Background

Lightdash is a Series A company backed by Tier 1 investors like Accel and Y Combinator. Lightdash instantly turns your dbt project into a full-stack BI platform. Analysts write metrics and Lightdash enables self-serve for the entire business.

They've been growing rapidly and this case study explores how they leveraged Pylon to help them build a scalable, unified customer support process for their Slack Connect Channels, Slack Community, Email, and In-App Chat.

Challenge

As Lightdash grew, so did the demands to meet their customers where they are. Fast customer support is a priority for the team and they needed a tool that would enable them to keep providing the level of support their community expected as they scaled. In their case, customers communications spans 4 different channels:

1. Slack Connect

2. Slack Community

3. Email

4. In-App Chat

They had quite a few challenges to solve for:

• No one place where they could view all of their customer conversations. Supporting customers across four different channels is extremely difficult, especially if you have to use multiple tools to do it.

• Lack of basic support tooling. They had no way to assign issues to team members, set up automations, get alerts for missed SLAs, or see relevant customer data while responding to customers.

• Concerns about cost. They were previously leveraging Intercom for their In-App chat, but it was extremely expensive and they didn't want to be spending double on two support systems.

• Activity not syncing to Attio. They wanted to setup up RevOps workflows based on customer conversation data but it was all manual.

  
  

Lightdash was able to solve all of these problems thanks to the help of Pylon.

Results

Lightdash's support team now operates completely out of Pylon where they can see a consolidated view of all of their customer issues.

They get complete unification across their 4 customer channels and were able to get rid of Intercom for their In-App Chat experience saving heavily on cost.

For their tracked messages they can now leverage support system functionality including assignment, private notes, viewing customer plan data from Attio while responding to issues, SLAs, and exporting issues data.

The metrics they pull include analytics on support volume, first response time, and resolution time. This is filterable by date ranges, accounts, groups of accounts (based on their HubSpot data), issue assignee, and custom fields.     

And finally they're finally able to run RevOps workflows off the synced activity data Pylon automatically syncs to their CRM Attio. Now they can setup alerts like:

• No activity in 3 days for any prospect in a POC

• Customers they haven't engaged with in 1 month