Sales teams are sitting on a goldmine of data. The challenge is that sales teams don’t have much time to actually take this data for sales analysis, whether by them or by someone else.
In this article, I want to help you understand how to actually process this data in a way that isn’t time-consuming and which could be automated in the future.
Let’s jump right in and talk about the different kinds of sales analysis your team could be running and how to pick the right one to focus on.
It might seem like there’s hundreds of potential analysis that you could be running on your data. In reality, we can organize different reports into general categories of focus. You might come across reports which are named differently but you should think about which category this report is meant to be tackling.
Using categories is helpful because it forces us to decide where we want to focus our energy and where we think we can make the biggest impact. Data can be addicting and you don’t want to end up mindlessly looking at reports without taking some kind of action.
Before you ever run any report, ask yourself: what part of my sales world do I want to improve? There are 5 broad categories including:
Once you make your choice, you can dive deeper into the relevant reports and the data for sales analysis. Let’s look at each category in more detail.
How to Improve the Conversion Process
This is category is all about the sales process itself. Should you qualify leads by phone or by email? Should you change how you present your products and services? Are there any steps that are redundant or steps that should be added?
Being able to look at your conversion process in a funnel is really helpful here as that will show you where prospects are dropping off and where you can make improvements. You can also look at the details behind each step including documents, scripts, and documentation that sales reps use.
How to Improve Individual Sales Reps
This category focuses on individual sales reps and how they could improve. You’ll know this category is relevant if you have a new sales rep that isn’t performing a well as they should or if one of your sales reps starts to struggle with their quota.
This category is tricky because you’ll be dealing with people problems that aren’t as straightforward as improving the conversion process from the previous section. You will need to understand what is the ideal behavior that you would like to see and what gaps exist to get there.
How to Improve the Products
This category focuses on your products and services and how they could be improved. In some cases, a product may have significant limitations that not even the best salesperson in the world could sell.
You’re interested in looking at any metrics that should help you understand customer satisfaction such as multiple purchases, refunds, complains and more.
How to Improve the Inbound Lead Generation (or Marketing)
This category focuses on improving the inbound lead generation campaigns which are typically under the marketing team. You’ll need to understand how inbound leads are currently generated and the performance of the individual pieces e.g. landing pages, content, call to actions, etc.
How to Improve the Outbound Lead Generation
This category focused on the outbound lead generation which is typically handled within the sales team. You’ll see typical sales numbers such as calls, emails and the conversion rate of these prospects through your conversion funnel.
Once you make a choice on the most relevant sales analysis type, we can move on to the nuts and bolts of how to work with your data.
Data can seem intimidating especially if you’re overwhelmed with hundreds of metrics out of the box. Like anything else, you simply need a process that takes you step by step through the most important actions.
You also want to remember that analyzing your data isn’t a one time process. You will need to keep going through the data to find insights and understand how things are changing over time. Data is best seen as a feedback mechanism that lets you know how your decisions or experiments are performing.
Step 1: What is Your Data
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 of your data to get a sense of what is available and what could be created through formulas and calculations.
Step 2: How to Easily Visualize It
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 the most comfortable with.
Don’t get caught up trying to find the perfect tool and instead focus on using the tool that you can manage. You would be surprised how much you can do in Excel if you know the right functions.
Step 3: Average vs Segments
Once you visualize it, it is time to analyze it. The first thing you want to do is to find segments of your data that could be relevant. Let’s imagine that 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 which will then tell you who does better or worse than the average.
These are your segments of rockstars 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.
Step 4: Low Hanging Fruit
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 on a regular basis and look for patterns and changes in your underlying data.
Step 5: Automating This Process
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 now look at some concrete examples of how you can analyze your data. This should give you some ideas of what you could tackle within your teams.
Finding Holes in the Sales Process
Let’s start by visualizing the sales process into some kind of funnel where we can see a drop off rates. You will end up with something like this though perhaps not as visually appealing.
We might want to focus on reducing the time deals spend on the first stage (Qualified) from 42 to 30. 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.
Discovering Weakness in Your Reps
We cover this example in a previous section and we expand on it here. 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.
Sales Trends and Forecasting
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 successfully take place.
The possibilities can seem endless when it comes to sales analytics but focus on the 1-2 categories with the biggest potential impact and start there. Also remember that the better structure your data is, the easier everything else will be.