How AI helps with ad campaign analysis

Introduction: AI is an accelerator of the "hypothesis → solution → money" cycle

AI is not a "magic button," but an add-on over clean data and disciplined processes. It reduces the time between an idea and a proven result: it tells you what to test, where to cut the spend, what creatives to scale and how to protect the margin.

1) Where AI has the greatest effect

1. 1. Quality and Payback Forecast

Early Quality (D1/D3): model based on early signals (source, device, geo, first actions) predicts' Prob (FTD) ',' Prob (2nd_dep) ',' ARPU _ D30 '.

Payback & LTV: regressions/gradient boosting rate 'Cum _ ARPU _ D30/D90' and payback day.

Mini-formulas:

`ROAS_Dn = NGR_Dn / Spend`, `Payback = min{n: Cum_ARPU_Dn ≥ CPA}`, `LTV = Σ NGR_t / (1+r)^(t/30)`.

1. 2. Optimize budgets and rates

Bandit models/renewal: transfer the budget to the best links with "fences" (cap, compliance, frequency).

Forecast pacing: the daily spend is distributed taking into account the probability of payback.

1. 3. Attribution and MMM

Composite attribution: models distribute the contribution of channels with partial data (post-privacy).

MMM (Marketing Mix Modeling): ML regressions evaluate elasticity and "diminishing returns," suggesting where to shift the budget.

1. 4. Creative Analytics

NLP/visual embeddings cluster creatives in "corners" (emotion, offer, social evidence) and associate with CR/ARPU.

Variant generation (copyright/visual) + predictive scoring of "probability of success" → test prioritization.

1. 5. Antifraud and anomalies

The combination of rules (IP/ASN/velocity) and ML (event sequence anomalies) reduces garbage and chargebacks, protecting ROI.

1. 6. Cohort analysis and CRM

Models classify cohorts by LTV/Retensh, launch CRM triggers (personal missions/offers) - in compliance with Responsible Marketing.

2) Data architecture for AI analysis

Collecting: UTM + 'click_id' → S2S of an event ('registration/KYC/FTD/2nd_dep/refund/chargeback') → GA4/MMP → payment logs.

Storage: DWH (BigQuery/Redshift), events in UTC, amounts in transaction currency + report currency.

Features: recency/frequency/monetary, geo/device/payment method, creative embeddings, early behavioral signs.

Models: classification (validity/fraud), regression (ARPU/LTV), bandits/pacing, NLP/vision for creatives, MMM.

Activation: biding rules, SmartLink/offer routing, BI reports, CRM segments.

Gardians: Compliance/Consent Mode, explainability, manual override, decision log.

3) Specific before/after cases

Direction	Before AI	WITH AI
Source selection	eCPC/EPC Solutions	Cutting off "dead" ligaments according to Early Quality (D1/D3)
Pacing/Betting	Manual + delays	Auto-pacing according to Prob (Payback_D30), more stable than delivery
Creatives	6-8 tests/week	40-60 variations/week + scoring success, faster exit from learning
Anomalies	After the fact	Alerts by lag/signatures/ASN, less losses S2S
Budget mix	Historical split	MMM budget transfer with uplift check

4) How to train models without self-deception

The goal is about money: optimize Payback/LTV, not clicks.

Temporal split: train/valid/test by time (roll-forward).

Leakage stop: no "future" information in features.

Explainability: SHAP/feature importance → business confidence and compliance.

Online check: A/B or holdout, report on uplift and confidence intervals.

5) Metrics to watch

Качество: `CR(click→reg)`, `CR(reg→FTD)`, `2nd_dep rate`, `Retention_D7/D30`, `Chargeback rate`.

Economy: 'CPA', 'ARPU _ D7/D30/D90', 'Cum _ ARPU', 'Payback', 'ROAS/ROI'.

Technique: delay of postbacks,% retrays, p95 latency, share of events without 'click _ id', discrepancy "operator↔DWH '.

6) Visualizations for the solution

Heatmap Cum_ARPU (cohort × days) - tilt of the tail.

Gain/response curves from MMM - where is the saturation and optimum of the span.

Feature impact on creatives - what angles drive CR.

Payback points by channel/creative - break-even CPA line.

7) Risks and how to reduce them

Raw data → smart garbage. Start with S2S hygiene and currencies/TZ.

Small sample overfitting. Keep power thresholds and regularization.

Compliance. Auto-filters of creatives (18 +/RG, prohibition of promises), targeting policies.

Ethics of personalization. Bonus/frequency restrictions, respect for RG and consents.

8) AI Analytics Implementation Checklist

Data

S2S: `reg/KYC/FTD/2nd_dep/refund/chargeback` (UTC, валюта, idempotency)
UTM policy and 'click _ id', redirect/postback logs, alert lag> 15 min
GA4/MMP linked, Export→DWH fx rate tables by date

Models and processes

Objectives: Payback_D30/LTV_D90/Prob (2nd_dep)
Temporal split, leakage control, baseline rules
Explainability + decision logs, ручной override
Activation channels: bid-rules, SmartLink, CRM, BI

Compliance/Safety

Consent Mode/privacy, no PII in URL
RG filters, creative audit, brand-safety
Incident and Dispute Policy, Model and Key Version

9) 30-60-90 plan

0-30 days - Framework and "clean" metrics

Standardize S2S and currencies/TZ; raise delay/error alerts.

DWH showcases: Cum_ARPU D7/D30, Payback by cohort, discrepancy report.

Pilot AI-creatives: generation of angles + auto-screening compliance.

Early Quality (Prob (2nd_dep )/ ARPU_D30) model in offline evaluation.

31-60 days - Models for production and risk control

Enable auto-pacing/reallocation of Payback_D30 forecast budget (guardrails).

Antifrod-ML on top of the rules; FPR/TPR metrics and appeals mechanism.

MMM draft: elasticity and what-if by CPM/rates; A/B validation of solutions.

61-90 days - Scale and sustainability

MLOps: drift monitoring, model/secret rotation, emergency scenarios.

Personalization of CRM-offers based on LTV/rates (with RG restrictions).

Regular retro by creativity/sources, updating UTM dictionaries/feature.

10) Frequent errors

1. Optimization by EPC/clicks instead of Payback/LTV.

2. Time zone/currency errors - floats D0/D1 and ROI.

3. No idempotency - FTD takes on retreats.

4. Zero explainability - business does not trust, the model "lies on the shelf."

5. Ignoring compliance - fast growth → quick sanctions.

AI helps not to "guess," but to choose faster and more accurately: which bundles to scale, where to squeeze, which creatives will come to Payback, and which will burn the budget. With a pure S2S circuit, cohort economics (by NGR, not GGR), UTM discipline and MLOps, AI transforms from a fancy term into a working engine of analysis - and makes your decisions reproducible and profitable.