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What is Appvertiser AI?

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Full AI-Native Growth

Fintech

May 20, 2026

Empower/Tilt: +101% ROAS and -24% CPA for Fintech App in LATAM

How Appvertiser AI helped Empower/Tilt, a fintech lending app, achieve +101% ROAS increase, -24% CPA reduction, and +51% monthly spend scale in the Mexican market through multi-channel UA optimization.

Trusted by teams behind leading apps & games

EXECUTIVE SUMMARY

Appvertiser AI scaled Empower/Tilt through AI-powered creative iteration and seamless asset adaptation during the brand transition.

REGION

LATAM

CHANNELS

Multi-Channel

CAMPAIGN GOAL

More than double ROAS and grow first-loan volume in Mexico, driving down the cost per first loan even as monthly spend scaled.

+101%

ROAS

+77%

Installs

-24%

CPA

+51%

Monthly Spend

01 / CHALLENGE

The constraint

Scaling a lending app in Mexico's competitive fintech market meant navigating variable user intent and strict financial-product compliance — and proving value on first-loan conversions, not just installs.

02 / STRATEGY

The operating model

Optimize against first-loan CPA as the core KPI, running cross-channel campaigns on Google, Meta, and TikTok across Android to reach high-intent borrowers.

03 / EXECUTION

The system in motion

Over a 19-month engagement (May 2024 — January 2026), Appvertiser delivered a +101% increase in ROAS, a 24% month-over-month decrease in CPA for first loans, a 77% increase in install volume, and a 51% increase in monthly spend — all managed across Google Ads, Meta Ads, and TikTok on Android.

04 / RESULTS

Measured outcomes

+101% increase in ROAS, a 24% month-over-month decrease in CPA for first loans, a 77% increase in install volume, and a 51% increase in monthly spend.

WHAT ENTERPRISE TEAMS CAN TAKE AWAY

A repeatable growth pattern, not a one-off win.

Frequently Asked Questions

Appvertiser AI is a recommendation AI for mobile user acquisition. Every morning it reads every campaign, ad set, ad, keyword and country in your ad accounts, judges each one against your own goal, and writes a short list of recommendation cards: raise a budget, lower one, pause an ad, raise a target ROAS, exclude a country, replace weak assets. It's a symbolic, graph-based decision model that reasons the way a senior UA manager does, so every card can say exactly why. Your team decides, and Apply makes the change in one click.

No. Your data is used to make decisions for your account only.

No. Every read and write is scoped to your account.

Yes, in a multi-tenanted, safe and secure environment.

Ad networks: Google Ads, Meta, TikTok, Apple Search Ads, Unity, AppLovin, Mintegral and Moloco, for spend, budgets, bids and delivery status. MMPs: AppsFlyer, Adjust, Singular, or your own BigQuery warehouse, for installs, revenue, in-app events and cohort revenue by install day.

Every night, for every app, day and metric, our figures are compared with the partner's own. Spend must match within 2%, installs and revenue within 3%. A day is checked once it has settled: one day for spend, two for installs, three for revenue and events. A card that includes a day that failed the check says so.

If a network's data hasn't refreshed, the AI writes no cards for that network that night. A failed or partial read is never treated as zero, and the AI makes no ROAS call where more than a tenth of the spend sits on days the data doesn't state.

No. Decisions come from a symbolic, graph-based decision model. No language model writes card text. It's a deterministic model rather than a probabilistic one.

The AI holds your account as a connected structure: network, operating system, campaign, ad set, ad, keyword and country, each linked to its parent and siblings. A campaign's verdict decides what happens inside it, a fix inside a campaign comes before pausing the whole campaign, and an ad is judged beside the other ads in its ad set.

Every sentence on a card is built from figures it read, so a card can't state a figure it didn't read.

It holds. A missing figure stays missing and leads to no verdict.

Yes. Each card shows the finding, figures, target, reason and exact change.

Yes. It remembers every change and grades every call; your team's feedback adjusts its confidence.

Every card is graded on the 14 days after it was made, once that data has settled, and the grade is never changed. Cards that can't be tested (missing data, no target, the campaign changed mid-window) are marked untestable, not scored. For each client, the AI weighs each kind of card by how often that kind of call has held up.

Each decline reason teaches something different. "I disagree with the call" lowers confidence in that kind of card for your account. "Not now" snoozes it for a week. "This campaign is off-limits" keeps the AI away from it for a year. "The numbers look wrong" goes to our team as a data question and doesn't count against the AI.

No. Nothing changes until someone with permission presses Apply.

Yes: budget caps, change-size limits, cooldowns, optional second approver and a kill switch.

Safety checks run before any change; every action is logged and graded. Mistakes can be corrected by giving feedback through the card, using the Decline button and adding an explanation.

Anything the network's API allows: campaign budgets, bids, target ROAS and status; ad set budgets and status on Meta and TikTok; ad set status on Google and Apple Search Ads; ad status on Meta and TikTok; keyword bids and status on Apple Search Ads; and country exclusions on Google, Meta and Apple Search Ads. Where the network refuses the change, the card shows Agree instead.

A preview runs every safety check and stops just before the network. You get a few seconds to undo, then the checks run again for real, the change is made and logged with before and after values, and the result is tracked in Active Decisions.

After a budget raise, the next raise waits until the first has matured: seven days plus your ROAS window, so 14 days on a D7 client. After a cut, the next raise within 30 days is half the usual size, and there's no second cut while the last one is still settling.

It judges on matured cohort days; projections only come from tested curves, and a projection never raises a budget alone. It also judges on matured early KPIs.

Your MMP for installs and revenue; spend from the network that charged it.

Nightly checks against partner figures; when data doesn't match, the card says so or the AI holds.

The one you set a target for: D1, D7 or D30. Most clients judge on D7. Targets can be set per app, operating system, network, country or campaign, and the most specific one wins. The AI never uses a platform default as your target.

Install days younger than your ROAS window are projected from the point they've reached (D1 or D3) on the campaign's own revenue curve. A curve only projects after 28 install days of evidence, with 70% of past projections in range, a median error of 15% or less and a bias of 10% or less.

Your risk profile sets the lines. On Balanced (default), a campaign under 85% of target gets a 10% budget step down, and under 65% a pause. Conservative uses 95% and 70%; Aggressive, 75% and 60%. A campaign under target keeps running while its operating system's total still meets target.

When a campaign is at or above target in its matured and newest days, spends at least 90% of its budget, and has no earlier raise still settling: a 10% raise. Raises are funded from your budget plan's headroom and from money that night's cuts free up.

If MMP installs drop sharply in the last two or three days while the network's own clicks and installs hold steady, the AI reads it as attribution still arriving and lowers its confidence in any cut.

Never. New kinds of change are proven on our own ad accounts first.

Every attempt, made or refused, is logged with before/after values. Access: details pending.

No. One night's pauses can never stop more than half of a network's active spend, each network gets at most ten cards a night, and new campaigns aren't cut before they've had their ROAS window plus a week to show their return.

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