Sales analytics gives you deeper insight into seller and buyer behavior. You can monitor and measure the performance of your field teams, understand what drives engagement and leads to conversions, and identify areas where training and coaching will help.
Sales analytics help you better forecast revenue, evaluate the impact of actions (or inaction) by your sales and marketing teams, and decide where to focus your attention to optimize.
This article highlights the nine key sales data metrics to track, the benefits of sales analytics tools and what to look for, and how sales analytics helps you improve performance.
Let’s start with a definition of sales analytics and how you can use sales data.
What is sales analytics?
Sales analytics are metrics that help sales managers and executives to forecast revenue, understand causality, and identify areas of improvement within revenue-generation processes. It helps you understand the top line, analyze everything that happens in pursuit of sales, and how certain areas impact others.
However, you’ve got to track the right sales metrics.
9 key sales analytics metrics to track
In mature sales organizations, you need a pathway to conversions. You likely already know how many opportunities you need in your pipeline, how many meetings or presentations need to occur, and each of the steps on the way to closing a sale.
When you analyze each of these steps, you can better manage performance. For example:
- Do you have a sales rep who makes cold calls all day but rarely secures meetings? They might benefit from more training on call techniques or using a script.
- Do your sales reps generate many qualified opportunities and meetings, but struggle to close them? Maybe they need training on deal closing or in-field coaching.
- Do you have a sales team member that delivers substantial sales volume, but finds it hard to upsell? It might help to create sales packages so they can sell bundles rather than individual products.
It all starts with identifying the key sales analytic metrics to track. Yours may be different, but here are some useful measures.
1. Sales per rep
Sales per rep is a straightforward measurement of how many sales and/or how much money each sales team member made during the period. This helps you uncover patterns, successes, and deficiencies.
When you see performance lacking, it’s a signal to analyze why and what can be done to effect change.
This measurement should never be used to compare reps. You would expect more experienced sales team members to generate higher dollar volume. In other cases, certain reps might have specific territories, products, or sales targets. Analyze sales per rep in terms of performance against personal budgets and forecasts.
2. Average contract value
This metric looks at the average value of transactions.
Average contract value formula:
Average contract value = Total revenue / number of sales
This number helps you understand how many deals you need to close to reach your goals. From there, you can work backward to figure out what kind of volume you need at each stage in your pipeline.
3. Pipeline Velocity
This helps you gauge how long it takes deals to move from lead to close.
Pipeline velocity formula:
Pipeline velocity = ( Sales-qualified opportunities x Average contract value x Average win rate ) / Current length of sales cycle
If you have 20 SQOs and your average sale is $10,000 with a win rate of 30%, and your sales cycle is 440 days, your pipeline velocity would be 25 x $10,000 x 30% / 30, or $2,500 per day flowing through your pipeline.
This helps to forecast and helps sales teams understand how multiple variables impact results.
4. Win Rates
This measures how many of your sales qualified opportunities are turning into customers. It’s important to know your average win rates so you forecast revenue based on what’s in the pipeline now. If your win rates drop, you can analyze your sales enablement tools and process for improvements.
Win rate formula:
Win rate = number of deals won / number of deals created
If your win rates look good, but you’re not hitting your revenue targets, you may have a marketing or pricing problem.
5. Sales growth
Sales growth measures revenue generation over a defined period to help you assess performance.
Sales growth formula:
Sales growth % = [ ( Sales for the current period – sales for the previous period ) / Sales for the previous period ] x 100
This will give you your percentage of growth (or loss). Most companies track sales growth in a variety of ways, including month-to-month, quarter-to-quarter, and year-to-year. Companies with seasonal selling patterns compare to previous months or quarters to look for patterns.
6. Quote to close
This metric tracks how many prospects that got to the quote stage became paying customers.
Quote to close formula
Quote to close % = ( number of deals closed / number of quotes produced ) x 100
You can also apply this same formula for other metrics to analyze the ratio of demos to closes, presentations to closes, engagement duration to closes, etc.
7. Orders captured
Orders captured tell how many sales occurred during a particular period. This helps you compare it to current sales to forecast volume and trendlines. To see if you’re on pace, you can use this to determine an average order volume per week, day, month, quarter, or year.
8. Customer acquisition cost
One of the biggest reasons startups and ecommerce stores fail is that they cannot control their customer acquisition costs (CACs). No matter how much you sell or how much money you generate, you have to manage your CACs to remain profitable.
Customer acquisition cost formula:
CAC = Total cost of sales and marketing / Number of customers acquired
If CACs eclipse your customer lifetime value, you’re in trouble.
9. Customer lifetime value
Customer lifetime value (CLTV) is one of the most critical metrics you need to measure, especially when comparing it to your customer acquisition costs. This tells you how long it takes to recover your investment.
Customer lifetime value formula
CLTV = Average order value x average purchase frequency x average customer lifespan
To calculate this, you’ll need to figure out each of these components first. CLTV helps you understand how much an average customer is worth.
The benefits of sales analytics tools and what to look for
Every sales organization has milestones and key events that help move prospects along in the buying journey. When you can determine what these events are and where they occur, you can focus your marketing and sales efforts in the most productive places.
By tracking every touch point, from logged calls to microsites visit duration to field time spent in-app, you can see how these metrics correlate to sales. When you break performance down by different channels, you can see the ROI for your sales and marketing spend.
So, what should you look for in sales analytics tools? Here are some of the key features you need:
Real-time data visualization
You want to see your key metrics at a glance to ensure you’re on track. Data visualization makes it easier to absorb information and notice anomalies. When something bears further investigation, you also want to be able to look closer for further analysis.
Different functional areas may be tracking slightly different metrics depending on their job and area of responsibility. You need to be able to customize your dashboards any way you want so each team member can measure what’s important to them.
The sales analytics tool you choose must integrate with your tech stack to take full advantage of all the data you gather, including platforms such as:
- Customer relationship management (CRM)
- Business intelligence (BI)
- Content management
- Sales automation and sales enablement
- Marketing automation
Ability to collect and consolidate customer data from various channels
We live in an omnichannel world. Customers interact with companies across multiple channels. You need to be able to tap into each channel, normalize the data so that it’s consistent, and consolidate it into one central platform for more comprehensive analysis.
Sentiment analysis to track customer interests
Your sales data analytics tools also need to measure and capture customer sentiments. The best tools can automate the process of discovering and tracking how customers feel about your brand, products, and services.
When you understand customer sentiment, you can more accurately predict churn (and take proactive steps to avoid losing customers). When your positive sentiment is high, it may be time for upsell opportunities or brand extensions.
Intuitive and easy to use
While sales analysis tools should provide the in-depth features needed for data scientists and BI specialists, they should also be easy to use so that anybody can leverage the benefits of data. If data tools are too complicated for the average user, you likely won’t get the full value.
Gain a deeper understanding of seller and buyer behavior, both online and offline
“A self-service analytics approach must build a holistic, balanced ecosystem that includes data, people, process and technology focused on delivering business value.”
With Pitcher, you also get an effective and easy-to-use set of data analysis tools with out-of-the-box dashboards. You can get an overview of your key metrics with the ability to drill down to groups or individual data points.
Marketing can track what content assets and key messaging are driving engagement. Sales management can monitor field team performance.
Pitcher gives you the tools you need to help monitor, manage, and improve your end-to-end sales process to provide you with a deeper understanding of buyer behavior and how your sales team is performing.
All of this lets you identify roadblocks and opportunities to optimize your sales process and grow your business.
Check out these success stories for more information about sales enablement and analytics.
Frequently asked questions (FAQs)
Here are some frequently asked questions about sales analytics.
Why are sales analytics important?
Sales analytics help you better forecast revenue, evaluate the impact of actions (or inaction) by your sales and marketing teams and decide where to focus your attention to optimize revenue. Sales analytics are important because they help you spot trends, forecast more efficiently, and improve team performance. With sales analytics, you get a deeper insight into seller and buyer behavior. You can monitor and measure the performance of your field teams, understand what drives engagement and leads to conversions and identify areas where training and coaching can help.
How can data analytics increase sales?
Data analytics can increase sales by providing deeper insights into buyer behavior and seller performance. Customers interact with companies across multiple channels. You need to be able to tap into each channel, normalize the data so that it’s consistent, and consolidate it into one central platform for more comprehensive analysis.