8. Advanced Targeting Strategies for Mobile Ad Networks
Chosen theme: 8. Advanced Targeting Strategies for Mobile Ad Networks. Dive into practical, privacy-first techniques that help you reach the right mobile users at the right moment, while building durable performance. Join the conversation, share your experiments, and subscribe for future deep dives.
Anchor targeting on consented first-party keys such as hashed emails, user IDs, or loyalty credentials, then match them in clean rooms to build high-fidelity cohorts. Share aggregated overlap, not raw data, and invite partners to validate results across campaigns.
Deterministic and Probabilistic Identity Without Breaking Privacy
Leverage publisher-scoped IDs and vetted device graphs to connect app behaviors across properties while honoring app-level consents. Validate match rates with transparent diagnostics, and comment with your experience on balancing scale, accuracy, and legal compliance.
Deterministic and Probabilistic Identity Without Breaking Privacy
Move past broad genres by mapping granular app subcategories, in-app screens, and feature use. For example, a travel app’s flight search screen signals near-term purchase intent, while inspiration boards indicate planning. Share your best taxonomy wins below.
Geo-Intelligence: From Geofences to Real-World Context
Target near relevant points of interest—airports, stadiums, car dealerships—with micro-geofences that respect accuracy thresholds and noise policies. A mobility app used stadium exits after games to boost ride bookings; what POIs drive your best post-event conversions?
Geo-Intelligence: From Geofences to Real-World Context
Overlay time-of-day, weekday trends, or weather to sharpen intent. Food delivery surged during rainy evenings in one case study, enabling bid boosts by neighborhood. Share if seasonal dayparts changed your CPI or retention curves.
RFM, Session Depth, and Dwell-Time Scoring
Build segments using recency, frequency, and monetary proxies like session depth and dwell time. A fintech app found that users with three consecutive days of activity were 2× likelier to convert. Comment with your best lightweight behavioral proxy.
Funnel Stage and Intent Micro-Moments
Map users to discovery, evaluation, or purchase micro-moments using events like wishlist adds, checkout opens, or tutorial completion. Adjust bids and creatives by stage. Have you tied micro-moments to improved ROAS or retention? Share your results.
Quality Filters and Negative Targeting
Exclude patterns correlated with low value—rapid uninstalls, repeated accidental taps, or suspicious click-to-install times. Negative targeting preserves spend for better cohorts. Which exclusion rules most improved your post-install events per dollar?
Predictive Audiences and Lookalikes for LTV
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LTV-Driven Lookalikes and Seed Hygiene
Construct lookalike seeds from stable, high-LTV users defined by durable events—subscription renewals, repeat orders, or late-stage retention. Clean seeds of fraud and outliers. Tell us your minimum seed size and how you ensure representative diversity.
02
Propensity Scores With Continuous Feedback
Train models on early signals—tutorial completion, category depth, and purchase intent proxies—to predict downstream value. Continuously retrain with post-install events. What feedback loop cadence yields the best balance of stability and responsiveness for you?
03
Segment-Specific Offers and Frequency Shapes
Let propensity tiers inform frequency caps and offer gravity. High-propensity cohorts may prefer fewer but richer messages, while exploratory users need progressive nudges. Share how you shape frequency curves by segment without overexposing audiences.
Privacy-First Targeting on iOS and Android
SKAdNetwork Conversion Values and Cohort Design
Design conversion value schemas that reflect early, predictive actions—subscription trials, level completions, or add-to-carts. Use cohort-level optimization instead of user-level targeting. What schema changes gave you the biggest performance lift after ATT?
Experimentation, Incrementality, and Continuous Improvement
Holdouts, Ghost Bids, and Geo Experiments
Use persistent holdouts, ghost bidding, and geo-based experiments to estimate true lift. A delivery app found 22% incremental orders by expanding context cohorts, not just raising bids. What experimental design has best de-risked your scaling decisions?
Switchback Tests for Fast Iteration
Rotate treatments across time slices to isolate effects quickly in volatile mobile traffic. Pair with Bayesian updates for early reads. Comment if switchbacks helped you converge faster than traditional A/B testing under budget constraints.
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