Every sales team has hidden performance boosters just sitting in plain sight. Sales teams collect a vast amount of data, but not all teams have the time to go through it. After all, sales reps are interested in things that will help them in the next sales call – not in what could help six months down the road.
Sales leaders need to find ways to get the most from their data without slowing down sales. Here are five steps to boost your team’s sales performance.
The biggest challenge I see with sales teams is low motivation to go through their data. I get it: Data seems great in theory, but it doesn’t always translate into something tangible.
You want to make data analysis a no-brainer, and I’ll show you how – a five-step process to start going through your data and mining it for insights. You’ll likely find the bulk of this data within your CRM or any of your other tools.
Let’s start by figuring out what data is available to use. This is where you can define KPIs and metrics to be analyzed. You want to spend some time with all your data to get a sense of what is available and what could be created through formulas and calculations.
Once you know what data you have, it is time to visualize it. This can happen within your sales CRM, or it might take place through Excel®. The choice will come down to your technical expertise and what you feel most comfortable with.
Don’t get caught up trying to find the perfect tool; instead, focus on using the device you can manage. You may be surprised how much you can do in Excel if you know the right functions.
Once you visualize the data, it is time to analyze it. The first thing you want to do is find segments of your data that could be relevant. Let’s imagine you’re looking at your sales reps to figure out who should be coached. You’ll start by determining the average performance among your sales reps – showing you who does better or worse than the average.
These are your segments of rock stars and diamonds in the rough. For the second group, you can then work on coaching the behaviors that are common among the average or the superstars.
Once you determine your averages and segments, we can move on to tackling the low-hanging fruit. Which segments are performing much worse than the average that we could easily tackle? Which segments are performing much better than the average that could be further improved?
You’ll then repeat this process regularly and look for patterns and changes in your underlying data.
Once you go through this process, you can consider automating the bulk of these steps. You can set up tools that will send you regular reports with changes to your KPIs and key segments. This can save you the manual effort of processing your data to get the same results. Look into tools like Domo, Tableau, and Databox to help you achieve this automation.
Let’s continue by looking at actual analysis workflows you can run. These are the areas I see the biggest opportunities when working with sales teams.
Let’s start by visualizing the sales process into some kind of funnel where we can see drop-off rates. You want to end up with something that shows you a funnel drop-off.
For example, let’s say you want to reduce the time it takes to qualify prospects. This could involve more aggressive outreach or using automated communication to reduce the effort needed by sales reps. You can continue exploring your overall conversion process to find large or small tweaks that could be done.
I find it helpful to define what a great, average, and below-average sales rep looks like in tangible ways. My list could include factors such as:
Based on that, I can start to work with an individual sales rep to figure out where they are struggling and how they could be coached through these challenges.
Forecasting is an interesting area because it’s looking at something that hasn’t happened yet. We need to start by running our forecast report broken down by month or quarter. I also think it’s helpful to include historical performance.
I can then see if my forecast is going to hit my targets – and why or why not. Are the deal sizes too small? Are there enough upcoming deals? Am I projecting issues with specific regions or sales reps? Forecasting is all about diagnosing the variables that go into the model and figuring out what needs to happen for a forecast to take place successfully.
The possibilities are endless. Remember: Data is meant to help you achieve tangible business outcomes. Reject any “quirky” insights unless they will translate into something that will appear in the bottom line.
Ruben Ugarte is founder of Practico Analytics, providing expertise in data analytics. His new book is The Data Mirage: Why Companies Fail to Actually Use Their Data. Learn more at rubenugarte.com.