5 Use Cases to Transform Your Marketing in 2025

Introduction

Working in marketing today can be as exciting as it can be challenging. With so much buzz around Artificial Intelligence (AI) and innovations, it’s easy for marketers to feel overwhelmed by the sheer volume of possibilities and unsure about where to start.

But marketing technology (MarTech) and Marketing Science aren’t abstract concepts and it’s not just about keeping up with trends—it’s about finding practical, impactful ways to solve real-world marketing challenges and drive business growth.

This blog post aims to demystify marketing solutions by sharing use cases that address some of the most pressing issues marketers face today. We’ll break these 5 use cases into manageable pieces, highlighting the challenges they address, the solutions available, and the potential impact on your business. The 5 use cases we are going over are:

1. Incremental Marketing Measurement & Attribution: Find the right combination of marketing solutions to effectively measure the incrementality of your advertising efforts.
2. Media Planning & Forecasts: Predict the effectiveness of media investments to allocate budgets across channels for maximum ROI.
3. Online Paid Media Optimization: Leverage saturation curves and historical performance to efficiently optimize digital campaigns.
4. Owned Media Personalization: Leverage machine learning models to generate data-driven content and engagement strategies that increase usage and revenue.
5. Gen AI Ads: Create and optimize ad content with AI, improving personalization, efficiency, and scalability in ad campaigns.

Hopefully, some of these challenges will resonate and the use cases will give you a better idea of how to start using Marketing Science and technology for your business.

The Use Cases

1) Incremental Marketing Measurement & Attribution

Challenge

Although there have been some shifts between performance and brand advertising, there’s a constant (and increasing) pressure to justify marketing investments. So how can marketers measure clear ROI across complex, multi-channel customer journeys? What is the best way to identify where to spend the next marketing dollar to ensure incremental growth? Traditional single-touch attribution models often oversimplify these interactions, giving disproportionate credit to the first or last touchpoint and leaving marketers with incomplete insights. Multi-Touch Attribution models still face privacy restrictions.

In sum: without effective attribution and measurement, it’s difficult to understand incrementality. It’s important to understand what is driving conversions to allocate budgets effectively.

Solution

If there’s one clear trend in measurement, it’s moving towards an incremental marketing framework. Depending on where you stand in your marketing journey, there will be different solutions available. But the end goal should be a holistic approach that combines Data-Driven Multi-Touch Attribution (MTA), Marketing Mix Modeling (MMM), and lift tests as each of these will provide different insights.

• Data-driven Multi-Touch Attribution (MTA): Granular, real-time insights into touchpoint effectiveness for tactical optimizations online.
• Marketing Mix Modeling (MMM): High-level, long-term impact analysis of marketing investments.
• Lift Tests: Ground-truth validation of MTA & MMM, measuring true incremental impact.

To effectively integrate these three approaches, businesses should use MMM to set a baseline for long-term marketing effectiveness and budget allocation, while leveraging MTA for tactical optimizations in digital channels. Lift tests should be conducted regularly to validate and calibrate both models, ensuring accuracy in measurement. By continuously cross-referencing insights from the three methods, companies can harness an attribution strategy that balances precision with scalability, enabling smarter data-driven decision-making.

📚 Recommended reading

• The Art of Correctly Assigning ROI
• MercadoLibre Case Study - Unlocking the Power of lift based Multi-Touch Attribution
• Think With Google | The MMM Handbook

2)Media Planning & Forecasting

Challenge

Even with a solid measurement and attribution framework, simple linear forecasts based on past performance ignore channel saturation, seasonality, and true incremental impact, often leading to misallocated budgets. Teams typically use spreadsheets to build spending plans, making it difficult to generate multiple scenarios that can adjust for daily/weekly trends. Additionally, they often divide spend evenly or use rough estimates, leading to inefficiencies. Additionally, ad platforms are constantly changing their algorithms, and costs keep rising due to increasing competition, making it harder to predict performance. Even with a plan in place, overspending or underspending happens due to unexpected performance shifts or economic changes, requiring constant recalibration to stay on track.

Solution

Use the output of the Marketing Mix Modeling together with performance or spend targets defined by the business and demand forecasting to quickly model automated scenarios and define the high-level spend and performance (ROAS/CPA) targets for each channel.

Application & results

1. Optimized Budget Allocation – Leverage historical data from past seasonality trends to redirect spend from over-attributed to high-incrementality channels, high-performing days, or moments.
2. Adjustable Media Plans – Pace the investment according to the data-driven prediction of campaign performance.
3. Effective Campaign-Level Constraints – Use this plan and historical campaign performance to estimate the best set of constraints to set at the campaign level to maximize the results of each group.

3) Online Paid Media Optimization

Challenge

Once the media plan is defined, most marketing teams set their budgets and ROAS/CPA targets, then let ad platforms optimize for them. The problem is platforms don’t treat all advertisers equally; sometimes they’ll push one brand’s ads over another’s, but it’s inconsistent. This misalignment means you can’t just "set and forget" your campaigns—you need to actively manage them to make sure you're getting the best return on your spend. In other words, the complex multi-channel campaigns that run 24-7 need more monitoring than is humanly possible.

Solution

An agnostic, campaign optimizer that helps you manage budgets and bids across channels like Google Ads, Meta Ads, and Google Analytics—all in one place. It lets you group campaigns across channels, set spend and performance targets (ROAS/CPA), and stay on track with your media plan. The platform predicts when campaigns might hit saturation and suggests budget and bid adjustments to get the best results—automatically applying changes if you choose. It also monitors performance in real-time, flagging and fixing overspending or underspending before it becomes a problem.

In short, it takes the guesswork out of campaign management so you can maximize performance with less manual effort.

Application & Results

• Daily Recommendations to Maximize Revenue – Optimizes campaigns and maximizes overall results with smart automation that monitors campaigns throughout the day, automatically alerting or pausing due to performance deviations. Depending on frequency of application and spend variations, customers have reported around 5-15% increased ROAS.
• Advanced Cross-Channel Insights – Access ad response curves for each campaign to understand saturation, marginal effectiveness, and identify cross-channel opportunities.
• Streamlined Workflow – Automation reduces campaign management time, allowing the team to focus on strategic initiatives, with customers reporting a 55% decrease in Time Spent on Campaign Management.

📚 Recommended reading

• Case Study - How does our Paid Media Optimizer Work

4) Owned Media Personalization

Challenge

Companies with owned media often struggle to drive higher engagement because capturing attention in a crowded digital space is harder than it seems. Simply having access to an audience doesn’t guarantee impact; the challenge lies in delivering the right message, at the right time, in the right way to influence behavior. Many brands rely on generic, one-size-fits-all communication, which fails to resonate with individual preferences.

In sum: without personalization, behavioral insights, and strategic timing, messages can feel irrelevant, leading to low engagement and service adoption.

Solution

We develop comprehensive recommendation frameworks using Machine Learning models to predict users propensity to use specific platform services. This system tailored recommendations based on delivery timing, channel interaction, service relevance, and message content.

• Propensity Model – Analyze historical seller and platform activity data to identify the key services that each seller is more likely to use.
• Recommendation Engine – Leverage historical data to identify what message to send, when, and through which channels to boost platform transactions.
• Gen AI Message Generation – Quickly generate variations for top messages to further boost engagement.

Application & Results

• Increased User Engagement & Service Adoption – By predicting each user's likelihood to engage with specific services, the system delivers personalized recommendations at the optimal time and through the right channel, leading to higher conversion rates and deeper platform usage.

  • Example: A similar project led to a 4% nominal increase in sellers selecting MercadoPago as their primary platform, growing from 6% to 10%. Additionally, the implementation of monitoring best practices enabled real-time metric tracking through user-friendly dashboards, further optimizing the platform's performance and decision-making.

• Optimized Marketing Efficiency – With machine learning-driven targeting, resources are allocated more effectively, reducing wasted impressions and irrelevant messaging while maximizing the impact of each communication.

• Higher Retention & Revenue Growth – Tailored recommendations enhance user satisfaction by delivering more relevant and timely offers, increasing customer loyalty, share of wallet, and overall revenue while minimizing churn.

📚 Recommended reading

• Case Study - The AdTech Advantage: Boosting Mercado Pago's Customer Base

5) Gen AI Ads

Challenge

One of the biggest challenges in marketing is keeping up with the demand for fresh, high-quality ads in a fast-moving, competitive landscape. Traditional ad creation is slow and resource-intensive, with long review cycles and limited variations. Marketing teams working to promote large product feeds want their messaging to be as dynamic as their strategies, requiring faster turnaround for special pushes and promotions, but working with designers and even with internal design teams can be costly and time consuming.

Solution

With our Gen AI Display Ads, anyone—regardless of technical or design skills—can generate high-quality ads with personalized images, product descriptions, and precise classifications.

• Top Product Selection – The best products are selected based on criteria that favor performance (views, sales, etc.), store representation, and aesthetic criteria (quality of image, lack of background, etc.).
• Description & Prompt – The product’s characteristics are fed to an AI model that describes a use-related scene, highlighting details that complement the product and its placement.
• Background & Composition – A Generative Model creates a background scene, while a computer vision model analyzes the composition and places the item on top to create a harmonious arrangement.
• Inpainting – Finally, an AI model enhances the composed image through inpainting, improving details like shadows, lighting, and reflections.

Output / Expected Impact

• Higher Efficiency – 1000x faster ad creation; automatic production enables high output with minimal allocated resources.
• Lower Costs – By reducing the required design budget, overall production costs are significantly decreased.
• Higher CTR – Faster ad renewal lowers the risk of ad fatigue, keeping customers more easily engaged. Some brands have seen CTR increases of up to 30%.

📚 Recommended reading

• Learn more about our Gen AI solutions
• Amazon Web Services Case Studies | GenAds: A New AI-Driven Product Imaging Service
• Case Study | GenAds for MercadoLibre

Other applications and customized marketing solutions

Not sure if these apply to your business? Do you have other challenges you'd like to address? Book a call with us to talk with one of our Data Nerds