Modern sales is more about long-term customer relationships than big, one-and-done deals.
This has always been true — lower customer churn decreases marketing costs — but it’s especially true today, as everything from designer clothing to SaaS products moves toward a subscription model.
“The best way to be successful in sales is to know yourself, know your customer and know how you create strong relationships with other people,” Samantha Harrington opined in Forbes.
“Once you’ve built that relationship, shown you care, and earned their trust, you are on the road to making [a prospect] a customer,” Lee Ann Obringe wrote in HowStuffWorks.
Building trusting, enduring relationships doesn’t sound like a process ripe for automation. Machine learning algorithms can master repetitive, predictable, and, in a word, mechanical tasks — but despite the future foretold by Her, artificial intelligence hasn’t yet learned to empathize or make jokes. (It hasn’t even learned what a cardigan is.)
Yet AI tools have taken the sales world by storm. The key to their popularity? They automate almost everything but actual socializing.
“I want tech-enabled humans, not human-enabled tech.”
“I want tech-enabled humans, not human-enabled tech,” Jim Benton, CEO of Chorus.ai, told Built In. “I want the human at the front and center.”
Chorus.ai software sticks to that vision, offering salespeople AI-based coaching and recapping of their calls. It surfaces tactics that have worked with similar clients on past calls — which could mean highlighting a specific product feature, or talking less and listening more.
Benton calls this “conversational intelligence.”
AI Can’t Hold a Conversation, but It Can Analyze One
Based on analysis of five million sales calls from more than 300 companies, Chorus.ai recently published a round-up of macro trends in sales calls in a report called “The State of Conversation Intelligence 2020.”
A smattering of findings: The average sales cycle takes 91 days, and it takes the average sales development representative roughly 106 cold calls to schedule one meeting. On successful calls, salespeople tend to speak for 40 to 60 percent of the “talk time,” and always set next steps, which Benton calls “a critical component.”
On successful calls, salespeople tend to speak for 40 to 60 percent of the “talk time,” and always set next steps.
(If Chorus.ai can’t see a rep planning next steps in a call, it flags that call in red.)
Since shelter-in-place orders rolled out across the country to combat the coronavirus, the structure of sales calls has also shifted slightly, Benton noted. For one, CFOs have been invited to 91 percent more sales calls. For another, reps have begun moving into their demos an average of two minutes later than they used to — likely because salespeople are doing longer check-ins, or “doing a little bit more empathy.”
These broad trends aren’t the company’s bread and butter, though. Ultimately, the ideal sales strategy varies depending on the product and the clientele. Chorus.ai helps companies find their own, personalized best moves, offering salespeople tips based on tactics that have correlated with success on their company’s other sales calls.
The software measures success, or lack thereof, through an integration with the client’s CRM. There, it tracks which calls move a deal through a sales pipeline, or correspond with increases in a deal’s price tag.
It works backward from there, scanning successful video call footage, via Zoom integration, for intelligence. It finds trends among the winning calls — phrases and tactics that crop up again and again — and encourages sales reps to try them out.
You could say it’s automating management, but let’s be honest: No manager has time to listen to all their salespeople’s calls.
Meet the All-Seeing AI Coach
In the era before AI, coaching meant a manager sitting in on a few randomly selected sales calls, or overhearing snippets of dialogue from afar and weighing in. Software like Chorus.ai opens up new possibilities — like coaching on every call.
The software offers two types of AI-powered feedback: recommendations before a call and recaps afterward.
It surfaces recommendations by compiling playlists of calls that have worked in similar situations in the past.
“We could build a playlist of the deals we’ve won in the mid-market category [where prospects] have had [certain] types of objections,” Belmont said. “We would just build an auto-curated smart playlist.”
After a call, the software can highlight key positive and negative signals from the prospect; if they brought up your competitors an above-average number of times, for instance, that’s not great. It can also highlight important metrics, like the percentage of the call the salesperson spent speaking, the number of questions they asked, and any traits that make the call quantifiably an outlier.
One type of feedback Chorus.ai has so far opted out of: real-time feedback during a call.
Benton worries this could be “distracting,” and corrode “the human connection” between reps and prospects.
Instead, it functions more like game tape for an athlete. It lets salespeople review what worked and what didn’t on calls across their team, and send key excerpts of their conversations to other teams. If a prospect has product feedback, a rep could forward that audio clip to the product and engineering departments. If a section of the call went in an unexpected direction, a rep could send that clip to their manager, who could then cite specific, time-stamped moments in their feedback.
Essentially, Benton said, the software turns the sales call into “an empowering event that helps mobilize product, marketing, support, engineering, and leadership teams,” rather than what it once was: an “interaction that disappears into thin air.”
The AI-Enabled Sales Software Market
In 2018, McKinsey reported that AI could create more than $1.4 trillion of value for marketing and sales teams. That’s thanks in part to these companies, whose software applies AI to different facets of the sales effort.
Lead Qualification Chatbots
Drift’s chatbots, Wall-E-esque cartoons that hover in the corners of companies’ websites, function like employees who never sleep. Around the clock, the bots conduct preliminary conversations with prospective clients, pushing qualified leads into the sales team’s pipeline. The company’s conversational AI is powerful enough to recognize questions phrased as statements or execute account-based marketing tactics, like greeting top prospects by name.
Infer helps salespeople decide which lead to call next. It sounds like a simple proposition, but as Benton noted above, reps typically make more than 100 cold calls to set up one meeting — which means they still bark up a lot of wrong trees. This software sorts prospects into an intuitive matrix, unleashing AI on client data enriched with in-house data from Infer, which captures millions of different companies’ sizes, demographics and tech stacks.
Gong’s revenue intelligence software offers salespeople real-time, AI-powered feedback on their calls, alerting them to client traits like price-sensitivity. More broadly, it functions as a user-friendly source of truth for sales teams, ingesting data automatically through integrations with Salesforce, Microsoft Teams and more. On the platform, users can easily scan all the sales in progress across their company, and managers can browse benchmarked assessments of their reps’ calls.
Personalized Communications at Scale
Conversica’s AI assistants for sales professionals function a bit like Drift’s chatbots. However, their medium is SMS and email — not real-time chat — and they operate deeper in the sales funnel. Using natural language generation and processing, they craft automated, personalized emails, arranging follow-up calls, checking in with prospects who have gone silent and executing other digital errands. Fluent in multiple languages, they can both initiate email conversations and respond to emails from clients.
An AI layer in the CRM
Einstein, AI support built into the Salesforce CRM, automates busywork that once slowed salespeople down. Its algorithms can learn to execute frequently used sales workflows autonomously, recommend next steps and unearth trends across a company’s client base. It also enables personalized tactics — its algorithms can figure out which communication channels each client prefers, and pinpoint the time of day when they’re most responsive.
What’s Next for Conversational Intelligence?
Conversational intelligence inherently improves over time. That’s just the nature of machine learning algorithms — the more a client relies on them, the more deeply they understand “the voice of the customer,” as Benton puts it.
However, Chorus.ai won’t just lean on its algorithms for product refinement going forward. The company has two big plans in the works, according to Benton.
One involves exploiting the “video” aspect of video sales calls. Though Chorus.ai can spot the difference between a talking head and a slideshow, in the future, the product team hopes to improve its sentiment analysis capabilities, so it can “see” laughter and non-verbal empathy cues.
Chorus.ai also plans to broaden the range of inputs its AI can parse — expanding from phone and video calls to include Zendesk tickets and emails. The more it can take in, the more holistically the software can understand salespeople’s relationships with their prospects.
Really, Chorus.ai’s conversational intelligence could also be termed relationship intelligence. It supplements human intuition about social cues and relationship-building.
“We want to help capture all the different interactions that sales teams are having with their prospects,” Benton said. “I think the focus is just: How do we make sure that you’re bringing the best of your company to every interaction?”