Modern sales teams face many challenges that eat away at their productivity and impact performance. From juggling admin tasks and internal meetings, to switching tools, to forecasting sales based on incomplete data, hitting quota is tough.
According to Forrester, only 23% of a rep’s time is spent directly selling. Given that deals hinge on human interaction, that’s a pretty devastating stat. Meanwhile, with Google and Yahoo changing their mass email rules to fight spamming, it’s clear the old ‘spray and pray’ approach to sales is set to become a thing of the past.
AI is promising to transform the sales process. Organisations can now capture and analyse client interactions and be far more data-driven than before. Meanwhile, advancements in generative AI offer reps and their leaders the ability to craft personalised emails at the touch of a button, summarise key call highlights, ask questions about deal risks and nudge sellers on action items to ensure things stay on track. Best-in-class revenue intelligence platforms use AI for conversation intelligence, forecasting, customer and prospect engagement, market insights, and coaching. So, the question then shifts from ‘Should we invest in AI? to ‘What AI-powered solution can best serve our long-term goals?’
What can revenue intelligence offer?
Despite having a range of forecasting and conversation intelligence tools at their disposal, sales teams are struggling. According to a 2022 survey from revenue intelligence provider Gong, 44% of UK businesses missed their earning targets at some point in 2022.
An average sales rep conversation with a prospect is 6000 words, yet the average number of words that make it into a CRM entry is 30
“Traditionally, companies do one of a couple of things. They either use their CRM as a system of record or they ask reps about the deal progress. Neither of these approaches gives a realistic image of the deal,” says Craig Hanson, senior director of market strategy at Gong. “An average sales rep conversation with a prospect is 6000 words, yet the average number of words that make it into a CRM entry is 30.”
That’s a lot of contextual data to lose during the sales process. Another fallacy which may give sales executives a false sense of security, Hanson explains, is the use of low-grade, simple conversation intelligence tools that can’t turn data into action.
“These tools have basic analytics around them, but you’re not able to deeply understand what’s going on in those interactions,” he says. “Maybe you can do a keyword search, but these are not accurate enough to be useful. The only way you can extract meaning is to listen to the entire call, which is not practical. That is why these tools don’t get adopted.”
Insights and predictability
In contrast to low-grade tools, an enterprise-level revenue intelligence platform can glean meaning from the hundreds of different ways a prospect may talk about a topic and provide tailored actions based on those insights.
Bryan Fong, director of product marketing at Gong, says that revenue intelligence platforms achieve this by processing high volumes of sales-centric customer data and tuning them to the context of the particular use case.
Efficiency and productivity
Eighty-four per cent of sales leaders say that improving pipeline performance is their top priority. With revenue intelligence, reps can streamline their workflows, offload repetitive tasks, learn from the winning plays of top reps, and get AI-backed recommendations on what to do, and when.
“In the past, there has been a reluctance to change because reps suffered from tool overload,” Fong says. “Whether it’s context switching, going into their CRM, going into the email inbox, going into the app notes – there’s a lot of tools that they need to jump through to get the context they need to be successful. That then impacts their productivity and how they engage with their customers.”
Driving growth
The success of any for-profit organisation hinges on its ability to create, manage and convert pipeline. Revenue intelligence can help pinpoint what top performers are doing differently, and emulate winning behaviours to drive higher participation and attainment rates. It also enables sales teams to onboard new hires faster with minimal hand-holding, improving a new rep’s time to close their first deal.
Another key growth driver is customer retention and expansion. Revenue intelligence not only provides actionable insight for new business but also offers infinite memory of every account, from pre- to post-sale, helping account managers understand which customers to focus on and what messaging is landing well with existing clients.
How can revenue intelligence transform the full sales cycle?
Just as an aircraft’s co-pilot is there to assist the pilot on a safe and smooth flight, AI can be seen as the co-pilot to the human sales rep, helping them expertly navigate every step of the sales process. It should be seen as a collaborative relationship where human skills are supported by technology and productivity is enhanced. Good revenue intelligence makes selling easier for the rep, the process more personalised for buyers and everything more efficient. So, what does it offer at each stage of the sales cycle?
Sales engagement
Sales engagement is experiencing a major facelift. Many traditional sales bulk email tools were designed for sales development executives and ignore account executives who prospect and sell. Having this centralised in one place improves cross-account visibility and requires less tool switching. Recent research by Gong has found that only 4% of emails get replies. This isn’t surprising when only 25% of buyers feel sellers understand their role in the business and only 13% of buyers feel outreach messages truly address a relevant challenge for the organisation.
Deal execution
Having access to all client interactions means that the subtle suddenly becomes obvious. Gong’s analysis of over 10,000 sales deals has found that there is a strong correlation between the deal size and the number of buyers involved in the decision-making process. The study also reveals that team selling has a tremendous impact on win rates regardless of the deal size.
Having these insights at their disposal, businesses can be far more intentional about who gets involved at what stage of the sales negotiations.
Coaching
Another element which is a big divider between high and low-performing sales teams is the leadership team’s approach to coaching. Sales teams can sometimes have a high turnover rate, which means sales leaders are reluctant to invest in elaborate or high-quality coaching initiatives. Instead, sales training often consists of scripts, call shadowing and occasional sales strategy courses. However, having the full history of client interactions alongside AI-driven action suggestions means that leaders can empower individuals to coach themselves with little to no effort.
Hanson adds: “Most revenue leaders realise that the difference between their middle performers and their top performers is probably not that they’re doing more activities. It’s the fact that they’re doing them better.” Being able to easily analyse these activities means that businesses have an endless catalogue of training opportunities at their disposal.
To drive engagement, especially as email restrictions change, sellers need to be far more strategic in how they talk to different types of buyers. For instance, Gong has found that individual-based personalisation is far more effective with low-level buyers while executives respond better to company-based personalisation. Providing sales teams with these insights early on can significantly boost response rates and revenue outcomes.
For example, BlueGrace Logistics, a third-party logistics provider and a Gong client, has reported a 52% increase in sales rep activities and an 80% increase in email response rates just five months after launching Gong’s sales enablement tool, Gong Engage.
Forecasting
Forecasting is key to business performance, yet only 27% of sales professionals say their forecasting is accurate. Sales leaders often fall short of their forecasts due to partial data, biased information or missed opportunities. Contextual AI can look beyond numbers and previous performance and capture nuances in conversations that people can otherwise miss.
For instance, looking at red flags such as how often prospects talk about scheduling a meeting or at what part of the conversation they bring up competitor names can significantly impact sales outcomes.
The road ahead
While the primary focus for revenue intelligence platforms is improved sales performance, there’s a great opportunity for other teams to benefit as well. Marketing teams can tune in to see how customers talk about their challenges and use that to plan their content development. Product teams can understand first-hand how clients feel about specific features and pain points so that they can better prioritise their roadmap.
Hanson likens this new level of insight to having the lights switched on. Suddenly, leaders can see more and take better action with more confidence. Gartner predicts that by 2025, 70% of all B2B seller-buyer interactions will be recorded and analysed using AI or machine learning.
Fong believes that, once AI becomes the norm across all customer interactions, hyper-personalisation and account-based selling will become a no-brainer. It is then up to companies to choose the right platform that can support them not only today, but for the years to come.
