SaaSProduct data
10×
revenue in one year
Data showed Optamark what to build next.
Optamark
◆ Data Accelerator™ · Revenue Data Engineering
Data engineering that ends at revenue.
We build one trusted customer model in your warehouse, from 200+ sources, and push it into the dashboards, CRM and AI agents your teams already use. Live in 2–6 weeks.
For teams with 500+ paying customers · data across 3+ tools · 2+ weeks per data question
The transformation2–6 weeks
Today
✕ Product, billing and CRM data never join
✕ Every question = 2+ weeks of eng
✕ Reps can't see product usage
✕ Three teams, three ARR numbers
→
With Optiblack
✓ One customer model in your warehouse
✓ Self-serve answers in minutes
✓ Usage and buying signals in CRM
✓ One trusted number for every team
$380M+
Revenue impact
100+
Businesses served
19M+
Users managed
2×
Mixpanel Partner of the Year
Databricks
Implementation partner
/ If you're stuck asking
The questions your data can't answer, because it doesn't join.
?Product + billing
"Which accounts are about to churn — and why?"
?Product + CRM
"Which free users should sales call today?"
?Marketing + billing
"Which campaigns bring revenue, not just signups?"
?Finance + CRM
"What's our real ARR and NRR per segment?"
?Product + support
"Where are paying customers getting stuck?"
?Usage + accounts
"Which features drive expansion?"
/ How it works
One customer model in your warehouse. Activated everywhere.
We engineer the layer between your tools and your teams. Data stays on your platform; we build, run and extend it.
01 · Sources
200+ tools you already use
◦ Product events
◦ Billing and revenue
◦ CRM and sales
◦ Support tickets
◦ Marketing and ads
+ 195 more
→
02 · Engineer
Pipelines and models
Ingest
Managed pipelines into your warehouse
Managed pipelines into your warehouse
Model
Tested dbt models for accounts, users, revenue
Tested dbt models for accounts, users, revenue
Resolve identity
User → contact → account → billing
User → contact → account → billing
Govern
Metric dictionary and quality checks
Metric dictionary and quality checks
→
03 · Single source of truth
Unified customer model
Accounts
Users
Revenue
Usage
In your warehouse: Databricks · Snowflake · BigQuery
→
04 · Activate
Where teams act on it
▸ Self-serve dashboards
▸ Scores and signals in CRM
▸ Slack alerts for owners
▸ Context for AI agents
Synced by reverse ETL
/ Identity resolution, explained
Illustrative example
Four tools, four versions of one customer. We join them into one account.
Product analyticsuser_8812 · jane@acme.com
CRMContact · Jane Doe · Acme
Billingcus_4F2 · Acme Inc · annual plan
SupportTicket #311 · jane@acme.com
→
Match rules
Email and domain
CRM and billing IDs
Account hierarchy
CRM and billing IDs
Account hierarchy
One account record
Acme Inc
Users
Active users, by role
Active users, by role
Revenue
Plan, MRR, renewal
Plan, MRR, renewal
Pipeline
Owner, open deals
Owner, open deals
Health
Usage trend, tickets
Usage trend, tickets
Without this step, "how many active customers do we have?" has four answers. With it, every dashboard, score and CRM field starts from the same account.
/ What we engineer
Five service lines. One revenue data layer.
01 · Start hereWeeks 1–6
Revenue Data Foundation
Warehouse or lakehouse, pipelines, dbt models, identity resolution and a metric dictionary.
Outcome: one trusted number
02Ongoing
GTM Activation
Reverse ETL, PQL and intent signals, and Slack alerts, pushed into HubSpot or Salesforce.
Outcome: reps act on live signals
03Ongoing
Revenue Intelligence
Attribution, health scores, churn and NRR models, and automated QBR data.
Outcome: prove ROI, predict churn
04Monthly
Managed Data Team
A dedicated team that runs, monitors and extends your stack. One new use case every quarter.
Outcome: no eng bottleneck
05Add-on
AI-Ready Data
Clean features and context so your AI agents work on trusted customer data.
Outcome: agents you can trust
Our stack
Databricks
Snowflake
BigQuery
dbt
Mixpanel
Segment
Hightouch
HubSpot
Salesforce
/ The Data2Dollar™ roadmap
Every GTM stage builds on one data foundation.
Start with Phase 1. Every later phase runs as a sprint on top of it, so nothing gets built twice.
Months 3–6Operationalize
Phase 2 · Activation
02Signal and intent routing
03AI SDR outreach
07AI sales enablement
09Integration-led growth
Sold as retainer sprints
Months 4–9Scale
Phase 3 · Measurement
06Multi-touch attribution
08Predictive churn and NRR
10Automated QBR
11Dark social attribution
Sold as retainer sprints
OngoingScale
Phase 4 · Monetization
05Usage-based pricing
12Benchmarks against your peers
Sold as a pricing project
Entry pointWeeks 1–10 · Establishment
Phase 1 · Foundation
Sold as a GTM Data Audit plus a Data Foundation build. Ends with one trusted number for every team.
01Data infrastructure and reverse ETL
04PLG telemetry and PQL scoring
How each engagement moves through the gates
0
Weeks 0–2
Audit
Map your stack, find the broken joins, and size the revenue leak.
- Stack and source map
- Metric conflicts found
- Scoped build plan
Gate: plan agreed
1
Weeks 1–6
Establishment
Build the foundation that makes data trustworthy and self-serve.
- Warehouse and pipelines
- dbt models, identity resolution
- Tracking plan, dashboards
Gate: one trusted number
2
Months 2–6
Operationalize
Turn the foundation into daily action for sales, CS and growth.
- Reverse ETL into CRM
- PQL and intent signals
- Slack alerts, automated reports
Gate: signals live in CRM
3
Ongoing
Scale
Compound the data into revenue, retention and margin.
- Attribution and ROI
- Health scores, churn, NRR
- Automated QBRs, experiments
Gate: ROI proven each quarter
Sold as
Fixed-fee audit
Sold as
Fixed-price build
Sold as
Monthly sprints
Sold as
Managed data team
/ Inside Phase 1
What happens in the first six weeks.
A typical Establishment plan. Exact timing depends on your stack and number of sources.
Wk 1
Wk 2
Wk 3
Wk 4
Wk 5
Wk 6
Audit and metric definitions
Pipelines into your warehouse
Tracking plan and events
dbt models and identity
Self-serve dashboards
CRM sync and team handover
Discovery
Engineering
Live for your teams
/ What your teams get
Every team, answering its own questions.
Sales
Call the right accounts
- PQL scores in CRM
- Buying-signal alerts
- Usage on every account
Customer success
Catch churn early
- Health scores
- Usage drop-off alerts
- Expansion signals
Growth and product
See what drives revenue
- Activation to revenue
- Attribution by channel
- Experiment results
Leadership and finance
One trusted number
- ARR and NRR that reconcile
- Revenue per segment
- Start the week with data
/ From signal to deal
Illustrative example
What "activated data" looks like on a Tuesday morning.
- 09:02 · ProductTrial user invites 3 teammatesEvent lands in the warehouse.
- 09:03 · ModelAccount crosses the PQL thresholdFit + usage score updates.
- 09:04 · ActivationCRM updated, owner alerted in SlackWith usage context attached.
- 09:30 · SalesRep calls with the right storyNo research, no guessing.
- Later · RevenueDeal tracked back to the signalAttribution proves what worked.
Without a data foundation, the same signal sits in a product tool nobody in sales opens.
/ Why Optiblack
Engineering depth and a revenue outcome, in weeks.
| What you need | Hire in-house | Data consultancy | Analytics agency | Optiblack |
|---|---|---|---|---|
| Live in weeks | ✕ 3–6 months to hire | ✕ Long projects | ✓ | ✓ 2–6 weeks |
| Warehouse and models you own | ✓ | ✓ | ✕ Tool-locked | ✓ On your platform |
| Signals pushed into CRM | Rarely | ✕ Stops at pipeline | ✕ Stops at dashboard | ✓ Plus CRM services |
| Starts from the revenue question | Depends | ✕ Starts from architecture | ✓ | ✓ Data2Dollar™ |
| Team that runs and extends it | ✓ If you can retain them | Extra contract | Limited | ✓ Managed data team |
/ Named teams
Measured outcomes.
2× Mixpanel Partner of the Year
Databricks partner
"Optiblack helped us decide the right ICP to go after for our go-to-market and built our entire data stack."
CGO, Luna
"We improved our trial rates by 20% in one week after working with Optiblack, leading to an increase in paid users."
CEO, Dictanote
"Now we look at data every day to make business decisions. Personally, the data is how I start my week."
CEO, Bettermode
/ Case studies
See all 56 customers →
Data that moved the number.
SaaSFull data stack
+20%
MRR
Luna used data to replace SDR outreach, on a stack Optiblack built.
Luna
eCommerceData strategy
30%
of revenue driven by data
A redesigned data strategy for youth sports camps.
Australian Sports Camp
Consumer techConversion
+20%
free-to-paid conversion
Trial rates up within one week, lifting paid users.
Dictanote
Public sectorData systems
<5 sec
for 200+ train allocations
Allocation at national scale, in seconds.
Indian Railways
SaaSData-led growth
Enterprise → PLG
growth model rebuilt on data
The CEO now starts every week with the data.
Bettermode
HealthcareModern data stack
Data stack
for healthcare contracting
A modern data stack built for a regulated industry.
Trekhealth
SaaSAnalytics foundation
Mixpanel
foundation for experimentation
Analytics rebuilt so every experiment is measured.
Convert
Fintech
Nuvama · Torus Digital · Tiers App · SalarySe
Healthcare and pharma
Dr Reddy's · Trekhealth · Medkart
SaaS
Bettermode · Convert · Snaptrude · Maxxton · Extramarks
Commerce and consumer
Landmark Group · Powerlook · Agrim · Fitpass · Vyro
/ How the engagement runs
Audit. Build. Run.
01
GTM Data Audit
We map your sources, find where revenue data breaks, and hand you a scoped plan. Fixed fee, credited to the build.
02
Foundation build
Warehouse, models and your first live outcome — a dashboard, a PQL score or a health score — in 2–6 weeks.
03
Run and extend
Your managed data team keeps it healthy and ships a new use case every quarter, tracked weekly against goals.
Not for: pre-revenue products, or teams that don't yet have much data.
/ Where are you today?
Level 2 of 5 · Tool dashboards
Find your next step on the data maturity ladder.
Pick the level that sounds most like your company.
Product, CRM and billing disagree.
What it looks like
Dashboards inside Mixpanel, HubSpot and Stripe, each with its own numbers.
The pain
Three teams, three ARR figures, and no view of the account.
Your next step
A warehouse, dbt models and identity resolution.
Start with: Phase 1 Foundation
Talk through my level →
/ 15-minute exploration call
Find where your revenue data is leaking.
We'll look at your CRM, product and billing data together and show you the fastest path to one trusted customer model.
Book a strategy call →