What challenges did GumGum face before AI implementation?

GumGum had scattered reporting data and KPI calculation inconsistencies across teams. Analysts were overloaded by ad hoc data requests from internal and external users. The company needed secure, scalable self-service access for more than 2,000 external clients.

What was the impact of the AI solution?

The portal eliminated practically all support tickets for data requests. The company improved cross-functional alignment and faster decision-making through consistent KPI definitions. Analysts shifted from answering routine requests to more innovative and revenue-generating work. Early tests suggest conversational analytics could reduce onboarding time and improve data accessibility.

Rank#273of 3,117

Source

GCP

GumGum — Conversational analytics and self-serve KPI portal using Looker (with Gemini in Looker)

Use case typeConversational analyticsUpdated Jul 9, 2026

GumGum, an ad tech company, needed a unified BI layer to centralize data silos, keep KPI definitions consistent, and remove analyst bottlenecks for more than 2,000 external clients. It implemented Looker and LookML as the primary BI and semantic layer, embedded dashboards into a self-service portal, and used row-level security and CI/CD practices to scale access safely. The company is also exploring Gemini in Looker and Model Context Protocol for conversational analytics and agentic data access.

Organization: GumGum
Industry: Tech & Comms
Location: United States
Published: July 2026

Reported outcomes

Strategic outcomes

Cost efficiencyEliminated nearly all data-request support ticketsBetter decisions & insightImproved cross-functional alignment and faster decision-makingEmployee experienceFreed analysts to focus on higher-value workOther strategic outcomePrepared for conversational analytics and agentic data workflows

Primary read

Use case focus

Showing 2 of 2

1Conversational analytics
2Customer service automation

GumGum had scattered reporting data and KPI calculation inconsistencies across teams.
Analysts were overloaded by ad hoc data requests from internal and external users.
The company needed secure, scalable self-service access for more than 2,000 external clients.

GumGum selected Looker as its enterprise BI platform and used LookML to define business logic, formatting, and calculations in one semantic layer.
It embedded dashboards into a self-service web portal with row-level security based on client IDs.
The team added continuous integration and deployment practices with Looker Continuous Integration to validate LookML code.
GumGum is exploring Gemini in Looker and Model Context Protocol for conversational analytics and future agentic workflows.

Technologies

Looker LookML Snowflake Gemini in Looker Model Context Protocol Looker Continuous Integration

The portal eliminated practically all support tickets for data requests.
The company improved cross-functional alignment and faster decision-making through consistent KPI definitions.
Analysts shifted from answering routine requests to more innovative and revenue-generating work.
Early tests suggest conversational analytics could reduce onboarding time and improve data accessibility.

Sources & evidence1

Groundedness: 4/5Type: Customer StoryPublished: Jul 9, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

Explore related AI use cases

Customer evidence