How to leverage AI and Data to acquire customers in your fintech

From MVP to 100 thousand customers

The fintech ecosystem has grown exponentially in recent years, challenging the dominance of traditional banking and offering more accessible and digital financial solutions. However, the key to a fintech’s success is not only developing an innovative product, but scaling its customer base efficiently. Going from a Minimum Viable Product (MVP) to 100,000 customers requires a well-structured acquisition strategy based on artificial intelligence (AI) and data.

Today, digital advertising has evolved thanks to automation and machine learning, which allows companies to optimize campaigns in real time, predict user behavior and maximize the return on advertising investment. However, many fintechs still face challenges in implementing these technologies due to the lack of maturity in their measurement and attribution processes.

This article will serve as a practical guide to understanding how fintechs can leverage AI and data to accelerate growth, optimize customer acquisition, and improve retention and monetization. We will explore the evolution of advertising technologies, the investments of tech giants in machine learning, and the maturation processes necessary for fintechs to be competitive in today’s market. In addition, we will discuss success stories and real-world examples that demonstrate the impact of an AI and data-driven acquisition strategy.

Throughout this tour, we will see how the right integration of attribution, measurement and automation tools can make the difference between a fintech that grows at an accelerated rate and one that gets stuck in the startup phase.

 

1. Evolution of Advertising Technologies

The world of advertising has undergone a radical transformation in recent decades. From newspaper and television ads to sophisticated digital marketing strategies based on artificial intelligence, companies have had to adapt to changes in consumer behavior and available technology.

In this section we will explore how digital advertising has evolved, the key role of machine learning in campaign optimization and how these advances impact customer acquisition for fintechs.

 

1.1. From Traditional to Digital Advertising

Before the digital era, advertising strategies were based on mass media such as television, radio and print. While these formats offered great reach, they lacked precision in audience segmentation and impact measurement.

With the advent of the Internet, companies began to adopt digital advertising, initially with banners on websites and later with the arrival of platforms such as Google Ads and Facebook Ads. Google Ads and Facebook Adswhich revolutionized the way advertisers could segment and measure the performance of their campaigns.

The fundamental change lay in the ability to personalize: instead of targeting mass audiences without clear criteria, digital advertising allowed for detailed, data-driven detailed targeting based on data such as browsing behavior, interests and geographic location. such as browsing behavior, interests and geographic location.

 

1.2. Advances in Programmatic Advertising and Automation

One of the most significant developments in digital advertising was programmatic advertising. programmatic advertisingprogrammatic advertising, which automates the purchase of advertising space through advanced algorithms and real-time bidding (RTB, Real-Time Bidding).

• Previously: Advertisers manually negotiated the purchase of ad space with publishers or platforms.

• Now: Programmatic advertising systems analyze real-time data and adjust bids automatically to maximize performance.

Platforms such as Google Display & Video 360, The Trade Desk and Meta Ads Manager use machine learning models to optimize ad impressions and allocate budgets more efficiently, ensuring that ads reach users with the highest likelihood of conversion.

 

1.3. The Integration of Machine Learning in Digital Advertising

Machine learning has completely changed digital advertising, allowing algorithms to make smarter decisions based on huge volumes of data. Some of the most relevant applications include:

• Automated optimization: Algorithms adjust in real time which ads to show, to whom and at what time to maximize conversion rate.

• Predictive segmentation: AI models can identify behavioral patterns and predict which users are most likely to become customers.

• Customer journey analysis: Allows you to understand how users interact with different touch points before making a conversion.

• Dynamic personalization of ads: Tools such as Google Performance Max and Meta Advantage+ generate ads tailored to each user’s profile in real time.

A clear example of this evolution is the shift in Meta’s (Facebook) approach from relying on manual targeting to an “AI-first” model. “AI-first”model, where advertisers simply upload their creative and the platform uses AI to optimize delivery based on multiple user signals.

 

1.4. The Critical Error: The Lack of Quality Real-Time Data

Despite the advances in machine learning applied to advertising, many companies fail to implement these strategies because they do not feed fail to implement these strategies because they do not feed the algorithms with quality data in real time..

 

The problem: Feeding algorithms only top funnel data.

Companies often turn on AI-based campaigns (AI-first campaigns) without connecting the actual data of customer interaction with the product. In these cases:

• Algorithms only learn from initial conversion events such as clicks or registrations, with no information about the actual value of the user.
• The system is not fed back with key activation, retention or monetization data.
• Optimization is performed on incomplete signals, making artificial intelligence less effective than a traditional well-structured strategy.

 

The impact: Inefficient learning models

If a fintech launches acquisition campaigns without sharing data with the algorithms about which users actually generate revenue or long-term retention, the system will not be able to optimize effectively. In many cases, this ends in:

• Poorly targeted advertising: Low quality leads are attracted that do not convert into active users.
• Waste of advertising investment: Budget is allocated to incorrect segments, raising customer acquisition cost (CAC).
• Optimization models less efficient than manual strategies: Without enriched data, AI models cannot differentiate between valuable customers and unprofitable users.

 

The solution: Real-time, comprehensive data integration

For a fintech to really benefit from machine learning advertising advertising machine learningis essential:

1. Implement post-conversion events that feed back to campaigns with key information on user behavior.
2. Connect monetization and retention signals with ad platforms for algorithms to optimize for high-value users.
3. Automate real-time data transfer between product, attribution systems and advertising channels.

Example: Companies that have optimized their campaigns through advanced machine learning have reduced CAC by up to 40%. 40% by integrating conversion events into their advertising strategies.

The impact on fintechs

For a fintech looking to scale its customer base, these technologies represent an invaluable opportunity. The ability to personalize ads, optimize budgets and measure results in real time allows companies to reach the right users with greater precision and efficiency.

However, in order to take full advantage of these tools, fintechs must mature their measurement and attribution processesensuring that their marketing strategies are aligned with the learning capabilities of the algorithms.

 

2. Investments by Large Companies in Advertising Machine Learning

Leading technology companies have realized that the future of digital advertising lies in artificial intelligence and machine learning. In recent years, companies such as Google, Meta, TikTok and Amazon have poured billions of dollars into developing machine learning algorithms that optimize media buying, audience targeting and ad personalization.

Why have you invested so much in this? Because screen time continues to increase worldwide and users’ attention is on digital devices. This is an opportunity to improve advertising efficiency and maximize revenue. However, these technologies only work if the companies using them are prepared to feed the algorithms with quality data.

In this section, we analyze the main investments in advertising machine learning, the changes in the industry and how fintechs can leverage these tools to accelerate their growth.

 

2.1. Why have the technological giants bet on machine learning in advertising?

The reasons behind these massive investments include:

1. Growth of digital consumption

The time users spend on digital devices has grown steadily across all demographic segments. According to DataReportalin key markets such as Latin America, users spend between 7 and 9 hours a day in front of a screen..

2. Saturation of traditional channels

Traditional advertising strategies (targeting by interests, demographics, lookalike audiences) have reached their efficiency ceiling. Platforms have opted for models “black box AI”where machine learning automatically decides the best way to impact each user.

3. End of manual segmentation

Meta, Google and TikTok have pushed automatic optimization models, reducing reliance on manual targeting and allowing algorithms to take full control of campaigns. An example of this is Meta Advantage+which optimizes campaigns without human intervention.

 

2.2. Examples of Key Investments in Advertising Machine Learning

Google: Performance Max and full automation

Google has evolved from its traditional system of segmented ads to a model where the advertiser only defines targets and the system optimizes everything else..

• Performance Max: Campaigns based on machine learning that automatically distribute budget across different channels (Search, Display, YouTube, Gmail, Discover).

• Smart Bidding: Algorithms that automatically adjust bidding in real time based on signals such as geolocation, device, browsing behavior, among others.

 

Goal: AI-driven advertising with Advantage+

Meta has migrated to full automation complete automation in advertising with products such as:

• Advantage+ Shopping Campaigns: Automatic optimization of creatives, segmentation and budget.

• Conversion API (CAPI): Allows sending product events directly to Meta to improve optimization accuracy.

 

TikTok: Recommendation algorithms for native advertising

TikTok has invested heavily in machine learning models that personalize the user feed in real time.

• Spark Ads and Smart Performance Campaigns: Allow you to integrate organic content with paid advertising, optimizing based on real interactions.

• Recommendations based on deep learning: Analyzes consumption patterns to show relevant ads without relying on predefined audiences.

 

2.3. How fintechs can take advantage of these investments

While these technologies are available to any advertiser, the reality is that fintechs must adapt their strategies to extract maximum value from them. fintechs must adapt their strategies in order to extract the maximum value from them..

 

Common mistakes when using machine learning in advertising

Many companies believe that turning on AI-first campaigns guarantees immediate results. However, without a well-structured data strategy, machine learning models can be inefficient.

• Do not feed algorithms with post-conversion data. → Optimize for low quality clicks and leads instead of high value users.

• Not using monetization signals → You lose the ability to target campaigns to users that actually generate revenue.

• Lack of integration with conversion APIs → Without this connection, AI-based advertising is less effective than a well-managed traditional strategy.

 

How to leverage the advertising technology of large companies

In order for fintechs to scale from one MVP to 100,000 customers with AI-optimized advertisingthey must implement:

1. Quality conversion events → Not only capture registrations, but also activation and retention events.
2. Integration with conversion APIs → Connect the product with platforms such as Google, Meta and TikTok.
3. Use of advanced attribution models → Measuring the real impact of advertising on high-value users.
4. Real-time data automation → Send constant quality signals to algorithms.

 

Tech giants have invested in machine learning because they know that the advertising of the future is fully automated and based on real-time data. However, in order for fintechs to take advantage of these tools, they must work on the maturity of its measurement and campaign optimization processes..

If a fintech does not integrate quality signals into its advertising, AI algorithms will not be able to improve results and the company will spend money without getting profitable customers.

3. Maturation Processes for Competitive Advertising Strategies

Implementing machine learning in advertising is not simply activating automated campaigns on platforms such as Google, Meta or TikTok. For these strategies to be truly effective, companies must go through a maturation process in their measurement, attribution and data optimization capabilities..

Fintechs that do not develop this process end up wasting investment in advertising without achieving profitable customers. In this section we will analyze the three essential pillars for a competitive advertising strategy based on machine learning: attribution, user behavior measurement, and real-time data quality..

3.1. Integration of Attribution Tools in User Acquisition

One of the biggest challenges for fintechs is knowing which campaigns actually generate valuable customers. Without a proper attribution system, ad spend decisions are based on incomplete or biased data.

 

Common mistakes in advertising attribution:

❌ Use of “last click” models → All value is attributed to the last click before conversion, ignoring the impact of other channels.
❌ Not measuring incrementability → No distinction is made between customers acquired through advertising and those who would have converted organically.
❌ Failure to connect conversion data with product usage data. → Campaigns are optimized to capture cheap leads instead of profitable users.

 

Solution: Implement advanced attribution models

✅ Multi-touch models → Evaluate all previous interactions of a user prior to conversion.
✅ Mobile attribution platforms (MMPs). → Tools such as. AppsFlyer, Adjust and Branch allow you to measure the impact of each campaign more accurately.
✅ Attribution based on monetization signals. → Integrate post-conversion events to target advertising to high-value users.

Example: A fintech that optimizes only for app downloads will see its CAC increase over time. However, if it optimizes its campaigns based on users who actually generate revenuewill reduce costs and improve profitability.

 

3.2. Integration of Measurement and Customer Interaction Tools

Having a registered user does not guarantee that they will become an active customer. Fintechs need to measure how users interact with their product in order to activate, retain and monetize them..

 

Problem: Lack of integration between advertising and user behavior

Many fintechs do not have advanced measurement tools to analyze how users interact with the platform after acquisition.

• They do not identify which users generate the most revenue.
• They do not personalize retention strategies based on user behavior.
• They do not automate the activation of inactive users.

 

Solution: Implement analytics and engagement tools

✅ Advanced analytics platforms → Tools such as. Mixpanel, Amplitude and Google Analytics 4 allow you to segment users according to their behavior.
✅ Engagement automation → Platforms such as. Braze, CleverTap or OneSignal activate users at key moments with personalized notifications.
✅ Churn prediction models → Detect users at risk of churn and reactivate them with offers or targeted content.

Example: A digital bank can identify that users who do not complete onboarding in the first 3 days have an 80% chance of abandonment. With automation tools, it can send reminders and personalized offers to improve conversion.

 

3.3. Implementation of Signals, Events and Quality Data Flows

The machine learning is only as good as the data it is fed with.. If a fintech activates campaigns on Google or Meta without sending quality conversion signals, algorithms optimize for cheap clicks instead of profitable customers.

 

Problem: Poor quality data in AI-first campaigns

❌ Optimization based only on clicks or registrations → Users are attracted who have no real intention of use or purchase.
❌ Do not send monetization signals → Algorithms cannot distinguish between profitable and unprofitable users.
❌ Lack of real-time integration → Missed opportunity to optimize campaigns based on up-to-date data.

 

Solution: Send quality events to advertising platforms.

✅ Advanced conversion events → Measure not only downloads or registrations, but also activations, purchases and recurrence.
✅ Meta and Google conversion API → Integrate real-time post-conversion data to improve optimization accuracy.
✅ Automation of high-value signals → Adjust advertising spend based on users who actually generate revenue.

Example: A fintech that connects its payment system with its Google Ads campaigns can automatically optimize to attract customers who make high-value transactions, rather than simply get more downloads.

 

Fintechs that want to scale from one MVP to 100,000 customers must mature their advertising strategies. The key is:

1. Integrate advanced attribution tools to accurately measure which campaigns generate valuable customers.
2. Use analytics and engagement platforms to activate, retain and monetize users.
3. Send quality conversion signals to ad platforms to optimize advertising with real data.

Without these processes, AI-first campaigns will not only be inefficient, but may be less effective than traditional manual strategies. less effective than traditional manual strategies..

4. Success Cases and Real Examples

The impact of artificial intelligence and the use of data in digital advertising is not only theoretical. Companies from different industries have managed to optimize their customer acquisition, reduce costs and improve profitability through strategies based on machine learning and real-time data.

In this section, we will look at specific examples of fintechs and other companies that have successfully applied these principles, comparing them with traditional approaches to understand the differential that a strategy based on AI and well-structured data brings.

 

4.1. Case 1: Fintech that reduced its CAC by 40% by integrating conversion signals into Google Ads

Initial problem:

A fintech in Latin America spent large sums on digital advertising but its customer acquisition cost (CAC) was high. customer acquisition cost (CAC) was high. and low user retention. They were using traditional Google Ads campaigns optimizing only for clicks and registrations, without considering which users actually generated revenue.

Solution applied:

✅ Implementation of post-conversion events in Google Ads and Meta Ads (example: users who made their first deposit).
✅ Use of multi-touch attribution models to understand the impact of each channel on the final conversion.
✅ Adjust advertising campaigns to optimize for high-value customers rather than simply increasing registrations.

Results:

📉 40% reduction in CAC in six months in six months.
📈 30% increase in active users after 90 days thanks to better thanks to better segmentation.
💰 Increasing the Lifetime Value (LTV) of customers by prioritizing the acquisition of users with higher monetization potential.

 

4.2. Case 2: Neobanco that improved its activation rate by 50% with engagement automation

Initial problem:

A neobank with operations in Brazil had a critical activation problem: only 25% of the users who signed up actually completed the onboarding process and use its financial products.

Solution applied:

✅ Use of platforms such as Mixpanel and Braze to analyze friction points in onboarding.
✅ Implementation of an automated sequence of notifications and emails with personalized incentives to complete the registration.
✅ Integration of this data into Meta Ads and Google Ads to optimize the acquisition of users more likely to activate their account.

Results:

📈 50% improvement in the activation rate of new users of new users.
📉 Reduction of churn in the first 30 days by 25%..
💳 Increased conversion rate to financial products (loans, investments, insurance) thanks to early activation.

 

4.3. Case 3: Digital lending startup that scaled from 5,000 to 100,000 customers in one year with AI-first campaigns optimized with real data

Initial problem:

A digital lending startup in Mexico wanted to scale its business but faced problems with:

• High fraud in credit applications.
• Difficulty in finding clients with a good credit profile.
• Meta and TikTok campaigns that generated low quality leads.

Solution applied:

✅ Implementation of a customer scoring system based on transactional data.
✅ Sending this data to Google Ads and Meta via conversion API to optimize segmentation.
✅ Adjust advertising campaigns to prioritize users most likely to approve and pay.

Results:

📈 Growth from 5,000 to 100,000 customers in a year.
📉 60% reduction in fraud thanks to better segmentation.
💰 Higher profitability per user by attracting customers with a better financial profile.

 

4.4. Comparison between Traditional Strategies and AI- and Data-Driven Strategies

Estrategia Tradicional	Estrategias con IA y Datos
Segmentación manual por intereses y demografía.	Modelos de machine learning que predicen qué usuarios tienen mayor probabilidad de conversión.
Optimización de campañas en base a clics o leads.	Optimización en base a señales de monetización y retención.
No hay conexión entre campañas publicitarias y datos del producto.	Integración de APIs de conversión para alimentar los algoritmos con datos en tiempo real.
Mayor CAC y menor retención.	Menor CAC, mayor LTV y mejor eficiencia en la inversión publicitaria.

The success stories analyzed show that a fintech that integrates machine learning with quality data can integrates machine learning with quality data can scale quickly and reduce its acquisition costs..

However, to achieve these results it is essential:

• Correctly measure the impact of advertising on high-value customers.
• Implement automated engagement and retention tools.
• Send quality conversion signals to advertising platforms.

Fintechs that adopt these strategies can scale their customer base efficiently and sustainably.

 

5. Conclusion

Scaling a fintech from an MVP (Minimum Viable Product) to reach 100,000 customers. is not just a question of increasing advertising investment. It is a process that requires the strategic integration of artificial intelligence, quality data and automation at every stage of the customer acquisition and retention funnel.

The evolution of advertising technologies has enabled companies to automate media buying, personalize ads and optimize ad spend in real time. However, as we have seen throughout this article, the success of these strategies depends directly on the quality of the data fed to the algorithms..

 

Main lessons learned:

1. Machine learning is only effective with quality data:
   The algorithms of platforms such as Google, Meta and TikTok cannot optimize campaigns if they only receive “top funnel” signals (clicks and registrations). The key is to send post-conversion events that reflect the real value of each user acquired.
2. Accurate attribution drives efficiency:
   Implementing advanced attribution models allows you to understand which channels, campaigns and creatives actually generate valuable customers. Without this visibility, fintechs run the risk of wasting their budget on low-quality leads.
3. Integration between product and advertising is essential:
   Campaigns AI-first must be connected to the product, sending activation, retention and activation, retention and monetization signals to advertising platforms. Only then will algorithms be able to identify and prioritize profitable users.
4. Engagement automation improves retention:
   It’s not enough to acquire users; you need to activate and retain them. Tools such as Mixpanel, Braze and Amplitude allow you to personalize the customer experience and reduce the abandonment rate.

 

Impact on fintechs:

Fintechs that have adopted these practices have succeeded:
📉 Reduce your CAC by up to 40%by optimizing campaigns based on user value.
📈 Increase your activation and retention rateby connecting product data with advertising platforms.
💰 Improve the LTV of your customersby focusing acquisition on high-value users.

Those that continue to run campaigns without quality data face rising costs and lower profitability, despite using advanced platforms.

 

The road to 100,000 customers:

For a fintech to scale efficiently, it must:

1. Implement advanced conversion events that reflect customer quality.
2. Integrate conversion APIs to connect the product with advertising channels.
3. Automate segmentation and personalization based on user behavior.
4. Adopt a continuous measurement approachoptimizing campaigns based on real data.

In an increasingly automated digital environment, companies that master the use of AI and data are the ones that will lead the market.. It’s not just about spending more on advertising, but building an ecosystem where every data signal strengthens the acquisition and retention strategy.