Does Your Hotel Speak the Same Language as Your Guests?

Imagine seeing an ad for a family vacation when all you’re looking for is a romantic getaway. Frustrating, right? Personalization is no longer a ‘nice-to-have’—it’s a critical consumer demand in the digital landscape, and this is where DCO for hotels (Dynamic Creative Optimization) comes into play.

In this article, we’ll walk you through how to simply and effectively phase a DCO strategy within the hospitality environment, leveraging tools like Campaign Manager 360 (CM360) and Google Studio. If you work in hotel marketing and want to boost your campaigns’ relevance (and results), keep reading.

Step 1: Define Your Audience and Identify Key Signals

Before firing up your creative or tech engines, start with the most important element: people.

Ask yourself:

• Who are my priority users?
• What digital signals can I use for segmentation?

Some key variables in the hotel sector include:

• Travel dates
• Type of accommodation sought
• Geographic location
• Originating channel
• Device type

🧭 A business traveler booking a weeknight stay from a mobile device should not receive the same ad as a family browsing on a tablet on a Sunday afternoon.

Pro Tip: Begin with 3-4 well-defined signals and a controlled set of audiences. Less is often more in the initial phase.

Step 2: Build Modular and Scalable Creatives in Studio

The magic of DCO for hotels lies in the ability to construct ads that automatically adapt to the user. To achieve this, you need to structure your messaging into reusable blocks within Google Studio.

Build a simple data feed featuring:

• Destination (city, region, or experience type)
• Room Type (suite, family, standard…)
• Promotional Benefit (discount, free breakfast, flexible cancellation)
• Call to Action (Book Now, Learn More, etc.)

🛠️ This modular approach allows you to mix and match elements without having to create hundreds of distinct creative variations.

Bonus Tip: Utilize templates that work across both display and rich media formats, ensuring they maintain visual brand consistency.

Step 3: Activate the Campaign in Campaign Manager 360

With the creatives finalized, it’s time to configure the campaign in CM360. This is where you connect the tailored messages with the defined audiences and establish the delivery rules.

Basic Checklist:

• Associate each dynamic creative with the appropriate target audience.
• Apply advanced segmentation (by geography, language, device, frequency, etc.).
• Use the dynamic preview to verify that every combination in your feed is displaying correctly.

🔍 Pre-flight review is crucial to prevent unexpected issues at launch.

Step 4: Analyze, Adjust, and Evolve

A DCO campaign is not a “set-it-and-forget-it” launch. On the contrary, it is a living system that improves with every data point it collects.

Key metrics to monitor:

• CTR per creative combination
• Conversions per message type
• Website dwell time
• Cost Per Acquisition (CPA)

Iterate and Optimize:

• Adjust the creative feed based on real-world performance.
• Add or remove modules according to seasonality or current offers.
• A/B test different CTAs and benefits.

📈 What works for romantic getaways today may not be effective for family stays tomorrow.

Conclusion: Personalization That Drives Bookings (and Results)

Phasing your DCO strategy for hotels isn’t complicated if you know where to start. Define your key signals, build a solid modular foundation, connect everything in CM360, and let the data be your guide.

In a competitive market like hospitality, differentiation means speaking to the right user with the perfect message at the precise moment. With the right tools, you can achieve this without an excessive workload.

👉 Ready to start your first hotel DCO pilot? At Making Science, we help you scale personalized campaigns with a data-driven and creative approach. Contact us and discover how to transform your campaigns into memorable brand experiences.

Introduction: Navigate the Data Maze and Decide with Confidence

CMOs, do you sometimes feel lost in a sea of data and measurement tools? You’re not alone. With so many options available, from attribution to Marketing Mix Modeling (MMM), knowing what tool to use and when is crucial for making informed decisions. This post is your compass. We’ll explain the main measurement tools (what they are, why, when, where, who, and how to use them) to help you build a solid data foundation and avoid the “AI chaos.” The goal is for you to master your measurement strategy, maximizing the impact of every marketing euro. Ready to unravel the mystery?

The CMO’s Challenge: Too Many Tools, Too Few Clear Answers?

The problem isn’t a lack of data, but rather data saturation and confusion about how to turn it into actionable insights. Many CMOs face the question: “How do I build my data foundation before diving into the ‘AI chaos’?” The answer lies in a clear understanding of the available measurement tools and how they fit into your overall strategy. Not all tools are suitable for everything, and knowing when to apply each one is the key to smart investment and data-driven decision-making.

Your Measurement Arsenal: When to Use Each Tool

For a complete and effective measurement strategy, you need a well-defined arsenal of tools. Here, we explain the most important ones and their purpose:

1. Attribution

What is it? Attribution assigns credit for a conversion to one or more touchpoints in the customer journey. Common models include first-click, last-click, linear, or data-driven. When to use it? It’s ideal for understanding short-term, user-level impact and optimizing specific digital campaigns. For example, if you want to know which ad or keyword led directly to a sale. It’s fundamental for granular measurement. Key Advantage: It allows you to optimize the performance of active campaigns and the allocation of your budget within digital channels.

2. Marketing Mix Modeling (MMM)

What is it? MMM is an econometric technique that measures the impact of all marketing activities (online and offline) on sales and other KPIs over time, considering external factors. When to use it? It’s perfect for medium- to long-term strategic planning, budget allocation between channels (TV, digital, print, etc.), and optimizing overall marketing ROI. It helps you understand diminishing returns and the real effect of your investment at a macro level. Key Advantage: It offers a holistic and platform-agnostic view, which is crucial in a cookie-less world, and it’s excellent for strategic measurement.

3. Budget Allocator

What is it? A tool that uses attribution and MMM data to recommend the optimal allocation of your marketing budget across different channels and campaigns, based on your objectives (maximize sales, ROI, etc.). When to use it? When you need to make quick and precise decisions about where to invest your next dollar, or to simulate different investment scenarios and see their impact. It’s the bridge between analysis and practical measurement action. Key Advantage: It translates insights into actionable recommendations for maximum efficiency.

4. Brand Lift Studies

What is it? Studies that measure the impact of your advertising campaigns on brand metrics such as awareness, consideration, preference, or purchase intent. When to use it? For branding campaigns or when the main objective is not a direct conversion, but rather building the brand and its long-term perception. It complements performance measurement. Key Advantage: It quantifies the intangible value of brand building.

The Perfect Synergy: How to Combine Your Measurement Tools

The key is not to choose one over the other, but to understand how they work together. A comprehensive measurement strategy often involves:

• MMM for long-term strategic allocation and understanding macro factors.
• Attribution for optimizing specific digital campaign performance and short-term impact.
• Budget Allocator for translating MMM and attribution insights into dynamic investment decisions.
• Brand Lift Studies to ensure that performance investments don’t sacrifice brand health.

By combining these tools, CMOs can build a robust Data Foundation that not only prepares them for the future of AI but also enables them to make much smarter and more efficient marketing decisions today. Effective measurement is the basis of everything.

Conclusion: Your Marketing Future Depends on Smart Measurement

In today’s complex marketing ecosystem, success is not a matter of luck, but of precise measurement and a clear strategy. By understanding the strengths and the right time to use tools like attribution, MMM, budget allocators, and brand lift studies, CMOs can move from uncertainty to confidence. It’s the path to building solid data foundations that will not only help you avoid the “AI chaos” but also drive you toward sustainable growth.

Ready to organize your measurement strategy and unlock the true power of your data? Start evaluating your tool arsenal today! If this post was helpful, share it and tell us, what is your biggest challenge in marketing measurement? Because at Making Science, we believe that to measure is to know, and to know is power!

What if your analysis tool could think with you instead of just giving you data?

Artificial intelligence, especially its generative version, is changing the game for data analysts. It’s no longer just about visualizing; it’s about discovering, predicting, and acting in real-time with unprecedented efficiency.

In this post, we’ll explain what generative AI applied to analytics is, why it’s transforming how data teams work, and most importantly, how you can start using it today to become more productive and generate a greater impact in your organization. Because the future of analytics is already here, and it’s intelligent.

What Does Generative AI Bring to Data Analytics?

Generative AI doesn’t just analyze information: it understands it, interprets it, and proposes solutions. Thanks to advanced models like LLMs (Large Language Models), you can now:

• Ask questions in natural language about your data.
• Generate dashboards automatically.
• Detect patterns or anomalies before they become a problem.
• Automate reports and data narratives in seconds.

This means less time writing code and more time making strategic decisions.

Why Is It a Revolution for Analysts?

Generative artificial intelligence acts as a co-pilot for any analyst: it reduces repetitive tasks, proposes new approaches, and allows for more creative data exploration.

According to Gartner, by 2026, over 50% of analytics tasks will be automated using generative AI.

With this technology, analysts stop being mere “data translators” and become true business strategists.

How to Start Applying It in Your Daily Work

Here are some practical ways to incorporate artificial intelligence into your workflow:

• Analytical chatbots: Ask your data questions without writing a single line of SQL.
• Automatic insight generation: Get summaries and recommendations effortlessly.
• Code assistants: Use tools like GitHub Copilot or Dataiku to speed up your scripts.
• Scenario simulation: Predict future outcomes by simply entering key variables.

🧠 Making Science Tip: Start with small tasks, measure the impact, and scale progressively. The key is to move from curiosity to real adoption.

What About the Risks? Do I Lose Control of the Analysis?

No. On the contrary. Generative AI amplifies your capabilities, but you are still the one making the decisions. The important thing is to maintain a critical eye, always analyzing the model’s outputs and combining them with your expert business knowledge.

Remember: artificial intelligence isn’t here to replace you, but to empower you.

Conclusion: The Time Is Now

Artificial intelligence is no longer science fiction. It’s a real, powerful, and accessible tool for any data analyst. If you want to be more productive, make more informed decisions, and have a greater impact on your organization, it’s time to give generative AI a place in your daily routine.

👉 Are you already using any AI tools in your work? Tell us in the comments!

And if you found this post useful, share it with that colleague who still thinks Excel can do everything 😉

Because in the world of data… Foresighted analysis is twice as effective.

Why do you need to analyze your app’s data?

Nowadays, almost everything goes through an app: ordering food, buying clothes, making a transfer… but how much do you really know about what your users do in it?
This post goes straight to the point: what app analytics is, why it matters, when and how to apply it, and who should pay attention to it.
If you have an app or are thinking of launching one, this is for you. We’ll tell you how data analysis helps you improve conversions, make informed decisions, and optimize every euro invested.
Did you know that 80% of users abandon an app after the first use if it doesn’t meet their expectations? The key is in the data.

📲 App analytics: much more than clicks

Data analysis in apps isn’t just about measuring how many people download your application. It’s about understanding:

• What do users do when they open it? 
• Where do they get frustrated and drop off? 
• Which features do they use the most? 
• Which channels generate the highest-value users? 

These insights allow you to make strategic decisions based on real behavior, not assumptions. The keyword here is data: without it, you’re flying blind.

🎯 Conversions: measuring what really matters

A conversion can be a sign-up, a purchase, or even a content view. But if you don’t know where they come from or why they happen, you can’t scale results.
App analytics lets you:

• Define key events: what action represents a conversion for your business? 
• Identify bottlenecks: where are users dropping off? 
• A/B testing and continuous improvements: change, measure, learn, and repeat. 

The key is knowing clearly what to measure and how to do it.
Pro tip: integrate tools like Google Analytics for Firebase or Appsflyer to have a complete and unified view.

🔗 Attribution: knowing which channel converts best

Not all users arrive by the same path. App attribution tells you who brought whom. This allows you to invest your marketing budget more wisely, optimize campaigns, and know which channels generate loyal users.
For example:

• Does the TikTok campaign perform better than Google Ads? 
• Which users have a higher retention rate: those coming from email or influencer marketing? 

Without a strong attribution strategy, you could be investing in the wrong places.

🔍 Smart data = smart decisions

Do you have a lot of data but don’t know what to do with it? That’s as useless as having nothing. What matters is turning data into decisions.
A strong app analytics strategy helps you:

• Prioritize development based on real behavior. 
• Personalize in-app experiences. 
• Detect errors or crashes in real time. 
• Increase business profitability. 

An app without analytics is like a car without a steering wheel: it moves, but you don’t know where it’s going.

📌 Conclusion: put your data to work (and not the other way around)

Deeply understanding app analytics is the difference between an app that merely exists and one that grows, converts, and retains.

Remember: data, conversions, and attribution aren’t just buzzwords. They’re the engine behind smart decisions.

👉 Do you have an app and want to take it to the next level? Contact us or leave a comment!
Because at Making Science, we know the real power isn’t just having data, but knowing what to do with it.
And you, are you already reading your app’s data… or just guessing it? 😏📊

Landing Page Optimization: The Secret to Boosting Your Paid Media ROI

Are you pouring budget into perfectly set-up paid campaigns… and still not seeing the return you expected? You’re not alone. But here’s the twist: maybe your campaign isn’t the problem—your landing page might be.

In this post, we’ll explore how Landing Page Optimization (LPO) can radically improve your Paid Media performance, help you make the most of your investment, and give you the tools to convince your clients and teams to prioritize it.

Whether you’re part of a Paid Acquisition team, a Making Science account manager, or a client trying to scale smarter, this guide is your shortcut to unlocking greater results—without touching your campaign setup.

Why CRO Is Your Paid Media’s Best Friend

Let’s be clear: Conversion Rate Optimization (CRO) isn’t just a nice-to-have. It’s the missing piece in many Paid Media strategies.

You’re likely optimizing your audiences, bidding strategies, creatives, and platforms… but what happens after someone clicks your ad?

If your landing page fails to convert, even the best campaign won’t deliver. That’s where Landing Page Optimization steps in.

Did you know?
Brands that actively run CRO tests can see up to 30% increase in conversions—without increasing ad spend.

What Is Landing Page Optimization?

Simply put, Landing Page Optimization is the process of improving the elements on your landing pages to encourage more users to take action—buy, sign up, request a demo… you name it.

It involves:

• Aligning messaging with your ad copy
• Improving page speed and UX
• A/B testing layouts and CTAs
• Removing friction and distractions

When done right, LPO doesn’t just improve conversions—it maximizes your ROAS (Return On Ad Spend) and makes every click count.

When Should You Prioritize LPO?

Let’s say your:

• CTR is strong ✅
• Campaign setup is solid ✅
• CPC is competitive ✅
• But ROAS is down ❌

That’s your cue.

If you’ve optimized your campaigns and you’re still not hitting targets, it’s time to look at your landing pages.

We often see Paid Media teams focus heavily on traffic—but forget that the true goal is conversion.

How to Sell LPO to Clients and Internal Teams

Here’s how you can pitch Landing Page Optimization with confidence:

1. Speak in ROI:
   Explain how LPO gets more value from the same budget.
2. Share quick wins:
   Clients love tangible results. Show A/B test examples with before/after metrics.
3. Highlight the opportunity cost:
   A weak landing page wastes media spend. It’s like sending traffic into a leaky funnel.
4. Align with business goals:
   Talk about revenue, leads, and growth—not just clicks or impressions.

💡 Pro tip: Ask the client, “What if we could increase your conversions by 15% without increasing your media budget?” That’s the LPO effect.

Real Impact, Real Fast

Unlike some long-term strategies, Landing Page Optimization can bring fast, measurable improvements.

Run a test. Get the data. Optimize. Repeat.

💥 Example: One Making Science client saw a 27% lift in lead volume after 3 weeks of LPO testing—with no increase in ad spend.

Conclusion: Don’t Let a Perfect Campaign Fall Flat

Landing Page Optimization is often the unsung hero of Paid Media success. When campaign setups are already tight, this is where real growth happens.

So next time your ROAS drops despite strong performance metrics, don’t panic. Think landing pages. Think CRO. Think opportunity.

Want to keep your media spend from going down the drain?
Start optimizing where it truly counts—after the click.

👉 Got a landing page in mind? Let’s test it. Let’s improve it. Let’s boost your Paid Media performance—together.

Still sending traffic to the same old page?
You might be paying for visits, not conversions. Let’s change that.

💬 Have questions or success stories? Drop them in the comments—we’d love to hear from you.

📊In today’s fast-paced B2C world, simply having customer data isn’t enough. Businesses are swimming in information – from website visits and past purchases to social media interactions and customer service queries.

💡The real challenge is making sense of it all and using it to genuinely increase sales.

💡This is where Salesforce Data Cloud steps in, offering a powerful way to transform raw data into actionable insights and drive significant growth.

❓What’s the Big Deal with Data in B2C?

Think about it: every customer interaction leaves a digital footprint. For B2C companies, understanding these footprints is key. If you know what your customers like, how they behave, and what their preferences are, you can offer them exactly what they need, at the right time, and through the right channel.

🎯This personalized approach isn’t just a nice-to-have anymore; it’s a sales imperative.

🚀How Salesforce Data Cloud Makes Data Work for You

Salesforce Data Cloud isn’t just another database; it’s a sophisticated platform designed to unify all your customer data in one place. Imagine having a single, comprehensive view of every customer, no matter where their information originated. This “single source of truth” allows you to:

• Understand Your Customers Deeper: By bringing together data from marketing, sales, service, and e-commerce, Data Cloud creates rich, 360-degree customer profiles. You can see their entire journey, from their first interaction to their latest purchase, and beyond.
• Segment Your Audience Smarter: Gone are the days of broad marketing campaigns. With unified data, you can create highly specific customer segments based on detailed behaviors, demographics, and preferences. This means sending the right message to the right person.
• Personalize Experiences at Scale: Once you understand your segments, Data Cloud enables you to personalize every touchpoint. From tailored product recommendations on your website to customized email offers and relevant ad placements, personalization makes customers feel understood and valued.
• Drive Proactive Engagements: With real-time data insights, you can anticipate customer needs and engage them proactively. For example, if a customer browses a specific product category repeatedly, you can send them a timely offer or information about those items, increasing the likelihood of a sale.

💰From Data to Dollars: The Sales Impact

The direct link between Salesforce Data Cloud and increased B2C sales is clear. When you understand your customers intimately and engage with them personally, several things happen:

• Higher Conversion Rates: Relevant offers and personalized experiences lead to more purchases.
• Increased Average Order Value: When customers find exactly what they’re looking for, or discover related products they genuinely need, they tend to spend more.
• Improved Customer Loyalty: Personalized interactions build trust and make customers feel valued, encouraging repeat business and long-term relationships.
• Optimized Marketing Spend: By targeting the right customers with the right message, your marketing efforts become more efficient and yield a better return on investment.

✅The Bottom Line

Salesforce Data Cloud empowers B2C businesses to move beyond scattered data and embrace a truly data-driven approach to sales growth. By unifying customer information, enabling smarter segmentation, and facilitating highly personalized experiences, it allows companies to connect with their customers on a deeper level, ultimately leading to significant increases in revenue and customer loyalty.

🤝Making Science has extensive experience in implementing and optimizing these types of Salesforce solutions, helping businesses unlock the full potential of their customer data to achieve their sales goals.

Are you tapping into the full potential of your digital data or just scratching the surface? Google Analytics 4 is here to change the rules of the game… but only if you know how to play.

📍 Introduction

Since its launch, Google Analytics 4 (GA4) has positioned itself as the new standard in digital analytics. But what makes it so special? And why are so many companies looking for Data Analyst profiles specialized in GA4?

In this post, we explore what GA4 is, why it has replaced Universal Analytics, what this change implies for your company, and how an analyst can turn data into real growth. If you manage digital channels, ecommerce, paid media or UX, then read on.

💡 Fun fact: according to Google, more than 50% of companies that have migrated to GA4 are still not properly exploiting their data due to a lack of analysts or advanced configuration.

🎯 What makes GA4 different?

1. It measures people, not just sessions

GA4 focuses on the user, not on visits. This allows you to analyze the real journey of your customers across channels, devices and platforms.

It no longer matters how many users visited your site, but who they are and what they do.

2. Personalized events: you decide what matters

No more measuring only page views. In GA4 you can track specific interactions: clicks, forms, scrolls, downloads etc. And the best part: everything is flexible, without depending on your IT team at every step.

3. Predictive models within everyone’s reach

GA4 gives you predictions such as conversion or abandonment probability, so you can prioritize actions based on data, not assumptions.

🧠 What if you knew which users are about to stop shopping… before they do?

4. Goodbye cookies, hello privacy

GA4 complies with regulations such as GDPR and adapts without cookies, which is key if your company wants to continue to scale without penalties.

👨‍💻 Why do you need a Data Analyst with GA4 experience?

It is simple: because having data is not the same as understanding it. Let alone acting on it.

✔️ An analyst turns data into decisions

• Configures relevant events and conversions
• Detects opportunities and problems before anyone else
• Relates data to business: sales, leads, profitability

✔️ Able to design useful dashboards for each team

A good analyst doesn’t just extract data, he translates it for Marketing, Product or C-Level. The result? Aligned teams and faster decisions.

✔️ Automate, predict and scale

With GA4 and BigQuery, your Data Analyst can build attribution models, automated reports, or identify hidden patterns that make (or lose) you money.

📌 Raw data is just noise. Advanced analytics turns it into music for your business.

🔚 Conclusion 

Google Analytics 4 has redefined how we measure and understand the digital experience.
However, to take advantage of its full potential, you need a Data Analyst who knows how to extract it, customize it and align it with your business strategy.

At Making Science, we help companies like yours turn analytics into impact. If you’re ready to transform your data into decisions, it’s time to take the next step.

🎯 Do you already have the person to turn your GA4 into a growth engine?

Did you find this article useful?
Share it, leave us a comment, or message us to show you how we work with advanced analytics at Making Science!

Because data doesn’t change your business. The decisions you make with it do.

Today’s media landscape is a maze. Multiple online and offline channels, fragmented audiences, multiplying touchpoints… In this complex environment, the pressure on marketing teams to demonstrate Return on Investment (ROI) and justify every dollar spent is more intense than ever. It’s no longer enough to “be present”; it’s crucial to understand what works, how it works, and, above all, where to invest the next dollar to achieve maximum impact. This is where precise measurement and intelligent budget optimization become fundamental.

Why do we need to measure accurately?

Without proper measurement, media investment decisions are based on intuition, partial data, or vanity metrics that don’t reflect the real impact on the business. The challenges are clear:

• Fragmentation: Consumers interact with brands through numerous channels and devices. How do you understand the complete journey and the role of each touchpoint?
• Data silos: Information is often scattered across different platforms and partners, making a unified view of performance difficult.
• Real impact vs. correlation: Did that campaign really generate sales, or did it simply coincide with a market trend? It’s vital to distinguish causality.
• Short-term and long-term vision: Some actions generate immediate results, while others build brand over the long term. How do you balance both needs?

The key question: Where do I allocate my budget?

Measuring is the first step, but the real challenge is translating that measurement into concrete actions, especially in budget allocation. The fundamental question every marketing manager must answer is: “Given my budget, how should I distribute it across different channels and campaigns to maximize my objectives (sales, leads, brand awareness, etc.)?”
This is where the need for a Media Budget Allocator comes into play.

Current solutions and their limitations

Currently, companies try to solve this puzzle in different ways, each with its limitations:

• Partner reports (agencies, publishers):
  • What they provide: Detailed reports on performance within their specific platforms or channels (e.g., social media campaign results, search ad clicks, GRPs on TV).
  • Limitations: Siloed view. Each partner optimizes for their own channel, without offering a global and neutral perspective on how different media interact. They may have inherent biases towards their own solutions. They make it difficult to compare “apples to apples” between very different channels (e.g., TV vs. TikTok).
• Other technologies (web analytics tools, isolated attribution platforms, MMM, BI): 
  • What they provide: Data on user behavior on the website, long-term insights, attribution models (often focused only on digital or last-click), dashboards with key metrics.
  • Limitations: They may lack a holistic view that robustly integrates offline and online. Digital attribution models alone don’t capture the impact of mass media or external factors (economy, seasonality, competition). MMM models focus on the long term, making short-term decision-making difficult. BI tools show the past but often lack the predictive or integrated optimization capabilities to guide future budget allocation automatically. They require complex integrations and expertise to extract actionable insights for planning.

The comprehensive solution: OptiPhi

This is where an advanced platform like OptiPhi makes a difference. It’s not just another measurement tool but a true strategic optimizer of media investment:

What does OptiPhi provide? EVERYTHING Necessary:

• Holistic and unified vision: OptiPhi integrates data from all relevant sources: online, offline, sales, external factors (competition, seasonality, macroeconomics). It breaks down silos.
• Robust measurement (MMM + Attribution): It combines the best of both worlds. It uses Marketing Mix Modeling (MMM) to understand the strategic impact of all channels (including offline and external factors) in the long term, and enriches it with a cookieless attribution model for tactical digital detail at the platform and strategy level on a daily basis. All within a Unified Measurement Framework.
• Risk-free testing: Conduct experiments to validate the introduction of new platforms or strategies into your models without risk, favoring model calibration and continuous improvement.
• Deep understanding of ROI: It measures the real impact of each marketing activity on business KPIs, separating the incremental contribution from background noise.
• Predictive capability: It doesn’t just look at the past. OptiPhi uses advanced models to simulate future scenarios, facilitating decision-making and investment justification. You can ask: “What would happen if I increase my TV investment by 10% and reduce search by 5%?”
• Budget optimization: This is key. Based on your objectives and constraints, OptiPhi recommends the optimal allocation of the budget across channels and campaigns to maximize return. It tells you exactly where to put each dollar.
• Speed and agility: It provides insights and recommendations month by month, allowing for faster adaptation to market conditions.
• Neutrality: As an independent technology platform, it offers an objective view without bias towards any specific channel or partner. Furthermore, the platform can be implemented in the client’s environment, maintaining the privacy of their data.

Conclusion: From intuition to intelligent optimization

In today’s competitive environment, managing the media budget based on partial data or intuition is too high a risk. The need for accurate measurement and, above all, for a tool that translates those insights into optimal budget allocation is critical.
Traditional solutions offer value but often in a fragmented or incomplete way. A true budget optimizer like OptiPhi provides the holistic vision, predictive capability, and optimization recommendations necessary to navigate the complexity of the media landscape and ensure that every dollar invested works as efficiently as possible to achieve your business objectives.
Are you ready to stop guessing and start optimizing your media investment strategically?
Discover more about OptiPhi here

Today, user research is a fundamental part of designing digital experiences. It helps us better understand users’ needs, behaviors, and expectations to create more intuitive and functional products. However, traditional research methods, such as interviews and usability testing, are often costly, time-consuming, and difficult to scale. This is where synthetic user research comes into play, an innovation that uses artificial intelligence and autonomous agents to create virtual user profiles and simulate their interactions.

In this article, we explore how synthetic users are changing the way we conduct UX/UI research, their current limitations, and how Making Science is addressing these challenges with its own advanced synthetic user model.

What Are Synthetic Users? 

Synthetic users are profiles generated by AI that simulate the characteristics, needs, and behaviors of real users. These profiles can be created automatically, using data and AI algorithms that mimic the diversity and complexity of a real audience. Tools like Delve AI and Synthetic Users allow anyone to generate detailed personas with information about their background, frustrations, goals, and psychological characteristics. Through these platforms, design teams can quickly create profiles representing a diverse sample of users without the need to recruit real individuals.

Advantages of Research with Synthetic Users

Scalability and Efficiency The main advantage of synthetic users is their ability to simulate multiple profiles in a short period. By eliminating the need to manage and coordinate groups of real users, design teams can quickly obtain relevant data from a wide variety of people. This makes research much more scalable and efficient, especially when fast results are needed.

Cost Reduction Recruiting real users, conducting interviews, and performing usability testing can be costly and logistically challenging. With synthetic users, these costs are significantly reduced, as there is no need to compensate participants or manage the logistical aspects of research sessions.

Simulating Controlled Scenarios With synthetic users, teams can simulate very specific scenarios and observe how virtual users interact with a product or service. This flexibility allows for usability testing with different types of users in controlled contexts, making it easier to identify problems before launching a product to the market.

Limitations of Synthetic Users

While synthetic users offer great advantages, they are not a complete solution. There are some important limitations to consider:

Lack of Real Empathy Although synthetic profiles can be incredibly detailed, they cannot replace genuine interaction with real users. Interviews and usability testing provide an emotional connection that helps designers deeply understand the motivations, friction points, and emotions behind user behavior. Synthetic users, no matter how sophisticated, lack the human complexity necessary to simulate authentic emotional experiences.

Superficially Accurate Simulations Synthetic users can provide valuable feedback, but they often lack the complexity that real users can provide. The insights generated by AI may be overly optimistic or simplistic, making it necessary to complement them with real-world research to obtain a more comprehensive view.

Reliance on Previous Data Synthetic users rely on previous data to create profiles, meaning their accuracy depends on the quality of that data. If this information is limited or outdated, the resulting profiles and insights may not reflect actual user behavior.

How Making Science is Redefining Synthetic Users

Understanding these limitations, at Making Science we have developed our own advanced synthetic user model designed to overcome many of these challenges. Unlike conventional solutions that focus mainly on demographic data, our model integrates multiple layers of information, including:

• Market behavior
• Technological context
• Socioeconomic aspects
• Behavioral trends
• Customer journey insights

Additionally, we enrich our model with sectorial and brand insights—incorporating information about the sector, competitive landscape, and brand-specific elements such as product offerings, pricing strategies, and positioning. This approach allows us to simulate more realistic user experiences that account for industry-specific dynamics and brand perception.

But what truly sets our model apart is its ability to incorporate real data from various sources, such as user interviews, focus groups, and client reports. It can even be enriched with call center data, ensuring a deeper and more authentic understanding of user behavior.

Moreover, our model excels in early-stage validation. It can test redesign proposals at the sketch or wireframe stage, providing actionable insights to determine whether the new approach improves the user experience. This capability accelerates decision-making and enhances design outcomes, helping teams iterate with greater confidence.

Use Cases in UX/UI Research

While synthetic users should not fully replace real-user research, they have an important role in the lifecycle of UX/UI research. Some of the most relevant use cases include:

Exploratory Research: Synthetic users are ideal for the early stages of research, where the goal is to explore potential issues and generate hypotheses. By creating synthetic personas based on demographic and behavioral data, designers can gain an initial understanding of their audience before diving into more costly and complex studies.

Concept and Prototype Testing: Before conducting tests with real users, design teams can use synthetic users to evaluate design ideas, prototypes, and features. This allows for quick validation of concepts and adjustments before rolling out prototypes to real users.

Feature Validation: Synthetic users can be used to validate new features or changes in a product. Through simulations, designers can observe how different user segments interact with the product and whether the new features meet their needs.

AI and the Validation of Interface Affordance One of the most exciting advancements that AI already enables is the validation of the affordance of our interfaces. Simply put, affordance refers to an interface’s ability to indicate how an element should be used intuitively. AI can analyze and predict how users will interact with certain interface elements, helping us improve accessibility, clarity, and usability in our designs. Thanks to simulations with synthetic users, we can observe and adjust interactions before implementing changes in a real product, allowing us to optimize the user experience more efficiently.

The Future of Synthetic Users in UX/UI Research

The field of synthetic user research continues to evolve. At Making Science, our advanced synthetic user model not only integrates complex layers of demographic and behavioral data but also enriches profiles with real user feedback from interviews, focus groups, client reports, call center interactions, and sector-specific insights. This unique approach allows for more nuanced and realistic simulations.

Additionally, our model’s ability to test wireframes and sketches sets it apart, enabling early detection of design challenges and quicker iteration cycles.

However, it is essential to remember that research with real users should always be the foundation of any design strategy. Synthetic users are a powerful tool to complement research, but they should not replace a deep analysis of users’ needs, expectations, and emotions.

TL;DR 

Synthetic user research is opening up new opportunities for design and product development teams. While it doesn’t replace real-user work, it provides a scalable and cost-effective alternative to obtain preliminary insights and validate ideas quickly. The key is knowing when and how to use this technology effectively, integrating it into a broader and balanced research approach.

At Making Science, we support our clients in implementing these innovative technologies to improve their user research processes. Our proprietary synthetic user model stands out by integrating both demographic insights and real user data, even from call centers, and enriching the analysis with sector-specific and brand insights. Additionally, we validate redesign concepts at the wireframe stage, optimizing user experiences from the earliest phases. The future of UX/UI research is promising, and we are ready to lead this change.

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As businesses strive to gain a competitive edge, the ability to transform raw data into actionable insights has become paramount. However, the path to leveraging data effectively has often been an uphill climb plagued by technical hurdles and siloed information. At our recent event “GenAI + BI: Accelerating Time-to-Insight with Looker” in collaboration with Google Cloud, we witnessed how generative AI is revolutionizing this paradigm, ushering in a new era of conversational analytics.

The Evolution of Business Intelligence

For decades, extracting insights from data meant wrestling with complex queries, static dashboards, and clunky reporting processes accessible only to the technically skilled. While tools like Looker provided powerful data exploration and visualization capabilities, ensuring data democratization remained an uphill battle. What set Looker apart was its robust data governance foundation – a centralized model that promoted trust and accuracy while unlocking self-service analytics across an organization.  However, even with these advancements, a gap persisted between businesses and the true democratization of data they sought.

This disconnect has been a driving force for our team at Making Science. As I shared during the event, “For too long, data democratization and getting insights from data has been an uphill climb.” But the integration of Looker and generative AI (GenAI) is profoundly changing the game.

Fostering True Data Literacy

At the core of this transformation lies the power of natural language interaction. Through Looker’s GenAI capabilities, users across an organization can simply ask questions and receive accurate, real-time insights without grappling with SQL, data modeling, or visualizations. This intuitive approach empowers teams to become active participants in data-driven decision-making, fostering a culture of true data literacy.

Fabrizio Russo, Cloud BI Specialist at Google Cloud, encapsulated this shift perfectly: “With Looker and GenAI, you can ask complex questions and receive answers instantly, without the need for technical expertise or data manipulation. This is data democratization in action.”

From Data to Profit: AI-Powered Monetization

But the potential of Looker’s GenAI integration extends far beyond interactive Q&A. Through live product demonstrations, we witnessed firsthand how these capabilities enable users to automatically generate visualizations, build custom dashboards, rapidly develop data-driven applications, and much more – all via simple natural language prompts.

This seamless translation from insights to action unlocks new frontiers in monetizing data. Tools like Looker Actions and Embedded Analytics empower businesses to package and monetize their analytics offerings, create tiered pricing models for differentiated data access, embed insights into customer-facing platforms, and drive sustainable revenue growth.

We had the privilege of hearing from Sergio Pescador, Corp Dev. & BI Director at Playtomic, the leading padel reservation app. Sergio detailed their inspiring journey of leveraging Looker to unify organizational data, enabling data-backed decision-making across departments while maintaining their competitive edge.

Looking Ahead: Embracing the Future of BI

As this groundbreaking event made clear, the fusion of Looker and GenAI is merely the beginning of a seismic shift in how businesses will interact with and extract value from their data. Generative AI will continue to push the boundaries of what’s possible, propelling us toward an era of hyper-personalized data experiences and unprecedented efficiencies.

At Making Science, we remain committed to staying at the forefront of these transformative AI capabilities in the BI space. Our team of seasoned experts stands ready to guide organizations in navigating this new frontier and unlocking the full potential of a conversational, AI-driven approach to data analytics.

The path from insights to impact has never been more clear or achievable. The dawn of conversational analytics is here, and those who embrace it will gain a decisive competitive advantage. I encourage you to explore Making Science’s comprehensive offerings and engage our team to begin your data transformation journey.