Airbridge Entry API

Miss the first session,
lose the user forever.

You spend $2-5 to bring each user. 77% never come back. Every personalization tool needs history they haven't created yet.

What if you could act in the first 5 minutes — before they leave?

The Problem

After the first session,
you're marketing to the 23% who stayed.

Without Entry API

1,000 install
open app
generic welcome
230 stay

CRM, retargeting, recommendations
all fight over these 230

77% lost

With Entry API

1,000 install
personalized in 5 min
right message, right user
550+ stay

Larger retained base
= every downstream effort scales

2x+ potential lift

$2–5

average CPI to acquire one user

77%

of users never return after Day 1

$0

invested in their first experience

Airbridge Entry API: personalize from the very first screen.

One API call, 5 minutes from install to prediction — 4 real-time predictions.

The Secret

How do you predict a user
with zero history?

Their history starts at the first ad impression — not at install.

Ad channels, click patterns, hesitation time — all recorded in the multi-touchpoint ad journey.

Meta Ad

Impression

Google Ad

Click

TikTok Ad

View

Google Ad

Click

Install + Open

After 5 min

Ready!

API Call

→ 4 Predictions

Every touchpoint is a signal. The platforms' billion-dollar ML optimization already sorted your users — we just read the result.

Dozens of signals from the ad journey

The DNA of user intent — extracted from ad platform ML across every channel.

Channel mixTouch frequencyClick vs impressionTime to installRecency pressureChannel entropyDA/SA splitTouch velocityand dozens more...

Every micro-action in the first 5 minutes

Every micro-action in 300 seconds reveals exactly where they are in the funnel.

Product viewsSign-inAdd to cartWishlistHome browseSign-upDeeplinkOnboardingand more...

70+ raw signals distilled into 25 precision features. The ad journey reveals who this person is — their intent, urgency, and value. The in-app behavior reveals what they want right now.

No other platform on earth has both. Only Airbridge, the MMP, has this data.

Why Us

Every other tool gives up on new users.

You need pre-install data. Only the MMP has it.

Only Airbridge combines ad journey + in-app behavior into a prediction API.

Device info (OS, model)

Almost noneAnyone

Multi-touch ad journey

HighOnly Airbridge

First 5-min in-app behavior

HighAny SDK app

Ad journey + In-app combined

HighestOnly Airbridge

What You Get

Personalized treatment. Predictive signals.

You define the treatment variants. Airbridge assigns the right one per user — plus three predictive signals to guide budget and retention decisions.

Per-User Best Treatment

You define the variants. A contextual bandit picks the right one for each new user.

"best_trigger": "<your_treatment>"

3-Day Purchase Probability

Focus budget on high-intent users. Save it for everyone else.

"d3_purchase_prob": 0.87

3-Day Churn Risk

Spot users about to leave. One shot to keep them.

"d3_churn_prob": 0.72

30-Day Predicted LTV

$500 customer or $2 customer? Know instantly.

"pltv": { "tier": "high", "percentile": 92, "tier_avg_ltv": 110000 }

Proven Results

Not a pitch. Real data.

Validated on e-commerce apps, 40K+ new installs, 30-day window.

Purchase Prediction

Model ranks users by purchase likelihood. Top 10% vs Bottom 10% actual purchase rate.

Top 10% predicted94.7%
Bottom 10% predicted0.6%

When the model says “this user will buy” — they actually buy.

154× difference

Churn Prediction

Top 10% churn-risk users: actual churn rate

81.2%

Of users the model flagged as “will churn”, 81.2% actually left within 3 days.

Bottom 10%: only 11.9% churned.

LTV Tier Separation

Average 30-day revenue by predicted tier

High tier$900
Medium tier$75
Low tier$2

440× difference

<200ms

real-time response

5 min

from install to prediction

25

precision features

The Process

From zero data to
personalized experience.

01RCT

Exploration

Week 1–2

We assign your treatments randomly to collect causal data.

02Causal ML

Optimization

Week 3+

A contextual bandit starts picking the best treatment per user.

03Auto

Gets Smarter Every Week

Ongoing

The model improves every week. Your code never changes.

Live Demo

See it for yourself.

Real API. Not a mockup.

terminal
curl -X POST https://airbridge-entry-api-prototype.onrender.com/v1/entry/predict \
  -H "Content-Type: application/json" \
  -d '{"app_id":"sample_1","airbridge_uuid":"363c178f-ad44-4e18-ad81-ed098e28919f"}'

Click the command to select, then copy and paste into your terminal.

Example Response

{
  "user_id": "363c178f-ad44-4e18-ad81-ed098e28919f",
  "best_trigger": "social_proof",
  "trigger_scores": {
    "price_appeal": 0.18,
    "social_proof": 0.28,
    "scarcity": 0.20,
    "novelty": 0.19
  },
  "is_random": false,
  "d3_purchase_prob": 0.22,
  "d3_churn_prob": 0.34,
  "pltv": {
    "tier": "medium",
    "percentile": 59,
    "tier_avg_ltv": 9338
  }
}

Treatment names (price_appeal, social_proof, etc.) shown above are the e-commerce defaults for this demo app. The keys in best_trigger and trigger_scores match whatever treatments you define for your app.

⚠️ Demo server may take ~30s to wake up on first request (free tier). Production API responds in <200ms.

Integration

You do 3 things.
We do everything else.

Models, experiments, retraining, serving — all handled.

01

Tag SDK events

Core app events (views, signups, purchases). Most already tagged if you use Airbridge.

02

Define your treatments

3–6 variants of the experience you want to personalize — banners, CTAs, onboarding paths. Same format, different hook.

03

Call one endpoint

POST with app_id + uuid. 5 min from install to treatment. We handle the rest.

Get Started →

Live in 1 week. No commitment required.

Get Started

Ready to personalize from day one?

Pilot setup in 1 week. No commitment.

No commitment. We respond within 24 hours.