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About This Episode
Is there a common thread that runs through businesses that survive and business that don’t?
Our guest today says yes — and it’s the sales organizations that know their clients the best through data analytics.
Some of us are actually starting to come out of COVID-19 survival mode, while others are realizing that something drastic about the business needs to change if survival is even going to be an option
Kevin’s answer to that is data.
“I can’t think of one industry that is getting any less technical or less data-oriented,” Kevin said.
This forced pause is a great opportunity to retool all of those processes that we’ve always told ourselves we’ll get to.
“Unquestionably, the ones who will survive and come out better, it’s the client-facing organizations that know their client the best,” he said
There are a few fundamental pieces of data that you have to track in order to start leveraging analytics.
Reps hate using CRM. “The only incentive is to avoid hassle. That’s not a long-term strategy,” Kevin said.
To make CRM a core piece for sales reps, you have to know what you’re looking for: KPIs.
“If you want more money at the end of the quarter than you had at the beginning, you only have four levers at your disposal,” Kevin said.
The only 4 KPIs
- You can have more opportunities in your pipeline
- You can close more of them
- You can close them at a higher dollar amount
- You can close them in less time
“Do you have the data in there to really understand how those metrics are interplaying with each other?” he asked.
This is what the CRM alone can tell you. Imagine what you could get when you leverage more than just first-party data.
“The key to getting accurate data is to make it as easy, seamless, and automated as possible,” Kevin said. “Make it very clear that that data comes back to help reps and their managers be more successful.”
Data analytics as customer knowledge
You probably have a system for assigning points to MQLs.
“If you belong to one of these companies that has actually built a scoring model, reviewed it against actual results, and gone back and refined your scoring model, hats off to you. You’re in the rarity,” Kevin said.
Well, the next step is comparing the MQLs to whether the opportunity closed… and then to how it compares to another lead with similar attributes.
“The whole point of connecting all this data is to feed the algorithm with as much potential relevant data as possible — and let it figure out what’s relevant or not,” Kevin said.
Ultimately, you’ve built an algorithm that will tell you which factors are correlated.
Algorithm: Did you know that companies with a deal size over 50,000 on the East coast or three times as likely to close?
Leaders: Great, let’s investigate what’s going on over there.
“The point is to allow you as leaders to lead, to not spend time having to crunch through dashboards,” Kevin said. (Honestly, geeking out over dashboards is Kevin’s job.)
First steps in data collection
Step 1: Ensure that your data are at least 85% complete and accurate.
Run through your system of objective measurement and assess how it’s been adopted.
Also investigate how you assimilate the information. Are you getting email alerts or are key insights front and center on your dashboard?
“it’s identifying what the sources of data are, and then feeling good about the accuracy of it,” Kevin said.
Step 2: Connect the data
This could be a few clicks or a several-month integration project.
“Interconnecting data is getting easier. Every org will have individual complexities, but in the same way,” Kevin pointed out.
Step 3: Data confidence
At the end of the process, you’re aiming for a simple number.
If you and your sales team believe that this opportunity scored 95% because of these 5 enumerated reasons, then your team will have confidence spending their time on that opportunity first.
Analytics can even tell you what is optimal to do next, like meeting with the senior decision maker within the next 7 days.
“The way to have something of value is to know something more about your clients, their business, or their industry than they know themselves. Your secret weapon is lying in your databases. It really is,” Kevin said.