Without the proper approach to sales data analysis, all those metrics and reporting tools are useless. You likely have access to all kinds of analytics—probably too much information if we’re being honest—but the question is: how do you make sense of it all?
An analysis is all about focusing in on the info that matters, diving into the insights that lead to actions that will move the needle. So what should you zero in on during a sales data analysis? From YoY rep performance to quota attainment to compensation spend, the possibilities are staggering.
To go from analysis paralysis to meaningful assessment you need to dive into the mix of macro and micro details that determine your team’s performance. Before we dive into describing those areas of interest, let’s define sales data analysis.
What is a Sales Data Analysis?
A sales-oriented data analysis can be best defined by its goal: to reveal anomalies and trends in your sales orgs performance that can lead to opportunities or help you avoid risks. In other words, a sales data analysis is the process of identifying, modeling, and critically considering data in order to uncover information that exposes areas of improvement and supports decision-making that boost sales.
With that said, using sales analysis to hit your numbers involves two basic operations: identifying the metrics that matter and truly understanding what those numbers mean for the health of your organization—the analytical part. To start, let’s talk benchmarks.
1. Compare Against Past, Present, and Competitors
Typically, the analysis used for forecasting, building comp plans, or performance assessments begins in the past. Year-over-year data is, of course, useful—but doesn’t paint the full picture. A cross-analysis between past, present, and competitive numbers has to happen.
Past benchmarks provide one important thing: trends. These could be good or bad trends, but both are equally valuable. Obviously, the latter are ones to avoid. The former ones should be replicated, but with the caveat that because it worked in the past doesn’t necessitate that it will work in the future. Queue in the importance of present data. Past benchmarks and understandings need to be continually held up to up-to-date reviews.
Tools like Xactly Insights for Sales™ make it easy to review the latest metrics across your organization while also providing pay and performance data from best-in-class companies. It’s from these comparative industry numbers that you can come to understand how competitive your reps really are, if you’re overpaying or underpaying, and generally where your sales org stands in the grand scheme of things.
2. Look Beyond Pipeline Numbers
Ask any sales manager how the quarter is going and you’ll likely receive a response regarding pipeline and a status report on some important deals. The annoying truth is that pipeline numbers don’t tell you enough about sales performance. It’s a surface-level answer that often can lead to an unreliable forecast.
In short, predicting sales numbers by pipeline alone is only a gut check. To get a reliable look into what the future of your sales org’s future, you need to analyze a few other dimensions, namely:
- Any and every sales metric must be compared to the same data from last year—just like a 10Q financial report shows performance against the same period in the prior fiscal year
- Don’t ignore data points that are critical to a well-functioning sales team: rep progress towards OTE (a rep’s number is their earnings, not their quota), turnover rates overall and by teams (watching for unusual spikes), tenure of the sales force and open positions
3. Review Performance Indicators that Predict the Future
Your sales data past and present is always telling a story. Anything that goes too far from that narrative calls for its own analysis. That’s if you can identify outliers in the numbers. This is often a serious challenge for modern sales leaders: they have the data, but don’t know what do with it or how to maximize its usefulness.
Tracking and analyzing anomalies is one of the most valuable ways to make meaningful predictions about sales performance.
This means looking at the previously mentioned “unusual spikes” in the data: everything from turnover and tenure rates, low performance compared to previous years, and discrepancies between what the industry and your company pays are all implied actions.
Sales data analysis can be a hassle, but when approached correctly it’s worth it’s wait quota attainment. And maybe not so cumbersome when you use tools with reporting templates/sales analytics dashboards. The trick is to remember that having the data is only half battle.
This starts with where you look and from there what your analysis or prescriptive analytics suggest you do. And this leads us to maybe the most important aspect of a sales data analysis: your findings should always lead to do something being done on your side. After all, analysis without action is about as useful as doing nothing.