Sales Analytics: Don’t Miss Out On Tracking These [10] Crucial Metrics

December 22, 2023

Whether it's personalization or customer relationship management, sales analytics play a key role. Take Shopify, for example; it monitors sales data for each customer and delivers ads based on people’s historical shopping behaviors –  who have never bought the premium, who bought only once, and regular subscribers who are up for renewal. 

72% of the fastest-growing companies agree that it’s the most important component of sales planning.

Sales analytics are accessible to everyone – regardless of the fact that they own a sales analytics tool or not. You just need a basic understanding of the metrics and some tools to tap into the power of sales analytics. 

In this blog, we dive deep into the concept of sales analytics and share the best ways to help you incorporate analytics into your organization. 

Shall we?

What is sales analytics? 

Sales analytics is the process of gathering and analyzing sales data to gauge and improve your sales performance. By analyzing your sales data, you can stay current on sales trends, customer behavior, and how your sales reps perform.

Let’s understand the 4 types of sales analytics by taking an e-commerce company called Comfort, for instance: 

1. Descriptive (the what)

In descriptive analytics, you track your past sales data to find what went right or wrong. Descriptive analytics also serves as a foundation for the next three analyses as it lets you create benchmarks to measure the changes that happen in your business. 

For example, Comfort, an e-commerce company, analyzes its past sales data over the last year, examining metrics such as monthly revenue, the number of new users acquired, and conversion rates. With this analysis, they found out that there was a significant increase in sales during holiday seasons but a decline in user engagement during certain months. This forms the basis for understanding historical performance.

2. Diagnostic (the why)

In this step, you analyze data gathered from descriptive analytics to determine why sales spiked or went down. Let’s continue with the same example to understand diagnostic analysis.

After identifying a decline in user engagement during specific months through descriptive analytics, Comfort conducts a diagnostic analysis. It looks into factors such as website traffic, customer feedback, and competitor activities during the low-engagement periods. They found that a website redesign during those months led to a less user-friendly interface, resulting in decreased engagement.

3. Predictive (the future)

In this step, you can find patterns in your sales performance and plan future scenarios. This lets you make informed predictions. 

For example, Comfort used the insights gained from descriptive and diagnostic analytics to apply predictive analytics. They identify patterns in user behavior and find a correlation between bad UI and low engagement. The predictive analytics model helps them understand that improving the UI will improve engagement on the website. 

4. Prescriptive (the plan or solution)

Prescriptive analytics is about assessing all data you have gathered until now and devising a sales strategy. In this step, you discuss key action items leading to your goal.  

For example, after predictive analytics, Comfort now moves to prescriptive analytics. It plans targeted marketing campaigns during anticipated peak seasons and invests in UI improvements to enhance engagement during slower periods. The prescriptive plan outlines specific action items to achieve sales goals.

How Are Businesses Steering Their Sales Decisions Using Sales Analytics Help?

With sales analytics, you start operating in a more customer-oriented way.  You constantly monitor their preferences, which naturally leads to better business decisions.

1. Businesses are making more data-driven decisions

Sales analytics replace your reliance on gut feelings to make confident sales decisions.  Sales analytics tools will also reduce human errors, improving the accuracy of insights into past, present, and future sales to increase operational efficiency.

Let’s consider an e-commerce store, for example. With sales analytics, the rep can find which products have sold well and which were left on the shelf in the past. They can also check which products customers are enquiring about the most. 

Based on this information, the store manager can increase the value of popular products and reduce unpopular products to enhance the store's bottom line. 

2. Sales teams are improving customer relationships 

The use of sales data analytics is proven to improve customer relationships as sales analytics helps uncover their buying behavior and preferences- especially in the B2B sector. With sales analytics, you gain knowledge and insights from multiple data sources, including social media, CRM, calls, and texts. 

You can use this intel to generate personalized customer experience, which is non-negotiable these days since 63% of customers will stop buying from websites with poor personalization tactics. 

For example, you see multiple customers enquiring about the same feature. You can segment them and create targeted marketing campaigns with how-to guides, emails, and infographics highlighting the benefits of that particular feature. 

3. Real-time Reporting helps sales teams stay on top of the performance of their sales activities

When you continuously track sales analytics with effective tools, you don’t have to wait for weekly or monthly meetings to find customer insights. Sales analytics tools help you monitor sales calls and engagement in real-time. 

Also, with the real-time reporting features in sales analytics tools, you can stay on top of your sales reps' performance. You can see how many calls they’re taking, how many texts or emails they’ve sent, and how many of those have been converted. This helps you identify potential bottlenecks in the sales cycle and resolve them right there. 

For example, while monitoring sales analytics, you see sales cycles taking longer than expected, increasing the time to close a deal. You can start removing unnecessary steps in the sales cycle and implement a sales automation tool that can reduce manual tasks and speed up the sales process. 

Source: Sellular

10 metrics you should track in a sales analysis

For efficient sales analysis, you need to set some metrics to measure over time. These metrics serve as benchmarks to compare sales performance over time. Take monthly sales for example. Once you measure your monthly sales, you can compare them each month to see how it improves or declines over time.

Here are 10 important metrics you should track:

1. Sales growth (over a period of time)

Sales growth is simply the percentage increase in your sales revenue over a specific period of time. For example, if your company's sales were $1 million in the first quarter and $1.2 million in the second quarter, the sales growth rate would be [(1.2 - 1) / 1] * 100 = 20%.

2. Lead scoring

Lead scoring is used to rank and prioritize leads based on their likelihood to convert. With lead scoring, you assign scores to leads based on certain criteria such as demographics, behavior, or engagement.

For example, your company can assign scores to leads based on criteria like job title, company size, and website activity. A lead with a higher score (e.g., 90 out of 100) indicates better chances of conversion.

3. Win rate

Win rate is the percentage of sales opportunities or meetings your sales reps successfully convert into customers. It’s called win rate, which indicates your sales team’s effectiveness in winning business.

If a sales team closes 20 deals out of 100 opportunities, the win rate is (20/100) * 100 = 20%.

4. Customer lifetime value

Customer lifetime value is the net profit your business expects to earn from customers throughout their relationship with your company. To calculate this, you consider a customer's average purchase value, frequency of purchases, and duration of the business relationship.

For example, if the average purchase value is $100, customers make 5 purchases per year, and the average customer lifespan is 4 years, CLV = $100 * 5 * 4 = $2,000.

5. Sell-through rate 

Sell-through rate is the percentage of inventory or products you sell within a specific period.

For example, a retailer starts with 1,000 units of a product and sells 800 units. The sell-through rate is (800 / 1,000) * 100 = 80%.

6. Sales per product 

The total revenue you generate from a specific product over a specific period is called sales per product. 

For example, a company sells two products, A and B. If product A generates $50,000 in revenue and product B generates $30,000, the sales per product are $50,000 for A and $30,000 for B.

7. Pipeline velocity 

Pipeline velocity is the speed at which leads move through the sales pipeline. Basically, it’s the time leads take to move from initial contact to closing a deal. 

For example, if a lead takes an average of 30 days to move from the initial contact stage to closing, the pipeline velocity is 1/30. It indicates that one stage is completed every 30 days.

8. Quote to close

Quote to close is the ratio of successfully closed deals to the number of quotes or proposals sent to potential customers. 

For example, if a sales team sends out 50 quotes and closes 10 deals, the quote-to-close ratio is (10 / 50) * 100 = 20%.

9. Repeat buy per product 

Repeat buy per product is the number of times a customer purchases a particular product from your business. The more times a customer buys a product, the more loyal they are.

For example, if a customer purchases a particular product three times over the course of a year, the repeat buy per product is 3.

10. Average order value 

As the name suggests, the average dollar amount a customer spends per transaction is your average order value. 

For example, if a customer places three orders with values of $50, $75, and $100, the AOV is (50 + 75 + 100) / 3 = $75.

Each of these metrics helps you track your business goals. For example, if your goal is to increase sales, you should track your win rate and pipeline velocity. These metrics will show how many customers you’re gaining and how fast leads are moving through the sales pipeline. If the win rate reduces, you can find solutions to improve it, as it will bring you closer to your goal. 

Similarly, you can connect different metrics to each of your business goals. Check out the table below to understand which metrics are linked to different business goals.

Business Goals Metrics you should track to achieve this goal
Increase Sales
  • Win Rate
  • Pipeline Velocity
Enhance Lead Quality
  • Lead Scoring
  • Win Rate
Improve Sales Effectiveness
  • Win Rate
  • Quote to Close
Maximize Customer Value
  • Customer Lifetime Value
  • Repeat Buy per Product
  • Average Order Value
Optimize Inventory Turnover Sell Through Rate
Boost Product Performance Sales per Product
Accelerate Sales Pipeline
  • Pipeline Velocity
  • Quote to Close
Streamline Sales Processes Quote to Close
Foster Customer Loyalty
  • Repeat Buy per Product
  • Customer Lifetime Value
Increase Transaction Value
  • Average Order Value
  • Sales per Product

Best Practices for Sales Analytics 

Consider the following factors before jumping into sales analytics:

1. Ask or begin with clear questions

They say you cannot grow what you don't measure. Similarly, you cannot start to analyze if you don’t know what the sales enigma wants to dig out the answer for.  

Ask your sales and business leaders what you want to measure from your sales analysis. This could be sales pipeline velocity or the performance of a sales channel. This will help you realise the objectives of your analysis. 

2. Use effective tools to gather sales data  

A good sales analytics tool will come with integrations, visualization, tracking, and reporting features to make analysis super easy. Developer dependencies shouldn’t slow down your sales reps - to integrate various tools, help prepare dashboards, etc. 

The simpler the UI of the tool, the better your sales reps will perform, and it will produce greater results. A sales analytics tool will also help you track and monitor your sales team’s performance and descriptive analytics. You will get insights into individual and team engagement, including metrics like number of calls and emails, call duration, email open rate etc, so you can diagnose what went right or wrong. 

An effective sales analytics tool ensures that data is accessible to everyone. Whether a sales manager or a junior sales rep, everybody can use an analytics tool to analyse sales data to make informed decisions to plan future sales strategies. 

3. Centralize your sales data

You will have difficulty analyzing sales data if scattered across multiple channels. That’s why you should condense all of it into one platform. You can do this manually in spreadsheets or slides, but that will take hours. That’s why it’s better to use sales analytics tools for it. 

For example, Sellular provides real-time dashboards comprising all sales analytics data. It also syncs your data with Salesforce automatically, so no matter what channel you prefer, you’ll find all sales data consolidated into one place. 

4. Visualize your data 

Long reports filled with numbers are hard to understand. Data visualization helps you convey insights at a glance. When you convert the data insights into charts and graphs, they become more digestible.

And the best (and fastest) way to convert boring data insights into engaging charts and graphs is to use an analytics tool that comes with visualization features. Sales analytics tools automatically convert data insights into charts and graphs to make them more digestible. This way, more people understand your sales analytics and you’ll make decisions faster. 

5. Drive actions based on data 

Once you grasp analytics, it’s time to make decisions. Here’s what you can do with sales analytics:

  • Segment customers based on preferences and personalize marketing campaigns for them. 
  • Identify high-value leads and allocate additional resources to vet them.
  • Evaluate lead sources and prioritize those with higher conversions 
  • Adjust pricing based on market demand.
  • Identify top-performing sales reps and leverage their strategies across the team. 
  • Optimize inventory levels using predictive analytics to meet anticipated demand. 

6. Measure outcomes 

Analytics is not a one-time task. You must monitor trends, create sales analytics reports, study them, and change your strategies based on the data. You must assess KPIs and metrics to evaluate the impact of sales analytics over time. 

You can also segment data based on customer demographics, product categories, or sales representatives. It will also help you better understand your sales performance across different subsets. 

What should you look for in a sales analytics tool?

  1. Data requirements: Look for a tool that can bring in data from multiple sources, such as your CRM, social media channels, text, and call interactions, for a 360-degree view of sales performance. 
  1. Integrations: A sales analytics tool should integrate seamlessly with CRM systems, marketing automation platforms, social media channels, and customer support systems, The integration ensures that data flows cohesively between these tools without the need for APIs and coding. 
  1. Ease of use: Look for a tool with a low learning curve so your sales reps can jump right into analysis without spending days on training. 
  1. Reporting features: Look for a tool with advanced reporting features so that your teams can track KPIs, identify trends, and communicate performance metrics effectively. 
  1. Budget: The cost of a sales analytics tool depends on various factors, such as the number of team members and monthly reports. The costs multiply as your requirements scale. So, consider pricing factors before swiping your card. 
  1. Customer support: You shouldn’t have to wait for days to get a response to your query. Look for a tool with reliable customer support to address any issue quickly.
  1. Scalability: Look for a sales analytics tool that can scale with your business and can accommodate increasing data volumes and user requirements. 

Sellular Makes Sales Analytics Easy & Intuitive 

Sellular is a multi-channel sales engagement software with advanced reporting and analytics It gives you a bird’s eye view into sales analytics. You can monitor your team’s performance in pre-built dashboards and get actionable insights into all sales communications. 

As you can see above, you can monitor daily calls, emails, sales figures, and conversion rates in the dashboard. Moreover, it showcases how many prospects open your emails, click links in them, and how many emails bounce. You also get a detailed analysis of calls and texts as well. 

You can also enhance your customer relationships with Sellular. Just collect potential leads on leaderboards and send personalized SMS text messages instead of sending generic follow-ups to increase the chances of conversion. 

The best part? Sellular has a two-way sync with Salesforce, so it uploads all data changes to Sellular. 

Sellular replaces all intuition with facts and data. There’s no room for guesswork. So, you can sit back, study trustworthy, real-time analytics, and enhance your sales performance with informed business decisions.

Get started with Sellular today.

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