Today, we have a lot of data that’s supposed to help us navigate the complexities of the market and make strategic decisions. The problem is, we’ve never felt more lost. After all, the abundance of information alone does not guarantee success.

The key lies in understanding your data and implementing it in a practical way. That’s where sales analytics comes into play!

In this article, I will delve into what sales analytics is, highlight the key metrics you need to track and guide you on effectively analyzing your data.

Sales Analytics in a Nutshell

In short, sales analytics is about collecting sales data using key metrics and using that information to identify market trends, set goals, find new opportunities, gauge sales performance, make sales forecasts and more.

For instance, you can use past sales data to predict future trends. If a store sees that certain products sell more during the summer, they know they should order more next spring. If you notice a specific segment of customers tends to upgrade to your product after performing Action A, you can guide the rest of the cohort in the same direction.

Another example is looking at the lead conversion rate to determine which marketing campaign works best. If more people buy after seeing one particular ad, you know what type of marketing strategy needs more resources the next go around.

Types of Sales Analytics

Not all data is the same, and that's where different types of analytics come into play. The type of sales analytics you need depends on your specific sales and company goals.

Diagnostic Sales Analytics

When you need to figure out why something happened, you can turn to diagnostic sales analytics. This type of analysis looks at historical data to get to the root cause of events – whether it's understanding customer behavior, evaluating marketing campaign performance or deciphering sales trends.

For example, if sales unexpectedly dropped during a certain period, diagnostic analytics would help uncover the reasons behind it.

Diagnostic analytics helps you pinpoint areas that need improvement and make smarter decisions regarding the factors that influence sales results.

Descriptive Sales Analytics

Like diagnostic analytics, descriptive sales analytics looks at past data, too. However, it tells you more about “what” happened instead of “why” it happened. It describes the features of sales data – including region sales, revenue, product adoption rate, etc.

It helps you get a better idea of what’s currently going on and make informative comparisons. And because it zooms in on past data, descriptive analytics can also be a good starting point for other types of analyses like diagnostic, predictive and prescriptive.

Predictive Sales Analytics

Think of predictive sales analytics as your business's crystal ball, almost as good as seeing the future. Typically, you’ll take historical data and run it through advanced AI and machine learning software to make pretty accurate predictions.

Typically, I find predictive sales analytics to work best when looking at customer behavior. Instead of guessing and hoping for the best, you can analyze how customers respond to specific actions and how they behave, so you find touchpoints where you can influence them.

Prescriptive Sales Analytics

When you need a solid action plan, turn to prescriptive sales analytics. It analyzes all the data to help answer, "What should we do next?".

Using advanced number-crunching algorithms, prescriptive sales analytics might suggest adjusting pricing for specific products, targeting a particular market segment or refining the sales process to boost efficiency.

For example, if you’re struggling with poor team productivity, prescriptive sales analytics might crunch the numbers to see that your reps spend too much time tracking down email addresses. Knowing this, you’ll also know you need a tool like Findymail to automatically pull verified email addresses into your CRM.

Ultimately, prescriptive analytics provides actionable, data-backed solutions. It doesn’t just plop a bunch of data into your lap, leaving you wondering: “Okay, so what should we be doing?”

Instead, it points you to problem areas so you can target them with an effective strategy.

11 Key Metrics You Need to Analyze in Sales

Of course, the sales analytics is only as good as your data. Here are some of the key metrics you should be keeping an eye on:

1. Average Purchase Value

Sometimes called average sale value or average order value, this metric gives you a glimpse into how much each customer spends on average when they make a purchase. It's a valuable metric for understanding the spending patterns of your customer base.

Here’s the formula for it:

Total Revenue / Number of Orders = Average Purchase Value

If you had a total revenue of $10,000 from 50 orders, the average purchase value would be $10,000 / 50 = $200. On average, each order contributes $200 to the company's revenue.

This metric is key in helping you tailor your sales strategies and determining your customer lifetime value.

For example, suppose you’ve been spending $210 to acquire a customer and they’re only bringing in $200 on average.

In that case, it becomes clear you should reduce your CAC (Customer Acquisition Costs) to increase your profits. Knowing that, you can look into more cost-effective selling methods, such as cold outreach.

2. Customer Lifetime Value (CLTV)

Customer Lifetime Value (CLTV or CLV) estimates the total revenue you can expect from a customer throughout their entire time with your business.

Here’s how to find it:

CLTV = Average Purchase Value x Purchase Frequency x Customer Lifespan

CLTV offers insights into the long-term financial impact of each customer. Let’s say your average purchase value is $150 and your clients typically buy quarterly. If the average customer stays for five years, you can plug in:

150 x 4 x 5 = 3,000.

So, the CLTV would be $3,000. Understanding CLTV helps you make decisions concerning customer acquisition costs, marketing strategies and more.

For example, if your average purchase value is low (like in the example above), but the customers tend to grow, then it makes sense to spend more on them and strengthen your qualification process, so you know which leads are worth the spend.

3. Customer Acquisition Cost (CAC)

Customer Acquisition Cost (CAC) tells you how much it costs to get a new customer. It will help you evaluate a customer’s profitability.

To find CAC, use this formula:

CAC = (Cost of Sales + Cost of Marketing) / Number of New Customers Acquired.

It's essential to know your CAC. If it’s higher than your LTV, you will end up in the black.

For example, you spend $10,000 on sales and marketing to get 100 new customers. If each customer brings in $300 over time, it's good.

But if the cost is more than what each customer brings in, like $400, it's a sign to check and rethink your strategy.

4. Win Rate

Win rate measures the success of your sales team by evaluating the percentage of deals they successfully closed out of the total opportunities presented.

The formula for the Win Rate is straightforward:

Win Rate = Closed Won Deal / Total Opportunities

This metric is valuable for assessing sales team performance. It provides a snapshot of how successful the team is in turning potential business into actual revenue.

Naturally, more wins means your sales strategy is effective or your salespeople are savvier at converting opportunities into deals. As a sales manager, you’ll use the win rate in sales analytics to determine how many more deals you need to hit targets.

For example, suppose your win rate is lower this quarter than the previous. You can dive into the numbers to see where the issues occur – are there specific issues across the entire team or do only some reps need your help?

5. Churn Rate in Sales Analytics

The churn rate measures the percentage of clients who decide not to continue business with you over a specific period. It gives you insight into how well you’re retaining customers.

The formula for the churn rate is simply:

Churn Rate = Customers Lost during a Period / Total Customers at the Beginning of the Period

A higher churn rate could lead to lower revenue, especially when new deals don’t “outrun it,” so it pays to be careful with it.

Beyond that, it can cost between 5 and 25 times more to get a new customer than to keep an old one. So, if you see a spike in churn rate, rethink your approach to customer relations and perhaps even initial qualification.

6. Sales Growth

Sales growth gauges how sales increase or decrease over a set period. And it gives a snapshot of your current sales performance.

The formula for sales growth is:

Sales Growth=  ((Current Sales Period − Previous Sales Period) / Previous Sales Period) x 100

If you want to boost sales growth, it’s time to reassess the validity of your current strategy and ICPs, as well as explore new approaches. For example, you could expand your sales channels or introduce new offers.

7. Sales per Product/Service

Sales per product/service lets you determine how much each product/service contributes to sales performance and revenue.

The formula for sales per product/service is expressed as:

Sales per Product = Specific Product Revenue / Number of Units Sold

Sales per Service= Specific Service Revenue / Number of Services Rendered

Then, you could compare the figures to see which products or services are your clear bestsellers and which are your rising stars. It’s not uncommon to see some products sold more often while their total percentage of revenue stays lower than others.

However, it’s important to become aware of the situation through sales analytics, so you don’t end up in an unfavorable position.

You can also use sales per product/service metrics to see what products/services perform well and adapt to give customers more of what they want. It allows for a more granular analysis so you can make informed decisions about the new features, pricing strategies and marketing efforts.

8. Sales per Rep

Sales per rep measures an individual sales rep’s to bring in revenue. It’s another straight-forward formula:

Sales per Rep = Total Sales / Number of Sales by Rep

It can be used to assess a rep’s performance. If they’re doing well and hitting targets, praise and reward them. Heck, take it one step further and help the rest of the team learn from their tactics!

On the other hand, if they are a poor performer, it could be time for coaching and training to help the sales rep with their weaknesses.

9. Lead Conversion Rate in Sales Analytics

Lead conversion rate is one of the most critical metrics you need to track, so remember this one! It tells you the percentage of leads that successfully convert into customers. Plus, it helps you gauge the overall performance of your efforts.

The formula to find your lead conversion rate is:

Lead Conversion Rate = (Total Conversions / Total Leads) x 100

For instance, if your team generates 500 leads in a month and 50 of those leads become paying customers, the lead conversion rate would be:

(50 / 500) x 100 = .10

This means you have a 10% conversion rate. Of course, the higher the number, the better. But this also helps you forecast: if you get 1,000 leads next month, 100 should turn into closed deals.

As with any data, I’m a big fan of analyzing why the numbers look the way they do. Yes, it’s nice to see a 10% conversion rate, but you need to understand why it’s so high:

  • Which activities are you performing?
  • What about your strategy resonates with the audience?
  • Which audience segments are the most profitable?

Don’t settle for cheering for the numbers. Understand why they’re worth cheering for!

10. Sell-Through Rate in Sales Analytics

The sell-through rate shows how much inventory you’ve sold over a set period (e.g., monthly or quarterly). The formula for it is:

Sell-Through Rate = (Units Sold / Total Units) x 100

It’s crucial for businesses with physical products, as it helps them understand the pace at which products move off the shelves. Typically, you want to aim for a sell-through rate of 80% or higher.

11. Sales Pipeline Velocity

Sales pipeline velocity measures the speed at which opportunities move through a sales pipeline.

The formula for sales pipeline velocity is:

Sales Pipeline Velocity = (Number of Opportunities x Average Deal Value x Win Rate) / Sales Cycle

The velocity gives you an idea of how efficient your sales process is. And you can also use it to help with forecasting and planning your next moves. What makes sales pipeline velocity special is that a change in any input can have dramatic effects, especially when you’re in B2B, which is notorious for buying committees and long sales cycles.

For example, if your pipeline velocity is subpar, you could determine where the bottlenecks and drags happen. Is it with big accounts that have 3+ stakeholders? Knowing this, you could try new approaches like targeted selling.

How to Analyze Your Data (Pro Tips)

Avoid Analysis Paralysis

When dealing with a wealth of data, you run the risk of analysis paralysis – meaning you have so much information that you don’t know where to start.

Clearly define your objectives and questions: what do you hope to discover by diving into the data?

You could even formulate hypotheses to make it easier to extract good answers and avoid getting overwhelmed by the sheer volume of information.

Use Proven Analysis Methods

Cohort Analysis groups users who share a common characteristic, helping you understand trends over time. (This is helpful if you’ve changed strategies from quarter to quarter, as you’ll be able to observe how segments exposed to different strategies may behave differently.)

For Customer Analysis, dive deep into individual customer behaviors, preferences and interactions to tailor your strategies.

Lastly, embrace Real-Time Analysis to stay agile and respond promptly to changing trends. Just don’t be too trigger-happy: Any effort needs a ramp-up period to evaluate its results properly.

Align Marketing Data with Your Sales Analytics

Yes, now’s the time to hop on a call with the marketing team.

Firstly, your revenue attribution models will be off if you don’t align marketing data with your sales analytics. And you need them to understand how each channel (marketing or sales) contributes to the final conversion.

Once you know the big triggers and touchpoints, it’ll be easier to allocate resources to the most profitable channels and see more ROI with fewer budget dollars wasted.

Sales Analytics: Your Key to Better Strategies

It’s not enough to collect data. You need to know how to approach it. That’s the only way to translate it from zeroes and ones into insights.

In this article, I’ve given you an overview of how sales analytics works and the key metrics you need to track. However, you can analyze any data you have on hand – who knows what valuable insights they might hold? The question of data is personal to every company (and so is implementation).

Just make sure you’re taking the time to sit down with your team and review it periodically. Your balance sheet will thank you!