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Index Marketing Mobile apps › Google App Campaigns vs MoEngage
Mobile apps · September 2026 Edition

Google App Campaigns vs MoEngage

Zero of twelve models named Google App Campaigns first on the direct prompt; one named MoEngage. Google App Campaigns was named by eleven of the twelve models and MoEngage by eight and both carry 17 labels, so the shares below are directly comparable.

Google App Campaigns

accepted challenger

By Google Search, United States, founded 1997. Named in one category this edition.

MoEngage

accepted challenger

founded 2014. Named in six categories this edition.

First-choice share9%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%12%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#8A position in a field of 16; printed, not drawn.
Labels1717Equal, which is what makes the shares comparable.
The two percentage rows are drawn on one 0 to 100 track, Google App Campaigns reading right to left. Rank and label count are printed, not drawn.Braze was named alongside these two in seven of the twelve direct answers. Meta Advantage+ App Campaigns vs Google App Campaigns · Meta Advantage+ App Campaigns vs MoEngage · Customer.io vs Google App Campaigns

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the mobile apps page.

By framing

How many of the twelve models made each the first choice, per way of asking, and how many argued against it.
Google App CampaignsFirst choices, of twelve modelsMoEngage
Direct01
Paraphrase012 against MoEngage
Comparative00
Budget-constrained40
Scale-constrained01
Negative10
Bars are first choices, 0 to 12 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to twelve.

Every model, every framing

The seventy-two answers behind the chart above, one cell each: where Google App Campaigns and MoEngage stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
Google App Campaigns MoEngage first choice named as an alternative argued againstblank: not namedEach cell is one answer, Google App Campaigns on the left and MoEngage on the right.

The direct prompt

The plain question, one answer per model, grouped by where Google App Campaigns and MoEngage stood in it.

MoEngage first, Google App Campaigns not the choice

1 of 12 modelsGoogle App Campaigns was named in the answer but not as the choice, or not at all.
Perplexity SonarHubSpot Marketing Hub, MoEngage alternatives: Braze, Customer.io

Neither was the first choice, one was named

3 of 12 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5no first choice alternatives: Acoustic, Braze, Customer.io, MoEngage
Gemini 3.5 FlashCustomer.io alternatives: Appcues, AppsFlyer, Branch, MoEngage, Pendo
DeepSeek V4 FlashOneSignal alternatives: Customer.io, MoEngage

Neither was named

8 of 12 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniBraze alternatives: CleverTap, Iterable, OneSignal
Grok 4.1 FastAppsFlyer alternatives: Adjust, Branch, Singular
Mistral SmallBraze, CleverTap alternatives: Adobe Marketo Engage, AppsFlyer, Firebase, Insider, OneSignal
Llama 4 Maverickno first choice
Qwen 3.7 FlashCustomer.io alternatives: Braze, HubSpot
Kimi K2HubSpot Marketing Hub alternatives: 6sense Revenue AI, Airship, Braze, OneSignal
GLM 4.7 FlashXAppsFlyer alternatives: Adjust, Branch, Braze
MiniMax M2.5OneSignal alternatives: Acoustic, Google Ads

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Google App Campaigns leads by four points.
Google App Campaigns4%#5 of 12
MoEngage0%#– of 12
The full small business standing →
Mid-marketThe figures above
Google App Campaigns leads by two points.
Google App Campaigns9%#5 of 16
MoEngage7%#8 of 16
The full mid-market standing →
Enterprise
The order flips: MoEngage leads at enterprise.
MoEngage4%#4 of 11
Google App Campaigns0%#– of 11
The full enterprise standing →

What the models said about Google App Campaigns

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.

“Why it’s best for budget: Uses automated bidding across Google Search, YouTube, Play Store, and Display Network. No minimum daily spend requirement.” Qwen 3.7 Flash · budget prompt · first choice
“is Meta Advantage+ App Campaigns and Google App Campaigns. They have no minimum spend requirement” Llama 4 Maverick · budget prompt · first choice
“Google App Campaigns: Similar to Meta — automated bidding with no minimum spend.” GLM 4.7 FlashX · budget prompt · first choice

What the models said about MoEngage

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.

“CleverTap and MoEngage are heavily tailored to B2C e-commerce” Gemini 3.5 Flash · paraphrase prompt · hard negative
“I would recommend MoEngage *if your main need is mobile engagement and growth across push, in-app, and lifecycle messaging*” Perplexity Sonar · paraphrase prompt · first choice
“Want omnichannel journeys, built-in analytics, mid-market pricing | MoEngage, CleverTap, WebEngage” Kimi K2 · scale prompt · first choice
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.