State lottery mobile app analytics on iOS and Android ask the same question: Are more players finding the official app, trusting it, and installing it?
App Store Connect and Google Play Console both answer that. They do not use the same labels, the same formulas, or the same units. This guide is the Rosetta stone: which fields you can compare as trends, which look alike but are not the same math, and how to put both stores on one MoM slide without lying to the board.
Deep dives: App Store Connect without the jargon · Google Play Console without the jargon
One rule before you open either console
Compare jobs, not identical field names. Ask: Did people see us? Did they open the listing? Did they install? Did they stay or leave? Is the listing earning trust? Are we healthy for apps in our lane?
Never paste Apple’s conversion percentage next to Play’s conversion percentage and call that a platform winner. The formulas are different on purpose. Track each store against itself month over month. Use this crosswalk when leadership asks “how do Apple and Android line up?”
The funnel map (four boxes)
Same story on both stores. Different names.
- Saw you: discovery
- Opened the listing: product page / store listing
- Installed: first-time download / new user
- Stayed or left: retention, deletions, user loss, stability
If a step is soft on one store, fix that store’s listing and trust cues before you buy more traffic. The lever is the same. The dashboard is not.
What you can compare (safely)
These pairs answer the same lottery question. Use them for direction and MoM trend on each platform. Do not expect raw totals or percentages to match across stores.
1. Discovery: impressions
- Apple: Impressions / unique impressions
- Play: Total impressions / user impressions / device impressions
Lottery read: How often people even see that the official app exists. Unique (Apple) and user/device (Play) are closer to “reach.” Totals are “volume of exposure.”
2. Listing interest: page views vs visitors
- Apple: Product page views / unique product page views
- Play: Store listing visitors
Closest funnel-step match. Someone stopped to look at screenshots, description, and ratings. On Apple, opening the product page also counts as an impression. On Play, visitors are people who opened the listing and did not already have the app.
3. New installs: first-time downloads vs new users
- Apple: First-time downloads
- Play: New users (or user acquisitions focused on first-time installers)
Best apples-to-apples install pair for lottery MoM growth. Apple total downloads also include redownloads. Play store listing acquisitions are installs that came through the listing for users who did not already have the app. Related, not identical.
4. Listing efficiency: conversion (trends only)
- Apple: Conversion rate = total downloads ÷ unique impressions
- Play: Store listing conversion (visitors → acquisitions), plus install clicks / CTR on conversion analysis
Same job: “Is the listing earning trust?” Different math. Apple’s denominator is people who saw the icon (wide). Play’s is often people who opened the listing (narrower), and Play may exclude people who already have the app. Apple can include redownloads and pre-orders in the numerator. Compare each store’s conversion to its own history. Do not rank iOS vs Android by percentage.
5. Who left
- Apple: Deletions
- Play: User loss / uninstalls (and related device loss)
Directionally comparable after a bad release or a trust scare. Definitions still differ (devices vs users, inactive windows on Play).
6. Stability and trust
- Apple: Crash rate (Analytics / Xcode; peer crash benchmarks)
- Play: Crashes and ANRs in Android vitals
Same lottery question: Did the last release hurt trust? ANRs (app not responding / frozen) are a Play-specific vital. Check after every ship on both stores.
7. Peer context (not shared peers)
- Apple: Peer group benchmarks (percentiles for conversion, retention, crash, and more)
- Play: Compare to peers (normalized so scale does not lie)
Use each against its lane. Apple’s peer set is not Google’s. Never say “we’re at the 60th percentile on Apple, so Android should look the same.”
8. Where they came from
- Apple: Territory, source type (search / browse / app refer / web refer), page type
- Play: Country and other Statistics slices; listing conversion filters (traffic, language, campaign when available)
For a state lottery app, both should show home-state / home-country concentration. Soft conversion outside the home market usually means localization or “official” clarity, not a missing jackpot.
Where the math lies to you
Put this near the top of any cross-store deck.
- Conversion is not the same metric. Apple ÷ unique impressions. Play often ÷ listing visitors (or CTR on install clicks). Apple’s rate is usually lower because the denominator is wider.
- Users vs devices. Play leans users. Apple Analytics often counts devices. One person with a phone and a tablet can be two on Apple and one on Play.
- First-time vs total. First-time downloads ≈ new users. Total downloads ≠ acquisitions. Redownloads and multi-device installs break the total.
- Retention is not “still installed.” Apple retention is mostly “opened again.” Play mixes opens, installed audience, and retention windows. For true MoM retention, prefer your own product analytics with one definition.
- Peers are not shared. Percentiles and peer groups stay inside each store.
- Opt-in and sample. Apple usage and retention lean opted-in devices. Play crashes and ANRs lean usage-and-diagnostics sharing. Read the help text. Still enough signal to steer week to week.
Quick crosswalk table
| Lottery question | App Store Connect | Google Play Console | Compare how? |
|---|---|---|---|
| Did people see us? | Impressions / unique impressions | Impressions (total / user / device) | Trend MoM per store |
| Did they open the listing? | Product page views | Store listing visitors | Closest step match |
| Did they install (new)? | First-time downloads | New users / user acquisitions | Best install pair |
| Does the listing convert? | Conversion rate (downloads ÷ unique impressions) | Listing conversion / install CTR | Trends only, never % vs % |
| Who came back to the store? | Redownloads | Returning users | Related idea, different rules |
| Who left? | Deletions | User loss / uninstalls | Directional |
| Still with us / active? | Active devices, retention | Installed audience, DAU, MAU, retention | Directional; define “active” |
| Stable after release? | Crashes | Crashes + ANRs | Yes, post-release habit |
| Healthy for our lane? | Peer benchmarks | Compare to peers | Context only, separate peers |
Do not force-match these
- Apple product page optimization / custom product pages / in-app events vs Play store listing experiments / custom store listings: same idea (test trust and creatives), different tooling. Compare learning, not metric IDs.
- Play installed audience / install base: weak Apple twin. Do not invent a fake Apple “installed audience” from deletions math.
- Play first opens: install without an open is a soft win on Android; Apple has installs and sessions, not the same “first open” story.
- IAP and proceeds: skip for most official lottery apps.
- Updates: Apple updates vs Play update adoption can be a side note, not a board KPI.
How to report MoM without mixing stores
Board and lottery leadership want calendar months: September 1–30 vs October 1–31. Run that range separately in App Store Connect and in Play Console.
On Play, some metrics only offer Daily or a 30-day rolling average (for example Installed audience). Use Daily inside the calendar month for the official MoM cell. Use rolling as a mid-month or end-of-month pace check (average × 30), never as a number labeled “October” unless you say it is a pace. Full walkthrough: Play Console guide.
Slide pattern that works:
- Column A: Apple September / October (first-time downloads, unique impressions, conversion, deletions or crashes)
- Column B: Play September / October (new users, impressions or visitors, listing conversion, user loss or crashes)
- One sentence: “Both stores: listing soft / discovery up / home state healthy.” Keep it as trends, not blended totals.
Do not sum Apple + Play into one “downloads” number unless you label it as a rough combined view and accept double-counting risk across ecosystems.
A simple weekly checklist (both stores)
- Apple: first-time downloads, impressions, product page views, conversion.
- Play: new users / acquisitions, impressions, store listing visitors and acquisitions (or conversion analysis).
- Home territory / country on both.
- Crashes (and Play ANRs) after any release.
- Peer glance on each store for context only.
- If conversion is soft on either store, fix that listing before you spend more on ads.
FAQ: Comparing lottery app analytics on iOS and Android
Can I compare App Store Connect conversion to Google Play conversion?
Only as separate trends. Apple divides downloads by unique impressions. Play listing conversion is built around visitors and installs (or install clicks). Do not declare a winner from the two percentages.
What is the closest install metric pair?
Apple first-time downloads and Play new users (first-time installers). Use those for MoM growth on each store.
Are impressions the same on Apple and Google Play?
Same idea: how often your app was seen in the store. Units and surfaces differ. Compare MoM per store, not absolute totals across stores.
Should I average Apple and Android into one conversion rate?
No. Keep two columns. Summarize in words: both up, both soft, or split.
How do peer benchmarks compare across stores?
They do not share a peer set. Use Apple benchmarks for Apple. Use Play Compare to peers for Android.
What should a state lottery team put on the MoM slide?
Per store: new installs, discovery (impressions or visitors), listing efficiency, home-state mix, and post-release stability. Add peer context only as a note.
Bottom line
App Store Connect and Google Play Console are two scoreboards for the same lottery job. Map saw you → opened listing → installed → stayed or left. Compare trends inside each store. Treat conversion and retention as the same questions with incompatible formulas. Fix the soft step on the store where it is soft.
Want the platform deep dives? Read the App Store Connect guide and the Google Play Console guide.
Want help building a weekly or one-time cross-store Analytics report for your lottery app? Email me. cbluford@lissiland.com