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How to Perform Sales Data Analysis? What are the Metrics & KPIs of Sales Data Analysis?

In hyper-competitive markets, there is a high focus on sales metrics. To drive revenue growth, salespersons must achieve monthly sales targets. It is the responsibility of sales leaders to provide the necessary resources and planning to help the salespersons achieve their goals.

Traditionally, sales success had been largely dependent on intuition and guesswork. Herein, salespersons analyze every prospective customer and target the most suitable ones. But this process is based on a trial-and-error approach where salespersons utilize their intuitions to understand customer expectations and offer a possible solution.

But this approach is no longer valid as agile companies now require their sales leaders to perform strategic sales analysis based on sales data using the latest data analysis tools. This data-driven strategy based on important sales metrics ensures that guesswork is eliminated from the equation and the efficiency of the sales process is enhanced. 33% of sales teams prioritise their leads based on data analysis compared to 16% of sales teams that base it on intuition.

What is Sales Data?

Sales data is a term used to describe any information sales teams can utilize to augment their decision-making process. Sales data offers valuable insights into the sales team's performance, enhancing the efficiency of sales teams with better strategies.

It is imperative to ensure that the sales data is of the highest quality to enhance the accuracy of the forecasts. But more than merely gathering quality data for important sales metrics is needed; it is vital to use the best data analysis tools to determine the scope and strategy for improvement in the sales processes.


Need for Sales Data Analysis?

Sales leaders are responsible for tracking the sales metrics based on organizational goals. 76% of sales professionals believe that sales analytics has empowered them to deliver consistent customer experiences.

After-sales data on all the sales reporting metrics have been collected; it is imperative to use the relevant data analysis tools for sales data analysis to avail benefits like:

  • Making data-driven decisions: Analysis of sales performance metrics helps gauge the sales plan's performance and ascertain every salesperson's performance in real-time.
  • Identifying best customers: Sales data analysis assists salespersons in identifying high-value customers and enables them to spend most of their time engaging those customers only. This strategy helps maximize revenue growth by ensuring such customers are engaged with the company for the long term.
  • Analyze market trends: By accurately identifying sales metrics to track, sales data analysis can help company leadership identify the latest trends and opportunities in the market.
  • Expand market reach: With automated data analysis tools, sales data analysis can help get crucial details related to untapped market segments. These insights can help the salespersons optimize the sales pitch to target new customers better.

Steps to Perform Sales Data Analysis

To gain the maximum benefits from the sales data analysis process, sales leaders must follow the three crucial steps detailed here. These steps will help the decision-makers draw actionable insights for informed decision-making.

1. Identify KPIs to Track

First and foremost, sales leaders must ensure that the collected sales data is valuable and would help draw useful insights to drive business growth and positively impact the bottom line. The focus here must be on key objectives related to products or services whose performance must be tracked. Some common objectives usually perused are measuring the efficiency of sales training, identifying traits of repeat customers, and several others. 80% of high-performing sales teams rank their training processes as extremely important.

After identifying the crucial objectives, it is vital to break them down into specific KPIs and sales metrics to track, such as: -

  • Increasing close rates
  • Reducing customer turnover
  • Enhancing profit margins
  • Improve upsells

After defining the KPIs, sales leaders must determine the relevant variables, data sources, and essential sales performance metrics. While tracking sales data regularly is vital, it is ideal for in-depth analysis at frequent intervals.

2. Choose a Data Analysis Tool

After identifying KPIs and important sales metrics, the sales leaders must select a suitable option from various data analysis tools. Almost 55% of leading sales-driven companies offer their sales teams access to sales intelligence solutions.

Microsoft Excel is the most commonly used sales data analysis tool worldwide. To start using Excel, sales teams must have high quality data ready to draw inferences. High quality data takes time to develop, so it can take a while to identify useful behavioural patterns.

Companies can also use other tools to analyze important metrics to complete the analysis quickly and focus on core activities. At this stage, leadership can draw preliminary conclusions.

3. Share the Results with the Sales Team

After the analysis is done, it is time to share the results with the sales team. Data analysis tools can present the data in graphs and visuals. This would help everyone understand the crucial sales performance metrics in a better manner.

It is imperative that the data analysis offers actionable insights and is easy to understand. To further improve the efficiency of this step, sales leaders can also suggest some recommendations to help salespersons improve their performances. 81% of salespersons believe that having a connected view of the data along the complete customer journey is vital.

4. Sales Data Analysis Metrics & KPIs

There are several sales reporting metrics that sales leaders must track for an in-depth sales data analysis. With the help of relevant data analysis tools, these important sales metrics can offer actionable insights to the sales teams. 66% of sales teams track customer satisfaction as the top KPI, followed by 65% for team quotas met. Some of the most important sales data analysis metrics & KPIs that sales leaders must track are as follows: -

  • Conversion rate: It is an important metric that measures the sales team's effectiveness in converting leads into new customers. It is measured by dividing the number of new customers onboarded by the total number of leads generated.
  • Conversion Time: It is one of the sales performance metrics that offer insights into the time taken to convert a lead to a customer. Companies can measure the efficiency of their sales funnel and sales pipeline with this KPI.
  • Return on Investment (ROI): This sales metric helps the company leadership understand the returns (or income) generated by the sales teams against the total investment undertaken by the business for the sales process. It is measured in percentage terms.
  • Sales Closing Ratio: It is among the most popular sales reporting metrics. The sales closing ratio determines the total number of deals closed during a period against the total number of prospects in the pipeline to whom formal proposals have been sent.
  • Average Purchase Value: Company leaders often use this metric for sales forecasting. Average purchase value is determined after dividing the total revenue generated during a period by the total number of customers.
  • Cannibalization Rate: It is used by sales leaders to understand the performance of their product offerings. The cannibalization rate is the percentage of new product sales that have replaced the sales of present products.

Sales data analysis is an ongoing activity in most sales-driven organizations. With the help of specialized data analysis software, sales leaders can easily draw actionable insights to help salespersons deliver their best efforts in the market. Sales leaders must be well-versed in the process of drawing insights from the data analysis and then integrating those insights with the sales strategy.



Q1. What are sales metrics?

A1. Sales metrics are defined as data points used by sales teams to measure sales performance at the team level as well as the individual level.

Q2. What is data analytics in sales?

A2. Data analytics in sales is the process used to model, identify, and forecast the latest sales trends in the market and then use the results to find the points for improvement.

Q3. Why is sales data analysis important?

A3. Sales data analysis is an extremely important function that helps sales managers understand the products and customers to focus on. It further helps determine the best strategy to reach out to the customers.

Q4. How do you measure sales performance metrics?

A4. Different sales performance metrics are measured in different ways. It is essential to have updated data available to get accurate results from the analysis.

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