Thinklytics

Technology & SaaS Analytics Consulting

Technology and SaaS analytics. The SaaS metrics layer, governed pipeline and revenue reporting, and AI readiness for fast moving B2B companies.

Topics

  • SaaS data analytics consulting
  • SaaS metrics layer
  • ARR analytics
  • product analytics consulting
  • revenue attribution analytics
  • SaaS BI consulting

Work delivered in this industry

AT&T

We retired 4,380 Tableau workbooks, cut server response time from 47 to 9 seconds, and avoided $6.2M in migration costs.

4,380 Workbooks retired.

Enterprise SaaS Company

We identified inconsistencies across six revenue metrics and created one certified ARR definition, resolving a $1.4M reporting gap between finance and sales.

$1.4M Revenue discrepancy resolved.

Lumen Technologies Division

We replaced nine outdated reporting systems with a single platform, reducing infrastructure costs by $3.1 million annually and cutting the monthly close process from 18 days to 3.

$3.1M Annual infrastructure savings.

Informatica

Implemented a data governance framework for four product lines that cut audit prep time from six weeks to four days and prevented $1.8 million in regulatory fines.

23 Audit deficiencies resolved.

Domo Analytics

Built and launched a churn prediction model in 10 weeks that flagged $2.6M in at-risk ARR and cut churn by 340 accounts in three months.

$2.6M At-risk ARR identified in Q1.

Rackspace Technology

Consolidated 14 business unit data warehouses into a federated data mesh, cutting $4.7M in annual infrastructure costs and unlocking cross-unit analytics.

$4.7M to $1.1M Annual infrastructure cost.

What clients in this industry usually arrive with

  • Reporting cannot be trusted, described as sales and finance numbers do not match or inconsistent KPI reporting. Resolved by Metric governance, data cleaning, master data management, data engineering.
  • Reporting takes too much work, described as automate monthly reporting or combine reports from multiple systems. Resolved by Executive reporting, integrations, ERP consolidation.
  • Employees waste time finding information, described as search internal company documents or find policies and procedures quickly. Resolved by Knowledge assistant, RAG, unstructured data platform.
  • Systems require manual handoffs, described as eliminate manual data entry between systems or automate approval workflows. Resolved by API integration, workflow automation, human review design.
  • AI projects are not delivering value, described as measure AI ROI or AI pilot not moving to production. Resolved by Strategy, readiness assessment, AI PMO, adoption.
  • AI is too risky to deploy, described as AI confidential data protection or control AI agent permissions. Resolved by Governance, AI security, agent identity, evaluation.

Other industries

Start with the 30-day Analytics Truth Audit.

SaaS data work that holds up in the board room.

Why SaaS metrics projects stall.

What SaaS and technology clients ask before they engage.

Often paired with SaaS and technology engagements.

Ready to fix your SaaS metrics layer?

Data team scaling: hiring plans, tooling decisions, and operating models

No unified customer health model connecting product usage, support, and billing data

How do you resolve ARR discrepancies between finance and product teams?

We build a single certified ARR definition in a semantic layer that both finance and product teams consume. We document every business rule, handle edge cases like multi-year contracts and mid-period expansions, and create a reconciliation process that makes discrepancies visible and resolvable before they reach the board.

Can you help us build a product analytics stack from scratch?

Yes. We help early-stage and growth-stage SaaS companies design and build their product analytics infrastructure on modern tools like Snowflake, dbt, and Looker. We make tooling decisions based on your current scale and growth trajectory, not on what is most complex.

Most SaaS metrics layer engagements run 8 to 12 weeks. The scope is typically smaller than enterprise data foundation projects because the data environment is more modern. We start with a 30-day Analytics Truth Audit that identifies the specific metric inconsistencies before any build work begins.

Do you work with early-stage startups or only growth-stage companies?

We work across the full SaaS lifecycle. Early-stage companies typically need help building the right foundation before bad habits become entrenched. Growth-stage companies typically need help cleaning up metric inconsistencies that emerged as the business scaled. Both are solvable.

Technology and SaaS data analytics consulting. Build the SaaS metrics layer, product analytics infrastructure, and revenue attribution models investors trust.

Data and analytics consulting for SaaS and tech companies. Product, pipeline, and ARR metrics.

SaaS companies live and die by their metrics: ARR, NRR, churn, CAC, LTV. We build the metric layer that makes these numbers consistent across product, finance, and go-to-market teams, and defensible in a board room.

Start with a 30-day Analytics Truth Audit. We identify exactly what is broken and give you a 90-day roadmap to fix it.