What is a Sales Analysis?

A sales analysis is the process of analyzing the sales revenue generated from a business within a period of time. It looks at various factors such as consumer demographics, products sold, time of sale, region and many more.

Why is a Sales Analysis Important?

A sales data analysis provides key insights into improving a business model. Teams can utilize a monthly sales dashboard to help forecast sales, improve team performance and make further data-informed decisions.

A sales analysis is important because it:

• Optimizes team performance
• Forecasts sales to help resource planning
• Optimizes the sales funnel and sales process
• Helps to improves product decisions
• Helps discover market trends

10 Effective Ways to Conduct a Sales Analysis

This will be an interactive tutorial so you can follow along!

Open the Polymer App to get started.

Polymer Search is an interactive web application that allows you to have access to the same dataset I’m using, as well as Polymer's powerful AI-driven analysis tools.

1. Sales Revenue Analysis

The most important part of your sales data analysis should be the ‘sales revenue analysis.’

This analysis looks at overall revenue/profits and how they are influenced by other variables in the data.

For example, you want to figure out how targeting a different customer demographic affects sales. Or you want to figure out whether offering multiple payment methods improves sales.

Here's how to conduct a sales revenue analysis:

1. Look through the columns in your data and make a list of all factors that could impact revenue.
2. Open data analysis tool and create a bar chart for each one. Let's say we want to see the revenue generated from each product line:

3. Observe the trends and set this up as a dashboard for the team:

Bonus tip: Use a sliced bar chart to show sales from each gender:

A sliced bar chart can be useful for showing the product preferences for each gender.

Here we can see males spend more on "health and beauty" whilst females spend more on "fashion accessories," "home & lifestyle," and "food & beverages."

If you have no data background but want to easily analyze your revenue data, try out Polymer Search.

2. Product Sales Analysis

A product sales analysis breaks down the number of sales, revenue and profits generated from each product.

How to conduct a product sales analysis:

The first step is to set up a pivot table which breaks down sales by products:

Here I've set up a pivot table showing the quantity of sales and the revenue generated from it. You can add in other metrics you want to display such as total profits or marketing expenditure.

This pivot table breaks down the sales by product. It's also good to set this up in a dashboard for the team to see.

To set up a pivot table like this and share it as an interactive dashboard with no coding, try using Polymer Search.

3. Customer Sales Analysis

A customer sales analysis breaks down sales by various customer metrics such as:

• demographics (age, gender, location, annual income, education level etc.)
• interests
• platform
• funnel stage
• and many more

These metrics greatly vary for each dataset, but the method for conducting a customer sales analysis is similar.

How to conduct a customer sales analysis:

1. Figure out who your audience is composed of. Start with something simple like gender, then move down the rest of the demographics (age, location, household income etc).
2. Analyze customer preferences: use sliced bar charts (as shown above) to reveal whether certain customer demographics prefer certain products.
3. Analyze customer responses to marketing: Do certain demographics respond better to your marketing campaigns?

Let's look at an example:

Let's say we ran a Google Ads campaign to generate some sales for our travel agency.

A simple pivot table should show who our audience are comprised of:

The same can be done for other demographic data such as "country."

Once we know who our audience are, we can dive deeper into the data and look at customer preferences:

This bar chart reveals who is responding better to our Google Ad campaigns.

4. Sales Trend Analysis

A sales trend analysis looks at how sales change over time. Micro trends can last for a week whilst macro trends can last a quarter. 

The best method to finding trends in the data is by using a time series. In a time series, the x-axis is always time, and the y-axis is whatever variable you’re measuring (gross income, number of sales etc.).

Time series allows us to easily find patterns in the data: 

• Do sales peak or drop during weekends? 
• Are there unusual peaks in the data? 
• Are sales increasing or declining over time? 
• How is the market changing over time?

‘Trend factor analysis’ allows us to determine whether the graph is going up or down.

To create a time series in Polymer, head over to the visualizations tab -> choose time series -> insert ‘date’ into the x-axis and ‘gross income’ (or whatever variable you’re measuring) into the y-axis. 

You can choose to bucket the data daily, weekly, monthly, quarterly or yearly (in this example, we'll be using daily).

5. Sales Team Analysis

To conduct a sales team analysis, you'll first need to identify what are you performance metrics. This can be general employee performance metrics or sales specific performance metrics.

List of sales team metrics to track:

• Monthly sales revenue
• Win rates
• Average deal size
• Percentage of revenue from new vs. existing customers
• Number of calls/demos scheduled

Setting up a Sales Team Dashboard

A sales team dashboard allows you to monitor the effectiveness of each sales rep. It lists all the important performance metrics for each individual such as monthly revenue, win rates and number of demos scheduled.

There are many dashboarding tools out there and Polymer is one of the easiest to use as it requires no programming skills and takes as little as 5 minutes to set up a real-time dashboard.

Optimize Sales Team

Another way to conduct a sales team analysis is to take the performance metrics mentioned above and find patterns/correlations with other variables. For instance: Is employee satisfaction related to monthly revenue, win rates, number of calls scheduled etc.

Scatterplots are very helpful for finding correlations, meanwhile bar charts can be helpful for seeing the relationship of two variables.

6. Predictive Sales Analysis

Sales forecasting allows management to make better decisions when it comes to hiring, goal setting and budgeting. For instance, if forecasts are suggesting a 100% uptick in interest of the products you’re selling, then you might want to hire more people and increase the budget for marketing.

Predictive sales analysis often requires analyzing past sales data and building models in R or Python, but there are tools out there that allow you to do predictive analysis without coding.

7. Sales Pipeline Analysis

First of all, what is a sales pipeline? A sales pipeline is a visual representation of the buyer’s journey that shows all the stages they go through from lead generation, to scheduling a meeting to closing the deal.

A sales pipeline analysis allows your business to get more prospects into the pipeline, and find out areas that need improving.

Analyzing your sales pipeline comes down to three parts:

• Sales pipeline velocity: This provides a holistic view of how well your sales pipeline is performing. It tells you how fast your prospects are moving through the pipeline, the average value of these deals, and the % of deals that get closed.
• Conversion rates: Looking at the conversion rates in each pipeline stage can be an indicator of health for that current stage in the pipeline.
• Drop-offs: Analyzing drop-offs and asking “why” the failure happened can give insight onto what are the best ways to move the deal forward.

With Polymer, you can easily conduct a sales pipeline analysis, by filtering parts of the pipeline using interactive tags.

8. Sales Audit/Diagnostic Analysis

A sales audit, also known as a diagnostic analysis, asks "why did it happen?"

It is a step between the descriptive analysis "What happened" and predictive analytics "what will happen?"

For example: The descriptive analysis shows that your product sales were lower than what was forecasted. The diagnostic analysis might say "This was due to the product's pricing which doesn't stand well against the competition."

There are 6 key areas to analyze when performing a sales audit:

1. Revenue metrics: Everything revenue related from average order size, quantity, cost and product popularity.
2. Performance metrics: What are the top indicators for measuring performance?
3. Competitive position: Where does your business stand compared to other competitors? What are the pros & cons of your product/service? How well does your sales strategy resonate with your audience?
4. Pricing: Cost of the product/service, knowing which ones are negotiable and looking at discount opportunities.
5. Team structure: Reward structures, sales processes, team culture and division of roles/responsibilities.
6. Customer service: A good sales audit requires looking beyond the number of deals closed and analyzing the post-sales process. 

9. Sales Gap Analysis

A sales gap analysis compares the “gap” between where your company wants to be and where it currently is. It involves 3 steps:

1. Identifying where the business is at currently. For instance, if growth is at +10% per annum, then that’s the current state.
2. Identifying where the business wants to be in a certain timeframe. E.g. Growth at +30% in 2 years. This is the target state.
3. Figuring out how to close that gap from current to target state, based on the company’s current resources.

A gap analysis should be a constant, reporting procedure to help move your business in the right direction.

Examples of when to use a gap analysis:

• Failed product launch: Your business launched a new product, but the sales number didn’t meet expectations. You perform a gap analysis to find out why and figure out ways to fix it.
• Low team productivity: A gap analysis needs to be carried out to find what processes need fixing.
• KPI metrics: A gap analysis can also be used on individual KPI metrics like “customer lifetime value.”

10. Market Research

Surveys are the bread and butter of market research. It requires it's own topic, but I've outlined a guide on how to easily analyze survey data here.

Survey data can be collected via phone, email or in-person. The great thing about surveys is that it's easy to perform at scale, which allows you to quickly understand the market conditions and how it changes over time. The worst thing a business can do is fail to adapt to changing market conditions.

How to Perform a Sales Analysis (3 Step Process)

Once you've familiarized yourself with these sales analysis techniques, you're ready to start the data analysis procedures.

Step 1: Identify KPIs and Sales Goals

Ensure you're collecting the right type of sales data that'll help move your bussines towards its sales goals.

Your overarching business goal might be "to increase revenue by 25% year over year" but that goal needs to be broken down into smaller steps. Examples include:

• Reduce customer churn
• Boost profit margins
• Improve upsells
• Increase close rates

These are all important KPIs to track.

Towards the end of this article, we've included a list of sales metrics you should be tracking.

Step 2: Choose a Sales Analysis Tool

My recommended stack for sales analytics is:

1. Microsoft Excel or Google Sheets: 
2. Polymer Search

Microsoft Excel prices start at $159.99 per user, but Google Sheets is a free alternative that does exactly the same thing. To start, I highly recommend picking one of these up. They are great for manipulating data.

Polymer Search is a layer you can add on top of Excel that provides more powerful, AI-driven features for sales analytics. Whilst Excel is great for storing data, manipulating and cleaning your data, Polymer Search is more for analyzing your data.

You simply upload your spreadsheet or connect a Google Sheets document, and Polymer will automatically analyze the data for you and provide you with powerful tools for exploring the data.

Step 3: Sales Intelligence

After you've analyzed your data, it's time to present your findings to your team and stakeholders. There are several tools which you can use, the popular ones being:

1. Polymer Search
2. Power BI
3. Tableau

These tools allow you to quickly create graphs and interactive dashboards and share them with clients through a web interface. With Polymer Search, you can create a shareable URL that other people can have access to (you can also password protect it).

So let's say you want to present your findings to the CEO. You connect your Google Sheets file to Polymer -> create an interactive dashboard (takes a few minutes) -> generate shareable URL -> Send to CEO via email with the password and they'll be able to access it.

How do you choose which graphs to use? Make sure to read my guide on data visualization.

Important Sales Metrics to Track

Every for-profit business deals with some kind of sales data whether it be restaurants, web design agencies or in-app purchases. What’s important is the type of metrics you’re measuring.

Here are some metrics you should be tracking:

Net Promoter Score (NPS)

Net promoter score (NPS) is an overall measure of your customer's perception of your brand.

It's based on the simple question: How likely are you to recommend this product/brand to a friend or colleague?

Only people who rate 9 or 10 are considered "promoters."

Note: a good net promoter score varies based on industry and country of the raters.

Close Rate

Close Rate = (the number of new customers / the number of qualified leads) x 100

The close rate is an indicator of sales rep/team performance and also the quality of leads you're getting.

Sales Cycle Length

The sales cycle length measures the time it takes for a sales rep to close a deal, starting from their first initial interaction with the customer.

The formula for calculating sales cycle length is: 

Sales Cycle Length = Total number of days to close all deals / number of deals closed

This metric is important for analyzing the sales process and finding ways to improve it's efficiency or address any delays. It can also be used for sales forecasting.

Average Deal Size

Average deal size = Total $ generated from sales / number of deals

This metric can be useful for monitoring upsell performance and providing training to sales reps about upsells.

Annual Recurring Revenue (ARR)

This is for subscription based models and it tells you the revenue per customer, per year of a multi-year contract. To calculate ARR:

ARR = Total cost of product / Number of contract years

Churn Rate

The percentage of customers who cancel or don't renew their subscription to your service. This is a critical retention metric you must track.

To calculate churn rate:

Churn Rate = (Number of customers lost / Starting number of customers) x 100

Average Profit Margins

Average profit margin = (Total $ Sales / Number of Sales) x 100

How do you know if your business has a good profit margin? And what's a good profit margin to aim for? Take a look at our analysis of average profit margins by industry.

Other Metrics:

• % of time spent on sales and non-sales activities
• Customer acquisition costs
• Customer lifetime value (LTV)
• Activity numbers e.g. number of cold calls per week, number of scheduled demos.
• Monthly sales growth
• Sales funnel metrics
• Pipeline Velocity
• Revenue per salesperson

Overall

Sales analysis shouldn't be a one time thing. It should be an on-going process that helps you refine your business model and adapt to market changes over time. Being able to extract insights from your data will put your business ahead of your competitors.