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Performing a regular and detailed sales analysis provides real-time insights into various aspects of the sales cycle. This helps to drive continual improvement and growth. When you analyze sales data and use it effectively, the entire team is better set up for success.
In this article, weâll discuss why every sales leader should have a comprehensive sales analysis system, as well as the common methods used to analyze sales data.
Analyzing sales performance is critical for any business that wants to grow revenue, retain customers, and beat the competition. But where do you start? What metrics and KPIs should you track? How do you turn raw data into actionable insights?
This comprehensive guide will walk you through the entire sales performance analysis process, from choosing metrics to presenting findings. Follow these steps to boost team productivity, discover sales opportunities, and make data-driven decisions.
Why Sales Performance Analysis Matters
Before we dive in, let’s look at why sales performance analysis is so valuable:
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Identify growth opportunities Analyzing sales data reveals poor performing products/services, changing customer preferences, and other trends you can capitalize on.
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Optimize processes: Identify inefficiencies in your sales process, like long sales cycles or poor lead follow-up, and fine-tune them.
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Boost profits: Use insights to focus on high-value activities, like pursuing qualified leads over cold calls. This directly improves revenue and margins.
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Retain customers: Discover how to better meet customer needs through sales analysis. Happier customers equal higher retention.
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Forecast accurately: Historic sales data enables accurate revenue forecasting when extrapolated into the future.
Clearly, sales performance analysis is indispensable for any business. Keep reading to learn how to implement it.
Step 1: Choose Metrics and KPIs to Track
The first step is choosing which metrics and key performance indicators (KPIs) to track. Start by considering your overall goals. Do you want to:
- Increase sales?
- Shorten the sales cycle?
- Identify poor performing products?
- Boost customer retention rates?
Once your goals are clear, here are some metrics and KPIs to consider tracking:
Sales Volume Metrics
- Total revenue
- Total sales
- Sales by product/service line
- Sales by customer segment
- Sales by geography
Sales Efficiency Metrics
- Win rate
- Average deal size
- Sales cycle length
- Lead conversion rate
- Lead response time
Growth Metrics
- Month-over-month sales growth
- Year-over-year sales growth
- Returning customer rate
- Customer lifetime value
Sales Activity Metrics
- Inbound leads generated
- Outbound calls made
- Sales emails sent
- Sales demos completed
Choose a balanced mix of macro sales metrics and micro activity metrics relevant to your goals. Avoid tracking too many metrics, which can become unmanageable.
Step 2: Collect and Organize Your Data
Now it’s time to start gathering sales data to analyze. Here are some ways to collect it:
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Your CRM platform: CRMs store a wealth of sales data. Export reports to collect info on leads, deals, sales activities, and more.
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Email providers: Email platforms like Gmail track metrics for sales emails sent and opened. Download this data.
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Call tracking software: These tools provide insights into sales calls made, call length, recordings, and conversions.
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Help desk systems: Customer service tools track support tickets, complaints, resolving time, and other customer insights.
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Finance systems: Your accounting software stores financial transactions, which feed metrics like revenue and customer lifetime value.
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Sales meetings: Have regular sales meetings where reps share progress and recorded activity.
Once you’ve collected data from relevant sources, organize it into a central database, spreadsheet, or BI tool. Choose a tool that allows customizable reporting.
Step 3: Analyze and Visualize the Data
Raw data doesn’t provide much value on its own. The key is conducting analysis to extract meaningful insights.
Start by calculating your chosen KPIs. For example, divide total revenue by number of sales to get average deal size.
Some other sales data analysis techniques include:
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Trend analysis: Identify patterns over time, like rising or falling metrics.
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Drill-downs: Break down summary data for deeper analysis, like sales by both product and region.
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Segmenting: Divide data into logical groups, like sales by customer persona. Compare segments.
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Correlations: Determine if changes in one metric impacts others, like shorter sales cycles increasing win rates.
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Benchmarking: Compare performance to past periods, forecasts, competitor data, industry averages, or other benchmarks.
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Statistical analysis: Apply methods like mean, distributions, standard deviation, and regressions.
Visualizing your findings also makes them easier to digest. Create charts, graphs, dashboards, and infographics to bring insights to life.
Step 4: Interpret Results and Make Recommendations
The most important step is interpreting analysis results to determine why they occurred and what actions they dictate.
For example, if revenue from offshore customers is growing 50% faster than onshore, you could recommend expanding offshore marketing efforts. Always tie insights to tactical next steps.
When presenting findings, focus on sharing:
- Major trends and takeaways
- Root cause hypotheses
- Recommended actions based on findings
Provide both the data and interpretations. This enables stakeholders to follow your logic and make informed decisions.
Sales Performance Analysis Best Practices
Follow these best practices to get the most out of sales performance analysis:
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Track consistently – Analyze metrics consistently, whether daily, weekly, or monthly. Irregular analysis misses important trends.
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Leverage technology – Use data analysis and visualization tools to quickly crunch numbers and create insightful reports.
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Involve sales reps – Get insights from frontline salespeople to supplement data-driven conclusions.
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Act on insights – The goal isn’t just reporting findings but driving continuous improvement.
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Automate where possible – Automated reporting through CRMs saves time over manual analysis.
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Secure stakeholder buy-in – Get leadership invested in regularly reviewing sales analysis results.
Key Takeaways and Next Steps
Successful sales performance analysis contains the right mix of metrics, efficient data collection, thoughtful analysis, compelling deliverables, and, most crucially, action.
The steps outlined in this guide should provide a blueprint to implement an effective sales analysis process at your own organization. Just remember to focus on driving measurable results, not simply reporting data.
To build on these fundamentals, here are some next steps to consider:
- Discuss metrics and analysis cadence with sales leaders
- Research and select tools for tracking, analysis, and reporting
- Build dashboards to monitor key sales KPIs
- Create templates for standard sales reports
- Train sales reps on providing inputs to analysis
Robust sales performance analysis translates raw data into insights sales organizations can act on. Follow this guide and make it a key part of your team’s success toolkit. Just don’t forget to regularly tweak the process to meet evolving needs.
Define your objective, then pick your method to analyze sales data
Before you can begin analyzing your data, you first need to understand what youâre looking for and why. What is your goal in analyzing your data? Whatâs the main data point you want to track?
For example, do you want to focus on customer retention? Then the main metrics youâll want to track are post-sales data like NPS scores, customer engagement numbers, and churn rates.
Once you determine your main objective, select the most suitable method (or methods) for your sales analysis. There are many types of techniques when it comes to performing a sales analysis. So, it will depend on your companyâs goals. Here are the most common sales analysis methods:
Revenue Analysis: This method focuses on the actual sales numbers for the products sold. This helps determine which products are top performers and which are underperforming for better sales planning.
Example: Letâs say your company offers 10 different products and you want to analyze how each product sold during Q1. Running a revenue-focused sales analysis at the end of the quarter shows you that eight of your products are hitting expectations, while two of your products are lagging. This insight can help direct your sales and marketing planning for the remainder of the fiscal year.
Pattern Analysis: A sales pattern analysis focuses on finding trends within your sales data, which can help you better understand your product demand â leading to more accurate forecasting and quotas.
Example: Letâs say your main objective for the year is to grow your product in one of your newly established regions. Utilizing this trend analysis method, you track the sales in the new regions and see which ones are currently trending in the right direction. From there, you can sharpen your sales approach by using even more distinctive details like buyer trend information.
Predictive Sales Analysis: This analysis uses sales forecasting and anticipatory customer behavior for dissecting data and predicting upcoming revenue.
Example: Letâs say your main objective is increasing your close rate percentage. Using a predictive sales analysis, you see that based on your historical data and current pipeline information, your forecasted close rate is still below your goal. By focusing on the predictive data in your analysis, you can hone in on where the conversions are struggling specifically within your sales cycle and work on that particular gap. This method is also particularly helpful when it comes to scaling since you are working with data that gives you insight into your future growth.
Performance Analysis: Sales performance analysis means specifically tracking the performance of your sales team over a specific period of time.
Example: Letâs say your goal for the year is to improve your sales teamâs revenue growth performance by 10%. With this method, you track the teamâs overall revenue performance each month. Data shows that while they are increasing their revenue, they are still below the 10% goal. With this information, you can work with your team to incentivize their selling with friendly sales competitions or utilize motivational tools.
Effectiveness Analysis: The sales effectiveness method also focuses on performance, but on an individual level. This includes the repâs specific quotas and goals, as well as tracking the effectiveness of your coaching.
Example: Letâs use the same example as above â you want to improve your sales teamâs revenue growth by 10%. But you also want to see individual revenue growth year over year for each of your reps. With effectiveness analysis, you focus on tracking each repâs individual KPIs, in addition to remaining consistent with their 1:1 meetings for regular feedback and coaching. Donât forget about the importance of analyzing the effectiveness of coaching different teams as well!
Pipeline Analysis: The sales pipeline method focuses on analyzing the opportunities in your pipeline, including the quality of the leads and your close ratio.
Example: Letâs say your main goal for your team is improving the number of quality opportunities in your pipeline. Using this method, you track the lead to opportunity ratio to see if there are any noticeable gaps. You notice that the number of outreach is high â based on the SDR teamâs activity â but the movement from lead to opportunity is stagnant in comparison. By utilizing the pipeline for analysis, you can see that the SDR team may need more training on how to identify quality leads instead of focusing on only their outreach numbers.
Diagnostic Analysis: The diagnostic analysis targets the reasons behind the sales metrics and helps with a potential strategy for improvements.
Example: Letâs say your company released a new product in Q1 of this year and you want to see how itâs been performing with your current customers. Using a diagnostic method, you run post-sales customer satisfaction reports. You find that while the product initially sold well, your retention rate was lower than normal â this means that there has been some disconnect between the product and the client after the sale has been made. This method helps you diagnose the specific area of concern, which allows you to focus your solution in the right area.
Prescriptive Analysis: Oftentimes, this method goes hand and hand with the predictive and diagnostic methods since it focuses on the best steps to take to close the anticipated sale and adjust for any potential problems.
Example: Letâs say that youâve used the predictive analysis method to determine that your sales for Q4 will be lower than previously anticipated. Using the prescriptive analysis method, you run a sales activity report which shows that your SDR reps are not consistently hitting their outreach numbers. This can be an area where you focus on increasing those sales numbers for the quarter. This method helps to find ways to increase productivity and find solutions for potential roadblocks to pave the way towards higher revenue opportunities.
Customer Research Analysis: This method is all about market research and how to utilize the information to grow your revenue and retain your clients.
Example: Letâs say your main objective for the upcoming year is to scale your product while keeping your retention rates high. Using this method, you run post-sales reports like NPS scores and customer satisfaction survey results. While your retention rates are still high for now, you see that the customer satisfaction levels are lower than you had anticipated. This means that you can focus on training your post-sales Account Managers on areas where they may not be excelling, like their response time or conducting effective business reviews. You can also adjust how often you reach out to your customers regarding marketing campaigns.
Why should you analyze sales data?
Analyzing sales data is a crucial part of being an effective sales leader. Especially when it comes to improving your sales team performance and reaching your companyâs goals. It can:
- Help boost revenue
- Improve team performance
- Prepare you to scale
Jarrod Wright, Marketing Director of Fi911, says the following about sales data analysis.
It helps determine the top-performing products or features as well as the ones that may be lagging.
In addition, a sales analysis gives you great market and client insights for growth opportunities. It also means you get a better understanding of your companyâs value proposition in the marketplace.
Analyzing your sales data helps narrow in on any issues within your sales cycle. Tracking data like sales activity or training progress will help determine how well your team is performing.
Spend the time in your numbers. It will allow you to regularly find ways to enhance your sales cycle for the most optimal revenue outcome. It also leads to more accurate forecasting, which means better-suited quotas and goals for your reps.
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What is sales performance analysis?
Sales performance analysis: Sales performance analysis is crucial for effective sales performance management. This type of analysis will help you gauge your sales team’s performance and evaluate the overall effectiveness of your sales strategy. Utilize it to compare actual results to expected outcomes, and then make necessary adjustments.
How do you do a sales analysis?
Here’s how you can go about a sales analysis step by step. 1. Collect your sales data Step one: gather all your historical sales data. This means pulling info from your CRM, sales reports, transaction records, and any other sources. The more hard sales data, the better the insights.
What is a sales analysis process?
This process helps evaluate sales team performance, identify trends, and uncover areas for improvement. Techniques like descriptive analysis, comparative analysis, and correlation analysis provide actionable insights for refining sales strategies.
How do I improve my sales analysis?
To improve your sales analysis, look for tools with this functionality: CRMs manage sales data of all kinds in one place: customer interaction, sales activities, and overall performance.
