BBD · DATA ANALYTICS & PERFORMANCE TRACKING SERVICE 08 / 16
CAPABILITY 08 / 16

Data,
that drives the call.

For operators tired of dashboards that don't change behavior — and reports nobody opens twice.

Diagnosis-firstDecision-gradeModeledTrustworthyOperational
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Capability
Data Analytics & Performance Tracking
Position
Between dashboard sprawl and gut-feel decisions
Entry
Data Diagnostic
Typical Deploy
2–8 weeks
Fit
Founder's Build · Targeted Build · Launch Retainer
Headquarters
Miami, FL · United States
DATA ANALYTICS & PERFORMANCE TRACKING

A capability brief from Bespoke Business Development — diagnostic-led, senior-run, and built to operate inside the business, not pitch around it.

BESPOKE BUSINESS DEVELOPMENT MIAMI · NEW YORK · LONDON · TOKYO
01
01 · The Shift

No longer a reporting layer.
An operating discipline.

Modern data work is not delivering more dashboards. It's delivering trustworthy answers fast enough that the business actually changes behavior. The teams winning treat analytics as an operational capability — not a service the BI team renders.

THE OLD ASSUMPTION

Data lived with a BI team. Stakeholders submitted requests; charts came back days later; decisions were already made on instinct.

Trust in numbers was a recurring debate. Definitions changed depending on who was asking.

THE NEW REALITY

Data is an operating capability. Modeled, governed, fast, and embedded in the cadence the business already runs.

Without trustworthy data wired into decisions, every meeting opens with a debate about whose number is right — and closes without a real call.

LEVERAGE

Trust

One source of truth. Same metric, same number, same definition — across every team.

LEVERAGE

Speed

Answers in minutes. Self-serve at the team layer; modeled at the warehouse layer.

LEVERAGE

Decisions

Dashboards built to change behavior — not to fill space in a deck.

02
02 · Two Traps

Most data programs collapse into
one of two failures.

The gap between data work that drives decisions and data work that produces backlog is rarely the warehouse. It's whether the work was scoped against actual operating moments.

TRAP 01
SPRAWL

Dashboards everywhere, nobody opens them.

Hundreds of charts across five tools. No source of truth. Every team has its own version of revenue, activation, and churn.

The cost is invisible — until a board meeting and three numbers don't match.

TRAP 02
BLIND

No tracking, no learning loop.

Decisions made on monthly P&L and gut feel. Funnel data missing, attribution broken, customer behavior unknown.

The cost is visible — every quarter — when the business can't explain why something worked or didn't.

What separates analytics that drives decisions from analytics that produces backlog is not tooling. It is whether the data is modeled, governed, and wired into the operating moments the business actually runs.
03
03 · The BBD Approach

Diagnose first.
Wire the data into decisions.

BBD treats data the same way every engagement is treated — by mapping the decisions the business is making blind before scaling instrumentation.

01

Data Diagnostic

Inventory current data, tracking, and dashboards. Map decisions being made blind. Find the warehouse, governance, and trust gaps.

02

Architecture & Model

Stand up the warehouse, build the semantic layer, define metrics in one place. End the 'whose number is right' debate.

03

Instrument & Build

Event tracking, integrations, and the dashboards built against decisions — not vanity metrics.

04

Operate & Govern

Embed analytics into the operating cadence. Govern definitions, monitor data quality, and retire dashboards no one uses.

WHAT YOU WON'T GET

A dashboard for every request. A vanity-metric scorecard. A warehouse no one trusts. A reporting deck nobody reads twice.

WHAT YOU WILL GET

A modeled warehouse, governed definitions, and dashboards wired into operating decisions — followed by the cadence that keeps the data trustworthy and acted on.

04
04 · Operational Scope

Three layers
of data work.

A complete analytics program extends across infrastructure, modeling, and operation. The scope below maps where the work creates measurable leverage.

01 / INFRASTRUCTURE

The pipes.

Warehouse, ingestion, and tracking — the foundation the rest of the program runs on.

  • Data warehouse (Snowflake, BigQuery, Redshift)
  • Ingestion (Fivetran, Stitch, Airbyte)
  • Event tracking (Segment, RudderStack)
  • Reverse ETL and activation
02 / MODEL

The truth.

Transformation, modeling, and governance — where definitions get standardized and the metrics layer is built.

  • dbt or SQL transformation
  • Semantic layer / metrics layer
  • Definition governance and ownership
  • Data quality and observability
03 / OPERATION

The decisions.

Dashboards, embedded analytics, and the operating cadence that turns data into changed behavior.

  • Executive and team dashboards
  • Self-serve and exploratory analytics
  • Embedded in operating reviews
  • Experimentation and causal inference
05
05 · The Practice Areas

Six practice areas.
One trustworthy data layer.

Each practice stands on its own or chains with the others. Most engagements begin with the diagnostic and move outward from there.

01

Data Diagnostic & Strategy

The diagnostic entry point. The decisions being made blind — and the data layer that would close them.
Founder's Build · Targeted Build

A fixed-scope diagnostic that produces a prioritized data action plan tied to business decisions, not tool taxonomy.

Decision inventoryThe decisions the business currently can't answer with data.
Source and data auditWhat's already in the building — and how to trust it.
Tracking and instrumentation auditWhere the funnel is broken or unmeasured.
Dashboard and tool inventorySprawl, duplicates, and abandoned reports.
Governance readDefinitions, ownership, and trust gaps.
Prioritized data planSequenced moves with timelines and ROI.
02

Data Architecture & Warehouse

Warehouse standup, ingestion, and the foundational layer.
Founder's Build · Targeted Build

Warehouse architecture is the foundation — done right, every downstream report inherits trust. Done wrong, every dashboard is a renegotiation.

Warehouse selection and standupSnowflake, BigQuery, Redshift — fit to scale.
Ingestion and ELTFivetran, Stitch, Airbyte, custom.
Schema and data architectureLake, lakehouse, and warehouse layering.
Reverse ETLOperational data back into tools (HubSpot, Salesforce).
Privacy and securityPII handling, retention, access controls.
Cost monitoringWarehouse spend kept proportional to value.
03

Tracking & Instrumentation

Event tracking, attribution, and the data the funnel actually generates.
Targeted Build · Launch Retainer

Most analytics gaps live at the source — tracking that's incomplete, inconsistent, or broken. The fix is rarely a new tool; it's a tracking plan that's owned.

Event taxonomyA measurement plan tied to decisions.
Web and product trackingSegment, RudderStack, server-side.
Server-side and CAPIRestoring data lost to iOS and ad blockers.
Customer data platformUnified profile across systems.
Identity resolutionAnonymous to known to customer.
Tracking QAValidation and data observability.
04

Modeling & Metrics Layer

Transformation, semantic layer, and governance.
Founder's Build · Targeted Build · Launch Retainer

The metrics layer is where 'whose number is right' debates end. Definitions get codified, owned, and reused — every downstream tool inherits the same truth.

dbt and SQL transformationModeled tables that hold up over time.
Semantic / metrics layerCube, dbt Semantic, LookML, or native.
Metric definitions and ownershipOne definition per metric, owned.
Data quality and testsTests that fail loud — not silent.
DocumentationLiving docs the team actually uses.
Versioning and changelogDefinitions that evolve with the business.
05

Dashboards & Embedded Analytics

The decision surfaces — built against operating moments, not vanity metrics.
Targeted Build · Launch Retainer

A dashboard is only useful if it changes a decision. The work is figuring out which decisions matter — and building a small number of dashboards that change them.

Executive dashboardsThe five-to-seven metrics leadership runs the business on.
Team-level dashboardsOperational data the team uses weekly.
Self-serve explorationLooker, Metabase, Hex, Mode.
Embedded analyticsCustomer-facing reporting in product.
Anomaly detectionAlerts that fire when something changes.
Dashboard governanceRetire unused; consolidate duplicates.
06

Experimentation & Causal Analytics

A/B testing, incrementality, and the analytics that prove what worked.
Targeted Build · Launch Retainer

Correlation reports are easy. Causal inference is the work. The retainer runs the experimentation program — so growth decisions are made on lift, not vibes.

A/B testing frameworkStatsig, Optimizely, custom.
Geo and holdout testingCausal incrementality on real audiences.
MMM (marketing mix modeling)Channel-level ROI without cookies.
Cohort and retention analysisThe metrics that decide product fate.
LTV / CAC modelingUnit economics tracked over time.
ForecastingOperational and financial forecasting.
TIMELINE

2–8 weeks

From diagnostic to a trustworthy warehouse, modeled metrics, and the first decision-grade dashboards.

TRUST

One truth

Definitions governed in one place — every downstream tool inherits the same number.

DECISIONS

Wired in

Dashboards embedded in the operating cadence — not delivered as a quarterly artifact.

DISCIPLINE

Retire unused

Dashboards no one opens get retired. The data layer stays signal, not noise.

06
06 · Platforms & Stack

The toolkit
that delivers.

The stack is built around a trustworthy modern data foundation — and dashboards that change decisions, not decorate them.

Warehouse
Snowflake · BigQuery · Redshift

Modern cloud warehouses sized to scale.

Ingestion
Fivetran · Airbyte · Stitch

ELT for SaaS, ad, and operational sources.

Tracking
Segment · RudderStack

Event collection and routing.

Transform
dbt

SQL transformation and modeling.

Semantic
Cube · LookML · dbt Semantic

Metrics layer with governed definitions.

BI
Looker · Hex · Metabase · Mode

Dashboards and self-serve exploration.

Product Analytics
Amplitude · Mixpanel

Behavioral analytics and cohort tools.

Reverse ETL
Hightouch · Census

Operational data into business tools.

Experimentation
Statsig · Eppo · LaunchDarkly

A/B and feature-flag platforms.

Observability
Monte Carlo · Datafold

Data quality and lineage.

Customer Data
Hightouch · Segment Unify

Identity resolution and CDP.

AI Layer
Claude · GPT

Analyst assist and natural-language queries.

07
07 · Use Cases

What this looks like
in a real business.

Nine patterns that show up across most engagements — grouped by infrastructure, modeling, and operation.

INFRASTRUCTURE
Warehouse standup

A scattered toolset gets unified into a modern warehouse — and the next quarter's reporting runs on one source of truth.

Leverage · Trust restored
INFRASTRUCTURE
Tracking rebuild

Server-side tracking and a clean event taxonomy replace a broken pixel layer — and the funnel becomes legible again.

Leverage · Funnel visibility
INFRASTRUCTURE
Reverse ETL activation

Modeled customer data flows back into HubSpot and Salesforce — and the GTM team starts acting on warehouse-grade signals.

Leverage · Operational data
MODEL
Metrics layer

Revenue, MRR, activation, churn — defined once, governed, and inherited by every downstream tool. The 'whose number is right' debate ends.

Leverage · One source of truth
MODEL
Cohort framework

Retention modeled in cohorts — not lump averages. The team finally sees which acquisition vintages compound.

Leverage · Honest LTV
MODEL
Forecasting standup

Operational and financial forecasts modeled and version-controlled — board decks stop being a fire drill.

Leverage · Forecast credibility
OPERATION
Executive dashboard

Five-to-seven metrics leadership runs the business on — embedded in the weekly operating review.

Leverage · Faster decisions
OPERATION
Experimentation program

A/B testing turned from one-off stunts into a weekly discipline — and growth bets are made on lift, not opinion.

Leverage · Compounding wins
OPERATION
Dashboard retirement

Unused dashboards retired, duplicates consolidated, and the surface area cut by 70% — and adoption climbs.

Leverage · Signal over noise
08
08 · Engagement Fit

How analytics enters
a BBD engagement.

Data work is a layer inside the three engagement models — not a separate analytics agency. The right entry depends on where the business is.

ENGAGEMENT 01

The Founder's Build

Analytics built before the business runs. Warehouse, tracking, and the first dashboards in the foundation — so the company launches with measurement, not retroactive instrumentation.

  • Warehouse and tracking foundation
  • Event taxonomy tied to decisions
  • Executive dashboard at launch
  • Metrics governed from day one
ENGAGEMENT 02

The Targeted Build

For businesses already running. A scoped intervention on the part of the data layer that's broken — usually warehouse standup, tracking rebuild, or metrics layer.

  • Warehouse and modeling rebuilds
  • Tracking and instrumentation audits
  • Metrics layer standups
  • Executive dashboard redesign
ENGAGEMENT 03

The Launch Retainer

Ongoing data operations after the build. Modeling, governance, dashboard work, and the experimentation program run as a continuous capability.

  • Ongoing modeling and governance
  • Dashboard development and retirement
  • Experimentation program operation
  • Quarterly data health review
09
09 · Frequently Asked

Questions we answer
before the consultation.

Plain answers to the questions that come up on most first calls.

Are you analysts or engineers?

Both. Most engagements include analytics engineers (warehouse, dbt, modeling), product/marketing analysts (instrumentation, dashboards, experimentation), and a strategy layer that ties data to decisions. Same team.

Do we need a warehouse if we already have GA4 and HubSpot?

Often yes. Tool-level analytics gives a partial view. A modeled warehouse is what produces a single source of truth across acquisition, product, revenue, and finance. Without it, every cross-tool number is a debate.

How do you avoid building dashboards no one uses?

By scoping every dashboard to a specific operating decision — and retiring dashboards that aren't getting opened. Dashboard sprawl is treated as a failure mode, not a feature.

What about data privacy?

PII handling, retention rules, and access controls are part of the architecture work — not a follow-up project. Server-side tracking and identity resolution are designed against the privacy posture the business needs.

Do you handle marketing attribution?

Yes — including server-side tracking, MMM, and geo incrementality testing. The truth layer is incrementality, not last-click. Platform self-reports get triangulated against modeled data.

How long until our team owns this?

From day one. The team is in every step — modeling decisions, definitions, tooling. The retainer transitions out as ownership transitions in. The goal is a self-sufficient data capability, not a permanent dependency.

Can you embed analytics in our product?

Yes. Customer-facing analytics — usage dashboards, billing transparency, performance metrics — are a common scope inside the product engineering work.