What Is Sales Analytics?
Does the idea of managing sales analytics data on your own sound scary? Don't worry, I'm here to help.
In this article, I’ll teach you what sales analytics is and how you can use it to harvest actionable data to improve any type of sales—both with and without software.
What Is Sales Analytics?
Sales analytics is when your company leverages predictive and prescriptive data to find patterns and surface insights from historical behavior and interactions.
You can use this information to predict the business potential of future prospects, forecast new market trends, and identify effective strategies to enhance your sales efforts.
Want to stay ahead in analytics? Check out the 12 best analytics conferences to sharpen your skills this year.
Considering Sales Analytics Software
Sales analytics software provides you with real-time CRM data management and the power of artificial intelligence to automate the processing of historical information on an ongoing basis.
This software will then assess the data using machine learning and AI to translate a massive amount of raw data into actionable predictions that are easy to understand.
When you have more immediate access to information about upcoming opportunities, risks, and breaks in your sales process, you can improve decision-making in your sales and marketing strategies, including identifying upselling opportunities, improving marketing messaging, and narrowing down your most popular products.
While software makes this process much faster and with fewer errors, you can run sales analytics manually. And it's always good to have an understanding of the process, even if you prefer software.
Sales Analytics Without The Software
While sales analytics software is practical and streamlined, it’s not the only way to run a sales data analysis. Check out this step-by-step guide to manually completing this process (hint: it's very similar to this CRM analytics process).
- Decide what or who you want to analyze
- Choose a specific and measurable objective
- Create a data analysis frequency cycle
- Compile this data manually or input it into your software
- Use data visualization tools to translate results
- Analyze the charts and graphs to look for trends
- Take action on your findings to improve sales performance
With this process in mind, let’s take a closer look at each step to better understand sales analysis.
1. Who Are You Analyzing?
While this is straightforward, it’s worth mentioning that you should get as specific as possible before running your sales data analysis. You won’t get very detailed results with general search metrics. Get a higher-level perspective to learn more about a department, team, campaign, product, or sales rep’s performance.
2. Identify A Measurable Objective
So you have your who, but now it’s time to narrow it down even further. For example, if your retail business ran a recent promotion where all your products were on sale. You might be interested in seeing which products sold best when compared to your regular prices over a similar time period.
Measure the sales by unit during the sales period, as well as a non-sales period, to get the sales trend data you want.
3. How Frequently Will You Analyze Your Data?
Depending on the objective and topic of your analysis, the frequency at which you need to review the data will change. You might need to check in on a daily, weekly, or monthly basis. Some sales strategy data analytics are longer term and require quarterly or annual reviews.
4. Compile Sales Activities Data For Predictive Analytics
You can manually compile sales data without software through spreadsheet configurations, like Microsoft Excel or Google Sheets. However, you might find that using a CRM or sales analytics software is easier and quicker.
With software, you can expedite the process of collecting the data and track individual information more quickly. You’ll also see automated resources for logging sales team performance. If this seems like the option for you, you’ll be able to explore my top recommendations for sales analytics tools further down in this blog.
5. Creating Charts with Data Visualization Tools
Just putting together the raw data isn’t usually enough for a sales report. You have to make it easier to read and understand. For this task, I would recommend checking out some data visualization tools to help you automatically create charts and graphs that explore various KPIs, like conversion rates, sales growth, sales revenue, win rates, and customer retention.
If you don’t have these tools, you can still rely on manual sales reports using your spreadsheets. I would recommend adding a bit of storytelling to help your viewers interpret those actionable insights without having to make sense of all the raw numbers.
6. Looking for the Trends in Your Data
So you have the key metrics you want available in a sales report, but what does it all mean? You need to analyze your findings, comparing them to your original measurable objectives, to see how they measure up. What conclusions can you draw from the historical data, and how can you make better data-driven decisions based on these answers?
7. Applying the Results to Workflows & Sales Pipelines
With the insights you’ve gained from sales forecasting and analytics, it’s time to shape your future efforts. Take what you’ve learned and apply it to improve marketing campaigns, sales funnels, sales productivity, or customer interactions. You might find that you’ll need to run multiple analyses to adjust every variable in your process to achieve all sales targets.
My Favorite Software Solutions
Now that you understand the process, I can show you some of the best tools for sales analytics. Take a look at the top five analytics solutions for more visibility into your sales cycle:
Your Next Steps
Whether you’re a sales manager trying to add automation to your workflow or a team leader working to improve lead conversion rates, sales analytics can help you achieve your sales goals. Add a sales analytics process to your daily operations or research a few software options to make any sales task a little bit easier.
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