What Sales Data Visualization can do for Your Sales Team and How to Execute it Like a Pro

As profoundly significant as our raw data is, it’s not very good at telling us a story. Spreadsheets often leave us confused and unenlightened, overwhelming us with static data that blinds us to critical insights.

Sales data visualization brings numerical and textual data to life. Visual representation communicates raw data clearly and concisely, enabling sales teams to extract meaning from large volumes of data.

What is data visualization? 

Data visualization is the graphical representation of data using visual elements such as graphs, charts, maps, etc. It transforms your complex raw data into an easily digestible visualization designed to identify trends, patterns, relationships, outliers and more. 

Data visualizations tap into the fact that we respond to visual information quicker than textual information. Presenting data in a visual format can empower sales teams to make faster interpretations and deliver timely results. 

It also facilitates effective communication with stakeholders and customers, who will likely engage better with your charts than your spreadsheets. 

What type of data visualization should I use? 

From bar charts to bubble graphs, there are many techniques you can use to visualize your data. The specific visualization you use for any given dataset will depend on the story you’re trying to tell.  

Here are some of the most popular sales data visualization techniques. 

Bar charts 

Bar charts are the most popular type of data visualization tool because they’re simple and easy to interpret. They’re used to compare quantities within related categories, such as the number of sales across years or customers by age. The x-axis highlights the categories, and the y-axis represents the value.

If you want to compare two or more values or identify your highest/lowest-performing activities, bar charts are an effective solution. Using stacked bar charts or grouped bar charts give deeper exploration, like the example below.

While using the right colors can enhance your visualization, using the wrong colors can ruin it completely. Beware of using too many different colors to encode data and too many shades of one color, contrasting colors, or arbitrary colors. Remember, the colors you use should aid the telling of your data’s story; they’re more than mere aesthetic tools!

Keep it clear and simple 

The whole point of data visualization is to take raw data–which is usually presented in a complex, unengaging way–and transform it into an easy-to-digest visual aid. If you stuff too much data into one graph or chart, you risk defeating the purpose of your visualization goals.

Less is more. Limit the number of categories you use in charts (between four and seven is a good rule of thumb). Omit information that isn’t essential to the immediate story you’re trying to tell. Don’t be afraid to use single-value charts to communicate straightforward data or to split your data into two separate graphs to improve clarity.

Strive to communicate your data’s story as succinctly as possible. 

The takeaway

Sales data visualization empowers sales teams to easily decipher raw data and identify the trends, patterns, opportunities and threats that govern sales processes and goals. With the ability to quickly and intuitively understand complex data, sales teams can execute time-sensitive business initiatives and take advantage of sales-boosting opportunities. 

Of course, for your graphs and charts to be impactful within your sales reports, they still need to be supported by text. However, for the swift delivery of actionable insights, visualizations are powerful solutions. 

This article is part of the Crunchbase Community Contributor Series. The author is an expert in their field and we are honored to feature and promote their contribution on the Crunchbase blog.

Please note that the author is not employed by Crunchbase and the opinions expressed in this article do not necessarily reflect official views or opinions of Crunchbase, Inc.

Pohan Lin is the senior web marketing and localizations manager at Databricks, a global data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture and azure data lake tutorial. With over 18 years of experience in web marketing, online SaaS business, and e-commerce growth, Lin is passionate about innovation and dedicated to communicating the significant impact data has in marketing.