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A Step-by-Step Guide to Performing Sales Analysis

Sales analysis is essential to avoid inaccurate forecasts and identify improvement opportunities. Whether you’re an enterprise team or a small startup, regular analysis—yearly, quarterly, and monthly—provides insights into performance, highlights areas for training, and boosts productivity. This article walks you through the key steps to take to conduct sales analysis, predict better forecasts, and increase your revenue.
Don’t agree that sales analysis can make or break your company’s revenue? Let me tell you a story:
We’ve all had family or friends who used Nokia phones. And why not? It was the market leader in the mid-2000s, with a 40% market share in 2007-2008.
However, by 2014, the market share dwindled, and the mobile company was sold to Microsoft. So, what happened in seven years? How did a market leader get obliterated?
Two things happened:
  • Apple entered the mobile market by launching the iPhone, the first-ever smartphone, in 2007.
  • Nokia underestimated the growing popularity of smartphones and delayed adopting the change.
 
The result: Underestimation and poor guesswork made a global leader disappear.
If this can happen to a well-established brand, the same could happen to you. When? One way is to ignore sales analysis and rely on ‘guessed forecasts.’
Just like an emerging trend, a prospect’s interest in your product is time-bound. So whether your goal is to reduce the sales cycle length, increase conversions, or optimize sales performance—a periodic sales analysis is non-negotiable.
Not sure how to do that? Keep reading.
This article is about sales analysis, including the key metrics to track and a step-by-step process for analyzing sales data. Ready? Let’s begin.

What is Sales Data Analysis?

Sales data analysis is collecting and analyzing sales data from different sources to extract insights and make data-driven decisions. By analyzing sales data, you review your sales performance to identify:
  • Loopholes hurting your conversion rate
  • Opportunities to optimize the sales process
  • Customer’s pain points for targeted messaging
 
The most common example of a successful sales analysis is understanding your team member’s performance. According to a Brevet report, 84% of the sales training is forgotten within three months. So, if you’re seeing a drop in a rep’s performance, it may be a signal to organize continuous training programs.

What’s Included in Sales Analysis?

A detailed sales analysis is about auditing every aspect of your sales, including:
  • Lead generation metrics
  • Customer data
  • Sales figures like total revenue, average order value, per-month sales, etc.
  • Sales team performance
  • Trends and patterns, previous year/quarter/month comparisons
  • Data analysis based on segmentation
  • Forecasts
 
Ideally, you gather everything related to sales and try to figure out what’s happening and why. You find things you’re doing right and wrong (to create improvement strategies) and analyze data to make an accurate sales forecast.

How Often Should You Perform a Sales Analysis?

There is no one-size-fits-all answer. How often you do it depends on many factors, such as resource availability, goals, sales data, and time.
That said, periodic sales analysis is a must-do for all businesses. Ideally, you should conduct:
  • Yearly reviews to analyze YOY growth
  • Quarterly assessments to analyze parameters like sales reps’ growth
  • In-depth monthly reviews to create next month’s forecasts
  • Reviews after new campaigns launch to analyze performance and set target expectations for the future

Why is Sales Analysis Crucial for Boosting Sales Productivity?

Most of us learn the importance of budgeting the hard way. When we receive our first few payments, we rely on our desires for purchasing decisions.
We would assume what we want and buy it without second thoughts. What happens? After analyzing our expenses, we find we’re spending unnecessarily on things we don’t need. In short, we’re wasting money—our resources.
Similarly, you may be wasting time and resources unnecessarily on your sales process if you’re not careful. A regular sales analysis process helps you identify those leaks. It can also help in many other ways:

1. Make Data-Driven Decisions

Next month’s sales target shouldn’t be your boss’ moonshot goal. It should be backed by data—realistic, achievable, and yet challenging. But it definitely shouldn’t be a moonshot. You can arrive at that number by adding 20-30% higher than whatever was closed-won in the previous month or quarter.
Companies with accurate sales forecasts, that is, companies that choose next month’s targets after proper sales analysis, are 10% more likely to grow revenue YOY and 2X as likely to be at the top of their field. So, start conducting regular sales analysis to forecast accurately and increase your revenue.

2. Boost Employees’ Performance

Sales analysis helps evaluate your sales team’s performance, revealing your top performers’ most effective selling strategies. By examining call recordings, you can understand their techniques for handling objections, negotiating, and closing deals.
This analysis also identifies sales reps who require additional training. For example, a sales rep who makes 100 calls monthly but only closes two deals likely has strong prospecting skills but needs to improve their closing skills.
Training these reps to adopt strategies and sales scripts of high-performing colleagues can boost their effectiveness.

3. Increase Conversion Rates

Analyzing your sales funnel can determine where your leads are falling from. Are they abandoning carts? Not responding to outreach? Or not showing up on sales calls? Regularly review your sales funnel to identify these patterns and optimize the bottlenecks for better conversion rates.
Often, it’s the small changes that make the big difference. For example, a lack of product demos on the website could make your prospects move to your competitors’ products over yours. Or a lengthy call scheduling form that makes them bounce.

What are the Key Metrics You Should Track in Sales Analysis?

Before conducting an in-depth sales analysis, identify your sales KPIs. These vary from business to business based on their goals and resources.
Sales Analysis
Here’s a list of some of the critical sales metrics divided into categories for your previous month’s sales data for inspiration:

1. Revenue Metrics

  • Total revenue: Revenue generated at the end of the month
  • Revenue growth rate: Increase in revenue compared to last month
  • Average deal size: Average revenue generated per account
  • Customer lifetime value (CLV): Predicted revenue per customer

2. Analytics Metrics

  • Sales productivity: Ratio of effectiveness vs. the cost of sales operations
  • Sales channel efficiency: Revenue generated through different sales channels compared to the cost of maintaining those channels
  • Customer acquisition cost (CAC): Average cost of acquiring a new customer
  • Sales cycle length: Average time to close a deal from initial contact to closing

3. Rep Activity Metrics

  • Calls made: Number of calls made by each sales rep
  • Meetings scheduled: Number of meetings set up with prospects
  • Lead response time: Time taken by reps to respond to leads
  • Average close rate: Ratio of the number of leads closed vs. leads contacted

4. Pipeline Metrics

  • Pipeline deals: Total number of deals at various stages
  • Deal conversion rate: Percentage of deals moving from one stage to the next
  • Pipeline velocity: The speed at which deals move through the pipeline
  • Win/Loss ratio: Comparison of closed deals to lost deals

5. Forecasting Metrics

  • Sales forecast accuracy: Comparison of forecasted sales to actual sales
  • Quota attainment: Percentage of sales reps meeting or exceeding quota
  • Revenue forecast: Projected revenue for a certain period
  • Opportunity win rate: Percentage of opportunities converted into customers

6. Customer Engagement Metrics

  • Customer satisfaction score (CSAT): Customers’ satisfaction with your products/services
  • Net promoter score (NPS): Customers’ loyalty and satisfaction
  • Customer retention rate: Percentage of customers remaining with your company over time
  • Churn rate: Percentage of customers who stopped using your products or services after a certain period
 
Of course, this list isn’t exhaustive—as the priority depends on your company’s goals and resources. You’ll have to track even more metrics for larger teams, but a few essential KPIs work fine for smaller teams, so gather your sales team and create a list of the critical sales metrics.
Focus on a few metrics that affect your business. Katie Devoe, Co-founder of CBD Nationwide, says, “I’d suggest simplifying the process by remaining focused on a few key metrics that best reflect your business goals and performance. Avoid ‘analysis paralysis’ by not getting lost in too much data. It’s about understanding what metrics really drive your business and focusing on those.”

How to Analyze Sales Data: 4-Step Process

Once you have your preferred sales KPIs, it’s time to start the sales analysis process:

Step 1. Collect Sales Data

Start by collecting sales data from various sources. You can use a Google sheet if you want, but CRM software makes it easier—it will save you time, stress, and resources and simplify the overall process.
 
Draven McConville, CEO of Klipboard.io, says, “Using CRM and its analytics make the data collection process easier and leads to automated reporting. These tools can automatically collect data and make reports already set up, so you don’t have to do anything by hand and save a lot of time.”
 
The key to choosing the perfect CRM for your business is to look for a platform with automated reporting and 24/7 customer support. This way, you can create quick reports and get your queries answered on time.
 
HubSpot is a good solution for complete sales management, but don’t limit yourself to sales, as it can leave blind spots in your understanding of your customers and market.
 
Gather data from various sources to view the complete picture, for example:
 
  • Google Analytics tells you the landing pages your customers most interact with, allowing you to identify and replicate their key characteristics on other pages and increase conversion.
  • Hotjar visualizes user interactions as heatmaps and session logs so you can see where the most engaged users are and where to place CTAs on your landing pages.
  • Spiff creates automated sales reports so you don’t have to spend time conducting a comprehensive data examination to distill meaningful insights and articulate findings in detailed reports.
  • Supademo tracks user engagement with interactive product demos. It shows a prospect’s demo experience, so you know exactly which step converted the lead.
 

Step 2. Choose a Sales Analysis Method

After you’ve gathered all the data, choose a sales analysis method based on your business goals, time, and resources. By selecting a suitable method before conducting one, you can increase its effectiveness and get relevant insights to answer specific questions.
 
For example, suppose your goal is to examine your sales pipeline’s health. In that case, you’d conduct a sales pipeline analysis and focus on metrics like lead-to-opportunity conversion rate, sales pipeline velocity, average deal size, etc.
sales analysis methods
Here are the seven types of sales analysis methods to choose from:
 
  • Sales trend analysis: Look at historical sales data to identify patterns, growth areas, and seasonality. Find what worked/didn’t work to inform future actions.
  • Sales performance analysis: Focus on how your sales team executes its strategy against sales goals. See who’s winning and where to improve.
  • Predictive sales analysis: Use data and algorithms to forecast future sales and trends and optimize resource allocation.
  • Sales pipeline analysis: Examine the health and progress of leads in your sales funnel. Identify loopholes and ensure consistent deal flow.
  • Product sales analysis: Look into individual product performance, including revenue, profitability, and customer preferences.
  • Predictive analysis: Use data and models to forecast future events and make informed decisions for sales and any area with data.
  • Market research: Gather information about your target market, competitors, and industry trends.

Step 3. Extract Valuable Insights and Forecast Sales

After selecting a sales analysis method, it’s time for the good stuff—digging into the data and analyzing it. Should you read data by yourself? Or is it helpful to use a tool?
 
Using a tool definitely helps, but the key is identifying where you need help and if there’s software available. For example:
 
  • If you’re doing predictive analysis, Clari, a revenue platform, will collect data from multiple sources, like CRM and Google Analytics. Its AI-powered algorithms will identify customer behavior patterns, such as typical steps they take before purchasing.
  • If you’re doing sales performance analysis, Gong.io’s coaching feature will help you collect and analyze sales reps’ performance. It’ll track topics and behaviors reps struggle with on calls and provide insights on soft and hard skills to develop for closing more deals.
 
For general analytics, Diana Zheng, Head of Marketing at ShipStallion, suggests using Tableau or Power BI, “ With these tools, you can view data in an interactive and visually appealing way, allowing you to understand trends and patterns more deeply. This data-centric approach has been instrumental in optimizing our marketing efforts and improving the overall customer experience.”

Step 4. Create a Sales Analysis Report

Finally, you should create a sales analysis report to present all your findings appealingly. This is your opportunity to explain strategies, next month’s targets, and the team’s growth to stakeholders, backed up by your findings in a sales analysis report.
 
Include visuals like charts and graphs to present complex information in a simplified manner. Here are the things a sales report should include:
 
  • A summary of the essential findings and recommendations
  • The methodology used to conduct the sales audit
  • Objectives and SMART goals decided
  • Findings and suggestions for improvement
  • Sales strategies with actionable steps
  • Conclusion with results to be expected

Forecast Accurately with a Detailed Sales Analysis Report

Regular sales analysis is critical. We started with Nokia’s tragic story—how a market leader went extinct because of poor guesswork. Let me end it with Glossier’s sweet story: A company (which started as a blog in 2014) reached a $1.8 billion evaluation by 2023. Do you want to guess what was its strategy for success?
You guessed it right—they forecasted demand accurately. How? Glossier established itself as a “community-driven beauty company,” using Instagram to engage with customers, understand their needs and preferences, and generate demand. The brand also analyzed data to predict consumer behavior and market trends.
The result? Sales grew exponentially.
It all starts with sales analysis:
  • Do it regularly and watch your sales grow like Glossier.
  • Ignore it and watch your sales crumble like Nokia.
 
With all that said, it’s time to take things seriously and start analyzing sales data monthly!

FAQs

1. What is the easiest way to analyze data?

The easiest way to analyze sales data is to collect it in a spreadsheet and compare it with last month’s or forecasted data. This will give you a good idea about what’s working and what’s not.

2. What is an example of data analysis?

A typical example of data analysis in daily life is the research done to invest in the stock market. After analyzing and interpreting vast amounts of data, you decide whether to buy or sell stocks.

3. How do you manage sales data?

Here are some of the effective strategies to manage sales data:
  • Choose a few essential key performance indicators (KPIs) to track
  • Choose the right tools with good reporting
  • Integrate all major platforms for seamless data tracking
  • Set up a good naming system
  • Encourage your sales team to learn and participate in sales analysis
  • Create a sales analysis timetable
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Ekta Swarnkar

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