I’m fascinated by sales teams. They naturally attract people who get things done, have no time for excuses and are able to drive towards their goal, typically their quota. This is why it seems like data for sales teams should be a no-brainer but I have seen it backfired spectacularly.
In this post, I’ll share my experience working with hundreds of companies and how they have used data to make better decisions. I’ll also show you how to think about using data for your sales teams and how it can amplify what you’re currently doing if you implement things properly.
Let’s start at the beginning, knowing why.
Every sales team has a group of KPIs that they look at on a regular basis. The number of deals closed, number of activities (phone calls, emails, etc), average deal size and so on. I don’t think sales teams need help figuring out what KPIs they should be tracking. In a lot of cases, their CRM system will give them an endless amount of metrics that they could look at.
Instead, you need to focus on the why behind any action. This will help you figure out what gaps exist in your data and how you can close it. Start simple and you can eventually upgrade to deal with the whole correlation vs causation question.
Let’s take a common scenario: lost deals. When sales reps mark a deal as lost, they will get asked for a reason. Possible reasons will include competition, couldn’t reach the person, etc. This is a great starting point but we want to understand WHY deals are being lost.
Here are a few ways to improve this one area:
We are trying to go beyond the surface level information of losing a deal to competition to understand the competitor’s name, how much activity was done on that deal and capturing the most common sales objections.
You can expand this process to every major KPI and step in your sales process. Why are some of your prospects more willing to close higher value deals? Why are some deals closed in half the average time? Why do some deals fail or lose momentum?
Digging through your data to find the why takes time but that’s where the gold lies. You should also note that we are trying to limit what we asked our sales rep to input. This is critical and is my next section.
Asking a sales rep to input a million things is a recipe for disaster. In the previous section, we cover quite a bit of question that we could explore within our sales data. However, I wouldn’t ask the sales rep to take time out of their busy schedule to answer them.
Instead, I want to keep the data collection as frictionless as possible. They will need to input data into a CRM but I want it to feel seamless. To do that, focus on the following 3 principles or ideas:
1. Make data capture relevant and timely
When a rep marks a deal as lost, this is the perfect time to ask them why. We can provide them a few standard reasons and also have the opportunity to expand in a few words. This is not the right time to ask for common objections, when did they last reach out to the prospect or what the prospects favorite blogs are.
2. Capture little bits of data at a time
You should be collecting little bits of data at a time instead of forcing reps to go through 25 different fields that will take them 30 minutes to fill out. This means that you could have one question that pops up after they have a meeting with a prospect asking them for any objections that came out. You could also ask them how the prospect found your company after the first phone call.
3. Remove anything that software could do
Even with the first two ideas, you could easily end up asking the sales reps for a lot of information. To balance this, we want to remove anything that software could do. When a rep marks a follow up as done, you could automatically create the next one based on default parameters. The rep could then just edit it as needed, saving themselves a few clicks.
Now that we know how to dig through our KPIs to understand the why and how to make data collection frictionless, let’s look at 7 practical ways that data can help your team hit your targets. You’ll be able to take at least one of these ideas and apply them to your team.
Optimizing lead generation
Data can help you optimize your lead capturing system by filtering through any prospects that are unlikely to buy. The data could be provided by the user or could be enriched into the deal by software like Clearbit.
For example, if you know that companies under 50 employees aren’t the right fit, they could be sent an automated email guiding them to other companies. The same could be for the geography of the prospect and even if they mention specific keywords in their initial message e.g. prospects who ask for pricing right away. You could even track where deals came from using UTM tags.
Lead scoring is one of the most popular ways that sales use data. They define a scoring method that takes into account any activity like visiting the website, emails and more to automatically sort through a large list of leads. This can be very helpful if done properly.
There’s a significant portion of sales communication that could be automated or augmented by software. This could include follow up but more commonly, nurturing emails. Setting this up in an automated fashion will result in consistency across all of your deals. Have a modular marketing stack will make something like this easier.
Forecasting can be broken down into two categories: what’s going to happen based on our current pipeline and what could happen in a future pipeline. Either option can benefit from data and being able to know the probability of a deal closing based on different factors.
Cross-sell or up-sell
Data can also help you figure out any potential cross-sell or up-sell opportunities. This could happen post-sales as your customer uses your product or during the sales process. If you know a deal includes certain products, data could be set up to surface potential up-sells that are relevant to this prospect.
Churn reduction can be especially helpful as a preventative measure instead of trying to convince a prospect to not cancel their plan. The preventative strategy here means measuring how satisfied a customer is with the service (going into the realm of product analytics) in the weeks or months leading up to renewal and taking action to fix any issues.
A/B test pricing
You could also use data for sales A/B testing especially around trying to find the optimal pricing. These tests might be tricky to set up but you can focus on having specific sales reps offer different pricing in a controlled fashion.
Besides the sales process itself, there are a lot of opportunities to better understand how customers actually interact with your product and how this information could be used to improve your sales process and conversion rates.
Data for sales is an untapped area of opportunities but teams need to keep in mind who their consumers of data truly are. They are interested in anything that will help them hit their target but you can’t expect them to change their workflow or go through endless forms providing information. Keep it simple and let software & machines do the heavy lifting.