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Why Revenue Teams Can’t Afford to Rely on ‘Gut Feelings’

3 min
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If you need next-level creativity in marketing or to build relationships in sales, there’s no replacement for leveraging the skills of qualified people. But when it comes to decisions based on data, it’s time we leaned into tech.

Why? For starters, AI doesn’t take days off. It continually consumes and analyzes datasets to learn, predict, and make recommendations. It’s thorough and unbiased. And it never experiences lapses in judgment like humans do — such as those influenced by greed, hopeful thinking, or just missing a good night’s sleep. 

Those human-powered variables play havoc with budgeting and forecasts, and often lead to waste across revenue teams. Yet over half of B2B marketers still rely on intuition and experience — not data — to guide their decision-making. And these educated guesses are breeding inefficiencies and missed revenue moments across the buying cycle. 

Here are four fundamental areas where revenue teams should stop relying on gut feelings and leverage AI to make better decisions instead.

Selecting the Right Accounts

Successful account-based programs begin by selecting the right accounts. If you miscalculate this foundational component, your revenue teams will wind up wasting time on the wrong areas. 

Case in point: Sales reps ignore 80% of marketing leads. You can sidestep this mountain of wasted time and resources by proactively educating your marketing team on ideal accounts and focusing on the right prospects. 

Despite this, half of sales and marketing leaders say they only “somewhat agree” on their target account lists (TAL). That’s bad news, since the old way of picking target accounts — often based on personal preference or best guesses — sets revenue teams up for failure. 

The smartest way to avoid this misstep is to pick accounts based on data, not gut feeling. AI can define your TAL by analyzing data sets such as:

  • Past performance
  • The kinds of deals you’ve won (or lost), and
  • How buyers have previously moved through the journey

This points your team toward the accounts most likely to close, eliminating guesswork and waste.

Timing Your Outreach

Revenue teams that engage accounts at the right time in their buying journeys experience a 120% improvement in revenue generation. But most sales teams approach accounts based on their timeline, not the prospect’s.

This speculative approach leads to clumsy outreach. Engage your prospect too late, and you’re likely to miss out on the conversation altogether. Approach too early, and your outreach will often be unwelcome.

And mistiming your deals breeds larger business problems beyond fumbled outreach, like:

  • Uncontested losses
  • Not shaping the narrative because you’re late to the conversation
  • Discounting deals to try to pry a deal away from a competitor
  • Making promises you can’t keep 

Ultimately these issues all lead to the same result — blown deals and missed revenue

Thankfully, you can fix this finger-in-the-air approach to timing your outreach. Artificial intelligence pinpoints exactly when an account is in-market for your solution, so you can reach out when your prospect actually wants to hear from you.

Creating Meaningful Experiences

Your prospects and customers want the same friction-free, personalized, timely, and relevant experiences they get when they’re shopping for themselves. Providing this B2C-like experience means really understanding a prospect’s wants and needs based on intent data — not making an estimation.

Generic outreach, cookie-cutter content, and one-size-fits-all website experiences won’t convince potential buyers. Instead your interactions should be tailored around how prospects are engaging with keywords, topics, website pages, and campaigns. 

For sales teams, this means crafting personalized outreach based on a prospect’s research (as demonstrated by our BDR team here).

Meanwhile, marketers should use these insights to create personalized digital experiences — aligning messages and content with your prospect’s interests and buying stage. 

Forecasting Pipeline

The way most businesses forecast pipeline is riddled with assumptions. 

The principles behind predicting pipeline seem straightforward enough:

  • Know the revenue number you need to hit
  • And then work backward to understand what you need to achieve at each stage of the buying journey to get there

But predicting each stage involves calculating conversion metrics, deal sizes, and velocities. And that’s before we consider differences across geographies, regions, or solutions. 

With so many variables, even small mistakes at the planning stage lead to big discrepancies down the line. Yet many businesses still rely on spreadsheets or homegrown tools, managed in costly and error-prone manual workflows, to forecast pipeline.

These manual processes leave pipeline forecasting open to guesswork in the planning stage … and to wishful thinking when the pipe’s not tracking well. 

The solution? You guessed it: AI-powered pipeline forecasting that lets you plan, track, and accurately forecast pipeline to revenue conversion.

Remove Guesswork and Eliminate Waste with AI

There’s still space for creativity, meaningful experiences, and individuality in business. But we shouldn’t confuse gut feeling with fact. 6sense Revenue AI™ applies the power of AI across the entire buyer journey to remove the guesswork and waste that often plague revenue teams.

Our customers see results including 2x higher average deal value, a 10% better win rate, and a 25% reduction in cycle time. Check it out for yourself in our Business Impact Framework.

The 6sense Team

6sense helps B2B organizations achieve predictable revenue growth by putting the power of AI, big data, and machine learning behind every member of the revenue team.

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