CDP and End-to-End Analytics
All abbreviations and terms marked *Clickable - lead to glossary at the bottom of the page.

CDP and End-to-End Analytics

Collect customer data from the web, applications, CRM and payments into a single 360° profiles* we're counting LTV*, CAC* ROAS*, build attribution* scoring* and then activate the audience through channels (Reverse ETLNo black boxes, just transparent calculations and dashboards.

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Why Businesses Need CDP and End-to-End Analytics

Disparate data breaks the marketing and the product. We build the contour: event collection (web/app/server). identificationAnd crosslinking users, storage and storefronts, metrics and attribution, segment activation in advertising, e-mail and instant messengers, and as a result, budgets go into efficient channels, and communications become personal and measurable.

Business effects

  • +10-30% of revenue from personalization and precision segmentation.
  • 15-35% of CAC due to net attribution and shutdown of “junk” sources.
  • +12-25% to retention due to the correct triggers and scorings.
All metrics are repeatable: formulas, sources, refresh rates and responsible ones are in the documentation.
360° client profiles
24/7 event-flow
<7 days before basic end-to-end analytics
Events & S2SIdentity GraphClickHouse DWH AttributionAudiencesReverse ETL

Tools and architecture

Implementing a mature stack: data collection, routing and cleaning; identification and profiles; storage and data marts; computation and models; dashboards and audience activation.

Data collection and routing

  • Tracking Plan*: Events, properties, identifiers, schemes.
  • SDK for web/mobile + Server-to-Server* for critical events (registration, payment).
  • Anti-AdBlock: First-level domains, retros, deduplication.
  • Import CSV/JSON/APIs: CRM, billing, offline sales, coltrekting.

360° identification and profiles

  • ID-Resolution*: user_id, device_id, email/phone hashes, identity graph.
  • Stitching*: Deterministic/probabilistic, windows and weight schemes.
  • Profiles*: Demography, events, transactions, channels, speed, RFM/CLV
  • Consent* and the status of data processing by the user.

End-to-end analytics and attribution

  • Attribution*: first/last touch, position-based, multi-touch*.
  • Imports of expenditure*: Cabinets/CSV, exchange rates, VAT/commissions.
  • ROI/ROAS metrics, CAC*, LTV*, margins by segment.
  • Attribution windows: click/post-view, de-duplication of conversions.

DWH, data marts and dashboards

  • Warehouse* (e.g. ClickHouse) + ETL/ELT* connectors.
  • Marts: events, orders, profiles, attribution, audiences.
  • Dashboards: Redash/Metabase/Grafana, Metrics Dictionary and SLA.
  • Alerts: event/conversion drop, order divergence, SRM.

Activation: Audiences and Reverse ETL

  • SegmentationRFM, behavior, probability of purchase/outflow.
  • Reverse ETL*: uploading audiences in ads/e-mail/CRM/push.
  • Triggers: abandonment, price-drop, back-in-stock, first value.
  • Control of frequency and cannibalization of channels.

Models and scorings

  • scoring*: propensity to buy/churn, next-best-action/offer.
  • Cohorts and retention*: N-day, rolling, survival analysis.
  • Incrementality: geo/holdout/PSA tests, clean-room approach.

Typical stack

Web/App SDK + S2S Identity Graph ClickHouse DWH ETL/ELT Redash / Metabase Audiences & Reverse ETL

Implementation process

Data audit and objectives

Inventory of sources (web/app/CRM/payments), clarification of KPI: LTV, CAC, margin, repurchases, ROI/ROAS.

Tracking Plan and collection

Uniform Event and Property Scheme, S2S for Critical Steps, Deduplication/Retrai, Consent Banner and Processing Statuses.

Identification and profiles

Rules for crosslinking identifiers (det/prob), graph identities, unification of attributes, control of collisions.

End-to-end analytics and attribution

Import of expenses, normalization of UTM, attribution windows, data marts on orders / leads, reports on channels / creatives / segments.

Audiences and activation

Segments RFM/behavior/scoring, Reverse ETL into channels, triggers and frequency rules, A/B circuits.

Dashboards and procedures

Role-oriented panels (CEO/marketing/product/CRM), dictionary metrics, SLA updates, alerts.

What do you get?

Artifacts

  • Tracking Plan* and the CDP data model.
  • ID-Resolution/Stitching rules and profile scheme.
  • Attribution methodology with formulas and windows.
  • Dictionary of metrics and update procedures.

Infrastructure

  • Deployed DWH, event/order/attribution/audience data marts.
  • Dashboards (marketing/product/CRM/finance) and allerts.
  • Reverse ETL connectors: ads, e-mail, CRM, push.

Activation and growth

  • Segments (RFM, propensities, churn) and trigger scenarios.
  • Experiments on channels/offers, control of incrementality.
  • Team training and joint weekly insights.

Examples of KPI

  • E-commerce: CR, AOV, repeat orders, margin, returns.
  • SaaS: Activation D7, Retention D30, ARPU/ARPPU, churn.
  • LeadGen: CPL/CPA, lead-transaction conversion, LTV by source.
Get an estimate and work plan

Cases

E-commerce: Profiles and Audiences

User_id/email/phone crosslinking, RFM + back-in-stock/price-drop segments. Reverse ETL in ads + e-mail gave +18% to retention revenue and -22% to remarketing CPA.

SaaS: End-to-end and attribution

Expense import, 7/28 day windows, position-based model, creative reports. Budget reallocation: -19% CAC, +11 pp D7 activation.

Offline + online: leads and sales

Collecting and CRM integration, user-level attribution, remarketing holdout, confirmed incrementality +9.5% to orders.

Checklists and quality control

Data Quality

  • Event coverage ≥ 95%, discrepancy of web ⁇ CRM orders ≤ 2%.
  • Scheme Validation: mandatory fields, types, ranges.
  • Attitudes for the fall in conversions / events, SRM / traffic distortion.

Identification

  • Hierarchy of identifiers: det → prob, windows and timeouts.
  • Anti-merger: Coincidence thresholds, manual moderation of conflicts.
  • Audit the shares of anonymous / crosslinked profiles through channels.

Attribution and expenditure

  • Normalization of UTM, deduplication of clicks / postviews.
  • Import costs with exchange rates and commissions.
  • ROI/ROAS versus P&L and unit economy

Privacy and compliance

  • Consent*: banners, consent logs, granular opt-in.
  • Masking/hashing of identifiers, role-based access.
  • Storage time, log of configuration changes.
Signal.ThresholdAction.
The difference between web vs CRM> 3% по заказамS2S/hour windows check, deduplication, retrai
Fall of events> 15% от медианыAlert, SDK/tag versions, rollback
Attribution bias> 20% vs исторический профильWindows/UTM/Post Viewing, Reassin
Percentage of anonymous> 60% в ключевых сегментахLogin strategy, triggers and identity bonuses

FAQ

How long does the basic implementation last?

Usually 5-10 business days: Tracking Plan → collection / S2S → DWH / data marts → attribution / expenses → dashboards → Reverse ETL and the first segments.

Do you need a separate team of analysts?

No. We leave clear rules, activation scenarios, and we train marketing/product, and we train maintenance if we want.

Can I count offline sales and calls?

Integration of CRM/telephony, adding offline events to profile and attribution, P&L check.

What about privacy and the law?

We implement the consent banner, opt-in/opt-out logs, minimization, masking of identifiers, roles and storage periods.

Decoding of terms

  • Tracking Plan: a formalized scheme of events/properties and rules of sending.
  • Server-to-Server: sending key events from the backend.
  • ID-Resolution: crosslinking different identifiers of one user.
  • StitchingTechnical rules for the unification of identities.
  • 360° profileA user card with events, attributes and transactions.
  • AttributionDistribution of channel contribution to conversion/revenue.
  • Multi-touch: Multi-channel Attribution Model.
  • Imports of expenditure: downloading costs from normalization offices/files.
  • DWHData Warehouse (Data Warehouse)
  • ETL/ELT: the conveyor of extraction / transformation / downloading data.
  • Segmentation: Breaking the audience down by traits and behavior.
  • Reverse ETLUploading storefronts and audiences back to external channels.
  • scoringProbabilistic assessments of behavior (buying, outflow, etc.).
  • Retention: Keeping users in time.
  • LTV: lifetime value of the customer.
  • CAC: Cost of attracting a customer.
  • ROAS: refund on advertising costs.

Ready to put data together and start earning more?

We'll set up collection and identification, build profiles and attributions, collect dashboards and run activations. A couple of weeks, and you'll have transparent numbers and managed campaigns.

Request an audit and work plan
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