Sales Data Analysis
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Sales Data Analysis
Sales data analysis is a complicated and multifaceted report giving you a deep dive into your sales team’s successes and shortfalls alongside other important information, like customer data and incoming revenue. It’s critically important to compile statistical analysis of sales data so you can break down why your performance is so high or low, and any other important statistics about your sales team.
Some important KPIs for your team include revenue and conversion rate, but quota-hitting percentages and average deal size are also among the illuminating data points that will help you determine areas where your sales team is excelling and areas where they could use some improvement. Once you’ve gathered this information and other data—orders/closed sales, number of contracts, and et cetera—you can compile that information into several different forms of sales analysis.
You may use sales trend analysis to see your conversions going up during the third quarter, or you might prefer to use a sales pipeline analysis to take a look at which point during your sales cycle you tend to lose leads. There are numerous forms of sales analysis to choose from, but whichever forms of analysis you decide to use, you’ll eventually compile that into a report.
That report will help you determine your team’s overall performance, including areas in which they’re excelling or falling behind. It can also reveal which products are selling the best and worst, and any possible faults in your sales pipeline. Therefore, the importance of sales analysis means you need to measure the correct data points and use the best analysis tools at your disposal.
This article will cover all this information in deeper detail, showing you everything you need to know about sales data analysis in a brief article.
Sales Performance Metrics
There are numerous important metrics you can use to track your team’s performance. Here are some common examples and what they mean for your team:
- Percentage of Sales Team Members Hitting Their Quota. If 60% of your team members or fewer make their quota, then either your team’s underperforming or the quota is unrealistic.
- Conversion Rate. You want your conversion rate to climb because that means overall sales performance is improving. If it’s dropping, then it’s likely an indicator something’s gone wrong.
- Revenue. Though there’s many different forms revenue analysis can take, revenue is possibly the most important KPI.
- Average Deal Size. This will help you determine which salespeople are playing it too safe and which are being too risky.
Sales Data Analysis Case Study
To examine how sales data analysis benefits companies, let’s take a look at a short case study. Consumer Company X had a program for training people who signed up, paid a membership fee, and got certified. They needed to grow sales and deal with a high number of members canceling their memberships. Company X concluded they needed better segmentation with their ideal customers and to develop a deeper understanding of each ideal customer.
They decided to research each touchpoint and discover which were having the biggest effect on customer satisfaction, including risk factors. Using data modeling, the company used information from customer profiles alongside transactional data and lifestyle data from a third party in order to build predictive models—including an algorithm for guiding new prospects to activities for their segment.
Once they accomplished these feats, they had more successful sales overall, alongside better opportunities for clients. Their sales data analysis allowed for a greater understanding of their customers and their needs and allowed them to fill those niches.
Similarly, anyone who utilizes effective sales data analysis can greatly improve their sales and increase their revenue. Once you discover which type of sales analysis you need to perform, you’re one step closer to achieving greater success.
Types of Sales Analysis
You can use various methods and techniques to analyze your sales so you can make informed decisions about your company. Depending on your goals, you’ll want to perform different kinds of analysis.
General Sales Analysis Methods
These methods apply to almost every form of sales out there, and are useful for a variety of reasons.
- Sales Trend Analysis is used to find upwards or downwards trends within a specific timeframe.
- Sales Performance Analysis is meant to evaluate certain parameters compared to specific goals.
- Sales Pipeline Analysis looks at the activities prospects go through before they convert or fall off.
- Diagnostic Analysis is about determining why certain points of sales data are the way they are, due to internal or external sources.
Inside Sales Analysis Methods
For metrics pertaining specifically to selling from an office—without meeting clients in person—these metrics might be helpful:
- Call to Connect Ratio refers to the number of calls made versus the number of times a prospect answered the phone.
- Connect to Opportunity Ratio is how many of your connections became new leads versus how many were a “hard no.”
- Call to Deal Ratio is the number of deals closed versus the number of calls made.
Sales Data Examples
To analyze data, however, you need to know what data to examine. With countless B2B and CRM Sales Metrics, it’s important to note which are the most helpful to analyze your sales team’s performance. Anything from your number of contacts to your customer service interactions can count as data. Other common examples include customer contact info, orders/closed sales, number of contracts, invoices, and payments. Though it might not be clear how all these data points may indicate your team’s performance, they may come in handy in surprising ways.
Here’s one sales data analysis example: you’re checking your CRM and notice that a lot of your customers not only work in the restaurant industry; you also have an overwhelming number of pizzeria owners as clients. When you pull up their invoices and payments, you realize they all purchased the same software. Now you know that there’s something about this software that fills a niche for pizzerias. Maybe that software makes processing payments more effortless for that specific type of restaurant. Once you figure out why this correlation exists, you can use that information to market to other cities with similar qualities.
How to Analyze Sales Performance
Once you know which trends to track, you’ve completed the first step of the sales analysis process. Naturally, you want to focus on points of data that affect your bottom line and provide the most meaningful information about your sales performance. For example, if you want to determine the characteristics of your repeat customers, you might want to determine where these customers are from, what they are buying, which sales representatives are bringing them in, and how long they wait between purchases. These data points will go a long way to determining the results of your analysis, so you should carefully consider each one’s relevance.
From there, you can determine where you’ll get the data from, any variables relevant to what you want to examine, and any sales metrics you’ll need. For the first point, you can likely pull most data from your CRM. For the second, it’ll depend on which metrics you’re analyzing and what you’re looking for. These variables can be anything from customer analysis variables to price/sales ratios.
Once you’ve settled on those important factors, you can choose a timeframe during which to collect your data, and which tools and sales metrics calculators you want to use.
Sales Analytics Tools
Just like you need the proper tools to accomplish any job, you need the proper sales analysis tools to measure data and formulate the best possible report. Sales analytics tools are necessary to make data clear and provide insight into various aspects of sales. While Microsoft Excel is one powerful tool for sales data analysis and interpretation, you may require more robust tools to help you get the most effective report possible.
There’s a lot of tools to choose from for various tasks, but you’ll want to compare tools from different providers in order to find the best fit for your needs. Some features may be identical or similar across all tools, but many will be unique to one or two. Canopy—a robust web-based tool offering all sorts of data and functions surrounding sales—is one example of such a provider.
When you choose Canopy, you choose to:
- Stay ahead of potential risks to your teams and pipeline
- Predict future outcomes based on available data
- Use data to drive improvement and increased revenue
- Uncover and analyze key insights
You can find simple data visualization tools, request a demo, or learn more on canopy.io. Your success is our only goal.
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