Growth Hacks – Moving the Metric
As the founder of Collective[i], a leading platform for AI-enabled digital sales, Stephen Messer spends a great deal of time thinking about how sales organizations can better use technology to drive intelligent transformations of their sales processes. As Stephen has seen firsthand, one of the biggest pain points for any sales organization is manual data entry. While the process can be cumbersome, the need for accurate lead capture is higher than ever. Sales decisions have been shifting away from one to two points of contact towards a circle of influence that can involve multiple members of a target business, and by leaving a bulk of those decision makers out of a CRM, both sales and marketing teams are denying themselves the ability to leverage their connections to this larger team of decision makers. What’s worse, they’re limiting their ability to analyze this data that would help them better understand a target’s buying decisions and optimize the best route to close deals and influence their own go-to-market strategies.
In the latest episode of Growth Hacks, Stephen breaks down for Kunal and Katja the reason that he believes the B2B sales industry is on the precipice of undergoing a major digital transformation that will move the field away from its existing qualitative mentality into one driven by data-heavy analysis that can actually move the needle. He walks us through some of the surprising takeaways he’s seen through Collective[i]’s Intelligent WriteBack product, such as the fact that most sales teams spend 20% of their time – up to an entire day per week – on forecasting and predictions that often don’t yield highly accurate results. He offers solutions for ways that sales teams can better think about forecasting and predictions, and explains how better data capture and data analysis will allow for better modeling and optimization of go-to-market strategies in both the short and long term for businesses that are willing to invest time into better data capture.
- Why companies are still wasting time on ineffective forecasting, and ways to do it better.
One of the major themes that Stephen has seen through Collective[i]’s platform is that organizations are still spending roughly 20% of their time working on forecasting. When looked at from a different lens, that’s one day per week that’s being dedicated to a non-revenue producing task. Compounding the issue is the fact that it is rare for marketers to predict the future, which means that one-fifth of each week is spent chasing an accuracy rate that may never be reached. Collective[i] instead leverages its AI-powered platform to better understand what’s changing in a business’ landscape on a day-to-day scale. While it can be easy to get sucked into the standard model of months-ahead forecasting, Stephen suggests using data to understand how the world is changing in the near term. As he puts it, “What [boards] really want to understand is how the daily change is affecting their likely future, so that they can decide, ‘Do I open up the budget or do I close it down?’ They want to make sure they’re on track, that it’s reliable, and that everything is predictable.”
- How the sphere of influence in purchasing decisions has grown to involve larger networks.
As any salesperson knows, one of the largest challenges of managing a CRM database is the time spent on manual data entry. While skipping the process of entering leads may seem like a minor trade-off to make in pursuit of revenue-generating activities, Collective[i] has seen that the sphere of influence in purchasing has expanded significantly. What used to be one or two contacts has now become seven to eight buyers involved in a transaction, many of whom remain unknown to the larger sales and marketing organizations. Stephen estimates that these days, roughly 70% of people involved in a deal never even make it into a CRM. But if sales organizations start paying attention to the importance of making sure those contacts are accounted for, it becomes imminently clear that purchasing decisions are influenced by a much larger group.
“It changes the way you think about how the buyer is going through their buying process, and that can give you a real advantage if you know who’s there,” says Stephen. “Take account-based marketing. If you never know who’s there, and you don’t know the personas, you’re not going to be able to get that marketing tailwind from your organization simply because you can’t get that information into the CRM.”
- Ways that AI can help sales teams to better understand buying decisions and optimize go-to-market strategy.
Once teams begin to internalize the idea that buying decisions are made by a larger circle of influence, they can unlock the value in all the data being collected around buying decisions. Companies can better leverage opportunities using the full force of their networks, and capitalize on the social connections that can be uncovered through that data. By using AI, sales organizations can take this one step further. Rather than sifting through contacts in a CRM to find the best set of first and second-degree connections to a circle of influence, B2B sales organizations can use technology to analyze large data sets and better understand how buying decisions are made by that buying group. “If I can observe that same buying group across multiple sellers, it allows us to really start making good predictions about when they do this, what it means, or what they’re going to do next. And then we can look across an even larger network to start to understand what people do that leads to certain wins or losses,” explains Stephen.
Once those predictions are being put into action, savvy sales organizations can even use the data from their hits and misses to optimize their go-to-market strategy for the future. “The cool part of AI is that you can run the time forward [and say] here is the stack pattern of what we’ve done today. What is the optimal thing [I] can do next? How do I personalize my sale to the way this buying group likes to buy?”
- Why Stephen is bullish on sales organizations changing their operational playbooks as the industry further digitizes.
As evidenced by the data Stephen has collected on time overspent on forecasting, it’s apparent that the sales industry is ripe for changing how it has operated in the past. For decades the industry has operated on a qualitative model of decision making, but Stephen and his team at Collective[i] are confident that the industry will begin to move towards a much more data-driven sales process. “The biggest myth is that sales organizations are going to continue to operate in the same way they’ve done over the last 30 to 40 years. I think a lot of people are tweaking around the edges. I see this as a transition from being a very qualitative, very opinion-based world, to a very quant heavy world,” says Stephen.
While the concept may seem cumbersome, leading organizations to believe they shouldn’t rock the boat, he implores companies to remember that change isn’t as hard as it seems – after all, brand marketers were able to adapt to a new generation of digital marketing over the past decade, and in the last couple years alone, many organizations that had never used tools such as video conferencing quickly adapted to a new remote normal in a matter of weeks. “I think sales is going through a huge transition as it digitizes, and that will change everything about how we operate for the better,” says Stephen.