number of appointments set
- Net new revenue
- Lead conversion rate
- Sales quota attainment
- Average time to close
- Win rate (the ratio of deals won to total opportunities)
- Average revenue per user (ARPU)
Sales performance analytics are usually reviewed on an individual basis during a sales QBR (quarterly business review) or performance appraisal.
For sales management, sales performance analytics are used to communicate team performance, identify individual sales reps’ strengths and weaknesses, and guide decision-making when assigning leads or setting targets.
Sales Pipeline Analytics
Sales pipeline analytics show businesses how efficient their sales process is at qualifying prospects and converting them into customers.
Tracking pipeline data helps organizations assess their internal workflows and identify potential problems.
Pipeline analytics provide key insights into:
- Lead conversion rate
- Deal closure timeline
- Number of touchpoints needed to close a sale
- Qualification criteria
- Sales cycle length
Managers use the sales pipeline analysis to set ambitious (yet attainable) sales targets for their teams and test the success of new initiatives.
Predictive Sales Analytics
Predictive sales analytics use historical data to make future predictions. This includes forecasting expected revenue, customer churn rate, and market trends.
Businesses use predictive analytics to gain a better understanding of their customers’ buying patterns and anticipate their needs in the future.
With this information, organizations can allocate resources, plan better marketing campaigns, and counter competition.
Churn analytics help businesses identify why customers are leaving and assess retention strategies.
They can also help businesses spot revenue leakage and fix causes of involuntary churn (e.g., negligence or customer service issues).
Data points that give insights into customer churn include:
- Average customer lifetime value (CLV)
- Leads lost in the pipeline
- Customer renewal rate
- Rate of upsells and cross-sells
- Involuntary churn percentage and causes
Churn analytics helps companies reduce customer attrition, identify high-value customers, and predict the impact of different retention strategies.
Market Research Analytics
Research analytics shows businesses what customers think about them and provides insights into market trends. This includes tracking:
- Customer surveys
- Social media sentiment
- Focus groups
- Product tests
- Competitive analysis
Businesses use research analytics to evaluate how customers and the market perceive their brand and inform product development and marketing campaigns.
10 Common Sales Analytics Metrics
Although there are numerous sales metrics to evaluate, these ten play a significant part in just about every company’s sales analytics process.
1. Sales Growth
Bottom-line growth is the most important metric to track because it reveals how well a company performs overall.
To make sense of sales growth, it’s essential to consider both short-term and long-term trends.
Short-term fluctuations may be due to seasonal factors, promotional campaigns, or other temporary influences. Long-term trends provide a more accurate picture of a company’s overall performance.
To calculate sales growth, follow these steps:
- Subtract the sales revenue from the previous year (or another chosen time frame) from the current year’s sales revenue.
- Divide the result by the sales revenue from the previous year.
- Multiply the outcome by 100 to obtain the sales growth percentage.
For example, if a company had $1,000,000 in sales revenue last year and $1,200,000 this year, the sales growth percentage would be:
- $1,200,000 – $1,000,000 = $200,000
- $200,000 / $1,000,000 = 0.20
- 0.20 x 100 = 20%
In this case, the company experienced a 20% sales growth.
It’s important to monitor sales growth regularly and compare it to industry benchmarks or competitors’ performance.
Depending on the context, a 5-10% sales growth may be considered good for large-cap companies, while mid-cap and small-cap companies might aim for over 10% growth on a trailing twelve months (TTM) basis.
2. Sales Pipeline Velocity and Value
Sales pipeline velocity is a metric that measures the speed at which opportunities move through the stages of a sales pipeline, ultimately resulting in closed deals.
A high pipeline velocity indicates sales efficiency and a strong product-market fit, as prospects are quickly converted into paying customers.
To calculate sales pipeline velocity, use the following formula:
Sales Velocity = (Number of Opportunities x Average Deal Size x Win Rate) / Length of Sales Cycle (in days)
For example, if a company has 50 opportunities in their pipeline, an average deal size of $10,000, a win rate of 30%, and 60-day sales cycles, the sales pipeline velocity would be:
Sales Velocity = (50 x $10,000 x 0.30) / 60 = $25,000 / 60 = $416.67 per day
A high pipeline velocity is generally considered a positive sign, but it’s also crucial to consider CLV, which represents the total revenue a company can expect from a single customer.
A business with a high pipeline velocity but a low CLV may indicate the sales team is not effectively qualifying leads.
In other words, they might close deals quickly, but those customers may not generate significant long-term revenue.
Sales-to-date highlights revenue generation over a specific period of time, usually a company’s year-over-year (YoY) growth. To calculate it, simply sum up the total sales revenue for the chosen time frame (e.g., from the start of the year to the current date).
Comparing sales figures to previous periods enables businesses to identify patterns and determine whether their sales are growing, declining, or remaining steady.
Sales-to-date figures also provide accurate financial health snapshots, even in seasonal or cyclical demand.
4. Lead Conversion Rate
The lead conversion rate is a critical metric that measures the effectiveness of a company’s selling initiatives by determining the percentage of sales leads that convert into customers or progress through various stages of the sales funnel.
A higher lead conversion rate indicates an efficient sales process and better alignment between sales and marketing efforts.
There are two primary ways to measure lead conversion rate:
Conversion from Lead to Customer
This method calculates the percentage of leads that ultimately become paying customers.
To determine this rate, divide the number of leads converted into customers by the total number of leads generated during a specific period.
Multiply the result by 100 to get the lead conversion rate as a percentage.
For example, if a company generates 200 leads and 40 of them convert into customers, the lead conversion rate would be:
(40 / 200) x 100 = 20%
Conversion Within the Sales Funnel
This approach measures the conversion rate at various sales funnel stages, such as the percentage of meetings booked from cold outreach campaigns.
To calculate this rate, divide the number of conversions at a specific stage by the total number of leads at the beginning of that stage.
Similar to above, multiply the outcome by 100 to get the conversion rate as a percentage.
For instance, if a company has 100 leads at the beginning of an outreach campaign and 25 of them book meetings, the conversion rate for that stage would be:
(25 / 100) x 100 = 25%
The quote-to-close ratio compares the number of buyers who received a quote to the number that actually turn into Closed Won deals.
This metric gives an indication of how effective sales reps are at closing deals and reveals which products, services, or pricing strategies work best.
To calculate the quote-to-close ratio, divide the number of won deals by the total quotes sent out over a specific period.
For example, if a company sends out 100 quotes and closes 30 deals, the quote-to-close ratio is:
(30 / 100) x 100 = 30%
The ideal quote-to-close ratio varies depending on a company’s industry. Businesses in the entertainment or food service industries typically have higher quote-to-close ratios than those selling cars, appliances, or enterprise software.
Companies with low quote-to-close ratios relative to their industry should investigate potential issues within their quoting process, such as quote accuracy, closing speed, or product complexity.
6. Average Purchase Value
Average purchase value is a sales metric that calculates the average dollar amount customers spend in an individual transaction for a product or service.
By understanding the value of the average first-time order, businesses can assess how effective their marketing efforts are in driving high-value deals into their sales pipeline.
To calculate your Average Purchase Value, use the following formula:
Total Value of Sales ($) / Number of Sales over a Defined Period = Average Purchase Value ($)
Average purchase value is a good metric for businesses monetizing primarily through ad hoc sales.
For companies using a subscription business model, a metric like average revenue per user (ARPU) would be more indicative of company success.
7. Sales by Region
Regional sales metrics are valuable because they show executive leadership and investors where the revenue opportunities are.
Growing companies can use sales by region to plan strategic growth initiatives, forecast sales in new or emerging markets, and allocate resources where they are most likely to find success.
Companies using predictive sales analytics can also use these metrics to identify and target potential customers, as well as predict customer behavior in different geographical regions.
8. Demo Calls Booked
One of the easiest ways to measure sales productivity is to look at how many demo calls each SDR is booking.
Assuming they’re with qualified leads, sales demos are the key to closing deals and gaining traction in new markets, so tracking them helps provide visibility into an organization’s sales process.
Demo calls are usually tracked in a CRM like Salesforce or HubSpot, which will contain information like the date of the call, total duration, and follow-up action items.
9. Customer Lifetime Value (CLV)
CLV helps businesses create sales goals by telling stakeholders exactly how many sales they need to make to remain profitable.
A business’s CLV is relative — high efficiency, low overhead costs, or high sales volume can offset low lifetime value and vice versa.
Increasing CLV through retention, higher customer satisfaction, or additional sales is one of the best ways to become healthier as a business.
10. Monthly Recurring Revenue
Compared to one-off sales, monthly recurring revenue (MRR) is a more reliable and scalable type of revenue. MRR is a subscription metric that measures the sales revenue its customers generate on a monthly basis.
By tracking this metric, businesses can gain insight into their growth, subscription churn rate, CLV, and ARPU. This information can then forecast future sales and improve marketing efforts.
MRR also helps companies identify new products or services to invest in based on how customers respond to certain offers or pricing models.
Benefits of Sales Analytics
Identify New Sales Opportunities
Companies with the right sales analytics and KPIs can identify new sales trends that point toward promising opportunities.
Predictive analytics are the best example of this — they help companies anticipate customer behavior and recognize leads that are more likely to buy.
This allows them to focus on the most profitable potential deals and optimize their lead generation process.
Effective Sales Forecasting
Analytics turn sales reports into data-driven insights that businesses can use to identify pain points, actionable goals, and opportunities for improvement.
Better sales forecasts also make communicating with investors and shareholders (or potential investors and shareholders) about sales team performance easier.
Set Sales Goals
Although healthy sales orgs typically aim for around 60% quota attainment, shockingly few (24.3%) of reps exceeded their quota, according to research from Sales Insights Lab.
There are dozens of reasons for this, including actual quota attainability.
If managers don’t have the right sales analytics, they aren’t able to forecast performance effectively enough to make sure their reps are set up for success.
Improve Customer Acquisition
Acquiring new customers is difficult and expensive without sales analytics to guide the process.
Analyzing sales helps sellers refine their ideal customer profile (ICP), hone in on the most profitable customers, and find the most effective channels for building relationships with new prospects.
With better customer acquisition data, teams can also make more informed decisions around marketing and sales campaigns to ensure they get the best return on their efforts.
Incentivize Sales Teams
Sales incentives are always a good way to motivate reps, but it’s important to make sure they’re rewarding the right actions.
Managers can create incentives that reward reps for hitting specific goals and closing deals within certain timelines using sales analytics.
This helps ensure that sales teams are focused on sales activities that will actually move the needle and incentivize behavior that leads to optimal performance.
Increase Customer Retention
When it comes to improved financial performance, customer retention is by far the path of least resistance.
When businesses use sales analytics to improve their retention rates, they save considerable time, effort, and money that would otherwise be spent on customer acquisition.
Analytics can help companies track their customers’ journeys and identify patterns in customer behavior so sellers can anticipate when someone might churn.
This allows them to offer timely intervention and retention strategies to keep customers engaged and encourage loyalty.
Data Sources for Sales Analytics
Customer relationship management (CRM) software is the first place businesses should look when they need sales analytics reports.
CRMs house important customer data, including contact information, purchase history, and notes of all sales touchpoints, which sellers can use to build buyer personas, analyze trends in customer behavior, and measure performance against key KPIs.
Lead intelligence systems are a type of automated tool that collect leads from multiple sources and present them in a single platform.
By centralizing lead data, sales teams can track customer interactions across multiple channels, measure the effectiveness of campaigns, and filter leads according to their ICP.
This helps them better prioritize prospects for maximum ROI.
Sales analytics don’t exist in a vacuum — they should be integrated with marketing automation platforms to track the effectiveness of sales and marketing campaigns.
Marketing automation analytics lets companies follow their customers through more stages of the purchase funnel.
Considering buyers are nearly three-quarters of the way through the customer journey before talking to sales, additional sales analytics from marketing tools completes the picture for sales teams and helps them find the best time to jump into the process.
Since CPQ is responsible for quoting the right products and services for each customer, its analytics can provide invaluable information on customers’ interest, the quote-to-close ratio, average purchase value, and sales pipeline velocity.
Billing software optimizes businesses’ billing, invoicing, dunning, and contract renewal processes.
It’s an excellent source of post-purchase data, such as CLV, ARPU, and customer retention.
Using billing analytics, businesses can better understand customer profitability and develop strategies for optimizing revenue.
Value of Sales Analytics Platforms
Sales analytics tools play a vital role in modern business operations because they provide valuable insights into sales performance, customer behavior, and market trends.
They enable organizations to make data-driven decisions, optimize the sales strategy, and achieve higher revenue growth through greater sales intelligence.
Suppose a company experiences stagnant sales growth and high customer churn rates. By adding analytics to its sales stack, its leaders can analyze historical sales data, identify patterns, and uncover the root causes of these issues.
Through this analysis, the company discovers that a particular segment of their customer base has a significantly higher churn rate than others.
The sales analytics software also reveals that customers belonging to this segment have lower average purchase values and shorter customer lifecycles.
Armed with this information, the company targets higher-value customer segments with longer lifecycles, leading to increased long-term revenue and reduced customer acquisition costs.
People Also Ask
What is the role of business analytics in sales?
In sales, business analytics serve to inform and optimize the sales process. Analyzing customer data helps businesses uncover trends in customer behavior to improve customer targeting, track performance against key KPIs, refine selling strategies, and create more effective sales incentives for SDRs and AEs.
What are the challenges of sales analytics?
The challenges of sales analytics encompass ensuring data quality and accuracy, effectively integrating diverse data, avoiding the over-collection of unneeded data, bridging the skills gaps in data analysis, managing adoption and change, as well as maintaining data privacy and security.