One Source of Truth for Every Revenue Decision.
Unified dashboards, data warehouse builds and automated reporting that replace spreadsheet chaos with reliable intelligence. Leadership gets clear metrics. Teams stop arguing about whose numbers are right.
Koldconvert Data Analytics and BI connects your CRM, marketing platforms, product database and billing system into a unified data warehouse. We model your metrics using dbt so that ARR, CAC, LTV, churn and pipeline coverage mean the same thing across every dashboard. Leadership stops making decisions based on last week's spreadsheet export and starts operating from live, trusted data every day.
What is business intelligence?
Business intelligence (BI) is the set of technologies, data processes and visualisation tools that transform raw operational data into consistent, reliable metrics that leadership and teams can act on. A BI system connects to all data sources across a business, standardises how metrics are calculated, stores the transformed data in a central warehouse and presents it in role-specific dashboards. The goal of BI is to eliminate the reporting lag, metric disagreements and manual data work that slow down decision-making in growing companies, replacing ad-hoc spreadsheet analysis with live, trusted intelligence available to every decision-maker without requiring technical skills to access it.
Revenue Intelligence Infrastructure
Data Warehouse
Centralised data warehouse in BigQuery or Snowflake connecting CRM, marketing, product and billing data. Built on dbt models with documented metric definitions agreed across the business.
Revenue Dashboards
Leadership-level dashboards covering MRR, ARR, churn, pipeline coverage, CAC, LTV and CAC payback. Built for the board meeting and the Monday morning review, not the data team.
Marketing Attribution
Multi-touch attribution models connecting marketing spend to pipeline generated and revenue closed. Understand which channels produce the highest-quality pipeline, not just the most leads.
Automated Reporting
Scheduled reports delivered to Slack, email or your CRM automatically. No one needs to pull a report again. Alerts trigger when metrics cross thresholds that require attention.
Signs You Need a BI System
- Finance and marketing have different MRR numbers in their spreadsheets and no one knows which one to trust for the board presentation.
- The Monday metrics review requires someone to spend Friday afternoon manually pulling and cleaning data across four different systems.
- You cannot answer basic revenue questions quickly: what is CAC by channel, what is NRR this quarter, which cohort of customers churns earliest.
- Investor and board reporting takes two weeks to prepare because data is scattered across your CRM, billing platform and marketing tools with no central connection.
- The sales team, marketing team and finance team each have their own definition of a customer, a lead and a sale, leading to constant miscommunication about performance.
From Data Audit to Live Intelligence
Audit
Audit all existing data sources, data quality issues, current reporting gaps and the key decisions leadership needs to make faster with better data.
Model
Design data models using dbt that define consistent metrics, dimensions and relationships across all source systems. Every metric definition is documented and agreed.
Warehouse
Build the data warehouse in BigQuery or Snowflake. Connect source systems via Fivetran or Stitch. Transform raw data into clean, consistent tables for reporting.
Visualise
Build role-specific dashboards in Looker Studio, Tableau or Power BI. Train the team. Set up automated alerts and scheduled reports for key metric thresholds.
The Koldconvert Revenue Intelligence Framework
The Koldconvert Revenue Intelligence Framework starts from the premise that most BI projects fail because they are built for data teams rather than decision-makers. We begin every engagement by identifying the five to ten decisions that leadership needs to make faster and better. Every data model, every metric definition and every dashboard is designed to answer those specific questions. We use dbt as the transformation layer so that metric logic is version-controlled, testable and auditable rather than buried in dashboard SQL that no one can find or modify. The result is a system that grows with the company: when a new data source appears, it gets modelled, tested and added to existing dashboards rather than triggering a rebuild.
The most common BI mistake we see is building dashboards before defining metrics. Companies stand up Looker or Power BI, connect it to their CRM and call it a BI system. Then in the first board meeting, someone asks what MRR means in the context of multi-year contracts with variable usage and the entire reporting framework breaks down. We define metrics before we build dashboards. What does a customer mean? What counts as churn? How do we handle upgrades and downgrades in NRR? These definitions are documented, agreed across finance, sales and product, and then encoded in dbt models that every dashboard draws from. That is what a single source of truth actually means.
Koldconvert Revenue Operations Team
What Clients Achieve
Benchmark Results for Revenue Intelligence Implementations
What You Receive
- Data warehouse in BigQuery or Snowflake with all source systems connected and syncing automatically
- dbt project with documented data models, metric definitions and automated tests for data quality
- Executive dashboard covering MRR, ARR, churn, NRR, pipeline coverage, CAC and LTV
- Department-level dashboards for sales, marketing and customer success with role-specific views
- Automated Slack and email alerts when key metrics cross defined thresholds
- Data documentation and metric glossary so every team knows exactly how each number is calculated
Tools and Platforms We Work With
Data Analytics and BI Across Sectors
SaaS
MRR, ARR, churn, NRR, CAC payback and cohort retention dashboards for subscription businesses. SaaS BI connects billing, CRM and product usage data to give a complete picture of revenue health and growth efficiency.
Fintech
Transaction volume, AUM growth, activation rates and regulatory reporting dashboards. Fintech BI must handle high data volumes with strict audit trails and compliance-ready metric definitions.
Ecommerce
Revenue attribution, customer LTV, repeat purchase rate and inventory performance dashboards. Ecommerce BI connects Shopify or WooCommerce with Google Ads, Meta and email to show true blended CAC and channel ROAS.
Healthcare
Patient outcome reporting, capacity utilisation, referral source analysis and operational performance dashboards. Healthcare BI is built with data governance and access controls that meet regulatory requirements.
Professional Services
Utilisation rate, project profitability, client retention and pipeline dashboards for consultancies, agencies and law firms. BI for professional services connects time-tracking, billing and CRM data into a single profitability view.
Marketplace
GMV, take rate, buyer and seller cohort analysis and supply-demand balance dashboards for two-sided marketplace businesses. Marketplace BI must track both sides of the network simultaneously.
Logistics
Delivery performance, route efficiency, warehouse utilisation and carrier cost dashboards. Logistics BI integrates TMS, WMS and carrier data to give operations leaders real-time visibility into the supply chain.
EdTech
Enrolment conversion, course completion, learner retention and revenue per cohort dashboards. EdTech BI connects learning management systems with billing and marketing to show the full learner journey from acquisition to completion.
Manufacturing
Production output, defect rates, equipment utilisation and supply chain performance dashboards. Manufacturing BI integrates ERP and IoT sensor data to give plant managers and operations directors a live operational picture.
CleanTech and Energy
Energy production monitoring, carbon accounting, grid performance and project ROI dashboards. CleanTech BI handles time-series sensor data at scale and produces the audit-ready reporting that regulators and investors require.
Koldconvert BI vs Spreadsheet-Based Reporting
| Factor | Koldconvert BI | Spreadsheet Reporting |
|---|---|---|
| Data freshness | Live or near-real-time from source systems | As old as the last manual export |
| Metric consistency | Single definition encoded in dbt, used everywhere | Finance, sales and marketing have different formulas |
| Reporting overhead | Zero manual work once built | Multiple hours per week of manual data pulling |
| Scalability | Handles billions of rows without slowing down | Breaks at scale, slow pivot tables, version chaos |
| Accessibility | Any stakeholder sees live data via a browser link | Must email the file, risk of sharing wrong version |
| Error risk | dbt tests catch data quality issues automatically | Formula errors discovered in board meetings |
Data analytics and BI, answered
Business intelligence (BI) is the technology, processes and tools that collect, integrate and visualise business data to support better decision-making. BI systems connect to operational data sources, transform raw data into consistent metrics and present it in dashboards that leadership and teams can use daily without needing to query databases manually.
A data warehouse is a centralised repository that consolidates data from multiple source systems — CRM, marketing platforms, product databases, billing systems — into a single structured environment optimised for reporting and analysis. Most growing B2B companies need one once they have three or more data sources that need to be combined for reporting.
A basic BI dashboard connecting 2-3 data sources can be delivered in 2-4 weeks. A full data warehouse build with dbt models and multiple departmental dashboards typically takes 6-12 weeks depending on data complexity, source system quality and the number of metrics required.
We use Looker Studio, Tableau and Power BI for dashboarding, dbt for data modelling, BigQuery and Snowflake for warehousing and Fivetran or Stitch for ETL. We recommend the right stack based on your existing infrastructure and team's technical capability.
Yes. We audit your existing stack first, then extend or rebuild components where needed. We work with most major cloud data platforms and can model within your existing warehouse if you already have one.
How to Work With Us
Data Audit and Roadmap
A focused engagement that audits your current data landscape, identifies the biggest reporting gaps and produces a prioritised roadmap for building your BI infrastructure. The starting point before a full build.
Full BI Build
End-to-end build of your data warehouse, dbt models and dashboards. Covers data source connection, metric definition, warehouse build and dashboard delivery across executive, sales, marketing and customer success views.
Ongoing Data Partnership
Ongoing support for your data infrastructure: new metrics, new data sources, dashboard updates and data quality monitoring. Suited for companies that want a fractional data team on retainer rather than a full-time hire.
Data Analytics Glossary
- Data Warehouse
- A data warehouse is a centralised storage system designed for analytical queries rather than transactional operations. It consolidates data from multiple source systems into a consistent schema optimised for the aggregation, filtering and calculation that BI and reporting require.
- ETL (Extract, Transform, Load)
- ETL is the process of extracting data from source systems, transforming it into a consistent format and loading it into a data warehouse. Modern ETL tools like Fivetran handle the extract and load phases, while dbt handles the transformation step inside the warehouse.
- dbt (data build tool)
- dbt is an open-source transformation tool that allows data teams to write SQL-based data models with version control, automated testing and documentation. It standardises how metrics are calculated inside the warehouse so that every dashboard draws from the same tested, auditable source of truth.
- NRR (Net Revenue Retention)
- NRR measures how much of the revenue from an existing customer cohort is retained and grown over a period, after accounting for expansion, contraction and churn. NRR above 100% means the existing customer base is growing in revenue without any new logo acquisition.
- CAC Payback Period
- CAC payback period is the number of months required for a company to recover the cost of acquiring a customer from that customer's gross margin contribution. A payback period under 12 months is considered strong for SaaS. Longer payback periods increase capital requirements and risk, particularly if churn is high.
- MRR (Monthly Recurring Revenue)
- MRR is the predictable monthly revenue generated from all active subscriptions, normalised to a monthly amount regardless of billing frequency. It is broken down into New MRR, Expansion MRR, Churned MRR and Contraction MRR to give a complete picture of how the recurring revenue base is moving each month.
Ready to replace spreadsheet chaos with trusted revenue intelligence?
Book a call. We will audit your current data landscape and design the right BI infrastructure for your stage and stack.