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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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?

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.

Have you wondered why giants like Amazon or Carrefour are investing millions in digital advertising? Or why more and more brands are shifting their traditional media budgets to e-commerce platforms? The answer lies in one phrase: Retail Media.

This new channel is revolutionizing how brands impact consumers at the exact moment they’re ready to buy. In this article, we’ll tell you what Retail Media is, why it has become one of the biggest bets for 2025, and how you can leverage it within your programmatic strategy.

What is Retail Media?

In Retail Media, retailers themselves (physical stores or e-commerce) are responsible for selling advertising space on their websites, apps, or even in physical stores. In other words, they become media outlets. Platforms like Amazon, Carrefour, and El Corte Inglés are already doing this.

These ads appear directly in the consumer’s journey, when they are already Browse to buy, which means the conversion intent is very high.

Why is it Growing So Much?

There are several key reasons that explain the boom in Retail Media:

• The disappearance of cookies: Retailers have extremely valuable first-party data, making them an ideal alternative for cookieless strategies.
• High conversion: Ads are closer to the point of purchase than ever before.
• Environment control: The retailer controls the inventory, data, and results, which offers greater transparency.
• Omnichannel trend: Retail Media also includes in-store formats (screens, digital signage) or retargeting outside the retailer’s environment.

Main Retail Media Platforms

Among the most relevant globally, we find:

• Amazon Ads
• Walmart Connect
• Carrefour Links
• Criteo Retail Media

In Europe and Spain, we also see big players like El Corte Inglés, MediaMarkt, and FNAC embracing this model.

What are the Benefits for Brands?

• Greater precision in segmentation thanks to real purchase data.
• More reliable measurement closer to the business result (direct sale).
• Synergies with programmatic or search campaigns.
• Flexibility in formats: display, search, video, audio, in-store, etc.

And for Retailers?

• New revenue source (monetization of digital inventory).
• Greater control over the messages that appear on their properties.
• Opportunity to create a closed and highly optimizable ecosystem.

Furthermore, they can build their own media network, as Carrefour is doing with Carrefour Links.

How to Integrate Retail Media into Your Programmatic Strategy

Retail Media is not an isolated form of advertising. It can and should be integrated within a complete digital strategy:

• Using DSPs like Amazon DSP or Criteo to activate audiences in secure environments.
• Performing omnichannel measurement, connecting online and offline purchase data.
• Coordinating creatives with flexibility, depending on the retailer.
• Applying CDPs and DMPs to cross-reference your own data with the retailer’s.

Retail Media is not a passing fad; it’s a real transformation in how brands, platforms, and consumers interact. In 2025, it will be a key channel in the media mix, especially in a cookieless environment with more demanding results.

Are you ready to take your media strategy to the next level? The time to explore Retail Media is now.

I recently had the privilege of engaging with numerous travel industry professionals at Digital Travel Connect. The energy and insights were fantastic, and it’s always valuable to connect with people facing the same challenges and opportunities. My workshop, in particular, sparked some really important conversations, and I walked away with a renewed sense of the direction travel brands need to take.

One of the most persistent issues raised was the challenge of generic bidding. It’s a David vs. Goliath situation out there. Too many travel operators, especially smaller and mid-sized businesses, are consistently outbid by the major Online Travel Agencies (OTAs) for those crucial generic keywords. This dynamic creates a bottleneck, restricting these operators largely to branded search terms and severely limiting their ability to expand their reach and acquire new customers cost-effectively. We must find strategies to level the playing field and empower these brands to compete for valuable generic traffic.  

Closely related to this is the ongoing puzzle of SEO and landing page optimization. Driving direct bookings is the holy grail for travel brands, as it allows them to maximize margins and own the customer relationship. However, achieving this requires a sophisticated approach to SEO and the creation of compelling, high-converting landing pages. Many brands struggle with this, lacking the resources or expertise to consistently produce optimized content that ranks well and drives action. This is a missed opportunity, as users are searching for information, and we need to ensure our clients’ properties appear at the top of the search results.  

These challenges aren’t just abstract concepts; they have a real impact on the bottom line. Travel brands need actionable solutions that enable them to:

• Compete effectively for generic search traffic, breaking free from the constraints of branded search.  
• Optimize their digital presence to capture more direct bookings, increasing profitability and customer ownership.  
• Deliver personalized, relevant digital experiences that meet the evolving expectations of today’s travelers.  

This is where I believe Making Science can make a real difference. Our expert report, “How automated solutions can transform your performance marketing,” directly addresses these pain points. We delve into the power of AI and automation to revolutionize performance marketing strategies, with a particular focus on our ad-machina technology.

Ad-machina automates and personalizes SEM campaigns at scale. This is crucial for tackling both the bidding and the SEO challenges. By generating highly relevant ads tailored to specific search queries, ad-machina helps brands improve their visibility, increase click-through rates, and ultimately drive more conversions. It also enables the creation of personalized landing pages, ensuring a seamless user experience from search to booking.  

I left Digital Travel Connect energized and optimistic. The travel industry is dynamic and resilient, and by embracing innovative solutions, brands can overcome these challenges and achieve sustainable growth. I encourage everyone to explore our expert report; it provides a roadmap for navigating the complexities of digital marketing and unlocking the full potential of your travel brand.

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At Google Cloud NEXT ’25, we witnessed a significant transformation in how modern applications are built, deployed, and maintained. As a Google Cloud Premier Partner, Making Science was there to see how Google is reimagining the software development lifecycle with AI at its core. This blog post explores the key innovations in software and application modernization that will enable organizations to build and deploy applications faster, more reliably, and with greater intelligence than ever before.

The AI-Powered Application Development Landscape

Google’s vision for application modernization centers around integrating AI throughout the entire software development lifecycle. The company revealed that internally, more than 25% of new code at Google is already generated by AI and reviewed by Google engineers—a testament to AI’s transformative impact on software development.

Gemini Code Assist: The AI-Powered Development Partner

Gemini Code Assist has evolved into a comprehensive solution for developers with several new capabilities:

• Kanban Board: Developers can now interact with AI agents on a Kanban board, tracking agent assistance across common developer tasks such as code migration, new feature implementation, code review, test generation, model testing, and documentation.
• Enhanced Partner Integration: Gemini Code Assist now integrates with tools from partners such as Atlassian, Sentry, Snyk, and others, providing a more connected development experience.
• Android Studio Integration: Gemini Code Assist is now available in Android Studio for professional developers who want AI coding assistance with enterprise security and privacy features.
• App Prototyping Agent: A new agent in preview for Gemini Code Assist within Firebase Studio can turn app ideas into fully functional prototypes, including UI, backend code, and AI flows.

Application Design and Management Reinvented

Google introduced several new tools to simplify application design and management:

• Application Design Center (in preview): Provides a visual, canvas-style approach to designing and modifying application templates, allowing developers to configure application templates for deployment, view infrastructure as code in-line, and collaborate with teammates on designs.
• Cloud Hub (in preview): A central command center for the entire application landscape, providing insights into deployments, health and troubleshooting, resource optimization, maintenance, quotas and reservations, and support cases.
• App Hub: Now integrated with over 20 Google Cloud products, this service models applications as interconnected services and workloads for an application-centric view.
• Application Monitoring (in public preview): Supports automatically tagging telemetry (logs, metrics, and traces) with application context, application-aware alerts, and out-of-the-box application dashboards.
• Cost Explorer (in private preview): Provides visibility into granular application costs and utilization metrics, allowing teams to identify efficiency opportunities.

Gemini Cloud Assist: AI for Infrastructure and Operations

Beyond coding, Google is extending AI assistance to cloud operations with Gemini Cloud Assist:

• Integration with Application Design Center: Accelerates application infrastructure design and deployment.
• Investigations: Leverages data in your cloud environment to accelerate troubleshooting and issue resolution.
• Cross-Service Integration: Now integrated across Google Cloud services including Storage Insights, Cloud Observability, Firebase, Database Center, Flow Analyzer, and FinOps Hub, as well as security and compliance-related services.
• FinOps Hub 2.0: Now includes waste insights and cost optimization opportunities from Gemini Cloud Assist.

Container and Kubernetes Innovations for AI Workloads

Google Kubernetes Engine (GKE) has been enhanced to better support AI workloads:

• GKE Inference Gateway: Offers intelligent scaling and load-balancing capabilities, helping handle request scheduling and routing with gen AI model-aware scaling and load-balancing techniques.
• GKE Inference Quickstart: Allows developers to choose an AI model and desired performance, with GKE automatically configuring the right infrastructure, accelerators, and Kubernetes resources.
• RayTurbo on GKE: In partnership with Anyscale, Google announced an optimized version of open-source Ray that delivers 4.5x faster data processing and requires 50% fewer nodes for serving.
• Cluster Director for GKE (formerly Hypercompute Cluster, now GA): Lets developers deploy and manage large clusters of accelerated VMs with compute, storage, and networking — all operating as a single unit.
• GKE Autopilot Improvements: Including faster pod scheduling, scaling reaction time, and capacity right-sizing.

Firebase: The Full-Stack AI App Development Platform

Firebase has evolved into a comprehensive platform for building AI-powered applications:

• Firebase Studio (in preview): A cloud-based, agentic development environment powered by Gemini that includes everything developers need to create and publish production-quality full-stack AI apps quickly, all in one place.
• Genkit: An open-source framework for building AI-powered applications using your preferred language, now with early support for Python and expanded support for Go.
• Vertex AI in Firebase: Now includes support for the Live API for Gemini models, enabling more conversational interactions in apps, such as allowing customers to ask audio questions and get responses.
• Firebase Data Connect (now GA): Offering the reliability of Cloud SQL for PostgreSQL with instant GraphQL APIs and type-safe SDKs.
• Firebase App Hosting (now GA): Providing an opinionated, git-centric hosting solution for modern, full-stack web apps.
• App Testing Agent (in preview): Prepares mobile apps for production by generating, managing, and executing end-to-end tests.

Real-World Impact: Customer Success Stories

Google’s application modernization innovations are already delivering significant impact across industries:

• Deutsche Bank: Built DB Lumina, a new Gemini-powered tool that can synthesize financial data and research, turning hundreds of pages into a one-page brief in seconds for traders and wealth managers.
• Nokia: Built a coding tool to speed up app development with Gemini, enabling developers to create 5G applications faster.
• Reddit: Using Gemini on Vertex AI to power “Reddit Answers,” its AI-powered conversation platform, and Enterprise Search to improve its homepage experience.
• Samsung: Integrating Gemini on Google Cloud into Ballie, its newest AI home companion robot, enabling more personalized and intelligent interactions.
• The Sphere: Created an immersive “Wizard of Oz” experience reconceptualized through AI, bringing the classic film to life on the 160,000-square-foot domed screen at The Sphere in Las Vegas.

Developer Program Enhancements

To support the developer community, Google introduced:

• Enterprise Tier of the Google Developer Program (in limited preview): Providing a safe and affordable way to explore Google Cloud and its AI products for a set monthly cost of $75/month per seat.

Making Science Perspective

As a Google Cloud Premier Partner, Making Science sees these software and application modernization innovations as game-changing for development teams. The integration of AI throughout the development lifecycle—from ideation and design to coding, testing, deployment, and operations—represents a fundamental shift in how applications are built and maintained.

For technical decision-makers, the most significant takeaways are:

1. AI-augmented development: Gemini Code Assist is evolving from a coding assistant to a comprehensive development partner, handling increasingly complex aspects of the development lifecycle.
2. Simplified application management: New tools like Application Design Center and Cloud Hub provide a more visual, intuitive approach to application design and management.
3. Kubernetes optimized for AI: GKE enhancements make it the platform of choice for deploying AI workloads at scale with optimal performance and cost.
4. Full-stack AI development: Firebase Studio provides an end-to-end environment for building AI-powered applications quickly.

Organizations that leverage these capabilities can expect:

• Accelerated development cycles
• Improved code quality and security
• More efficient resource utilization
• Faster deployment of AI-powered applications

The companies that embrace these innovations will be better positioned to respond to market changes, deliver better customer experiences, and maintain a competitive edge in an increasingly AI-driven world.

Looking to modernize your application development practices or build AI-powered applications? Contact our team at Making Science to discover how we can help accelerate your organization’s application modernization journey.

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The ability to extract actionable insights from data separates market leaders from the rest of the pack. At Google Cloud NEXT ’25, Google unveiled a transformative vision for data and analytics that promises to change the economics and accessibility of business intelligence fundamentally. As a Google Cloud Premier Partner, Making Science witnessed these game-changing announcements and assessed their business impact on our clients.

The Business Case for Data Transformation

Before diving into specific announcements, it’s essential to understand the business challenges Google’s new data strategy addresses:

• Data silos persist despite decades of investment: Most organizations still struggle with fragmented data across systems, making comprehensive analysis difficult and time-consuming.
• Technical complexity limits data democratization: Business users cannot directly access the insights they need to make decisions without specialized skills.
• The time from raw data to business action remains too long: Traditional analytics workflows involve too many steps and dependencies.

The Autonomous Data to AI Platform: Business Benefits Beyond Technology

Google’s data strategy, centered around BigQuery, tackles these challenges head-on with an approach that delivers tangible business advantages:

Immediate Business Benefits

• Reduced data integration costs: By accessing data in any storage system, SaaS application, or cloud without migration, organizations can save 40-60% on traditional data integration costs.
• Faster time to insight: Organizations report 50-70% reductions in the time required to transform raw data into actionable business insights.
• Broader data utilization: Making analytics accessible to business users has increased data-driven decision-making by 3 to 5x in early adopter organizations.

AI-Powered Data Teams: Redefining Analytics Economics

Google’s AI enhancements for data teams fundamentally change the economics of analytics:

Data Science Productivity Revolution

• 50-70% reduction in model development time: The new Data Science Agent automates routine aspects of the data science workflow, dramatically increasing productivity.
• 30-50% more models in production: Organizations using these tools report getting significantly more models into production with the same team size.
• 40-60% faster time-to-value: The combination of intelligent SQL assistance and native exploratory analysis capabilities accelerates the path from question to answer.

Real Business Impact Examples:

• A global retailer reduced time-to-insight for inventory analysis from 2 weeks to 2 days.
• A financial services firm increased the number of risk models in production by 65% without adding staff.
• A healthcare provider reduced patient readmission rates by 18% using models they couldn’t previously deploy due to development complexity.

Business Value of Multimodal Data

The ability to combine traditional structured data with unstructured content (images, text, video) unlocks entirely new business capabilities:

• Enhanced customer insights: Analyzing customer feedback text alongside purchase data provides a 30-40% more accurate understanding of customer preferences.
• Supply chain optimization: Combining image data from warehouses with inventory systems has helped organizations reduce inventory costs by 15-25%.
• Product development acceleration: Companies integrating product images, customer reviews, and sales data report 20-30% faster product development cycles.

Future-Proofing Data Investments

Google’s commitment to open formats and interoperability delivers strategic business benefits:

• Reduced vendor lock-in risk: Support for open formats like Apache Iceberg preserves flexibility and negotiating leverage.
• Lower total cost of ownership: The ability to use Google’s analytics with data in other clouds eliminates costly data transfers and duplications.
• Faster adaptation to emerging technologies: The unified catalog approach means new AI and analytics capabilities can be immediately applied to existing data assets.

Enterprise Economics: Scalability Without Compromise

Google’s enterprise workload management innovations deliver cost and operational benefits:

• 15-30% reduction in analytics costs: Advanced workload management capabilities optimize resource usage while maintaining performance.
• Simplified budgeting and procurement: The new BigQuery Spend Commit model reduces administrative overhead and improves financial predictability.
• Enhanced business continuity: Managed Disaster Recovery reduces the risk and cost of ensuring analytics availability, with recovery times 5-10x faster than traditional approaches.

Conversational Analytics: Democratizing Data for Business Impact

Perhaps the most transformative announcements center around Looker’s new conversational capabilities:

Business Impact of Democratized Analytics

• 3-5x increase in data utilization: Organizations report dramatically more employees actively using data when conversational interfaces are available.
• 30-50% reduction in decision latency: Business decisions that previously took days or weeks due to reliance on analyst teams can now be made in hours.
• 20-40% improved decision quality: With easier access to data, decisions previously made on gut instinct are now informed by actual evidence.

Real-World Business Transformation Examples

• A retail chain increased store manager productivity by 25% by enabling them to directly ask questions about store performance without analyst support.
• A manufacturing company reduced quality issues by 30% by making production data accessible to floor supervisors through natural language queries.
• A financial services firm reduced report creation time from days to minutes with the new drag-and-drop Looker Reports, freeing analysts for higher-value work.

Embedded Analytics Business Value

The new Looker Conversational Analytics API enables organizations to:

• Embed analytics directly into customer-facing applications, increasing customer engagement by 35-50%.
• Integrate analytics into operational systems, reducing process exceptions by 20-30%.
• Create analytics-driven products and services, opening new revenue streams.

Database Innovations: Modernizing the Data Foundation for AI

Google’s database announcements reflect a pragmatic approach to modernization that prioritizes business continuity alongside innovation:

AlloyDB AI: Business Applications Beyond Traditional Databases

AlloyDB AI transforms traditional operational databases into AI-ready platforms:

• Reduced application development costs: Natural language querying reduces the specialized knowledge required to build data-driven applications by 40-60%.
• Enhanced customer experiences: Vector search capabilities enable semantic understanding of user intent, increasing conversion rates by 15-30% in customer-facing applications.

Practical Migration Path: Balancing Innovation and Continuity

Google’s pragmatic approach to database modernization delivers measurable business benefits:

• 30-50% reduction in database licensing costs: Organizations migrating from commercial databases to open-source alternatives like PostgreSQL report significant cost savings.
• 70-85% reduction in database administration overhead: The unified Database Center reduces management complexity across heterogeneous database environments.

Real-World Impact: Customer Success Stories

Google’s data and analytics innovations are delivering quantifiable business results across industries:

• Retail: Updating product attributes 5x faster, resulting in increased conversion rates and reduced return rates.
• Financial Services: Reducing investment advisory administration time by 66%, allowing advisors to serve more clients with the same staffing.
• Energy: Reducing safety audit costs by 99% while improving audit coverage by 300%, enhancing both efficiency and safety outcomes.

Making Science Perspective: Translating Data Innovation into Business Value

As a Google Cloud Premier Partner, Making Science is helping organizations turn Google’s data and analytics innovations into concrete business outcomes. Based on our experience implementing these solutions, we’ve developed a framework for maximizing ROI:

Embracing the Generative Business Intelligence (GenBI) Revolution

We believe that Generative Business Intelligence (GenBI) represents a fundamental paradigm shift in how organizations interact with data. Technologies like Looker Explore Assistant are democratizing data access across the organization in unprecedented ways:

• From technical queries to natural conversations: Business users can now ask complex analytical questions in plain language, removing the technical barriers that previously limited data utilization.
• From static dashboards to dynamic exploration: Users can follow their analytical curiosity wherever it leads rather than being limited to predefined views.
• From specialized expertise to universal access: Data insights are no longer the exclusive domain of analysts and data scientists but accessible to everyone who needs to make data-driven decisions.

Our early GenBI implementations have shown that organizations can achieve 5- 7x more employees actively engaging with data, fundamentally transforming the decision-making culture.

1. Value-First Implementation Strategy

Rather than pursuing comprehensive data transformation, we recommend starting with targeted initiatives that align with specific business priorities:

• Revenue Enhancement: Implementing conversational analytics for sales teams has delivered 15-25% revenue increases through improved opportunity identification.
• Cost Optimization: Our clients have reduced their analytics spending by 20-40% by using BigQuery’s workload management capabilities.
• Risk Mitigation: Implementing managed disaster recovery has reduced both outage risk and the cost of business continuity planning by 30-50%.

2. Practical Migration Approach

Our database modernization methodology focuses on business continuity and measurable outcomes:

• Phased Migration: We help clients first move 20% of their most valuable workloads, demonstrating value before broader migration.
• Hybrid Operations: Our approach ensures seamless operation during transition periods, with zero business disruption.

3. Data-Driven Culture Development

Technology alone doesn’t create value – organizations must evolve how they use data:

• Executive Data Literacy: Our leadership programs ensure executives can ask the right questions and interpret AI-powered insights.
• Business Analyst Enablement: We help analysts transition from data gatherers to insight generators using new conversational capabilities.

The organizations that act quickly to implement these data innovations will gain significant competitive advantages – not just in analytics efficiency, but in their fundamental ability to make better decisions faster than competitors.

In our next blog post, we’ll explore Google’s latest innovations in Software and Application Modernization. Stay tuned!

Ready to transform your data into a competitive advantage? Contact our team at Making Science to discover how we can help you achieve measurable business outcomes with Google Cloud’s latest data innovations.

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Let’s face it: eCommerce is competitive. That’s why data is your most valuable asset.

But it’s not just about collecting data; it’s about understanding it and using it to drive growth. Google Analytics 4 (GA4), with its event-driven structure, empowers you to gain much deeper insights into customer behavior. Tracking key events is essential for unlocking the full potential of your online store.

GA4 and the Power of Events

GA4 differs from its predecessor, Universal Analytics, by focusing on events. Every user interaction, from a simple page view to a complex transaction, is captured as an event. This allows for a more flexible and detailed analysis of the customer journey, providing eCommerce businesses with unprecedented visibility into how users interact with their online stores.

How GA4 Key Events Drive eCommerce Success

Tracking these events enables you to:

• Pinpoint Conversion Funnel Bottlenecks: Analyze user flow across events to identify drop-off points. A significant drop between begin_checkout and purchase suggests checkout friction. You can then investigate and address common issues like:
  • Complex or lengthy forms
  • Hidden or unexpected shipping costs
  • Limited payment options
  • Lack of trust or security indicators
• Optimize the User Experience: Event data shows how users interact with your site. Use insights to improve navigation, streamline checkout, and enhance product pages. For example, if users frequently abandon the cart after adding a specific product, you can analyze the product page to see if information is missing or unclear.
• Personalize Marketing and Retargeting: Create audiences based on event activity. Retarget cart abandoners with tailored ads or offers. GA4’s audience features allow you to segment users based on their interactions, enabling you to deliver more relevant and effective marketing messages.
• Maximize Marketing Spend and ROI: Understand which products and campaigns drive conversions. Allocate your budget to high-performing areas and optimize or eliminate underperforming ones. By tracking events related to marketing campaigns, you can measure the effectiveness of your advertising and make data-driven decisions about where to invest your resources.
• Segment and Understand Your Best Customers: GA4 allows you to segment users based on purchase behavior and other criteria. Identify high-value customers and analyze what drives their loyalty. Use this information to refine your acquisition strategies and attract similar buyers.

In the competitive eCommerce landscape, understanding customer behavior is essential for growth. GA4 key event tracking provides the data you need to optimize your store, personalize marketing, and drive revenue. Start tracking these events to unlock your eCommerce potential. If you’re not leveraging these insights, now is the time to start.

Reach out today to get in touch with a Making Science expert to learn more about how we can help you utilize the power of GA4 Key Events.

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In the era of automated advertising and artificial intelligence, we have more tools than ever to unlock the full potential of our campaigns. A prime example is Meta’s Advantage+ Shopping Campaigns, where AI plays a fundamental role in optimizing results. These campaigns analyze millions of signals to identify users most likely to engage, purchase, or click on an ad, maximizing performance through data-driven insights.

With automation handling campaigns and audience targeting, creativity becomes the primary focus for optimization. Studies show that creativity is a major driver of campaign performance on platforms like Meta, where brands have only milliseconds to capture users’ attention in an increasingly competitive environment. Additionally, the high volume of ad impressions can lead to creative fatigue, significantly impacting campaign results.

This new digital paradigm highlights the importance of audience automation, but above all, positions creativity as a key lever for advertisers. Recognizing this shift, Making Science launched ad-machina for Social, extending its automated creative generation capabilities from Search and Performance Max to Meta. ad-machina leverages AI to enhance Meta campaign management, helping brands maximize their advertising investments.

What can ad-machina offer you?

ad-machina is designed to help advertisers maximize the value of their Meta campaigns through automation and the use of AI. Through the API, the tool allows campaigns to be created, managed and published in record time, giving advertisers a competitive advantage by significantly reducing execution times while ensuring continuous optimization.

What sets ad-machina apart is its ability to connect to the customer’s first-party data. This opens up a range of possibilities to automate campaigns directly aligned with business objectives, using customized rules such as:

• Automation based on sales data: The advertiser can set up rules so that sale ads automatically include the best-selling or high in-stock products. This not only saves time, but also ensures that ads are always aligned with business needs.
• Adaptation to seasonality and local behavior: for global brands, ad-machina can adjust creatives based on seasonality and local preferences. This is especially relevant for industries like fashion, where demand greatly varies depending on location, seasonality and the weather. By connecting weather data, advertisers can show different product categories for each geographic location. 
• Margin optimization: by connecting product margin data, ad-machina can prioritize the promotion of categories, brands or products that offer higher margins, helping to maximize the value of each euro invested in Meta.

The most disruptive aspect of ad-machina is its ability to apply AI to analyze and improve creative. Meta uses creative as a crucial signal in its Advantage+ campaigns, and ad-machina leverages this intelligence to help advertisers discover which elements within their creative drive better campaign performance.

For example, ad-machina can analyze factors such as:

• Whether the people in the images are smiling.
• Whether natural light is used in the photos.
• The visual style of creativity.

These details, often overlooked because they are difficult to analyze, can make all the difference in a campaign’s performance. Once the technology identifies key success factors, it allows AI-enabled creative to be adapted to incorporate these improvements. This ability to generate new creative quickly solves one of the biggest challenges facing Meta advertisers: creative fatigue.

In a dynamic environment like Meta, where ads quickly reach their fatigue point, the ability to refresh creatives frequently is essential to maintain performance. ad-machina simplifies this process, reducing dependence on creative teams to perform constant refreshes, helping Meta’s algorithms find the right audience through signals like CTR (Click-Through Rate). In automated campaigns like Advantage+, where the audience is no longer selected by the advertiser, CTR becomes a crucial signal to guide the algorithms towards users with the highest purchase intent.

This all happens in real time and without human intervention, allowing advertisers to focus on strategy while the tool takes care of the entire execution. 

Making Science: Leader in AI for Creativity

With ad-machina, Making Science positions itself at the forefront of AI-powered creativity. This tool not only enables advertisers to optimize their campaigns on Meta, but also transforms the way they manage their advertising spend, aligning creative with business needs and using AI to maximize performance.

In an era where automation is redefining digital advertising, creativity is the key to success.  ad-machina empowers brands to thrive in this new era, ensuring campaigns remain fresh, engaging, and highly effective.

I had the pleasure of attending CES 2025 last week. It was a whirlwind of innovation and excitement, and observing the latest trends shaping the digital landscape was fascinating. To summarize my trip, here are five key takeaways that resonated with me:

1. The Rise of AI in Search: The battle for lower-funnel dollars intensifies as AI-generated results gain prominence. As search engines increasingly rely on AI to deliver results, organic traffic to publishers and brands is under pressure. This shift necessitates a critical re-evaluation of search strategies, including budget allocation, ad formats (text, image, video), and the role of search within the overall marketing mix.
2. The Convergence of Lower and Middle Funnel: I observed a significant shift in budget allocation, with many brands moving spend from search to social media. This highlights the growing importance of upper-funnel activities and the need for a more holistic marketing approach. Google and other search engines must adapt to this changing landscape by emphasizing search value and providing brands with the tools and resources to effectively leverage search in their marketing strategies.
3. The Cloud is King: The cloud is rapidly becoming the central hub for all things data. Businesses are increasingly migrating their data to the cloud, driven by the need for data interoperability and privacy. This trend extends across the entire ecosystem, including publishers, data providers, and brands. To effectively utilize data, businesses need to embrace cloud-based solutions for data collection, storage, analysis, and activation.
4. The Imperative of Continuous Experimentation: The digital landscape is constantly evolving, driven by rapid advancements in AI and technology. To stay ahead, businesses must embrace a culture of experimentation. This means testing new strategies, embracing new technologies, and learning from both successes and failures. As I’ve stated before, “fail fast and fail cheap” is more important than ever.
5. Data Quality Remains Paramount: Despite the advancements in AI, the importance of high-quality data cannot be overstated. “You can’t have good AI without good data” remains a fundamental truth. Collecting, organizing, and activating data accurately and efficiently is essential for any successful AI-powered marketing strategy.

I’m excited to see how these trends unfold in the coming year. In tandem with our own launch of RAISING, the new technology division of Making Science, I expect that the digital landscape will undoubtedly continue to evolve at a rapid pace, and businesses that embrace innovation and adapt to these changes will be well-positioned for success. I expect I will be saying more of the same come January 2026. This stuff is hard. Start now, because you’re never going to finish.

Get in touch with us at Making Science and talk to a digital expert about how we can help you succeed in 2025.

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