DECISION-DRIVEN ANALYTICS
Data Analysis & Insight Engineering
We wire marketing data into decision mechanisms, not dashboards. KPI tree, dbt modelling, Bayesian MMM, incrementality testing and self-serve analytics — the infrastructure of action, not measurement.
Analytics isn't 'building dashboards'; it's an operating system where every chart triggers a decision.
Most companies drown in 40+ dashboards yet get five different answers to the same question from five different sources. KPIs become debates, decisions get deferred, HiPPO wins. Roibase's analytics operation clears this uncertainty through six principles; every principle produces decisions, not dashboards.
METHODOLOGY
DIAGNOSE to MODEL to BUILD to AUTOMATE to VALIDATE to EDUCATE
Six layers of the analytics operation; each produces distinct artifacts and feeds the decision loop.
DIAGNOSE
Decision inventory + question map
The 30 questions decision-makers ask weekly are listed; source of answer, frequency, SLA and impact are made explicit.
MODEL
KPI tree + data model
dbt models + LookML or Metabase semantic layer; versioned, testable, documented.
BUILD
Dashboard + alert system
Dashboards organized by decision category (CAC, retention, revenue quality); threshold-based alerts + trigger templates.
AUTOMATE
Pipeline + refresh + monitoring
Refresh orchestration via Airflow / Dagster / dbt Cloud; pipeline health + data quality tests + Slack bot.
VALIDATE
A/B + incrementality + MMM validation
Model outputs are compared against experiments; calibration via incrementality testing + MMM scenario simulation.
EDUCATE
Data council + self-serve enablement
Monthly data council: which question went unanswered, which dashboard went unused, what self-serve training is needed.
— COMPARISON
Where we differ? Classic BI vs decision-driven analytics
A company can mistake 100 dashboards for 'analytics'. The real value emerges only when every dashboard is tied to a decision and every decision to an action.
| Dimension | In-house BI alone | Classic reporting agency | Roibase decision-driven analytics |
|---|---|---|---|
| KPI definition | Overlaps across teams | Agency template | KPI tree + written ownership |
| Dashboard philosophy | Chart abundance | Quarterly PPT focused | Every chart a decision |
| Data modelling layer | Ad-hoc SQL + Excel | Platform-native reporting | dbt + versioned + tested |
| Cohort + LTV engineering | Limited to average metrics | Not delivered as a report | D1-D90 + segment + LTV curve |
| MMM + incrementality | None | Excel-based attempts | Bayesian MMM + geo-holdout |
| Anomaly / alert system | Manual checks | None | ML drift detector + Slack/email |
| Self-serve culture | Data team bottleneck | Report-driven | Business units self-query |
| Governance + PII | No policy | Unaware | PII tagging + retention + audit |
PROOF
Outcomes, measured
Strategic questions that become answerable in the first sprint.
Hours reclaimed from the marketing team's manual dashboard prep.
Refresh cadence based on seasonality + channel mix changes.
Minimum daily data window required for MMM + forecast.
dbt + Airflow + monitoring SLA; data quality tests included.
Average time from brief to live for a new decision panel.
WHAT WE DO
Engagement scope
Every offering is an outcome-based work package. Roibase blends strategy and execution inside a single team — no hand-offs.
KPI tree architecture
Every marketing metric links directly to business output; every metric has an owner, a source, a threshold and a triggered decision.
Decision-tree dashboards
Not charts, decisions: panels designed with 'at this threshold, take this action' logic; each panel for a role, at a frequency.
dbt + warehouse + BI layer
Versioned + testable data models with dbt; on BigQuery / Snowflake / Redshift; surfaced through LookML / Metabase / Lightdash.
Cohort & retention engineering
D1/D7/D30/D90 cohort tables, LTV curves, segment-level churn and resurrection analysis — the real behavior under the average.
Bayesian MMM
Media, promo, seasonality and macro variables modelled together; Robyn + PyMC; quarterly refresh + confidence bands.
Attribution modelling
GA4 DDA + multi-touch attribution + Shapley value approaches; a decision model beyond platform-biased reporting.
Incrementality testing
Geo-holdout + matched-market tests; Meta Lift, GeoLift, in-house framework; the reference accuracy for budget decisions.
Anomaly detection
ML-based drift detector + forecast band + Slack/email alerts for silently deteriorating metrics; hourly, not morning-after.
Self-serve analytics
An environment where business units answer their own questions (Metabase, Lightdash, Hex) + training + mentoring.
Data governance
PII tagging, schema registry, retention policy, data access audit, documentation pack; KVKK + GDPR compliant operation.
— OUTCOME
The decision-side impact of a data operation
The faster, more data-grounded and more repeatable an organization's decisions are, the further ahead it stays in unpredictable market conditions.
Decision speed
All 30 strategic questions have answers on the panel; meetings debate action, not data.
HiPPO reduction
Data triggers decisions, not the highest-paid person's opinion; debate is referenced to metrics.
Reporting time saved
The marketing team's manual Excel routines end; reclaimed hours go into strategic analysis.
Early warning + action
ML drift detector + threshold-based alerts catch deteriorating metrics within hours.
Self-serve culture
Business units answer their own questions without waiting on the data team; the data team focuses on strategic work.
MMM + forecast accuracy
With Bayesian MMM + incrementality calibration, forecast deviation stays within ±8%; budget decisions are safe.
DELIVERABLES
Monthly + quarterly outputs
Concrete artifacts of the analytics operation; each is handed over to your team, and by month 12 the runbook enables fully independent operation.
Decision inventory + 30-question map
The list of questions decision-makers ask weekly, with source of answer, SLA and missing data needs.
KPI tree
Every metric's source, owner, threshold and triggered decision — a single Miro / FigJam board, versioned.
dbt repo + models
Versioned + testable dbt project; staging / intermediate / marts layers, documentation included.
Semantic layer (LookML / Metabase models)
The shared metric definitions layer behind every question business units will ask.
Dashboard pack
First 15-25 panels organized by decision category (CAC, retention, revenue quality); each by role + frequency.
Threshold-based alert system
ML drift detector + forecast band + Slack/email integration; deteriorating metrics trigger alerts within hours.
Cohort + retention report
D1/D7/D30/D90 tables + LTV curves + churn segment analysis + resurrection rate.
MMM model + report
Bayesian MMM (Robyn/PyMC); channel contribution + saturation + adstock + confidence bands.
Incrementality test protocol
Geo-holdout and matched-market test framework; planning + execution + analysis templates.
Data governance runbook
PII tagging, schema registry, retention policy, access audit — KVKK + GDPR compliant.
Monthly data council summary
Which questions got answered, which didn't, which dashboards got used, and a priority list for next month.
Self-serve training material
Metabase / Lightdash / Hex training videos for business units + SQL / jargon glossary + practice dataset.
— SCOPE
What's included, what isn't?
The boundaries of the analytics operation are clear. Seeing scope up-front removes wrong expectations and scope creep.
What this service covers
- Decision inventory + 30-question first sprint
- KPI tree + written ownership + versioned document
- dbt repo setup + staging/intermediate/marts layers
- Warehouse integration (BigQuery / Snowflake / Redshift / Databricks)
- LookML or Metabase semantic layer
- First 15-25 dashboards + quarterly additions
- ML-based anomaly detection + threshold-based alerts
- Cohort + LTV + retention analytics — quarterly refresh
- Bayesian MMM (3 refreshes per year)
- Incrementality test protocol + execution
- Data governance runbook (PII, retention, audit)
- Monthly data council + self-serve training flow
What's not included (optional extensions)
- Finance / accounting BI (ERP-side is separate consulting)
- Warehouse compute / license costs (customer's contract)
- Custom ML model training (beyond forecasting)
- Real-time streaming infrastructure (Kafka, Kinesis — separate scope)
- Data privacy / legal counsel (with a partner lawyer)
- BI tool license renewals
- Third-party data purchases (panel, survey)
- Marketing operations themselves (PPC / SEO / CRO are separate services)
HOW WE WORK
Process: analytics operation from Week 1 diagnosis to Month 6+ governance
Weeks 1-2 — Decision inventory + audit
The list of 30 strategic questions, current dashboard inventory, data source health, and SLA diagnosis.
Week 3 — KPI tree + schema
Written KPI tree, metric definitions, ownership; warehouse schema + staging layer decisions finalized.
Weeks 4-5 — dbt models + first dashboards
dbt staging + intermediate + marts; first 5-8 dashboards publish; stakeholder review.
Weeks 6-8 — Alerts + cohorts + refresh
Threshold-based alert system, cohort + retention reports, dbt Cloud / Airflow refresh pipeline.
Month 3 — MMM train + first result
Bayesian MMM on 18 months of history; channel contribution + saturation + first budget revision recommendation.
Month 4 — Incrementality test protocol
Geo-holdout or matched-market framework; first test goes live, results in 4-6 weeks.
Month 5 — Data council + self-serve training
Monthly data council routine starts; Metabase / Lightdash self-serve training flow for business units.
Month 6+ — Quarterly refresh + governance
Quarterly MMM refresh, incrementality test cycle, data governance audit; full handover possible at month 12.
— TOOL STACK
From warehouse to BI — the analytics stack
We work tool-agnostic; but at every layer, there are clear picks that produce the most value. We adapt to your existing stack.
WAREHOUSE
MODELLING & TRANSFORM
BI & VISUAL
ML & MMM
QUESTIONS
Frequently asked
— GLOSSARY
Analytics terminology
When teams use the same term to mean the same thing, debate accelerates the decision; when they don't, doubt accelerates instead.
- KPI Tree
- The hierarchical tree of metrics that cascade down from a core business output; every node is a decision trigger.
- dbt
- Data build tool — an SQL-based, versioned, testable data transformation framework; the standard of analytics engineering.
- Semantic Layer
- The shared metric definitions + business logic layer behind the BI tool; implemented with LookML, Metabase models, Cube and similar.
- Cohort
- A group of users that share a defining property (signup date, acquisition channel); their behavior is analyzed over time.
- LTV (Lifetime Value)
- A customer's total lifetime value; gross margin x retention x order frequency x basket value.
- Retention
- The percentage of acquired users still active in a given time window (D1, D7, D30, M1, M3). In SaaS and mobile games it is a direct read on product-market fit; a cohort curve that flattens out is the signature of a healthy product.
- Churn
- The percentage of users leaving the active customer base in a given time window. In subscription businesses it hits MRR directly; in e-commerce it is the inverse of repeat rate. Split into voluntary (cancelled) and involuntary (payment failure); reduced via onboarding, pricing and lifecycle messaging.
- MMM (Marketing Mix Modeling)
- A Bayesian-statistics model that estimates channel contribution; requires 18-24 months of historic data.
- Incrementality
- The extra conversion that wouldn't have happened without a channel; measured via geo-holdout tests, independent of attribution.
- Anomaly Detection
- An umbrella for techniques that automatically flag values outside the expected range in time-series metrics (KPI, conversion, latency, fraud signal). Tools include STL decomposition, Prophet, isolation forests and neural OoD models; the brain behind alerting and observability dashboards.
- Self-Serve Analytics
- An analytics environment where business units answer their own questions without waiting on the data team; delivered via Metabase, Lightdash, Hex.
- Data Governance
- The combined policies for data quality, access control, PII management, retention and audit; KVKK/GDPR compliant.
- ETL / ELT
- Extract → Transform → Load (legacy) vs. Extract → Load → Transform (modern). Approaches to moving data from source to warehouse. ELT relies on cheap cloud-DW compute; dbt + BigQuery/Snowflake/Databricks is today's standard.
- Data Lake
- A central store for all structured and unstructured data (logs, images, video, raw events) without enforcing a schema. Built on S3, GCS or ADLS with Parquet/Iceberg/Delta Lake; complements the warehouse and forms the basis of the lakehouse architecture.
- Stream Processing
- Processing data as a real-time event flow rather than in batches. Common stacks: Kafka + Flink/Spark Streaming/Kinesis + ksqlDB; use cases include fraud detection, real-time personalisation, IoT telemetry and anomaly alerting.
- Data Contract
- A pre-agreed contract between data producers and consumers covering schema, semantics, SLA and ownership. Operated with dbt + Great Expectations + JSON Schema; the most reliable wall against the "a downstream model just broke" surprise.
— QUICK DIAGNOSTIC
Are you ready for an analytics operation?
A four-question interactive guide that points to the program level that fits you. Yes / no answers, result in 30 seconds.
01 / 04
Do you currently have more than 10 active dashboards or Excel reports?
Dashboard abundance is a classic symptom of decision deficit.
— LET'S BEGIN
Are your dashboards triggering decisions — or just decoration?
A 60-minute analytics diagnostic: your current KPI inventory, dashboard dependency graph, data source health and a 90-day roadmap — on one panel.