10 minutes

How a Revenue Analysis can Profit Your Business

A wise man once said: "not conducting a revenue analysis is like trying to lose weight without keeping track of the scales. You won’t know what works, what doesn’t, and whether any progress is being made." A sales analysis is crucial to every business.

But what exactly is a sales revenue analysis and how do you get started on it? Here’s what you should know:

What is a Sales Revenue Analysis?

A sales revenue analysis is an analysis of the history of sales year-by-year, or quarter-by-quarter, to see how a business is performing and make predictions on how it will perform in the future (sales forecast). 

A sales revenue analysis not only looks at the revenue generated but also other factors that might influence the revenue. This includes looking at product launch dates, popular products, customer demographics, ad spend, and more.

In conjunction to a sales revenue analysis is a sales trend analysis which looks at the fluctuation of sales over time: 

Do sales dip or peak during weekends? Do certain products sell better at different times (e.g. Christmas)?

Why is a Sales Revenue Analysis Important?

A revenue analysis allows businesses to keep track of where they're at, and make accurate assessments on where to spend their resources in order to maximize profits.

A revenue analysis will guide your business on how to manage its inventory: when to increase or decrease the stock of a product based on supply/demand.

4 Types of Revenue Analysis

The 4 types of revenue analysis are:

  1. Sales revenue analysis
  2. Customer revenue analysis
  3. Product revenue analysis
  4. Revenue trend analysis

1. Sales Revenue Analysis

A sales revenue analysis looks at the total revenue generated over different time periods. It also looks at the sales across different products and customers.

Revenue analysis formula: Revenue = Number of units sold x Average price of each item

Revenue analysis example:

Last year, Bob sold 600 iPhones retailed at $900 each. The total revenue = 600 x $900 = $540,000

However, 100 of those iPhones were discounted 10% to $810. The new average price for each iPhone will be $885, so the total revenue generated is 600 x $885 = $531,000.

Creating a sales revenue report:

A sales revenue report is a document outlining all sales a company has made and the total revenue generated over a certain time period. This data is plotted onto a time-series graph.

If you’re doing it in Excel, it can be time-consuming, but with a sales report tool like Polymer Search, you can instantly generate a sales revenue report in seconds. 

Example sales revenue report template: 

  1. Example revenue analysis table outlining all sales
  2. Time series graph

Note: Cogs = cost of goods.

example revenue analysis report
example revenue report (time series)

2. Customer Revenue Analysis

A customer revenue analysis looks at the revenue generated from your customers and breaks them down into different categories around demographics, products purchased, and purchase dates.

  • Who are your customers?
  • What products do they purchase? What is their lifetime value?
  • When do they purchase?
  • Where do they purchase from?
  • Why do they purchase?

Who are your customers?

customer revenue analysis

What are the demographics of your customers? Important demographic data to collect are:

  1. Age
  2. Gender
  3. Location and country
  4. Ethnicity
  5. Education level
  6. Household income

Other important information include: marital status, children (if any), occupation, political views, and religious views (if any).

When and Why do they purchase?

Some customers may only purchase an item only when it’s on sale, or during special seasons like Christmas. It’s important to identify these reasons and key sales dates.

3. Product Revenue Analysis

product revenue analysis

A product revenue analysis looks at how each product/service is performing and aims at finding the top performers.

What exactly defines a “top performer?”

In order to find the top performer, we need to follow the Pareto principle, which is a well-established phenomenon in economics and often applied to business.

The Pareto principle states that 80% of the results come from the top 20% of performers. And that’s how we’ll define “top performer.”

The top performers are often the top 20% of products.

4. Revenue Trend Analysis

example revenue trend report (time series)

A revenue trend analysis looks at a business’s revenue over a certain time period: usually quarterly or yearly. 

Revenue trend analysis allows us to see fluctuations in sales over time. Come fluctuations are:

  • Traffic/sales dipping or peaking during weekends. For instance, it’s common for entertainment services to peak during weekends whilst work-related services dip during weekends.
  • Seasonality: Certain items become popular on special days like Christmas. This is why there is so much fluctuation in prices during Christmas. Many places offer several discounts, whilst other items rise in prices.
  • Sales Periods: The number of sales might fly through the roof during special sales. Some customers may also only purchase items that are on sale.

Why is a revenue trend analysis important?

Picture this:

You run a cake store. In order to run this store, you need to buy the ingredients + bake the cake. This takes time, effort, and money. 

Now your store offers two types of cakes: sponge cakes and chocolate cakes.

You realize that 80% of the sales come from chocolate cakes. And sales double during weekends and holiday seasons.

If you didn’t take into account the data, you would’ve wasted a lot of money and time making sponge cakes nobody will buy. On top of that, you’ll be losing profits for not having enough supply during weekends and holiday seasons.

A revenue trend analysis allows you to discover these trends, make sales forecasts, and plan resources accordingly to maximize profit and revenue.

How to Perform a Revenue Analysis

Step One: Data Collection

Before any analysis can be performed, you need to be collecting the right data. What data should you be collecting? Here’s a shortlist:

  • Customer demographics
  • Product sales 
  • Unit price
  • Product line
  • Discounted item sales
  • Sales velocity
  • Time of sale
  • Person/branch responsible for the sale
  • Expenses
  • Reasons for purchase (collected through surveys)
  • Revenue and profit generated

This will be a good starting point.

Step Two: Choose a Revenue Analysis Tool

Having a tool that makes data analysis quick and easy is ideal for your business as it won’t require much training.

Tools often have data connectors that allow you to directly import from the original source and update the data in real-time. This can save a lot of time and energy from manual data entry work.

Polymer Search is a sales analysis tool that’ll allow you to quickly draw insights from your data without any technical knowledge required. It’s faster, simpler and more powerful than Google Sheets or Excel.

Step Three: Connect Your Data and Start Analyzing

If using Polymer, upload your data to the Polymer web tool and choose “launch app.” This will create an interactive application where you can use AI to help you analyze your data.

Common techniques for analysis are: interactive tables, pivot tables, time series, bar charts and the auto-insights tool.

Start your analysis by looking at: 

  1. Who your customers are. Which ones are bringing in the most sales?
  2. Top performing products.
  3. Time series analysis for discovering trends over time.

Once that is done, perform some more in-depth analysis by looking at: 

  • Do certain customer demographics prefer certain products or product lines?
  • Make a sales forecast based on past sales history
  • Analyze your sales team

Practice, Practice, Practice!

Finally, to kickstart your data analysis, you can practice using a sample dataset from Polymer Search.

Sales data analysis isn't difficult, especially with all the modern tools at your disposal. No more headaches with Excel or Google Sheets.

Get started using Polymer Search and find meaningful conclusions from your data.

Posted on
May 24, 2022
under Blog
May 24, 2022
Written by
Ash Gupta
Former Tech Lead for Machine Learning at Google AdWords (6 years) and a quant developer on Wall Street. Co-Founder & CEO of Polymer Search.

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