Why Marketers Can’t Afford to Ignore This Now

Travel and hospitality marketers are constantly chasing the edge—the new signal, platform, or data set that promises to unlock higher intent and stronger ROI. With audience fragmentation at an all-time high and competition fiercer than ever, the temptation to bet big on new forms of hypertargeting is real.

App install data has emerged as one such opportunity. The logic is compelling: if consumers have already downloaded airline, hotel, or trip-planning apps, they must be active travelers with high propensity to book. In theory, this deterministic signal should outperform traditional demographic and behavioral targeting.

But theory and reality don’t always align. Apex put this hypothesis to the test for a leading travel brand, running a controlled campaign to see if app install hypertargeting could outperform tried-and-true strategies.

The Hypothesis

We expected travel-related app installs to represent bottom-funnel intent. By building audiences around these signals—paired with curated media and tailored creative—we anticipated:

• Higher engagement
• Stronger conversions
• Improved cost efficiency vs. broader behavioral/demographic targeting

The Results

The outcome was clear: app install hypertargeting underperformed.

Over a 90 day period:

• CPA was 1,295% higher than behavioral/demographic audiences. ($2.93 vs $0.21)
• CTR was flat, showing no lift in engagement (0.11% vs. 0.11%)
• Spend: ~$15K across Display over across 3 U.S. DMAs.
• Impressions served: 8.4M+, with identical creative, frequency caps, and channel mix to ensure parity.

Why installs ≠ intent

• Decay: Travel app retention by day 30 averages ~2.8%; many “installed” users are inactive when ads are served.
• Coverage gaps: On iOS, ATT opt-in hovers at ~14% globally (mid-2024), so third-party install segments are sparse and skewed. This narrows reach, drives up frequency, and inflates cost.

Why Hypertargeting Underperformed Here

Mechanisms at play:

• Low audience volume ⇒ auction scarcity ⇒ higher effective CPMs and CPAs.
• Weak recency (stale installs) ⇒ poor in-market match.
• Signal loss on iOS (SKAN/AdAttributionKit) ⇒ aggregate postbacks only, no user-level recency.

Bottom line: Precision narrowed the pool without improving intent, leaving us with higher costs for the same performance.

The Counterfactual

At equal reach, equal frequency, and equal spend, broad behavioral/demographic targeting outperformed across:

• Lower eCPA per valid, viewable, non-MFA impression
• Steadier conversion rates at scale
• Consistent ROI across geos and formats

This strengthens the conclusion: precision did not equal performance.

Where App Install Data Does Fit

App install data may still be useful in specific roles:

• As an exclusion: suppress recent bookers or current brand-app installers.
• As a modifier: combined with fresh in-market signals (search, itinerary, booking events).
• For reactivation: lapsed brand-app users with strict recency gates (≤30/60/90 days).

Future tests will constrain install audiences by recency windows and measure them against broader cohorts at the same reach and frequency.

The Takeaway

Not every shiny new signal is a silver bullet. Hypertargeting with app install data showed clear limitations:

• Deterministic ≠ effective. An install doesn’t equal active intent.
• Broader signals win on scale and cost-efficiency.
• Testing is protection. By pressure-testing hypertargeting, we validated behavioral approaches and set clearer guardrails for future data use.

The real takeaway: Precision alone isn’t the answer—it must be balanced with context, scale, and timing. That’s how smarter targeting drives performance.