For years, SEO professionals treated monthly search volume as the compass that guided every strategy. We picked topics, set priorities, and measured the potential of a keyword almost exclusively by that number.

But by 2025, that approach feels outdated. Google isn’t just showing blue links in the SERPs anymore. It now presents results through features like AI Overviews and AI Mode (already live in some regions), delivering direct, AI-generated summaries instead of a list of links.

Meanwhile, users are shifting their behavior. Increasingly, they’re asking questions directly to ChatGPT, Gemini, Claude, or Perplexity rather than typing into a search bar. In this context, searches aren’t keywords anymore, they’re conversational prompts. And right now, there’s no reliable public data to tell us the “volume” of those queries.

If you’re waiting for your SEO tool to catch up with the trend… you’re already behind.

Why Predictive SEO Matters

Predictive SEO doesn’t sit around waiting for data to confirm a trend. It takes the lead.

• It spots early signals using tools like Google Trends or AnswerThePublic.
• It creates content before the metrics are visible, by tapping into Reddit, X, Quora, Feedly, niche forums, or Google News.
• It’s optimized for conversations, thinking about how a user would phrase a question to a chatbot — or how Google might condense an answer in AI Overviews.

The First-Mover Advantage

In this new world of conversational search, being the first source cited by an AI model is a huge competitive edge.
Once a chatbot or Google references your content in its responses, dislodging it becomes extremely difficult.

How to Adapt Today

1. Keep an eye on forums and social platforms to spot weak signals early.
2. Write answers to complex questions that are clear, structured, and genuinely helpful, the way an AI assistant would.
3. Shift your focus to intent, not exact keywords: create content that solves problems, not just content that ranks.

Conclusion

Predictive SEO is the strategy that ensures your brand shows up in AI-driven answers.

It’s no longer just about hitting Google’s front page. It’s about becoming the trusted source that AI models cite when users ask questions. That’s the real visibility frontier in 2025.

At Making Science, our SEO & Content team has already made this approach to the foundation of how we create and optimize content.

👉 So let me ask you: are you making sure your brand — or your clients’ brands — appears in AI’s answers?

The eCommerce landscape is undergoing a seismic transformation, and at the heart of this change is artificial intelligence in ecommerce. From dynamic personalization to intelligent automation, AI is revolutionizing how online retailers engage with customers, streamline operations, and scale rapidly. But beyond functionality, AI also plays a pivotal role in optimizing ecommerce SEO by enhancing user experience, content performance, and search visibility. This article explores how AI is reshaping eCommerce, with a special focus on SEO applications, AI-powered content creation, and strategic implementation for sustainable growth.

The rise of AI in eCommerce

Once a futuristic concept, artificial intelligence (AI) is now a core driver of innovation in eCommerce. Technologies like machine learning, natural language processing (NLP), and computer vision enable online businesses to analyze behavior, predict intent, and personalize experiences at scale.

Several key factors are accelerating the integration of AI in eCommerce and SEO:

• Rising customer expectations: Shoppers expect seamless, personalized journeys. AI delivers relevant product suggestions, smart search, and real-time support, all of which improve engagement and SEO performance.
• Data overload: Every action online produces data. AI helps process this information to refine targeting, content, site structure, and user flow. These are essential elements of modern SEO.
• Accessible tools and platforms: AI-driven technologies for SEO, automation, and analytics are now widely available, even for smaller businesses.
• Competitive pressure: Businesses leveraging AI stay ahead in rankings, customer experience, and content strategy. Those who don’t risk falling behind in the SERPs.

Key applications of AI in eCommerce

AI is transforming every stage of the customer journey and its impact on SEO is tangible. Here are the most relevant use cases:

1. Personalized product recommendations

AI algorithms analyze browsing and purchase behavior to deliver product suggestions tailored to each user. This boosts click-through rates, conversion, and engagement.

2. Intelligent site search

AI-powered search understands natural language and user intent, delivering more relevant results. This improves UX and reduces bounce rates.

3. AI chatbots and virtual assistants

Chatbots offer instant assistance, helping users navigate, answer questions, and discover products. They increase time on site and reduce exit rates.

4. Fraud detection

While more focused on security, AI’s ability to detect fraud ensures business continuity and protects brand trust, which indirectly supports long-term organic reputation.

5. Inventory optimization

AI helps predict demand, manage SKUs, and automate reordering. This ensures product availability, prevents 404 errors, and supports healthy indexing for an SEO strategy.

6. Dynamic pricing

AI adjusts prices based on demand, competition, and behavior. When integrated with structured data, this can boost visibility in SERPs and AI platforms.

AI-powered content creation and optimization

One of the most disruptive innovations in SEO is AI-generated and AI-optimized content. AI is now capable of supporting the entire content lifecycle, from ideation to performance monitoring.

• Scalable content generation: AI can produce large volumes of SEO-friendly product pages, category descriptions, or blog content quickly and efficiently.
• Real-time SEO optimization: AI tools assist with keyword placement, heading structure, internal linking, and metadata optimization, all essential for ranking.
• Content refreshing and repurposing: AI can identify outdated or underperforming content and suggest updates based on current trends and algorithms.

Content remains king, but AI might be the strategist. For more on this topic, read our article AI Mode: What does it mean for SEO?.

Benefits of using AI for SEO in eCommerce

AI doesn’t just automate, it enhances your entire SEO strategy. Here’s how:

• Improved rankings and traffic: Smarter keyword targeting, optimized content, and enhanced UX drive stronger organic performance.
• Higher engagement signals: Personalized experiences and faster site interactions translate into better dwell time and lower bounce rates.
• Consistent content quality: AI helps maintain tone, structure, and SEO integrity across large-scale content production.
• Automated technical SEO: AI tools flag issues like broken links, duplicate content, or slow pages—and can sometimes fix them automatically.
• Better decisions, faster: AI surfaces actionable insights from SEO data, allowing marketers to test and iterate in real time.

How to implement AI in your SEO strategy

Integrating AI doesn’t require a full overhaul. You can begin with incremental changes:

• Set clear SEO objectives: Define KPIs such as impressions or ranking improvements and align your GEO strategy accordingly.
• Start with SEO quick wins: Use AI for content briefs, metadata generation, keyword clustering, or product copy creation.
• Choose scalable tools: Opt for AI platforms that integrate with your CMS, analytics, and SEO software stack.
• Leverage expert support: Collaborate with SEO and AI specialists to create a roadmap that balances automation with strategy.
• Continuously test and refine: Use testing and search performance data to evaluate impact and guide improvements.

The future of AI in eCommerce and SEO

AI is not just the present, it’s defining the future of how we search, shop, and grow digitally. Expect to see:

• Visual search optimization: AI allows users to search via images. Brands that optimize images with alt text and structured data will stand out.
• Voice search and conversational SEO: AI voice assistants are changing how queries are made, requiring long-tail and natural language content strategies.
• Immersive experiences through AR and AI: AI will support augmented reality shopping environments that are SEO-optimized and user-intent aligned.
• Hyper-personalized content: AI will tailor every interaction and content piece to individual users, increasing relevance and improving rankings.

Our AI and SEO approach at Making Science

At Making Science, we stay ahead of these shifts through a dedicated SEO and Content TECH & AI friendly strategy, powered by our proprietary technology Trust Generative AI. This platform transforms how we scale and optimize content, adapting it to current search engine demands and user behavior.

We guide our clients through this transition, helping them identify opportunities, scale content production, and strengthen long-term organic visibility with confidence.

Want to know how we can help your brand grow? Let’s talk.

Conclusion

Artificial intelligence in ecommerce is more than a trend, it’s the strategic engine behind personalization, automation, and search performance. From improving UX and engagement to optimizing content and metadata, AI empowers brands to scale efficiently and win in the SERPs. As AI continues to evolve, integrating it into your SEO strategy will be key to staying competitive in today’s digital economy.

The search scenery is evolving. We’re witnessing a shift from simple keyword matching to complex interpretations of user intent. Thanks to advances in natural language processing and generative AI such as ChatGPT or AI Mode, search engines are no longer just answering queries. They’re starting to engage in conversations with the users.

This evolution is reshaping how people interact with search engines and, as a result, what SEO means today. Instead of focusing only on keyword placement, we must now understand context, nuance, and user expectations. SEO has become less about visibility and rankings and more about relevance within AI-driven answers.

Generative AI in action: generated content, instant answers & brand visibility

Generative AI is revolutionizing the way companies approach content creation. Brands can now produce SEO-friendly content at scale, brainstorm topics quickly, and streamline their editorial processes. AI tools enable marketers to respond faster to trends and user questions.

On the other side, search engines are leveraging AI to provide instant answers directly within the search results without the need for users to click, altering the value of traditional content placement.

Now, the strategic goal is not just ranking, it’s getting your brand cited as a trustworthy source within those AI-generated responses. Visibility in this new environment requires a deep understanding of how AI interprets and works through information.

Does Google penalize AI-generated content?

One of the most common myths today is that Google penalizes content created with AI. This is not true. What Google penalizes is low-quality, unoriginal, or unhelpful content, regardless of how it was produced.

The foundation remains the E-E-A-T principle: experience, expertise, authoritativeness, and trustworthiness. AI can help scale content, but human review and editing are very important to ensure that the output meets quality standards.

We should think of AI as our first draft assistant, not the final editor.

How keyword research is evolving with AI

Traditional keyword research focused on finding exact-match phrases and long-tail variations, but with AI, we can do better. AI tools now can identify emerging trends, related questions, and semantic clusters that reflect how people actually speak and search.

The new approach changes from asking, “Which keyword should I use?” to “How can I completely cover a user’s intent?” By combining AI-driven insights with real search data, marketers can discover opportunities based on actual conversations rather than isolated keywords.

What do we do in Making Science to appear in conversational search

Appearing in traditional featured snippets is one thing, but getting your content referenced in generative AI answers requires a more refined strategy. Here are some best, tested practices we follow to boost your visibility:

• Answer clear, specific questions within the first few paragraphs.
• Use natural, conversational language. Avoid jargon if possible.
• Implement structured data.
• Build topical authority by covering topics from multiple angles (topical clusters).
• Optimize brand-entity associations by consistently linking your brand to specific themes.

The goal is no longer just to “rank #1,” but to be seen as a credible source by AI systems interpreting search queries.

Key takeaways

• AI is transforming every phase of SEO, from research to content, to the search results themselves.
• Content is still the king, but it must be genuinely useful, accurate, and crafted for real human intent.
• Brands that blend AI tools with strategic thinking and human creativity will gain a significant edge in the evolving search landscape.

In 2025, Google rolled out a major update with the launch of AI Mode, which is already live in the U.S., UK, and India (as of 30th July). This new feature is shaking up how people search. Unlike the AI Overviews we’ve seen in the past, AI Mode is a separate tool that uses generative AI to give you detailed answers directly on the search page. In this post, we’ll take a closer look at how it works, what it means for SEO, and how businesses can adapt to this new way of searching.

1. How does Google’s AI Mode work?

AI Mode is powered by Google’s Gemini 2.5 model, a super advanced language model that is designed to give you detailed answers to complex searches. Instead of showing directly a list of web pages, AI Mode breaks down your search into smaller pieces of content (chunks), running parallel searches to get data from different sources and creating a single response. This answer appears directly on the search page, so you don’t have to click through to other websites.

What makes AI Mode different? 

The main difference it brings to the table is that it can handle different types of inputs, like text, voice, or even images. Users can upload a photo and ask follow-up questions, interacting with the AI in a more conversational way. Plus, the AI can adjust its responses over time based on your previous conversations. For example, if you ask a broad question and then follow it up with something more specific, AI Mode will refine its answer to give you more detailed info.

2. What does AI Mode mean for SEO?

AI Mode brings a new set of challenges for SEO professionals, especially when it comes to traffic and click-through rates (CTR). In traditional search, users are shown a list of websites to click on, but in AI Mode, users are getting answers directly on the search page. This could mean fewer clicks to external websites, as the AI can sometimes fully answer the query right there.

Some reports have shown a drop in CTR, since users can get the information they need without leaving the search page. However, this doesn’t mean you lose visibility entirely. Even if users don’t click through, your content can still be exposed if it’s referenced in the AI-generated answers. Plus, Google has noted that users are often spending more time on the search page, asking follow-up questions and diving deeper into topics. This could actually lead to more engagement in the long run. Besides, boosting brand awareness is a crucial element of any successful marketing strategy, and this presents a prime opportunity to gain an advantage. 

3. New SEO metrics to focus on

With the rise of AI Mode, some traditional SEO metrics like clicks and rankings don’t carry the same weight anymore. Impressions are now more important than ever. However, there are also new metrics to keep an eye on:

• Visibility in AI Search features: This metric tracks how often your content appears in AI-generated elements like Featured Snippets, Knowledge Panels, and AI Overviews. Tools such as Semrush and Ahrefs can help monitor these appearances, providing insight into your content’s reach within AI interfaces.
• Branded search volume trends: Monitoring the frequency of searches for your brand name helps assess brand awareness and the effectiveness of AI-generated content in driving recognition. An increase in branded searches indicates that AI systems are referencing your content while also enhancing your brand’s visibility.
• User engagement metrics: Metrics such as bounce rate, time on page, and interaction depth provide insights into user satisfaction. AI systems consider these signals when determining content relevance, making them crucial for understanding how users interact with your content.

These new metrics focus on user intent and understanding how well your content is engaging with people—rather than just focusing on clicks alone.

4. What do we do in Making Science to perform better in AI Mode?

To help you get noticed in AI Mode, we follow these proven best practices:

• Ensure crawlability and indexing: AI Mode relies on Google’s crawling and indexing system, so we have to be sure our content is easy to access and index by Google systems. This means checking for any crawl errors and ensuring we’re not blocking anything relevant with robots.txt.
• Create high-quality content: Content that is thorough, authoritative, and effectively answers users’ questions is more likely to be featured by AI Mode. Google systems still favor high-quality content, so we should keep focusing on providing value (maybe more than ever!) for them.
• Review and apply structured data: Structured data like schema markup can help Google have a better understanding of our content, especially when it comes to media like images or videos.
• Optimize for user experience: While AI Mode may reduce clicks, the overall user experience still matters. A fast, mobile-friendly, and easy-to-navigate site is key in our strategy.

Key takeaways

Google’s new AI Mode is essentially a smart chatbot built directly into the search page, designed to answer complex questions in conversation style. This is changing the game for websites because the AI often answers questions directly, meaning fewer people click on the traditional blue links.

For businesses, the new goal is to become a trusted source that the AI quotes in its answers. The best way to do this is by building a strong, reputable brand and creating genuinely helpful content that offers unique, real-world experience and insights that only a human expert can provide.

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

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

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

What Does Generative AI Bring to Data Analytics?

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

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

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

Why Is It a Revolution for Analysts?

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

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

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

How to Start Applying It in Your Daily Work

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

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

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

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

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

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

Conclusion: The Time Is Now

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

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

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

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

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

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

📲 App analytics: much more than clicks

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

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

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

🎯 Conversions: measuring what really matters

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

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

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

🔗 Attribution: knowing which channel converts best

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

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

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

🔍 Smart data = smart decisions

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

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

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

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

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

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

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

In the fast-paced world of customer service, every second counts. Customers expect quick, efficient, and personalized support, while businesses strive to optimize their operations and keep agents productive. This is where Salesforce Service Voice comes in, a game-changer designed to seamlessly integrate voice capabilities directly into the agent’s workspace, leading to significant enhancements in both agent productivity and customer satisfaction.

The Challenge: Balancing Speed with Quality

Traditionally, handling customer calls has often meant agents juggling multiple screens, systems, and applications. This fragmented workflow can lead to delays, errors, and a frustrating experience for both the agent and the customer. Agents spend valuable time searching for information, while customers wait on hold or have to repeat their issues. This inefficiency directly impacts resolution times and, consequently, customer happiness.

How Salesforce Service Voice Transforms the Service Experience

Salesforce Service Voice tackles these challenges head-on by bringing the power of the contact center directly into the Salesforce Service Cloud console. This integration offers several key benefits:

• Unified Agent Workspace: Agents no longer need to switch between different applications. Voice calls are seamlessly routed and managed within the Service Cloud console, where all customer data, case history, and relevant information are readily available. This single pane of glass reduces clicks and context switching, allowing agents to focus on the conversation.
• Real-time Customer Insights: As a call comes in, Service Voice intelligently pulls up the customer’s complete profile, including past interactions, purchase history, and even sentiment analysis from previous calls. This instant access to crucial information empowers agents to provide personalized and informed support from the very first “hello.”
• Automated Call Logging and Wrap-up: After a call, Service Voice can automatically transcribe the conversation and even summarize key points, creating a detailed call log. This drastically reduces the time agents spend on post-call administrative tasks, freeing them up for more customer interactions.
• AI-Powered Assistance: Leveraging Salesforce’s Einstein AI, Service Voice can offer real-time suggestions and knowledge articles to agents during a call. This intelligent guidance ensures agents have the right answers at their fingertips, leading to faster resolutions and fewer escalations.
• Enhanced Coaching and Quality Assurance: Call recordings and transcriptions provide valuable data for supervisors to coach agents effectively and ensure consistent service quality.

The Impact: Happier Agents, Happier Customers

The benefits of Salesforce Service Voice are tangible and far-reaching:

• Increased Agent Productivity: By streamlining workflows, automating tasks, and providing instant access to information, agents can handle more calls, resolve issues faster, and reduce their average handling time. This leads to a more efficient contact center operation.
• Boosted Customer Satisfaction: Customers receive faster, more personalized, and accurate support. They feel heard and understood, leading to higher satisfaction, increased loyalty, and a stronger brand reputation.
• Reduced Agent Burnout: A less fragmented and more efficient workflow reduces stress and frustration for agents, contributing to a more positive work environment and lower attrition rates.

The Bottom Line

Salesforce Service Voice is more than just a telephony integration; it’s a strategic tool that redefines how B2C companies deliver customer service. By uniting voice capabilities with comprehensive customer data and AI-powered insights, it empowers agents to be more productive and effective, ultimately leading to exceptional customer experiences and a significant competitive advantage.

In the ever-changing and highly competitive retail landscape, achieving sustained growth requires an approach that leverages customer data and evolving shopping patterns, embraces cutting-edge AI technology for speed and scale, and seamlessly integrates online and offline customer journeys. 

At Making Science, our proven omnichannel retail innovation framework, powered by advanced AI and built upon the robust Google Marketing Platform (GMP), is designed to help retailers not only meet but exceed their performance objectives.

Our framework is designed around three foundational pillars: unifying and leveraging the deep first-party data that almost all retailers have within their walls, activating marketing strategies through best-in-class technology solutions, and enabling your organization’s data readiness for a future with advanced AI. 

Unifying First-Party Data: Your Foundation for Success

At the core of any successful AI-driven strategy lies high-quality data. Most omnichannel retailers have no shortage of first-party and sales data stored within their walls; it’s just a question of how to use it in a privacy-compliant way. As a retailer, we emphasize the critical importance of collecting comprehensive, accurate, and real-time customer and conversion data, whether you operate online, offline, or both.

By leveraging the Google Marketing Platform, organizations can effectively unify and deploy their first-party data, creating a rich and actionable foundation for AI success. This robust data pipeline is the fuel that powers AI, enabling it to remix, scale, and optimize campaigns with unparalleled efficiency. Ensuring privacy-compliant data inflow into Google platforms is paramount for achieving optimal and lasting results. 

Fueling Intelligent Activation: Maximizing Performance Across Channels

Leveraging the cutting-edge AI innovations within Google Ads and Google Marketing Platform is a key step in driving superior digital ad performance. 

This includes embedded AI tools like Performance Max, which optimizes campaigns across Google’s entire inventory, from Search and Shopping to YouTube and Gmail. When activated within GMP’s Search Ads 360 (SA360), retailers are able to maximize performance through goal-based bidding with deduplicated conversion data, powered by Google’s auction-bidding technology. This ensures that every bid aligns with real business goals, maximizing conversion rates and return on ad spend.

Shifting from Search to Video and Display, Google’s Demand Gen campaigns, run in GMP’s Display & Video 360 (DV360), are utilized to spark discovery and capture new demand earlier in the marketing funnel. By serving visually appealing, multi-format ads on Google’s most impactful surfaces like YouTube and Discover, we can boost brand presence and foster increased consideration. The integration with DV360 unlocks advanced capabilities such as unified customer views, reach and frequency management, and third-party brand safety verification. 

Enabling Data Readiness for Advanced AI: The Test & Learn Imperative

The journey to AI excellence in retail is continuous and can feel uncomfortable at times. Adopting a “test and learn” mindset, encouraging ongoing experimentation and optimization, can help to ease this transition and allow you to see which combination of tools works best for your organization.

Through the Google Marketing Platform, retailers are best positioned to integrate and scale more advanced AI features, including by licensing specialized third-party AI technology. This readiness ensures that as AI evolves, so too does your marketing strategy, allowing for agile adaptation and sustained competitive advantage.

Key Takeaways for Retail AI Innovation:

• Your Data Equals Your Success: Data is the fuel that powers AI to remix, scale, and optimize your campaigns. AI can only be effective when we feed it good data to make good decisions. From tagging to 1PD connections, ensuring that your privacy-compliant customer and conversion data coming into Google is accurate, complete, and real-time will ensure your success.
• Leverage Google AI Innovation: Here we have shown the strategies and benefits of combining Google Ads AI innovation with best-in-class enterprise Ad Tech from the Google Marketing Platform. From Pmax to Demand Gen, retailers who activate within GMP are able to unlock advanced marketing features.
• Think Omni Retail: We have to meet our customers where they are today. While e-commerce and online shopping get a lot of attention, offline physical stores remain the core of many retailers’ businesses. For these retailers, we must consider foot traffic in physical stores.
• Embrace a Test & Learn Mindset: And finally, it is essential that we embrace a test and learn philosophy. Working with partners like Google and Making Science, we can help to separate the signal from the noise, working in collaboration over time, to grow your business.

Retailers can unlock unprecedented levels of performance, drive profitable growth, and remain at the forefront of AI-powered marketing. To learn more about how to adopt this framework, get in touch with our specialist team at Making Science.

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In today’s crowded digital marketplace, e-commerce businesses face a relentless challenge: standing out and converting browsers into loyal buyers. Generic, one-size-fits-all approaches simply don’t cut it anymore. Customers expect experiences tailored specifically to them. This is where Salesforce Personalization unlocks a powerful advantage, transforming how online retailers connect with their audience and directly leading to a significant boost in e-commerce sales.

Why Personalization is No Longer Optional for E-commerce

Think about your own online shopping habits. Are you more likely to buy from a store that remembers your preferences, suggests products you genuinely might like, or sends you offers relevant to your past purchases? Chances are, the answer is yes. In fact, studies consistently show that consumers prefer personalized experiences and are more likely to convert when they receive them. Without personalization, e-commerce businesses risk losing customers to competitors who offer a more relevant and engaging journey.

How Salesforce Personalization Fuels Sales Growth

Salesforce Personalization isn’t just about adding a customer’s name to an email. It’s a sophisticated platform that uses data and artificial intelligence to understand individual customer behavior and deliver truly unique experiences across every touchpoint. Here’s how it drives sales:

• Real-time Behavioral Insights: Salesforce Personalization continuously collects and analyzes customer interactions on your website, app, and other channels. It understands what products they view, what they add to their cart, what they click on, and even how long they linger on a page. This real-time understanding allows for immediate, relevant responses.
• Dynamic Product Recommendations: Based on individual Browse history, purchase patterns, and even the behavior of similar customers, the platform can display highly relevant product recommendations. This could be “customers who bought this also bought…” or “you might like this based on your recent views,” significantly increasing the likelihood of an impulse or add-on purchase.
• Personalized Website Experiences: Imagine a homepage that changes for each visitor, highlighting products they’ve shown interest in, or promotions relevant to their past interactions. Salesforce Personalization can dynamically adjust content, banners, and even navigation paths to create a unique and highly engaging Browse experience.
• Tailored Email and Mobile Campaigns: Moving beyond generic newsletters, Salesforce Personalization enables segmented and individualized email campaigns. This means sending abandoned cart reminders with relevant product suggestions, personalized sale alerts, or follow-up emails based on recent purchases, all designed to encourage conversion.
• Optimized Offers and Promotions: By understanding a customer’s value and preferences, businesses can deliver targeted discounts and promotions that are more likely to resonate and drive a purchase, avoiding wasted marketing spend on irrelevant offers.

The Tangible Impact on Your Bottom Line

The direct result of implementing Salesforce Personalization is clear:

• Higher Conversion Rates: When product suggestions are relevant and experiences are tailored, customers are far more likely to complete a purchase.
• Increased Average Order Value (AOV): Effective recommendations encourage customers to add more items to their cart, boosting the overall value of each transaction.
• Enhanced Customer Loyalty: A personalized experience makes customers feel valued and understood, fostering stronger relationships, encouraging repeat purchases, and turning one-time buyers into lifelong advocates.
• Reduced Cart Abandonment: Timely, personalized reminders and incentives can bring customers back to complete their purchases, recovering potentially lost sales.

The Bottom Line

In the competitive e-commerce landscape, personalization is no longer a luxury; it’s a strategic imperative for sustainable growth. Salesforce Personalization provides the tools to understand your customers deeply and engage with them on a truly individual level, directly translating into higher conversion rates, increased sales, and a stronger, more loyal customer base.

Last week, Campaign’s Media360 event in Brighton brought together a vibrant community of marketing and media professionals. The strong attendance and a packed agenda covering key trends, challenges, and opportunities clearly demonstrated the industry’s keen engagement. For Nick Waters, Global CEO Northern Europe at Making Science, the event offered a unique lens through which to view the evolving UK media landscape, delivering some surprising insights beyond the usual headlines.

Beyond the Usual Agenda: The Power of Resilience and Contrarian Thought

The event kicked off with a truly engaging interview with Jamie Laing, whose natural energy immediately set a dynamic tone. From his conversation, two powerful messages resonated deeply with Nick: the vital importance of resilience – the unwavering ability to push past countless “no’s” – and the courage of contrarian thinking, daring to make the illogical choice when intuition calls. This prompts Waters to question whether our industry has perhaps become too comfortable simply following the obvious path.

The Elephant in the Room: Where Was the AI Conversation?

Perhaps the most striking observation from Media360 was the unexpected underrepresentation of Artificial Intelligence. In an era where AI dominates global headlines and is undeniably central to the future of nearly every industry, its relatively limited presence in the content stream was notable. This raises crucial questions for Nick: Does the UK marketing community feel entirely on top of these rapid developments, is there an element of complacency, or has the truly transformative power of AI yet to be fully appreciated within this market? A direct consequence was also a noticeable lack of discussion around the agency ecosystem’s role in guiding advertisers through this monumental shift.

A Clear Spotlight: The “Media for Good” Movement

In stark contrast to the quietness around AI, the theme of “media for good” commanded significant attention. Discussions prominently featured topics such as diversity in media, pressing sustainability initiatives, decarbonization efforts, and the critical importance of supporting quality journalism. This strong emphasis clearly signals a significant and positive priority for the UK industry.

Navigating Channel Dynamics and Demographic Shifts

The event also provided fertile ground for debates on evolving media channels. The enduring relevance of Television, despite fragmentation and the rise of social media, was a key point. The notion that “social media killed the TV star” was confidently dismissed, underscoring TV’s continued powerful influence on national social culture. Discussions around the impact of Gen Z were also present, though the consensus suggested that their material influence on reshaping the industry is still an evolving story, perhaps predictably so given their nascent professional journey.

Conclusion: Re-evaluating Industry Priorities for the Future

Media360 offered a compelling snapshot of the UK media landscape’s current focus. With “media for good” so prominently featured and AI surprisingly relegated to the periphery, Nick Waters’ core question echoes: Has the UK industry truly found the right balance in its attention to what truly matters for tomorrow? At Making Science, we firmly believe that future success lies in a holistic approach, where societal responsibility goes hand-in-hand with an aggressive embrace of technological innovation, particularly AI. It’s about leveraging cutting-edge tools to enhance human impact and drive meaningful, sustainable growth for our clients.