1. What is a Sales Analysis?

Sales analysis is the process of integrating, analyzing, and understanding various data related to sales activities such as sales, customers, and transaction data.

It allows managers to look at sales from many aspects and decide what is working and what is not working.

It also provides information and reporting so sales managers and executives can ascertain what region is achieving its goals and which areas are not achieving goals, which salespeople are doing well and which are not, etc. 

2.Benefits of Sales Analysis

A useful sales analysis has the following three benefits：

Understanding customer needs

One of the critical reasons for poor product sales is that we don’t accurately capture customer needs.

Through sales analysis, you can find the needs of your most profitable customers and potential customers, then formulate sales measures to improve business performance.

Sharing sales skills and know-how 

Traditional sales activities rely on the intuition and experience of the person in charge, so the useful skills and knowledge to sell products have not been systematized.

Through objective sales analysis, it is intuitive to discover the causes of failure and success, and share sophisticated sales know-how with your team.

Being aware of the market trend

Based on past data, sales analysis allows you to identify market opportunities, grasp trends in sales performance, and predict “how much your company’s products will sell in a certain period in the future.” 

3. 5 Types of Sales Analysis Methods and Techniques

There are various data analysis methods, such as cross-tabulation, association, and decision tree. But for beginners, none of them are applied to sales analysis immediately. Here are five easy and practical ways to analyze sales.

3.1 Factorization

First of all, the method of sales analysis that beginners need to manipulate is factorization.

By factoring sales into various aspects, you will understand the factors behind the decrease and increase in sales.

Let’s analyze sales on an EC site as an example.

(1) Product sales = sales volume x unit price. If sales decline, is it due to low sales volume or low unit price?

(2) Sales volume = Sales volume of sales channel A + Sales volume of sales channel B + Sales volume of sales channel C. Analyze the sales volume for each sales channel to see which one is lower.

(3) Sales volume of sales channel = number of clicks x turnover ratio. If the sales volume of sales channel A is low, is it due to the low number of clicks or low turnover ratio? If the turnover ratio is low, you have to double-check whether the target customer of the channel matches the target customer of the product.

(4) The number of clicks = number of times displayed × click rate. Is an insufficient number of impressions or a low number of clicks cause a low number of clicks? If your clicks are low, why not improve your ad content?

In this way, sales can be factored, and through an in-depth analysis of data, you can find the flow from the process to the results and the essential factors that cause the decrease.

3.2 Association analysis

If you know data analysis, you may know that association analysis is often used.

Association analysis is an analysis method that analyzes the accumulated transaction data for each customer and finds the law that “X% of people who buy product A also buy product B.”

The most famous example of association analysis is “diapers and beer”. It has shown that many men who come to their supermarket to buy diapers also buy beer.

The results of association analysis are useful for understanding which products sell and which do not, and for conducting effective sales promotion activities to increase sales.

3.3 Regression analysis

The multiple regression analysis is to analyze the factors (explaining variables) related to the results (objective variables), which factors affect the results and the extent to which the future is a statistical method for predicting.

When used for sales analysis, it predicts future sales by regression analysis of what influences sales among multiple factors. These include the number of employees, number of products sold, product price, and so on.

3.4 RFM analysis

RFM analysis is an effective method for finding profitable customers in sales analysis.

Customers are ranked by three indicators: Recency (last purchase date), Frequency (cumulative number of purchases), and Monetary (cumulative purchase price). 

RFM score=RS*100+FS*10+MS*1. In this way, you can identify profitable customers and customized marketing services to provide strong support for more marketing decisions.

3.5 ABC analysis

ABC analysis is an analysis of a range of items that have different levels of significance and should be handled or controlled differently.

It is an application of Pareto analysis (80:20’s law). In other words, 80% of sales volume is generated by 20% of all products.

The products are grouped into three categories (A, B, and C) in order of their estimated importance. ‘A’ items are essential, ‘B’ things are important, ‘C’ items are marginally valuable.

You can use ABC analysis to find out “selling products” and “dead products” and use them for product ordering, inventory management, sales management, etc.

4. How to Perform Sales Analysis ？

The above sales analysis methods can give you ideas for sales analysis. Practically, you can perform sales analysis from the following three levels.

• Real-time & cumulative indicator monitoring
• Regularity analysis of indicators
• Comparative analysis of indicators

4..1 Indicators Monitoring 

It is common to monitor the indicators. The traditional way is mail reporting. The modern way is real-time monitoring from the dashboard on the large screen.

Now, many companies have realized the automation of indicator monitoring, multi-platform integration and tracking on the mobile apps

Here is an example of a sales report built with FineReport:

As a sales manager, you never want to miss any progress and activity of your sales team.

Now you can easily monitor the real-time sales information by product, region, customer, and more with this dashboard.

You will have at-a-glance details that show the progress of the sales, and gain an insight into how this sort of real-time sales monitoring can augment your operational management.

4.2 Regularity analysis of indicators

It is difficult to find any abnormalities when looking at things independently. But when you expand the time dimension, there will be many discoveries.

For example, you can expand the time dimension by year

Or by month. In this way, you can know which periods of time your sales are doing well and which periods of time your marketing strategy are useful.

Or by order data. Maybe the distinct sales performance is due to holidays such as Christmas.

4.3 Comparative analysis of indicators

For example, from the regional dimension, comparing the differences between regions from multiple angles, the data is used to give invisible pressure to the relevant teams, remind each team of abnormal situations and help them promptly deal with the issues.

In the above figure, the maps are used to display the sales situation in various regions visually, and you can choose different comparison standards to display.

The two charts on the right form a linkage with the map, show the product and customer information related to each region. For more detailed sales information, you can drill through the chart to get it. 

It is also valuable to compare the value contribution degree of different products from the commodity dimension, which will provide a reference for brand managers reference to adjust the commodity strategy.

For example, in the pie chart above, it is clear to see the profit contribution of each product category. The following detailed list provides you with more detailed sub-products, so you can find which product affects the sales performance of categories.

5. Recommended Sales Analysis Tools

Current sales data and information are scattered across various in-house systems such as Point-of-Sale, CRM and inventory management. Outputting data from each system and processing it in Excel is the traditional way of sales analysis. In addition to the labor and time required for manual work, we cannot guarantee the accuracy of the analysis results. 

Therefore, I would like to recommend to use BI tools for sales analysis.

BI tools are tools for gathering and analyzing vast amounts of data to help make quick decisions. The ” FineReport ” has a full range of functions for sales analysis.

• Extract, aggregate and analyze sales data from different data sources
• Can create schedules and sales analysis reports by dragging and dropping
• Real-time data update

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