Investigate why conversion fell after a Tableau launch

Problem Statement Description

Product context: Salesforce is an enterprise CRM and cloud software company; its products include Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Data Cloud, Einstein AI, Tableau, and Slack.

Salesforce has recently launched a Tableau-related experience aimed at admins in enterprise accounts, such as setup, onboarding, connector configuration, workspace provisioning, or a new analytics workflow tied to CRM data. Soon after launch, the team observes a meaningful drop in conversion. Conversion may refer to admins moving from awareness or trial into activation, completing setup, enabling Tableau for users, connecting data sources, or progressing to a paid/expanded deployment.

You are asked to investigate the drop as a root-cause analysis problem. The focus is not to redesign Tableau or propose growth tactics upfront, but to structure how you would confirm the anomaly, isolate where the funnel changed, identify whether the launch caused the decline, and determine what evidence would support the next action.

The situation sits in an enterprise environment where admins are highly sensitive to trust, permissions, governance, data connectivity, performance, user provisioning, and integration complexity. Because Tableau often depends on CRM data, identity systems, connectors, licenses, and organizational approval flows, the investigation should account for both product behavior and go-to-market or implementation factors.

The experience should consider:

- How to define the conversion metric precisely, including numerator, denominator, funnel step, time window, and admin cohort.

- Whether the drop is broad-based or concentrated by segment, such as company size, industry, region, license type, Salesforce edition, new vs. existing customers, or admin experience level.

- Funnel stages where admins may be abandoning, such as landing page, trial start, login, connector setup, permission granting, data refresh, dashboard creation, publishing, or inviting users.

- Instrumentation checks to confirm whether tracking, attribution, event definitions, or dashboard logic changed during the Tableau launch.

- Product and technical hypotheses, including latency, errors, broken connectors, permission failures, SSO issues, confusing setup flows, data governance warnings, or compatibility problems.

- Business and operational hypotheses, such as pricing/package changes, sales enablement gaps, customer success readiness, documentation quality, support ticket volume, or competitive displacement.

- Evidence needed to separate correlation from causation, including pre/post trends, control groups, rollout timing, experiment exposure, release versions, and comparable unaffected cohorts.

- Mitigation and prevention considerations, including severity assessment, rollback or hotfix criteria, customer communication, monitoring, and future launch safeguards.

Your goal is to present a clear investigation plan that helps Salesforce determine why Tableau conversion fell, how severe and localized the issue is, what data would validate or reject leading hypotheses, and what decision the team should make next without jumping prematurely to a solution.