RevOps Champions Podcast

4 Steps To Provide Clarity For Sales Teams

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Episode Summary

Sales visibility makes sales management much more accessible. And sales visibility creates clarity, camaraderie, and empathy — foundations you can scale your business on. 

But while sales managers are responsible for enabling the entire team's success, your sales team is often focused on their efforts and results. That is why it is necessary that an individual or a team is evaluated based on their performance or behavior related to what they are responsible for.

According to Aaron Rickard, the VP of Operations at Denamico, when a team is aligned and working well, you're still going to have problems; however, the team will be able to solve them.

In this episode of RevOps Champions, Aaron explains the biggest problems companies face regarding sales visibility. Aaron and our host Tiffany Cavegn get into the importance of clarity, clean data, horizontal accountability, and sales methodology for business and also discuss the automation process.

 

Guest

Aaron Rickard

VP of Operations

Company: Denamico

Noteworthy: Aaron has experience and achievements in finance, sales, IT, and management for premium brands operating in manufacturing, wholesale, retail, and professional services. Aaron is the head of operations at Denamico and works with all of their strategic consultants to help clients identify their pain points and set a consistent pattern of growth and improvement for their success over time. In addition, Aaron's hobby is throwing darts.

 

Key Themes & Episode Highlights

  • What are the biggest problems that companies face regarding sales visibility? Denamico's mission is to humanize the HubSpot experience to delight customers, increase revenue, and eliminate worries about technology usage and system integration. Their operations team focuses on a business problem and uses technology to solve that problem, and Aaron explains the biggest problems companies are facing right now when it comes to sales visibility. "You've had a lot of people at your company that have a lot of feelings on how things should be done, and you're trying to meet somewhere in the middle with everyone, and it leads you to chaos probably in many areas. So, one, you gotta realize that that's happening and enjoy it. Like, no one's perfect, and you're trying to build a team together, which is part of that. I do think, though, even in young and mature organizations, you're still seeing problems with clarity in the right information. When I say clarity, I mean everyone can understand the information. It's partly there, and it's in a digestible and understandable format. The second part of this is — is the data accurate? Sometimes there's a lot of information, but is it based on someone's feelings? Is it quantifiable data? […] Another part of this is the sales methodology. So, as you're getting information and insights into things, are your humans interpreting what the system is saying? Do the humans understand what they should be looking for, what they should be doing, and how to interpret that information themselves?"
  • The importance of sales methodology for business. Sales forecasting is estimating future revenue by predicting the amount of products or services a sales unit (an individual salesperson, a sales team, or a company) will sell in the next week, month, quarter, or year. According to Aaron, good visibility, data, and sales forecasting start with a unified methodology across the business. "Talking about sales, there's a sales process, and then there's a sales methodology. Think of sales methodology as the pulse or the heartbeat of how things should operate; the process is the actual tangible things happening. In order to get good pros, in order to get good forecasting, you need a great process. In order to have a great process, you need a good methodology or a standard methodology."
  • Some things must be automated. Data cleansing is the process of repairing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled. As Aaron points out, the potential for error is greater if a human rather than a computer performs this process. "If there's anyone in your company who is doing data entry or import type behavior, that's generally something you should automate through a computer. If it's data cleaning, just refreshing records, trying to get things organized; generally, computers can automate that stuff and keep it the way you need it to be. As soon as you put humans into that data hygiene type stuff, you're likely to have errors just because humans are not perfect. We're not meant to do that. We're meant to be troubleshooters and solve problems creatively and use our brains to their full potential."
 

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