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Sales doesn't seem Mature for Automation, But it is

Mahesh Pardeshi

a year ago

sales automation | Insideaiml
Table of Contents
  • Algorithms cannot sell like a human being, but they can teach us on sales fundamentals
               1. DRIFT
                2. INFER
                3. GONG

Algorithms cannot sell like a human being, but they can teach us on sales fundamentals

          Modern-day sales are more about long-term customer interactions than big, one-and-done deals.
This has always been true — smaller customer churn cuts promotion charges — but it’s especially true today, as everything from designer clothing to SaaS products moves toward a subscription model.
“The best way to be effective in sales is to understand yourself, know your customer and know how you create strong interactions with other people,” Samantha Harrington opined in Forbes.
“Once you’ve built that relationship, shown you care and won their trust, you are on the road to making a customer,” Lee Ann Obringe wrote in HowStuffWorks.
Building trusting, enduring relationships does not 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 has not yet understood to empathize or make jokes.
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, told Built In. “I want the human at the front and center.” 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 underlining a specific product feature or talking less and listening more.
Benton calls this “conversational intelligence.”


          Based on the assessment of five million sales calls from more than 300 companies, recently circulated a round-up of macro trends in sales calls in the 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 the next steps.
(If cannot 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 strong trends aren’t the company’s bread and butter, though. In The Long Run, the ideal sales plan differs depending on the product and the clientele. facilitates 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 calculates success, or lack thereof, through an integration with the client’s CRM. There, it tracks which calls move a deal through a sales channel or communicate with expansions in a contract's price tag.
It drives backward from there, checking successful video call footage, via Zoom integration, for intelligence. It finds trends among the successful calls — phrases and strategies that crop up again and again — and encourages sales reps to try them out.
You could say it is automating management, but let’s be honest: No manager has time to listen to all their salespeople’s calls. 
Conversation Intelligence | Insideaiml


          In the era before AI, coaching meant a manager sitting in on a few randomly selected sales call, or overhearing snippets of dialogue from afar and weighing in. Software like 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.
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.”
AI Coach | Insideaiml


          In 2018, McKinsey reported that AI could create more than $1.4 trillion of value for marketing and sales teams. That is thanks in part to these companies, whose software applies AI to different facets of the sales effort.
AI-Enabled Sales Software | Insideaiml


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.


Lead Prioritization

          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.


Revenue Intelligence

          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.
revenue intelligence software | Insideaiml


           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, 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 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.
“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?”
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