Attention uses natural language processing to help sales reps sell faster

Updating CRMs after each call is an important task for sales representatives, but it means a lot of administrative work that takes time away from actually selling. Attention wants to fix that with its sales assistant, which uses AI tech and natural language processing to automatically fill in CRMs after calls and draft follow-up emails.

The New York-based startup announced today it has raised $3.1 million led by Eniac Ventures, with participation from institutional investors Frst, Liquid2 Ventures, Maschmeyer Group Ventures and Ride Ventures. The round also included the founders of Ramp, Pawp, Truework and CBInsights.

Attention was founded in September 2021 by CEO Anis Bennaceur and CTO Matthias Wickenburg. The two met while running Swipecast and Mixer, competing job platforms for creative professionals. After five years of being rivals, the two got coffee and realized they face many of the same challenges with sales, like needing to constantly update Salesforce and onboarding new sales reps as quickly as possible.

“After many back-and-forths, we decided to work together,” said Bennaceur. “I had hundreds of conversations with sales leaders and junior sales reps, asking about their pain points, digging into potential desired solutions, and continuously iterating, while Matthias would build those solutions in parallel. After numerous iterations, we knew that we were onto something.”

One of the things Attention helps with is CRM hygiene, which means making sure CRM software is updated with clean and accurate data. Bennaceur explains this is important because chief revenue officers and vice presidents of sales rely on their organization’s CRM to track interactions with customers, manage leads and analyze sales data. This lets them make decisions on how to increase revenue.

But there are several barriers to maintaining CRM hygiene. For one thing, it’s a lot of administrative work for sales reps and takes time away from actually selling. It’s also easy to miss data when sales reps leave their jobs or pass accounts onto other reps. This results in lost leads and customer attrition. Finally, without any way to track what is said during sales calls, revenue leaders have a harder time deciding how to advance potential deals.

Attention fixes this by automatically exporting data from calls into CRMs. For example, if a sales team uses the MEDDIC sales methodology, a framework of questions that includes six steps, Attention knows if each step has been covered in a conversation, and exports that information into the relevant Salesforce or Hubspot fields. This reduces the amount of busywork sales reps need to do, while giving revenue leaders more insight into sales leads and revenue opportunities.

By using natural language processing, Attention is also able to identify content for sales coaching in calls. During a call, it displays battlecards in real-time to help sales reps figure out what to say. “Let’s say a prospect asks you a question on how compare to your competitor on a specific capability. A battlecard would contain the elements to answer that question appropriately, and appears on your screen during your conversation,” says Bennaceur.

To increase deal velocity, or the speed at which a sales organization is able to negotiate and sign contracts, sales teams need to send a lot of emails, quickly. But the followup email templates they often rely on are impersonal, while catered emails sometimes leave out important data, says Bennaceur. Attention is able to draft emails after calls based on what was said during the conversation. For example, a sales rep can ask Attention to “write an email recapping our conversation. Mention our prospect’s challenges and how our product can help them. And talk about next steps.”

Attention’s competitors include Gong and Chorus, both of which analyze customer conversations. Bennaceur says that Attention’s advantage is its ability to flexibly understand conversations, display real-time prompts during calls and provide A/B testing for its coaching. “We haven’t seen any of these players flexibly export conversations into CRMs, and this is a strong edge that we currently have,” he said.

In a statement about the funding, Eniac Ventures’ Hadley Harris said, “We’re thrilled to partner with Anis and Matthias as they leverage the latest developments in AI generation and natural language understanding to superpower sales organizations. We love working with repeat founders and couldn’t be happier with the strong pull they’re already getting from the market.”

Attention uses natural language processing to help sales reps sell faster by Catherine Shu originally published on TechCrunch