CASE STUDY Uses Deepgram’s Tailored Speech Models to Drastically Improve Sales Analysis

One-size-fits-all speech recognition models miss too much vertical-specific terminology.

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How - testimonial Benefits from Deepgram

Better insights from more accurate audio transcriptions
More insights into sales conversations
Increased accuracy of topic and issue detection
Increased accuracy of topic detection, including industry-specific jargon powers high-performing teams with real-time guidance. By surfacing and recommending what works best, enables hundreds of customers like HPE, Nutanix, and AWS to deliver predictable results and optimize their entire revenue operation.
IndustryRevenue Operations Software
Business NeedsHelp customers… Unlock human potential with real-time guidance Know exactly where, why, and how they win. Scale demand, improve win rates, and accelerate deal velocity Unify revenue stakeholders with a single source of truth
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The Landscape

Bringing the Patchwork of Sales Enablement Solutions Under One Platform

Some RevOps solutions surface insights. Others give customers the tools to act on them. does it all—so customers can optimize revenue performance in real-time. Converting conversations into an actionable data source is critical for revenue teams. Conversations are where deals get won or lost, but many companies don’t have direct insights into these conversations at scale. helps customers understand what habits of top performers should be scaled across the team. In addition, reps are led by real-time guidance that helps teams grow revenue.

The Challenge

Analyzing Sales Team Conversations from Various Industries

As with all data analysis, garbage in is garbage out. For sales conversations, that means you need to be able to transcribe all the company names, product names, terminology, and specific words so you can analyze when the conversation is about a specific product or company. As Scott Hoch, Head of Data of, said, “You can build the most cutting edge AI model, but if you don’t put good data in, you’re not going to get good results.” Because different industries and companies have different terminology, jargon, product, and company names, found that improving the specific word accuracy of the one-size-fits-all Speech to Text (STT) solutions for specific customers and use cases were not possible.

Scott clarifies what he means by accuracy: “There are little things like making sure your company name is transcribed correctly. To bigger things where if you change one word in a sentence or you miss one word, it can change the entire interpretation of a sentence.” Good data is not just defined by the overall accuracy or word error rate (WER) but by the accuracy of important, content-heavy words in the conversation. For their analysis, getting all the “a”, “the”, and “of” words right doesn’t matter as much as getting the important words: product or company names like “XL40” and “” or sentiment words like “great” or “poor”.

The Solution

Tailored Speech Models That Perform Better than Big Tech

Before selecting Deepgram, compared many ASR providers including the Big Tech STT providers. Their models were initially a bit better, but with Deepgram, they were able to train and customize a speech model using audio from’s own platform, which led to a dramatic accuracy increase beyond what the all-in-one ASRs could reach. This training was not a resource or time burden for They trained tailored speech models for their customers within weeks with no resources needed from except providing 20+ hours of audio.