Sales and marketing departments were early adopters of digital transformation. Digital content expanded the touchpoints that they could use to personalize engagement. This transformation has been especially useful for B2B sales teams, with their longer, high-touch sales cycles.
Artificial intelligence (AI) tools are again changing expectations of B2B sales team performance. Recent McKinsey research into why some B2B sales teams outperform others determined that “those willing to shake up their sales models and embrace next-generation capabilities are growing revenue at twice the rate of GDP.” Technology plays a foundational role for outperformance, bringing enhanced insights and agility to sales teams.
According to the research and insights, which McKinsey defines as data-driven decision making, adds a 2 to 5% boost in sales. Agility, which includes scaling sales and reprioritizing accounts, can lead to a 5 to 10% boost.
AI is a critical technology that sales teams can use to generate the insights and agility needed to be top performers.
Using AI To Serve Up Valuable Intelligence
The typical B2B salesperson conducts hours of research before reaching out to a prospect. AI turns that process around by serving up valuable prospect intelligence about the most valuable prospects.
It starts with AI tools facilitating the flow of marketing information to sales. A salesperson invests hours on prospect research but still may not have insight into the prospect’s specific pain points, even though marketing has that intelligence based on an analysis of which marketing campaign and content engaged the prospect. After marketing passes a lead to sales, that prospect continues to engage with digital content. This is all evolving internal intelligence that sales teams need to reach a high level of performance.
Social listening AI tools surface external intelligence about prospects and contacts. Rather than salespeople running Google and LinkedIn searches on industry trends and prospect news, AI utilities can provide the updates and analysis to them. Using AI to identify and package prospect intelligence will save salespeople countless effort hours.
AI Tools Optimize Resources On Highest Value Prospects
Sales teams interested in shortening sales cycles and improving close rates use AI tools to refine their lead scoring and timing. AI-enhanced lead scoring and personnel grading helps sales teams find out who to contact and when. Understanding the context of how a prospect is engaging with which types of content, and how it’s engaging with them, all adds nuance to lead scoring calculations.
This nuance clarifies what sales sees are indicators of buy intent and weighs insight into the social relationships among contacts at a prospect level. With B2B sales decisions done by committee, insight into the “who is engaging” and what their relationships are helps sales focus on the right people.
Different types of engagement are indicators that a prospect is moving out of the research phase and into a buy phase. AI plays a role in identifying which actions are genuine indicators of buyer intent.
As trends and positions change, actions that reflect buy-intent change. Using the ongoing accumulation of data across the marketing and sales engagement platforms, machine learning can provide enhanced analysis of what the current buy-intent flags are. It helps keep sales agile by focusing them on hot prospects and their decision-makers. The AI-enhanced analysis pushes sales away from past assumptions that may no longer be accurate.
Machine Learning Adds Layer Of New Data
AI, through machine learning utilities, can also generate new data and related insights that sales wouldn’t have otherwise. Providing ongoing buy intent analysis is one example. Sales can also use machine learning to boost their sentiment intelligence and analysis.
Facial and voice sentiment analysis tools are a growing segment of AI capabilities. Facial sentiment analysis lags behind the current state of voice sentiment analysis, but will become more prevalent in the coming years. Voice and facial sentiment analysis of a sales presentation can help sales pinpoint which tools and positions elicit positive or negative responses.
You can use the machine learning analytics on top of the sentiment analysis to see where you’re successful and where you failed. It provides insights into how to handle similar situations for similar customers.
Machine learning also has potential to refine a sales team’s vision of the company’s ideal customer. Account-based marketing has raised the stakes for finding ideal customer look-alikes to target. The first step to building a look-alike hot prospect list is having deep analysis of who really is your ideal customer. AI and machine learning can help uncover the customer details that contribute to their lifetime value.
AI Fuels The Continuous Feedback Loop
AI provides hefty utility for B2B sales with its ability to keep teams operating with detailed analyses based on the most current data. Whether it’s refining prospect scoring, perfecting the timing of buyer intent, or generating high-value look-alikes, the analytical strength of AI continually improves sales’ efficiency.
The speed and breadth of AI-driven insights allow B2B sales teams to scale their operations for greatest impact even as conditions change. This pairing of insight with agility strengthens sales teams’ potential to achieve maximum acceleration towards greater revenue goals.
This article is written by Selva Pandian and originally published here