12 minutes

How to Effectively Analyze Sales Data

Businesses tend to get overwhelmed by data that gets ignored because no-one likes dealing with data or it's too expensive to hire a data analyst. Luckily, sales analysis doesn't have to be so daunting. Here's how to easily extract valuable insights from your data.

What is Sales Data?

In essence, sales data is a broad term used to describe any kind of data that relates to the sales process.

Any business that sells a product or offers a service has some kind of sales data, whether it be restaurant food sales, Facebook ad lead generation, software sales, cold calling data to car sales and real estate. These are just a few types of sales data.

Why is Sales Analysis Important?

The main reason why collecting & analyzing sales data is important is because it provides key insights into improving your business model. Any team or company that is simply relying on monthly dashboard reports without intepreting what the data means are being lazy and aren't setting themselves up for success. Here are 3 ways a sale analysis can benefit your business:

1. Improve Sales Team Performance

A sales analysis can supercharge your team performance by identifying weak areas in the sales process and improving upon them. Performing a sales analysis over time will allow you to really finetune the sales process to improve team efficiency.

Learning how to collect and analyze this data can also lead to better recruiting and training programs.

Team KPIs that every business must track are: % of deals closed, average deal size, revenue per salesperson and average sales cycle length (how long it takes to close a deal on average). More on this later.

2. Forecast Sales

A sales forecast predicts how many sales your business will make within a certain time period (usually quarterly or yearly).

Sales forecasts are highly important as it allows businesses to plan their resources accordingly, for instance, a computer hardware manufacturer has to plan how many graphics cards they want to produce in order to meet the market’s demand. 

Too little supply = lost profits, delayed shipping times and unhappy customers.

Too much supply = waste of resources which could’ve been used elsewhere (e.g. marketing).  

3. Make Data-Informed Decisions & Discover Market Trends

What exactly does your customer want? How do you find the most valuable leads that sales reps should be spending their time on?

Using your 'gut feeling' for these decisions is a hit or miss strategy. Instead, it's better to allow data to tell the story. Data allows you to find the traits of your top customers and show you how to serve them better.

Sales data can also reveal patterns, correlations and trends that are crucial for businesses to make smarter decisions, for example, choosing the right launch date can significantly boost your chances of a successful product launch. A market trends report can also help identify gaps in the market.

9 Ways to Analyze Sales Data

Polymer App

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 how your revenue/profits are influenced by other variables in your data.

For example, you want to figure out how different customer demographics affect sales. Or you want to figure out whether offering unique payment methods is a good idea.

In a sales revenue analysis, the key question to ask is: What factors are has the biggest influence to my business’s revenue? 

To find this out, you'll need a data analysis tool. The best tool for this is the 'auto-insights' feature of Polymer.

To get started, head over to the ‘auto-insights’ tool from that link I sent earlier:

Auto Insights

Put 'gross income' into the 'metric to maximize field.' This is because 'gross income' is the variable we're trying to maximize, so it should go here. The resulting output should look like this:

sales revenue analysis

As you can see, the tool automatically ranks each variable from rank #1 to last rank. What this means is the variables at the top have the biggest influence on 'gross income.' The variables at the bottom have the least impact (although there may still be some impact).

Since 'date' was at the top of the list, it means 'gross income' varied a lot over different dates. We should take a closer look at it. To do that, put that variable into “breakdown by segments.”

breakdown by segments

This is really useful, because it will show you all the dates and how well your business performed on those dates.

  • Noticed an outlier where sales peaked? Try to figure out what exactly happened on those days.
  • Seeing trends? Perhaps it's normal for sales to dip (or peak) during weekends.
  • Want a broader picture of how your business is performing? You can also change the grouping of dates into 'quarterly' or 'monthy' or 'yearly' instead of 'daily.' Note: This dataset only contains the sales for Jan to March (a 2 month period), so bucketing dates into 'quarterly' or 'yearly' isn't possible.

Want to compare multiple factors at the same time? You can put in multiple variables into the 'breakdown by segments' field. This is useful for finding top performing combinations. Feel free to experiment with the data.

2. Product Sales Analysis

If your business offers multiple products or services, you should perform a product sales analysis of each product that you offer to find out:

  1. How well each product is selling (popularity).
  2. How much profits that product is generating.
  3. Which demographics use the product most.

Pivot tables are one of the most important tools for product sales analysis. What they allow you to do is answer any questions you have about the data within a few clicks.

So I have this bloated dataset that I want to figure out: Which product line is generating the most income?

I simply input “product line” and “gross income” into the “Smart Pivot” field. This will create a pivot table that tells me everything I want to know:

pivot table for product sales

The #COUNT# column tells us the sample size which is useful for spotting outliers. There’s no outliers to be worried about here, so we just focus on the other 2 columns: Product Line and Gross Income (SUM).

The black number shows the true value, whilst the green/red numbers tells us how far above/below average that value is. It’s a great way to get contextual information about the value. 

  • ‘Food and beverages’ generated the most income which is 4.3% above average. 
  • Meanwhile health and beauty products generated the least income at $2,343 profit which is 8.61% less than average.

Now, taking this even further, we can add another variable to the equation: gender. 

This allows us to see whether a certain gender prefers certain product lines.

  • Interestingly, females prefer buying food and beverages much more than males (+23% vs. -15%). 
  • Males prefer health and beauty products much more than females (+14% vs -31%).

Another way to conduct a product sales analysis is by using bar charts. Head over to the ‘visualize’ tab at the top and input these variables:

  • Y-axis: Product Line
  • X-axis: Gross Income
  • (optional): Slice by gender
Product sales analysis

Overall: Use pivot tables to get answers to any questions you may have. It only takes a matter of seconds to set up. Alternatively, bar charts are better for presentation purposes.

3. 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).

sales trend analysis

4. Sales Team Performance Analysis

For this example, we'll be using a different example dataset: the employee details dataset.

This dataset looks at a large list of employees at a company and shows their 'performance score' along with various factors such as gender, education level, employee satisfaction, salary, and much more.

It might be a big scary dataset with many rows and columns, but I'll show you how to easily extract the insights you want from this data.

Sales Team Dashboard

First of all - it’s important to have a team performance dashboard like this so you can monitor the effectiveness of each sales rep and team. If you've been using spreadsheets, it may be time to switch to Polymer.

Second of all - you might already have questions about the dataset such as "does gender influence performance rating"? 

Or "Is age correlated to salary?"

The best way to get the answer to these questions are through pivot tables and visualizations. Start with a pivot table to answer the first question:

performance rating vs. gender

The data shows there's very little difference between performance rating and genders. So we got our first answer.

Sales Correlation Analysis

Now for the second question: since we're comparing two measurements aka numeric variables, we'll need to use a scatterplot to do a correlational analysis.

Making one is very easy: just input your 2 variables into the x and y axis (it doesn't matter which order). So putting in "age" and "monthly income" gets us this plot which indeed shows a correlation between age and salary:

correlational sales analysis scatterplot

5. 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.

predictive sales analysis

6. 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.

sales pipeline

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.

7. 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. 

8. 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.”

9. 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

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.

Posted on
April 5, 2022
under Blog
April 5, 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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