How AI optimizes media buying and targeting

Introduction: AI = "brain" on top of clean data

AI does not replace strategy, it makes the procurement circuit faster and more stable: it predicts the quality of the cohort by early signals, distributes the budget, selects audiences and creatives, observing compliance. Key - S2S data, UTM discipline and guardrails.

1) Where exactly AI is having an effect

1. 1. Biding and pacing

Dynamic bid/CPA/ROAS with an eye on 'Prob (FTD)', 'ARPU _ D30' and risk.

Smooth pacing: Keeps the flow in the Payback corridor, avoids burning in the morning and undercooking in the evening.

1. 2. Targeting and audiences

Propensity models: probability of FTD/2nd-dep/Retention → look-alike segments and priority clusters.

Exclusion models: probable churn/low LTV/fraud → exclude from shows or reduce the rate.

Context/semantics: NLP on content sites for pre-bid filtering.

1. 3. Creatives and offers

Visual/NLP embeddings → angle clustering and bandit rotation (ε -greedy/Thompson).

Predictive scoring odds of "getting out of learning" and holding CR/ARPU.

1. 4. Budget Allocation

Multi-market portfolio approach: shifting the span between channels/geo/devices is Payback_D30 likely.

What-if scenarios from MMM/causal models.

1. 5. SmartLink/Offer

Redirect traffic to offers with the best eCPA/cohort quality, taking into account caps, compliance and priorities.

2) Data architecture for AI targeting

Collection: UTM + 'click _ id', s2s events' reg/KYC/FTD/2nd _ dep/refund/chargeback ', GA4/MMP, redirect/postback logs, creative metadata.

Storage: DWH (UTC time, transaction currency + "report currency").

Features: recency/frequency/monetary, device/geo/payment, session/engagement, creative embeddings, source/placement.

Models: classification (fraud/validity), regressions (ARPU/Payback), bandits, NLP/vision, MMM/causal machine.

Activation: biding/pacing rules, audiences (in offices, CDP), SmartLink API, CRM.

Gardians: Consent/RG, whitelist GEO/age, rate/frequency limits, manual override and decision logs.

3) Decision mathematics (in the outline of marketing metrics)

Money goals:

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

Beading (idea):

`bid ∝ Prob(FTD) × E[ARPU_D30	signals ]/target Payback ', with reduction factors for fraud risk/chargeback.

Audience prioritization:

`score = w1·Prob(FTD) + w2·Prob(2nd_dep) + w3·E[ARPU_D30] − w4·Risk_fraud`.

Bandit rotation of creatives/offers:

redistribute shows in proportion to the posteriori probability of victory, leaving 10-20% for exploration.

4) AI targeting practices

4. 1. Growth audiences

Seed: cohorts with fast Payback (historically) → LAL 1-2% with guardrails by geo/age.

Contextual ML: select inventory/topics, where CR (reg→FTD) is higher.

Moment-based: dayparting and "freshness" (recency) of events: we catch hot users with a high bid, cold ones with cheap shows.

4. 2. Savings audiences

Exceptions: highly probable churn/bonus hunters/low LTV - exclude or cut the bet.

Frequency capping: ML-curve of decreasing return on frequency (we cross the optimum, set the ceiling).

4. 3. Creative targeting

Corner × segment matching: for example, social proof better goes to returning/Android LATAM, and gameplay goes to new users/iOS EU.

5) Compliance, privacy and ethics (mandatory framework)

Responsable marketing: 18 +/21 +, no "easy money," explicit promo terms.

Consent Mode/PII hygiene: no personal information in the URL, conversion server-side.

Without discrimination: exclude sensitive attributes from features; fairness audit.

Guardrails: min/max bid, caps, manual stop for quality deviations.

6) AI purchasing "health" metrics

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

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

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

Creative/targeting: win-rate option, time to exit learning, response-curves by frequency/rate.

7) Frequent mistakes and how to prevent

1. Click/EPC optimization instead of Payback/LTV.

2. Raw UTM/time zones/currencies - floats D0/D1 and ROI.

3. There is no idempotency in the S2S - FTD doubles for retrains.

4. Bias in exploitation: exploration was turned off - creatives "die," audiences burn out.

5. Compliance ignores - bans and loss of inventory.

6. No A/B in sales - "models on the shelf," no trust.

8) Checklists

8. 1. Before launch

UTM policy, 'click _ id', s2s: 'reg/KYC/FTD/2nd _ dep/refund/chargeback' (UTC/currency, idempotency)
Conversion API, delay alerts> 15 min, redirect/postback logs
Seed segments for LAL, whitelist GEO/age, RG disclaimers
Base models: Early Quality, fraud-risk, creative-scoring
Guardrails: min/max bid, caps, frequency, quality stop conditions

8. 2. First week

Creative bandit pilot (10-20% exploration)
Auto-pacing by Prob (Payback_D30); deviation report
Anomaly alerts: CR failures, ASN spike, EMQ/postback drop

8. 3. By day 30

Cohort Reports: Cum_ARPU D7/D30, 2nd-dep, Payback by Segment
LAL resampling on winning cohorts, update of exclusion lists
DDA/Last click vs. MMM elasticity, mix adjustment

9) 30-60-90 implementation plan

0-30 days - Framework and "early truth"

Standardize S2S, currencies/TZ, enable Conversion API and alerts.

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

Run Early Quality + fraud-risk; connect creative-scoring and basic bandit rotation.

31-60 days - Auto rules and scale

Turn on auto-biding/pacing by Prob (Payback_D30) from guardrails.

Extend LAL/context-ML targeting, add frequency-optimizer.

Connect SmartLink-rooting of offers, anti-fraud appeals procedure.

A/B-uplift validation by channel/geo.

61-90 days - Strategy and sustainability

MMM/causal models → budget mix optimization.

MLOps: drift monitoring, model/secret rotation, emergency drills (DLQ/retrays).

Regular retro by segment/creative, updating UTM dictionaries/feature.

10) Mini playbooks

Auto bet rule (pseudo):

If'Prob (Payback_D30) ≥ θ 1 '→ increase bid by x%;
if 'θ 2 ≤ Prob <θ 1' → left;
if'Prob <θ 2'or'CR (reg→FTD)' drops by X σ → reduce bid/turn on cap.

Rotation of creatives:

New creatives receive 15% of traffic; at 100 + clicks without regs or CR <0.7 × median - auto-stop. The winner → up to 60-70% of impressions.

Audiences:

Segments with Ret_D7

AI takes media buying and targeting out of the "manual craft" into a controlled system: predicts quality, manages rates/budgets, finds audiences and rotations, protects against fraud and targeting errors - all within the framework of compliance and Responsible Marketing. With a pure S2S circuit, cohort economics in NGR, UTM discipline and clear guardrails, algorithms stabilize Payback and grow LTV, and the team focuses on strategic hypotheses and new growth points.