Twine is an AI-powered platform that helps product teams get actionable insights from sales reps’ conversations with customers and prospects.

Twine needed to integrate their platform with customers’ CRM solutions to help product teams get the context necessary to understand, prioritize, and address customer insights.

Chris Manouvier, Twine’s CTO, adds more:

“By pairing relevant CRM data on accounts, like account names and associated ARRs, with insights from recorded calls, product managers can better understand the revenue opportunities or risks associated with product requests and feedback. This helps them prioritize effectively.” 

While the value of supporting CRM integrations was clear, the path to building and maintaining them at scale wasn’t. 

“We had several CRM integration requests, and we simply couldn’t afford to invest internal developer resources there instead of our core product. It was clear that we needed to outsource these integrations.”

As they evaluated different vendors, they ultimately decided between two unified API solutions: Merge and Nango. 

Chris explains why they went with Merge:

By using Merge’s comprehensive and accessible docs, Twine was able to build to Merge’s Unified API in less than a week. From there, they were able to offer integrations with widely-used solutions—like Salesforce and HubSpot—as well as more niche vendors, like Salesflare and Nutshell.  

Here’s how their CRM integrations work:

1. Once a user logs into Twine, they’ll navigate to integration settings and then click on the CRM system they want to connect to. This triggers Merge Link—an embeddable UI component—to appear.

2. If the user has any custom fields they want to map, they can do so in Merge Link (using Field Mapping) before going on to authenticate the integration.

3. Once the integration gets established successfully, data like account names, stages, and industries populates in the customer’s instance of Twine for every call that’s imported. This data is also re-synced every 24 hours to ensure it stays accurate over time.

The integrated CRM data enables Twine’s machine learning (ML) model to power several customer use cases.

For example, for a given customer, Twine’s ML model can analyze the transcripts of all the client conversations every week, extract the key parts the customer wants (e.g., product feedback), and then summarize the key themes that come up via plain text and video snippets. 

At the same time, the ML model can collect data related to these accounts in the customer’s integrated CRM to provide insights like the specific customer segment(s) that’s affected and the total ARR across these customers.

Finally, the ML model would provide its response to the customer’s product team in a platform like Slack, allowing the team to quickly understand the feedback and weigh it appropriately.

Twine’s prospects use a broad set of CRMs, so every integration they add through Merge unlocks market share.

Since they’ve managed to add more than 20 integrations, that’s led to a massive increase in their total addressable market (TAM).

According to Chris:

Chris and his team also appreciate how flexible and reliable Merge’s CRM integrations are.

“We never have to worry about Merge’s CRM integrations. It’s been ‘set it and forget it’ from the onset. When we couple the engineering hours saved by not having to perform ongoing maintenance with the initial integration build, we’re able to save at least $450k every year in engineering costs by using Merge.”

He goes on to explain that “Our customers have heavily customized CRM instances. To that end, Merge’s Field Mapping feature has been incredibly helpful in allowing our customers to add CRM integrations that accommodate their unique data models.”

We look forward to seeing Twine grow through their elevated TAM.