As the industry moves away from user-level tracking, capturing and measuring marketing value requires a fundamentally new approach. Signal fragmentation and degradation are the defining challenges, leaving traditional attribution models unable to cope. This has prompted marketing and analytics leaders to transition toward sophisticated predictive methodologies, including Marketing Mix Models and experiments, to maintain a competitive advantage by controlling for incrementality.

Working with clients across the spectrum, we often find a lack of confidence, if not an outright gap in attribution, that prevents marketers from applying insights to budget and channel-level optimizations.

The Strategic Anchor: Google Meridian

Google’s Meridian is a next-generation, open-source Marketing Mix Model (MMM) designed for modern marketers. The model moves beyond the black box of proprietary solutions and offers a transparent ground truth for evaluating marketing across channels.

• Privacy-Durable Modeling: Meridian mitigates the risks of signal loss by leveraging aggregated data, ensuring long-term measurement stability.
• Total-Market View: The methodology can incorporate the impact of both digital and traditional media, providing a macro perspective on how cross-channel investments drive business outcomes.
• Transparency for Stakeholders: As an open-source framework, Meridian allows for rigorous auditing and customization to improve both Analytics and Marketing leadership confidence in the underlying data science.

While Meridian provides a strategic measurement foundation, the challenge remains: how do marketers turn macro insights into performance gains?

The Execution Engine: Closing the MMM Insight-to-Action Loop 

OptiPhi, a Making Science proprietary tool, is a bridge between MMM insights and marketing execution. The offering focuses on transforming open-source MMM (Meridian or others) insights into an “always-on” optimization engine. Built to complement a MMM and augment its utility, OptiPhi ensures that high-level media mix goals are reflected in attribution and can inform campaign optimization.

• Reducing Utility Latency:  Traditional MMM results are often delivered quarterly or annually, making them “post-mortems” rather than real-time activation resources. OptiPhi solves this by ingesting the most up-to-date data available and performing monthly retraining. While some tools claim “real-time” updates, we’ve found a monthly cadence is a strategic sweet spot: frequent enough to capture new market shifts, stable enough to filter out daily statistical noise. The result is near real-time insights that allow marketers to pivot budgets with the agility of digital-first attribution without the volatility of over-optimization.
• Scenario Planning: Post campaign measurement is valuable, but OptiPhi empowers marketers to forecast the impact of different budget allocations. By combining optimization results from historical MMM data with OptiPhi predictive models, teams are able to simulate spend scenarios before a single dollar is committed.
• Holistic Attribution: OptiPhi goes beyond high-level media goals by evaluating how different marketing strategies work together to drive conversions. It calculates the incremental value each strategy adds to the collective results. By identifying which tactics are complementary and which are redundant, OptiPhi provides more granular, actionable conclusions. 

The Value Delivered

In an environment defined by signal fragmentation, the combination of Meridian and Making Science’s OptiPhi solution provides the durability of a world-class MMM, the speed and actionability of attribution and budget planning grounded in the MMM.

Leaders who adopt a unified or “trifecta” approach to modern measurement aren’t just measuring performance; they’re using MMMs, Attribution, and Experimentation to engineer their success.

Ready to transform your macro insights into budget-level performance gains?

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 The days of endless online browsing are over. Welcome to Agentic Commerce, the era where your shopping is done for you, not just by you.

For retailers and brands using the Google Marketing Platform (GMP), this isn’t just a technical upgrade; it’s a fundamental reimagining of how you connect with high-intent shoppers. From autonomous digital assistants that research on a customer’s behalf to protocols that turn a search query into a completed purchase in seconds, 2026 is the year retail gets “agentic”.

What is Agentic Commerce? (And Why Should You Care?)

Think of your favorite, most helpful sales associate. Now, imagine that an associate lives in your customer’s pocket, knows their exact style, budget, and past purchase history, and can scour thousands of listings in milliseconds to find the perfect match.

Agentic commerce is an era where consumers delegate parts of their buying journey, like discovery, comparison, and even payment, to intelligent AI systems.

• Discovery moves from keywords to intent: Shoppers no longer type “red sneakers size 10.” Instead, they tell an agent, “Get me ready for the New York Marathon,” and the agent manages the shoes, the energy gels, and the training plan.
• Zero-click transactions: By the end of 2026, experts predict many consumers may stop visiting brand websites entirely for routine purchases, relying on agents to handle the transaction directly.

The New Open Standard: Universal Commerce Protocol (UCP)

To make this seamless future a reality, Google and industry leaders like Shopify, Target, and Walmart have launched the Universal Commerce Protocol (UCP).

Universal Plug-and-Play for Retail

In the past, every retailer had to build a unique connection for every new shopping app or platform. This created a mess of bespoke integrations. UCP essentially provides a common language for agents, businesses, and payment providers to talk to each other effortlessly.

Native Checkout in AI Mode

Google is adding a “Buy” button directly on surfaces like AI Mode in Search and the Gemini app. Because UCP standardizes the commerce workflow, shoppers can check out from eligible U.S. retailers right as they’re researching, using payment methods and shipping info already saved in Google Wallet. Shopify merchants’ products are also now available for purchase on AI chatbots for checkout, including Chat GPT, Microsoft Copilot, and Gemini.

Retailer Tip: Even with this frictionless flow, the retailer remains the Merchant of Record. You maintain control over your business logic, inventory, and, most importantly, the customer relationship.

Supercharging GMP for Commerce

For advertisers, the “agentic” shift is starting across GMP. The goal is simple: move away from fragmented tactics and toward intelligence-driven systems that make marketing more accountable.

1. Activating Commerce Audiences

DV360 now allows brands to leverage first-party shopper data from Commerce Media Networks (CMNs). This means you can target shoppers based on actual purchase intent rather than just broad demographics.

• Precision Targeting: Access audiences like “Toddler shoppers” or “Laundry detergent buyers” directly within the DV360 UI.
• Unified Reach: These audiences can be activated across 3rd-party exchanges, YouTube, and even Demand Gen campaigns.

2. Measuring What Matters (SKU-Level Insights)

The era of “spray and pray” is over. New measurement betas allow retailers to upload offline and online sales data to track performance at the SKU level.

• Closed-Loop Reporting: Finally, retailers will be able to bridge the gap between a CTV (Connected TV) impression and an actual purchase.
• Incremental Growth: Use real-time visibility to adjust campaigns in the moment rather than waiting for end-of-month reports.

3. Branded “Business Agents”

Retailers can now launch their own Business Agent directly on Search. Think of it as a virtual sales associate that speaks in your brand’s voice.

• Custom Training: Agents will have the ability to be trained on your specific product data and customer insights.
• Direct Offers: While a shopper is chatting with your agent, you can present exclusive Direct Offers to close the sale right when they’re ready to buy.

Strategic Trends to Watch in 2026

To succeed in this new era, your data strategy must evolve as fast as AI. Here are three trends that will define the winners:

Moving from Experimentation to Integration

Innovation in the retail space is exploding rapidly. Retailers went from processing 8.3 trillion tokens on Google’s API in late 2024 to over 90 trillion tokens in 2025. This 11X increase shows that retailers aren’t just thinking about agentic commerce – they are already implementing it.

The most successful brands in 2026 won’t be the ones with the biggest budgets, but the ones with the most unified operational foundations. By connecting your first-party data, media, and commerce through tools like DV360 and protocols like UCP, you ensure your brand is “answer-ready” the moment a consumer’s personal AI goes looking for a recommendation.

To learn more about ensuring your brand is ‘answer-ready’ in this new ecosystem, get in touch with our team of experts.

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Are you feeling like your emails don’t always land at the right time? Looking for a more instantaneous and personal connection with your customers? Discover how WhatsApp as an additional communication channel can transform your strategy, allowing you to activate more efficient and direct campaigns through Marketing Cloud.

In this post, we explain the what, why, and how so that you—the Marketing Cloud expert—can integrate it seamlessly, solving the challenge of immediacy in your communications. Get ready to take your interactions to the next level!

1. Why Include WhatsApp in Your Marketing Cloud Strategy?

In an information-saturated world, WhatsApp as an additional communication channel has become the preferred messaging tool for billions. Ignoring it means missing a golden opportunity. But why should you activate it specifically within Marketing Cloud?

• Immediacy and High Open Rates: WhatsApp messages are read almost instantly. We are talking about open rates exceeding 90%—unthinkable in the world of email!
• Two-Way Communication: It’s not just about broadcasting. WhatsApp allows for real-time conversations, making it ideal for support, feedback, or quick resolutions.
• Personalization at Scale: Leveraging the power of Marketing Cloud, you can segment and personalize messages far beyond the classic “Hi [Name].”
• Enhanced Customer Experience: Offering a familiar and convenient communication channel boosts satisfaction and long-term loyalty.

Imagine sending a cart abandonment reminder, a shipping confirmation, or a back-in-stock alert directly to your customer’s favorite chat app.

2. First Steps: Requirements to Integrate WhatsApp in Marketing Cloud

Before diving in, it is crucial to understand that setting up WhatsApp as an additional communication channel requires a specific configuration. It’s not as simple as clicking “send” on an email.

a) The WhatsApp Business API: Your Access Gate

You will need a WhatsApp Business API account. This isn’t the app you use on your phone; it’s an interface that allows companies to send messages at scale. Various Business Solution Providers (BSPs) can help you set this up.

b) Business Account Verification

Meta (formerly Facebook) has a strict verification process for Business accounts. Ensure you have all your legal documentation ready. This process guarantees security and prevents your brand from being flagged as spam.

c) User Opt-in: Absolutely Essential!

To use WhatsApp as an additional communication channel, you must have the user’s explicit consent (Opt-in). This is non-negotiable and critical for the legality of your communications. How can you get it?

• Web forms.
• During the checkout process.
• A clear CTA in an SMS.
• Via an email campaign.

3. Best Practices for Activating WhatsApp Campaigns from Marketing Cloud

Once the basic setup is done, the magic happens within Marketing Cloud. Here are the keys to ensuring your campaigns are a total success:

a) Content Strategy and Approved Templates

WhatsApp is not email: messages should be shorter, more direct, and maintain a conversational tone.

• Message Templates: To initiate a conversation outside the 24-hour window, you must use templates pre-approved by WhatsApp.
• Personalization: Use Marketing Cloud contact attributes to make your templates relevant. Example: “Hi {{1}}, your order {{2}} is on its way!”

b) Integrating Journey Builder

This is where WhatsApp as an additional communication channel truly shines. You can include WhatsApp activities within your Journeys.

• Omnichannel Logic: If a customer doesn’t open an important email within 2 hours, trigger an automatic WhatsApp message.
• Wait Activities: Respect the user’s time. Don’t send messages in the middle of the night; use “Wait Until” activities to optimize delivery times.

c) Measurement and Optimization

What isn’t measured cannot be improved. Use Marketing Cloud’s analytics to track:

• Delivery rates.
• Open rates (Read receipts).
• Click-through rates (CTR) on your links.

Conclusion

In summary, activating WhatsApp as an additional communication channel within your Marketing Cloud ecosystem is no longer an “extra”—it is a necessity for brands that want to stay relevant. By following these best practices, from securing the correct Opt-in to automating via Journey Builder, you will bridge the gap between your brand and your customers.

At Making Science, we believe that the best communication is the one that happens where the customer feels most comfortable. And right now, that place is WhatsApp.

Ready to start? If you need help with the technical setup or defining your conversational strategy, we are here to help you activate WhatsApp in Marketing Cloud and turn every message into a meaningful connection.

Don’t leave your customers on “read”—start the conversation today!

Ever felt like your ad spend was working hard, but maybe not smart enough? For years, advertisers have chased clicks, conversions, and impressions, often treating all conversions the same. But what if there was a smarter way to tell your digital marketing campaigns to not just find conversions, but to find the best conversions?

That’s what Value-Based Bidding (VBB) does for smart advertisers. This isn’t just a fancy new buzzword; it’s a fundamental shift in how you optimize your ad budget within the Google Marketing Platform, designed to supercharge your return on ad spend (ROAS) and connect you with customers who genuinely matter to your bottom line.

Beyond the Click: The Paradigm Shift to Value

Let’s rewind a bit. Traditional bidding strategies often focus on volume. You might bid to get as many conversions as possible, or to hit a certain Cost Per Acquisition (CPA/targetCPA). And while those metrics are important, they don’t always tell the whole story.

Imagine you’re selling custom-designed sneakers. One customer buys a basic pair for $100. Another buys a premium, limited-edition pair with custom artwork for $500. If your bidding strategy treats both conversions as equal, you’re missing a massive opportunity. The $500 sale is clearly more valuable, yet your campaigns might be optimizing just as hard for the $100 sale. This is where VBB can make all the difference.

Value-Based Bidding assigns a monetary value to your conversions. Instead of simply telling Google to get you “a conversion,” you tell it to get you “a conversion worth X dollars.” This empowers Google’s machine learning to prioritize serving your ads to users who are more likely to generate higher revenue or profit for your business.

Why Google Marketing Platform and VBB are a Match Made in Heaven

The Google Marketing Platform (GMP) is not just a collection of tools; it’s an integrated ecosystem designed for serious advertisers. With components like Search Ads 360, Display & Video 360, Analytics 360, and Campaign Manager 360 working in harmony, GMP provides the robust data infrastructure and powerful machine learning capabilities that VBB absolutely thrives on.

Here’s why GMP is the perfect platform for VBB:

1. Unified Data Insights: GMP allows you to consolidate data from various touchpoints – search, display, video, analytics, and more. This unified view gives you a much richer understanding of customer journeys and, crucially, the actual value different customer segments bring. When you feed this comprehensive data into your VBB strategy, Google’s algorithms have a clearer picture of who your high-value customers are.
2. Advanced Machine Learning: At its core, VBB leverages Google’s sophisticated AI. The algorithms learn from your historical conversion values, user behavior signals, and real-time market dynamics to predict the likelihood of a high-value conversion. Within GMP, these algorithms are supercharged by the sheer volume and quality of data flowing through the platform.
3. Cross-Channel Optimization: With GMP, you’re not just optimizing a single campaign in isolation. Search Ads 360 can manage your search campaigns, while Display & Video 360 handles your programmatic efforts. VBB strategies, especially when implemented across these platforms, allow for a holistic approach where your budget is dynamically shifted towards the channels and audiences most likely to deliver high-value conversions, regardless of where they are in their journey.
4. Granular Control & Automation: While VBB automates much of the heavy lifting, GMP still provides the granular controls marketers need. You can set specific value rules, define custom bidding strategies, and observe the performance with detailed reports. This blend of automation and control is what makes VBB within GMP so powerful.

The Nitty-Gritty: How Does Value-Based Bidding Actually Work?

Implementing VBB effectively involves a few key steps:

Step 1: Defining Your Conversion Values

This is the foundational step. You need to assign a monetary value to each conversion action. This isn’t always as simple as a direct e-commerce sale. For lead generation, you might assign values based on:

• Lead Quality: A whitepaper download might be worth $10, a demo request might be worth $50, and a qualified sales lead might be worth $200.
• Customer Lifetime Value (CLTV): If you have historical data, you can estimate the average revenue a customer brings over their entire relationship with your business.
• Profit Margins: Focus on the profit generated, not just the revenue. A high-revenue, low-margin product might be less valuable than a lower-revenue, high-margin one.

The more accurate and detailed your conversion values, the better Google’s algorithms can optimize. This often involves integrating your CRM or internal sales data with your analytics platform (like Google Analytics 4, which is built for this purpose and integrates seamlessly with GMP).

Step 2: Implementing Conversion Tracking with Value

Ensure your conversion tracking is set up to pass these dynamic values.

• E-commerce: For online stores, this is typically handled by passing the value parameter with your purchase event data.
• Lead Forms: For lead generation, you might need to use custom JavaScript or a Google Tag Manager setup to dynamically assign values based on the form type, product interest, or even post-submission qualification.
• Offline Conversions: For sales that happen offline, you can upload offline conversion data back into Google Ads (and subsequently into GMP) with associated values.

Step 3: Choosing the Right Bidding Strategy

Once your values are flowing, you can select a VBB strategy:

• Maximize Conversion Value: This strategy aims to get the highest total conversion value for your budget. It’s great for when you want to maximize revenue without a specific ROAS target.
• Target ROAS (Return on Ad Spend): This is the holy grail for many businesses. You set a target ROAS (e.g., “I want to get $4 back for every $1 I spend”) and Google’s algorithms will adjust bids in real-time to try and achieve that target by prioritizing high-value conversions.

Step 4: Patience and Iteration

VBB, like any machine learning strategy, needs clean data and time to learn. Allow your campaigns to run for a few weeks, gather sufficient conversion data with values, and then review the performance. You’ll likely need to iterate on your conversion values and ROAS targets as you gain more insights.

The Exciting Benefits: What Can VBB Do for You?

• Boosted ROAS (Return on Ad Spend): By focusing on higher-value conversions, your ad dollars are spent more efficiently, leading directly to a healthier bottom line.
• More Profitable Customers: VBB helps you identify and acquire customers who are more likely to spend more, become repeat buyers, and contribute significantly to your long-term success.
• Smarter Budget Allocation: Instead of simply pouring money into campaigns that generate many low-value conversions, VBB intelligently shifts your budget towards opportunities that promise greater returns.
• Deeper Business Insights: The process of defining conversion values forces you to think deeply about what truly drives value for your business, leading to a better understanding of your customer segments and product profitability.
• Competitive Edge: While many advertisers are still stuck in the “clicks and conversions” mindset, embracing VBB gives you a significant advantage, allowing you to outmaneuver competitors by focusing on actual profit.

Real-World Scenarios Where VBB Shines

Let’s look at some examples:

• E-commerce: A fashion retailer notices that customers who buy high-end jackets also frequently purchase accessories. By assigning a higher value to jacket purchases (due to higher average order value and potential for add-on sales), VBB helps them acquire more of these valuable customers.
• SaaS Company: A software company offers different tiers of subscriptions. A free trial conversion is worth $X, but a conversion to their “Premium” plan is worth $5X. VBB allows them to optimize for users more likely to subscribe to the higher-value plan.
• Education Sector: A university has various courses. Applications for a highly competitive, high-tuition Master’s program are far more valuable than inquiries for a short online certificate. VBB ensures ad spend focuses on driving applications for the most lucrative programs.

Why Now is the Time to Embrace Value-Based Bidding

The advertising landscape is more competitive than ever. Relying on basic bidding strategies means leaving money on the table and falling behind. With increasing privacy regulations and the rise of zero-click search due to AI, first-party data (which forms the bedrock of effective VBB) is becoming even more critical.

Google Marketing Platform, with its robust infrastructure and sophisticated machine learning, is perfectly positioned to help you leverage VBB to its fullest potential. It’s about moving beyond simply counting conversions to truly understanding and optimizing for the value those conversions bring.

Ready to Change Your Strategy?

The transition to Value-Based Bidding might seem daunting at first, especially if you’re accustomed to traditional methods. But the rewards are substantial. It requires a thoughtful approach to data, a clear understanding of your business objectives, and the right tools.

That’s where Making Science comes in.

Our team specializes in helping advertisers like you unlock the full potential of Value-Based Bidding within the Google Marketing Platform. Stop chasing every conversion and start chasing the conversions that truly matter. Let’s make your ad spend work smarter, not just harder.

Ready to transform your Google Marketing Platform strategy with Value-Based Bidding? Contact us today for a free consultation!

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Moving Beyond Conventional Smart Bidding

Automated bidding strategies have evolved from a competitive edge to a market standard. However, for today’s marketing professional, relying solely on standard pixels is no longer enough. The real challenge of Value-Based Bidding (VBB) isn’t just collecting conversions; it’s feeding the algorithm with high-quality signals that reflect your business’s actual financial reality.

Optimizing for “conversion volume” is a thing of the past; optimizing for Customer Lifetime Value (LTV) and profit margins is the present.

The 3 “Blind Spots” Stalling Your Performance

Even with top-tier Search or Social campaigns, most advertisers hit three critical barriers:

1. The Attribution Gap (7-Day Window): Google and Meta algorithms prioritize recent signals. If your sales cycle is long (Real Estate, Education, Banking), the algorithm “forgets” the source of success before the final conversion occurs.
2. Offline Signals and Data Noise: Cancellations, returns, or low-quality leads clutter AI learning, causing you to over-invest in users who don’t generate real value.
3. Signal Scarcity: In niche markets or upper-funnel channels (Display/Video), a lack of conversion volume prevents Smart Bidding from ever leaving the “learning phase.”

Gauss Smart Advertising: The Intelligence Layer for Your AdTech

To bridge the gap between what the algorithm sees and what your business actually needs, we developed Gauss Smart Advertising. This isn’t just a data connector; it’s a proprietary Machine Learning technology that acts as a predictive brain for your campaigns.

How Gauss Transforms Your Bidding Strategy

Instead of waiting for a conversion to happen, Gauss analyzes user behavior in real-time and anticipates their future value:

• Propensity Scoring: Predicts a lead’s purchase probability the moment they enter the funnel.
• Predictive LTV: Estimates a customer’s long-term value, allowing you to bid aggressively for high-value users while saving on low-margin ones.
• Agnostic, Real-Time Activation: Unlike static models, Gauss integrates with any platform (Google, Meta, TikTok, Amazon), sending signals before cross-device traceability is lost.

Real Impact: With Gauss, we have successfully increased our clients’ conversions by 20% and reduced CPA/CPL by up to 50% by eliminating budget waste on non-profitable users.

Take the Leap Toward Profit Bidding

Success today doesn’t depend on the bidding tool itself, but on the quality of the signal you feed it. If you are ready to stop bidding for volume and start bidding for real business value, our team of data scientists and performance experts is ready to help.

Introduction: The Attribution Challenge in a Cookie-less World

In today’s dynamic digital marketing landscape, CMOs face a monumental challenge: how to measure the true impact of every marketing investment when user privacy is paramount? The post-cookie era has left a void, making it difficult to understand which channels and touchpoints truly drive conversions. This is where attribution becomes the indispensable compass. But how can we gain a clear, actionable view of attribution in an environment with increasingly strict data regulations? The answer lies in the adoption of advanced predictive models. This post will explore how these tools are redefining budget decision-making, allowing marketing leaders to optimize their strategies with confidence and precision.

The Problem with Traditional Attribution

For years, attribution relied on simplistic models like “last-click” or “first-click,” which failed to capture the complexity of the customer journey. These models, often dependent on third-party cookies, are now insufficient. The demise of cookies and growing privacy concerns have created a “privacy gap” that directly impacts CMOs’ ability to make informed decisions about their marketing budget allocation. Without a clear understanding of what drives performance, decisions become less strategic and more speculative, potentially leading to inefficient spending.

The Solution: Predictive Models and Machine Learning

The key to closing this gap lies in the power of predictive models and machine learning. These technologies allow for the analysis of large volumes of first-party and contextual data, identifying patterns and correlations that traditional methods cannot. By leveraging sophisticated algorithms, we can go beyond simple correlation to understand causation, assigning appropriate credit to each touchpoint in the customer journey, even in scenarios with limited or anonymized data. This not only improves the accuracy of attribution but also allows CMOs to anticipate trends and proactively optimize their campaigns.

Tangible Benefits for CMOs

For CMOs, implementing predictive models for attribution translates into tangible benefits:

• Optimized Budget Decisions: By understanding the true ROI of each channel, CMOs can allocate their budgets more intelligently, maximizing the impact of every dollar spent.

• Improved Customer Experience: Accurate attribution allows for personalized interactions and more relevant messaging, enhancing the overall customer experience.

• Privacy Compliance: These models are designed to operate with aggregated and anonymized data, ensuring compliance with privacy regulations like GDPR and CCPA.

• Competitive Advantage: Companies that master predictive attribution will gain a significant advantage by being able to react faster to market changes and consumer preferences.

Conclusion: The Future of Attribution is Predictive

In summary, attribution in the privacy era is no longer an unsolved puzzle. Predictive models offer a clear roadmap for CMOs, enabling them to make informed and strategic budget decisions, even in an evolving data landscape. It’s time to move beyond guesswork and embrace the power of artificial intelligence to unveil the true impact of your marketing efforts.

Are you ready to transform your attribution strategy and take your marketing to the next level? Share your thoughts and experiences in the comments! Because as the famous marketing consultant said, “To measure is to know, but to predict is to master.”

Introduction: Are You Ready to Optimize Your Media Investment Like Never Before?

In the competitive world of marketing, CMOs know that every dollar counts. It’s no longer enough to launch campaigns and hope for the best; you need data, strategy, and the ability to adapt quickly. This is where Optiphi comes in. But what is Optiphi? It’s our advanced solution that integrates Marketing Mix Modeling (MMM), attribution models, and a powerful Budget Allocator to give you a 360° view of your media performance. Why is it crucial for you? Because it allows you to simulate and compare different investment scenarios to maximize your results, regardless of your time horizon. How does it achieve this? Let’s dive in.

The Challenge of Media Planning in the Short, Medium, and Long Term

Media planning is both an art and a science. The real challenge for CMOs is balancing short-term tactics to achieve immediate goals with long-term strategic investments that build brand and loyalty. How do you ensure your budget is optimally allocated for both, without sacrificing one for the other? Traditional approaches often fall into the trap of short-term vision, neglecting the cumulative impact. This is where the versatility of Optiphi becomes your greatest asset.

Optiphi in Action: 3 Scenarios for Your Media Strategy

Optiphi offers you the flexibility to model and compare different budget allocation scenarios, allowing you to tailor your strategy to your specific objectives. Let’s look at three clear examples:

Scenario 1: Short-Term Conversion Maximization

If your goal is to drive immediate conversions and meet quarterly sales targets, Optiphi helps you identify the channels and investment combinations that generate the highest sales volume in the shortest time. Using the latest attribution insights and channel elasticity analysis, Optiphi will recommend where to invest aggressively to see quick results. This is ideal for promotional campaigns or periods of high product demand.

Scenario 2: Balancing Growth and Sustainability in the Mid-Term

For a mid-term view, Optiphi allows you to balance achieving sales goals with building a solid foundation for future growth. Here, the tool considers not only direct conversions but also the impact on brand awareness and customer loyalty. Optiphi will show you how to distribute your budget to maintain a steady flow of new acquisitions while strengthening your relationship with existing customers, optimizing your investments with a 6 to 12-month perspective.

Scenario 3: Long-Term Brand Building and Leadership

When the goal is market leadership and building a strong brand in the long term, Optiphi focuses on MMM to analyze the long-term effects of your investments. It considers carryover effects and synergies between channels that may not show immediate ROI but are fundamental to the brand’s sustainable health and growth. Optiphi will help you justify investments in channels that have a slower but deeper impact on brand perception and preference, ensuring your long-term budget aligns with your company’s strategic vision.

Conclusion: The Power of Flexible Planning with Optiphi

In summary, Optiphi is more than just a tool; it’s a strategic partner that empowers CMOs to make budget allocation decisions with confidence and precision. By integrating MMM, attribution, and a budget optimizer, it offers you the ability to simulate and adapt your media strategy to any time horizon, maximizing your ROI and ensuring the fulfillment of your objectives, whether short, medium, or long-term.

Ready to transform your media planning and see the true potential of your investment? Discover how Optiphi can be your competitive advantage! If you found this post helpful, share it with your team, and tell us, which of these scenarios resonates most with your current challenges?

Introduction: Before AI, the Data Foundation. Are You Prepared?

Artificial Intelligence (AI) is transforming the business landscape, promising unprecedented efficiencies and new avenues for growth. But how ready is your company to ride this wave? Many CMOs jump into the “AI chaos” without a solid foundation, forgetting that AI is only as good as the data that feeds it. This post will guide you on what you need to build a robust data base that allows your company to fully leverage the potential of AI. We’ll help you understand what, why, when, and how to prepare your data for this revolution. Because without a good Data Foundation, AI is nothing more than an empty promise.

The CMO’s Dilemma: Data for AI, Where to Start?

The enthusiasm for AI is contagious, but it can also be overwhelming. Many CMOs ask themselves: “Do I need a massive data lake? Is my current data good enough? How do I prevent AI investment from becoming a black hole with no return?” The main problem is that without a clear strategy for data management and quality, AI implementation becomes a challenging road full of obstacles. It’s not just about collecting data, but about having correct, clean, and accessible data. This is where the “Data Foundation” becomes your number one priority before diving into AI projects.

The Pillars of Your Data Foundation for AI

For AI to truly work for your company, especially for a CMO, you need to build a solid Data Foundation. Think of it like the foundations of a building: if they’re not strong, the structure will collapse.

1. Impeccable Data Quality:
   • What does it mean? Your data must be accurate, complete, consistent, and up-to-date. Incorrect or incomplete data will lead to flawed AI models and wrong decisions. Imagine a personalization campaign based on outdated customer data: the result would be a poor experience and a loss of trust.
   • How to achieve it? Implement rigorous data cleaning, validation, and standardization processes. Regular audits are key.
2. Unified Data Integration:
   • What does it mean? Data from different sources (CRM, ERP, marketing platforms, web, apps) must be connected and accessible in a single location or through a unified system. AI needs a holistic view of the customer to generate truly valuable insights.
   • How to achieve it? Use Data Warehouses, Data Lakes, or Customer Data Platforms (CDP) that allow for consolidating and harmonizing information.
3. Clear Data Governance:
   • What does it mean? You must have clear policies and procedures on who can access data, how it’s used, and how it’s kept secure and compliant with regulations (GDPR, CCPA, etc.). Trust and privacy are fundamental to any successful AI initiative.
   • How to achieve it? Establish clear roles and responsibilities, define security standards, and ensure staff training. AI is a powerful tool, but its ethical and legal use is non-negotiable.
4. Data Access and Usability:
   • What does it mean? Data must be easily accessible to the teams that need it (data scientists, marketing analysts) and be in a format that allows them to work efficiently with it.
   • How to achieve it? Invest in visualization tools and analytics platforms that facilitate the exploration and use of data to feed your AI projects.

AI Doesn’t Wait: How to Start Your Data Transformation

You don’t need to have everything perfect to start, but you do need a clear strategy. Here’s a simple guide:

• Assess your current state: Conduct a data audit to identify gaps in quality, integration, and governance.
• Prioritize: Focus on the most critical datasets for your initial AI projects (e.g., customer data for personalization).
• Invest in technology and talent: You’ll need appropriate tools and a team with the necessary skills to manage and prepare data for AI.

By laying these foundations, your company will not only be ready to implement AI solutions effectively but will also build a sustainable competitive advantage.

Conclusion: AI Is the Future, But Your Data Is the Present

Artificial Intelligence offers a universe of possibilities for CMOs, from campaign optimization to personalization at scale. However, the success of AI directly depends on the quality and structure of your data. Don’t leap into the void; invest first in a solid Data Foundation. It’s the key to making your AI models accurate, relevant, and most importantly, actionable.

Are you ready to build the Data Foundation your AI strategy needs? Start auditing your data today and lay the groundwork for an AI-powered future! Leave your comments or share this post if you believe data quality is the true superpower behind successful AI. Because in the world of AI, data isn’t the fuel, it’s the engine!

How do you measure the success of a campaign if no one clicks? In the new cookieless era, the answer is not only possible but essential.

Clickless advertising has gone from being a rarity to a priority for brands focused on brand recognition, mass reach, and effectiveness in environments where the click simply doesn’t exist. With the rise of channels like YouTube on Smart TVs, DOOH, digital audio, and CTV, it is time to move beyond models based solely on direct interaction.

In this post, we tell you what clickless advertising is, why it matters in 2025, and how you can measure it effectively with Google Marketing Platform (GMP) to demonstrate real results in campaigns where visual impact is worth more than the click.

What is Clickless Advertising and Why is It Gaining Ground?

Clickless advertising encompasses campaigns where the goal is not for the user to click, but for them to remember, recognize, and interact with the brand later on. This type of impact is generated in environments where direct interaction is not possible (or does not make sense):

• YouTube on Connected TV
• Spotify, podcasts, or programmatic audio
• DOOH (Digital Out Of Home), such as digital billboards or screens in shopping centers
• CTV (Connected TV)

With the progressive phasing out of third-party cookies and the increase in media consumption without a browser, measuring without clicks is no longer an option—it is a necessity.

📊 According to Nielsen, 65% of offline sales are influenced by advertising without direct interaction.

GMP: Your Ally for Measuring What Cannot Be Clicked

The key is to adopt new measurement tools and methods based on aggregated data, modeling, and first-party data. This is where Google Marketing Platform shines as a comprehensive solution:

🎯 Campaign Manager 360: Measuring Impressions and Viewability CM360 allows you to track impressions, viewability, frequency, and cross-channel reach. It is the central hub for validating that your message has reached the right audience.

🧠 Google Analytics 4: Behavior After Exposure Even without clicks, GA4 helps analyze user behavior after viewing an ad: direct traffic, brand searches, completed forms, etc. Thanks to its advanced attribution models, it compensates for the lack of cookies and protects privacy.

🔗 Integrated Platform: GMP as an Ecosystem The combination of CM360 + GA4 + DV360 + SA360 allows for a holistic view of campaign performance, uniting awareness and performance metrics under one umbrella.

Strategies That Work (Even if No One Clicks)

🧪 Brand Lift and Brand Awareness Use studies like YouTube Brand Lift or post-exposure surveys to measure ad recall, consideration, or purchase intent.

🗺️ Geo-testing and Incrementality Compare exposed and unexposed regions with tools like GeoLift to evaluate the real impact on searches or sales.

👤 First-Party Data and Indirect Signals Analyze direct traffic, brand searches, and completed forms after exposure. Supplement with user panels or surveys.

🔒 Consent Mode v2 and Conversion Modeling Even without cookies, you can estimate conversions with modeling and platforms like Ads Data Hub, ensuring compliance with privacy regulations.

Real-Life Cases That Prove It

• YouTube on CTV: Campaigns with a +20% brand recall, without clicks or calls to action.
• Spotify Ads: Direct correlation between full ad listens and peaks in brand searches.
• DOOH in shopping centers: Increase in store visits (footfall) and geolocated sales following campaigns without digital interaction.

Conclusion: Measuring Without Clicks Is Not Only Possible, It Is Essential

In a cookieless world with increasingly dispersed consumers, clickless advertising represents the natural evolution of digital marketing. It is no longer just about chasing interactions, but about understanding the real impact of the message on the user’s mind and decisions.

GMP gives you the tools to do this with precision, respect for privacy, and an omnichannel approach.

💬 And you, are you ready to measure without relying on the click? If you want to know how to implement this type of measurement in your strategy, let’s talk. Because the future of marketing is not measured in clicks, but in connections.

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.