Marketing BI Dashboards
The single source of truth for marketing and sales: from connectors and data marts to attribution. ROAS*, CPA*, cohort LTV*, retention and MMM. Solutions are on numbers, not in Excel.
Request an audit and a prototype of dashboardsWhat the BI circuit does
We collect data from ad accounts, web/product analytics, and CRM, normalize it, and build data marts and visualizations. Dashboards show channels, campaigns, creatives, segments, and products in money terms: revenue, margin, payback, and LTV contribution.
Business effects
- 15-35% of CPA due to transparency through channels / creatives and quick cleaning of noise.
- +10-28% to ROAS due to the redistribution of the budget to profitable segments.
- A single vocabulary of metrics and versioning – less disputes, more decisions.
Tools and architecture
The stack closes all stages: sources → loading → normalization → modeling → data marts → visualization → alerts.
Sources and connectors
- Advertising cabinets: search, social networks, marketplaces, advertising networks.
- Web / product analytics: events, goals, funnels, CWV*.
- CRM/billing/acquiring: transaction statuses/payments, returns, margins.
Storage and modelling
- DWH (column storage) – cheap aggregations and fast requests.
- Layer of transformations: naming schemes, deduplication, unification of currencies / VAT.
- Models: expenses → clicks → sessions → orders → revenue → margin.
Marketing data marts
- Attribution Mart: post-click/view, 1/7/30 windows, last non-direct and data-driven.
- Commerce Mart: SKU/categories, promotions/discounts, returns, contribution margin.
- Retention Mart: cohorts, D7/D30, LTV by segment/channels.
Dashboards and reports
- Channels→campaigns→groups→creatives: CPA/ROAS, CR, revenue, margin.
- LTV/Retention cohort panels, channel contributions to CLV and payback.
- Data quality feeds: UTM gaps, duplicates, S2S discrepancies.
Data Alerts and SLAs
- Anomalies: CR drop, CPA spike, lack of data/cost, 4xx/5xx on landing pages.
- Threshold rules: ROAS < X for N days creates a task/chat flag and an automatic comment for the owner.
- Catalogue of quality tests: freshness, completeness, uniqueness, reference with CRM.
Advanced analytics
- MMM panel (multiregression): channel contribution to revenue, taking into account seasonality / price.
- Experiments: geo/cluster tests, incrementality, holdout zones.
- What if scenarios for media plan: budget → ROAS/CAC/LTV forecast
Typical stack
The Dashboards you are getting
Executive Overview
- Revenue/Margin/ROI through WoW/MoM, contribution to the plan.
- Top 5 growth/fall drivers, owner comments.
- KPI heat map by channel/geo/devices.
Performance by Channel
- CPA/ROAS/CR/AOV, costs and coverage, frequency, saturation.
- Creative matrix: CTR→CR→ROAS, burnout, frequency limits.
- Search / product / social network / retarget - in one scheme.
Attribution & Incrementality
- Comparison of models, contribution of assist transitions.
- Holdout/geo tests: net effect vs control.
- A summary of deviations and recommendations to the budget.
Commerce/SKU panel
- Revenue/margin on SKU, promotional influence, returns.
- Rules of priority: balances, markup, seasonality.
- Share of “minus” margins, allerts and stop-lists.
LTV & Retention
- D7/D30/D90 cohorts, cumulative LTV and payback.
- Cut: channel/campaign/segment/geo/device.
- CRM: Re-purchases, NPS/refunds.
Data Quality & Monitoring
- Fresh sources, UTM skips, double clicks.
- S2S/client discrepancies, 4xx/5xx landing pages.
- Error Code: Responsible, SLA, status of fixes.
Implementation process
(1) Diagnosis and objectives
Audit of sources/reports/dictionary metrics: KPI: revenue, margin, ROAS/CPA, LTV/CAC, retention, data speed.
(2) Data diagram and connectors
Tracking plan, UTM standards, channel/campaign mapping, story downloading, deduplication.
(3) Models and data marts
Transformations, attribution windows, Marketing/Commerce/Retention data marts, quality tests.
(4) Visualization and alert
Dashboard design, access/role, alerts and data update procedures.
(5) Training and support
Guide to metrics, documentation, weekly insights and roadmap improvements.
What do you get?
Artifacts
- Data Scheme (ERD), Dictionary Metrics and UTM Standard.
- Marketing/Commerce/Continuance with SQL scripts.
- A set of dashboards (Executive, Channel, SKU, LTV, Quality).
Infrastructure
- DWH and task orchestration, schedule, download logs.
- Alerts (chat/mail), incident log and SLA.
- CSV/JSON upload templates and APIs for CRM/ERP.
Growth and support
- Weekly reports and action lists of what to scale/cut.
- The “what if” panel: budget → CAC/ROAS/LTV forecast (simple scenarios).
- Development plan: MMM, incremental tests, margin data marts.
Examples of KPI
- E-commerce: ROAS, CPA, AOV, share of promotional revenue, returns, margin.
- SaaS: CAC, signup→activation, PQL→SQL, LTV/CAC, churn, ARPPU.
- B2B: CPL/CPA, lead→meeting→deal, win-rate, project margin.
Cases
E-commerce: margin transparency
Built data marts for costs, revenue and returns, a SKU dashboard, and ROAS alerts. Result: negative-margin share below 1.2%, ROAS +22%, CPA -17%.
SaaS: CAC and LTV
Cohort LTV via funnel and funnel stages, attribution to SQL/payment, total: CAC -19%, LTV/CAC 2.1→3.0, payback -23 days.
B2B: CRM seam
BI trade statuses and sales funnel, win/loss report: CPL -27%, lead→meeting +16 p.p., forecast accuracy +12 p.p.
Checklists and quality control
Data and tracking
- UTM standard and click-ID are implemented in all campaigns.
- S2S events for key conversions, anti-double enabled.
- Source reports converge with CRM/billing ±5%.
Models and data marts
- Attribution windows and assist rules are agreed and documented.
- Refunds/cancellations/discounts are accounted for in the period and by source.
- Each showcase is covered with freshness/completeness/uniqueness tests.
Dashboards and alerts
- Rights and roles: Marketing/Sales/Finance – its panels and levels of access.
- CPA/ROAS/CR/download errors/4xx-5xx/downfall CWV.
- Each chart is clickable before the transaction/session/order.
Processes and procedures
- Weekly report on what to do: scale/cut/test.
- Versioning metrics (changelog), SQL review and code style.
- Catalogue of incidents with cause and time of recovery.
| Signal. | Threshold | Action. |
|---|---|---|
| The spa has grown | > target by 20%+ | Check traffic/creatives/landings, exceptions, redistribute budget |
| ROAS has slipped | < threshold for 3 days | Shift to top segments, stop low-margin SKU/campaigns |
| Disparity with CRM | > 5% | Recalculation of data marts, S2S / Currency / VAT, deduplication |
| No fresh data. | > 6 hours | Task restart, owner notification, post mortem |
FAQ
How long does the base launch last?
Usually 7-10 working days: connectors → transformations → data marts → dashboards → alerts → training.
Can we do without a heavy DWH?
At launch, yes (CSV/API). For stability and speed, we recommend DWH and task schedules.
How do we reconcile metrics between departments?
A single dictionary and a “source of truth” in the windows; metrics are versified, all formulas are documented in the panel.
What about privacy?
Pseudonymization of identifiers, differentiation of access by role, log of requests and anonymization of exports.
Decoding of terms
- ROAS: refund on advertising costs.
- CPA: the cost of the targeted action.
- LTV/CLV: lifetime value of the customer.
- CAC: Cost of attracting a customer.
- Payback: the payback period of the attraction.
- Core Web VitalsLCP/INP/CLS are key speed/UX metrics.
- Attribution: rules for the distribution of revenue between touches.
- MMMMedia mix modeling – contribution of channels to revenue.
- Holdout: control area unchanged for incremental assessment.
- DWHData storage for reports and analytics.
- Mart: Thematic data showcase (model for a specific task).
Do you want to see marketing on the money, not on clicks?
We will connect sources, build data marts and dashboards, configure alerts and quality rules. In a couple of weeks you will have a single source of truth and a list of specific growth actions.
Request an audit and demo