Unlock the Power of Treasure Data with Strategic Data Segmentation
By Madeline Calvert, Business Development & Partnerships Manager, AMER
In today’s competitive digital landscape, brands are constantly searching for ways to transform customer data into actionable insights. While many marketers dream of achieving 1:1 personalization, the complexities and challenges make it an elusive goal. Instead, brands can unlock massive potential with a smarter approach: Strategic Data Segmentation, powered by the capabilities of Treasure Data’s Customer Data Platform (CDP).
But what makes Strategic Data Segmentation different from regular data segmentation? While traditional segmentation divides customers into basic groups based on common attributes—like demographics or location—strategic segmentation dives deeper with behavioral clustering to create more meaningful and actionable segments based on real customer interactions and predictive patterns.
Now, here’s where AI comes into play: AI drives this segmentation to the next level, using machine learning to uncover patterns, predict behaviors, and create hyper-targeted segments in real time. Let’s explore how Silverbullet’s approach to Strategic Data Segmentation—enhanced by AI and powered by Treasure Data CDP—transforms your data into a revenue-driving personalization engine, all while maintaining efficiency, scalability, and compliance.
What is Strategic Data Segmentation?
At its core, Strategic Data Segmentation is the art and science of categorizing your customer base into meaningful segments that align with your business objectives. This goes beyond traditional methods, leveraging behavioral clustering and AI-driven analytics to group customers based on their actual behaviors, engagement patterns, and intent—ensuring relevance and effectiveness in your marketing efforts.
How AI Makes It Smarter: AI doesn’t just group customers into clusters; it constantly learns from the data, adapting segments as customer behavior shifts. It enables predictive analytics, ensuring that segments evolve over time, leading to more dynamic and accurate personalization that scales with your business.
How is it Different from Regular Segmentation?
Regular data segmentation typically categorizes customers based on simple factors like age, gender, or location, which often results in broad, generic groupings. These segments may not fully reflect customer behaviors or provide much actionable insight. For instance, segmenting a group as “Millennials” is too broad and does not account for the different purchasing patterns, product preferences, or engagement levels within that group.
Strategic Data Segmentation uses behavioral clustering and AI-driven insights to analyze how customers interact with your brand—tracking data points like browsing patterns, purchasing frequency, and product preferences. AI enhances this process by automating the identification of high-value segments and predicting future behaviors, creating an always-on learning system that continuously refines and optimizes your audience clusters.
By leveraging Treasure Data CDP and AI-driven models, this process becomes even more powerful. Here’s how it works:
01: Data Collection and Unification: Treasure Data CDP acts as the hub that unifies all customer touchpoints—whether from websites, email interactions, or point-of-sale systems—into a single source of truth. Silverbullet helps organize this data so it’s clean, structured, and ready to be analyzed.
02: Behavioral Clustering through Advanced Analytics and AI: With your data unified, AI-driven behavioral clustering automatically identifies key patterns within your audience. AI models can group customers based on similarities in their actions—such as their purchasing habits, browsing behavior, or engagement levels—allowing for real-time, dynamic segmentation.
03: Creation of High-Impact, AI-Powered Segments: Instead of managing countless groups manually, AI analyzes vast amounts of data to create high-impact segments that are both profitable and easy to scale across your marketing campaigns. AI ensures these segments evolve in real-time, adapting as customers’ behaviors shift.
Understanding Behavioral Clustering with AI
Behavioral clustering is at the heart of what makes Strategic Data Segmentation so effective. By clustering customers based on their behaviors, you unlock deeper insights into how different groups engage with your brand, allowing you to develop highly relevant marketing strategies.
How AI Enhances Behavioral Clustering: AI processes vast datasets far more efficiently than manual methods, identifying nuanced patterns that are often missed in traditional segmentation. It enables brands to create dynamic segments that adapt to real-time changes in customer behavior, making your marketing more timely, relevant, and impactful.
For example, behavioral clustering can group customers who:
- Frequently browse but rarely make a purchase (window shoppers).
- Purchase only during sales (discount hunters).
- Interact heavily with new product drops or limited editions (early adopters).
With AI, the system not only identifies these clusters but predicts which customers are likely to change their behavior. For example, it can highlight window shoppers likely to convert with the right promotion, or flag early adopters for exclusive pre-launch campaigns.
Real-World Example: AI and Strategic Segmentation in Action
Imagine a retailer like Nike. Instead of attempting to personalize every interaction for every customer, Nike could use AI-powered Strategic Data Segmentation to focus on key segments:
- Segment 1: Loyalists – Customers who have purchased three or more products within the last six months.
- Segment 2: New Product Enthusiasts – Shoppers who consistently interact with new releases, such as the latest Kobe basketball shoes.
- Segment 3: Price-Sensitive Buyers – Customers who frequently shop only during sales or promotions.
AI takes this further by learning from past behaviors and predicting future actions, enabling Nike to target each segment with precise, personalized offers. For example, AI could predict which segment members are likely to respond best to a limited-time discount or exclusive product drop, allowing Nike to tailor offers more effectively and improve engagement.
Why Strategic Data Segmentation Powered by AI is the Future of CDPs
As brands look to future-proof their personalization efforts, Strategic Data Segmentation with AI-driven insights will be the backbone of their customer data strategies. Here’s why:
- Scalable Personalization with AI: While 1:1 personalization is resource-intensive, AI-powered segmentation allows brands to personalize at scale—balancing cost, time, and impact.
- Faster Time-to-Market: With AI constantly refining segments, brands can launch personalized campaigns faster and with greater agility, keeping their marketing relevant and timely.
- Dynamic Evolution of Segments: AI ensures that customer segments are not static. Instead, they evolve as customers’ behaviors and interactions with the brand change, keeping campaigns fresh and aligned with customer needs.
Build Your Data Strategy with Silverbullet and Treasure Data
With the right approach to data, your CDP can become a powerful engine for revenue-driving personalization. Silverbullet’s Strategic Data Segmentation, enhanced by AI-powered behavioral clustering and supported by Treasure Data, offers the scalability, efficiency, and personalization that modern brands need to thrive.
Ready to transform your CDP into a strategic powerhouse? Reach out to Silverbullet today to learn how we can help you develop an AI-driven strategic data segmentation plan that optimizes your customer data for personalized digital experiences that deliver ROI.
Transform Your Digital Customer Experience
How Silverbullet’s Strategic Data Segmentation Turns Your CDP into an AI-Driven Personalization Engine
By Madeline Calvert, Business Development & Partnerships Manager, AMER
The Allure of Personalized Marketing
Imagine this: you visit the Nike website, excited about the release of the new Kobe basketball shoes. You sign up for email alerts to stay informed, and from that moment, Nike’s system starts tracking your every interaction. Every product you click on, every purchase you make—Nike’s Customer Data Platform (CDP) logs it all. The system learns what you like, how often you buy, and what promotions might catch your eye.
Nike could, in theory, use Generative AI to analyze all that data and send you personalized emails, exclusive promotions, and targeted ads that keep you engaged. They could even tailor your entire shopping experience to fit your unique preferences. Sounds like the ultimate personalized marketing strategy, right?
But here’s the catch: is this level of 1:1 personalization really the best approach? Can it truly deliver the scalable, profitable results that brands expect?
The Dream of 1:1 Personalization
The vision of 1:1 personalization is captivating—AI systems creating individualized messages for each customer, analyzing their every move, and tailoring content to their exact preferences. For a brand like Nike, the idea of building a direct, personalized connection with each customer is powerful.
But while it sounds amazing, there’s a significant challenge: is this dream realistic for most brands?
The Realities and Challenges of 1:1 Personalization:
Although the idea of sending millions of personalized messages at scale is appealing, it presents serious challenges:
Legal and Compliance Concerns: If a brand like Nike is generating millions of pieces of personalized content, how can their legal team possibly review all of it? There are strict privacy, advertising, and regulatory guidelines that must be followed. If AI generates all this content automatically, the risk of something slipping through the cracks grows exponentially.
Brand Consistency: Even if the AI follows brand guidelines, ensuring consistency across millions of unique messages is incredibly difficult. How do you guarantee that every message reflects your brand’s tone, voice, and style?
Cost and Infrastructure Overload: How much does it cost to manage the infrastructure required to deliver millions of personalized messages? The sheer volume of data, storage, and computing power needed to support this level of personalization can become overwhelming. You could easily end up with more data than you know what to do with, creating data chaos that slows down decision-making and performance.
The Smarter Solution: Strategic Data Segmentation
This is where Silverbullet’s strategic data segmentation approach comes in. Rather than focusing on the complexity of 1:1 personalization for every customer, strategic data segmentation offers a process to help brands organize their data and create personalized experiences for well-defined customer segments.
Strategic data segmentation helps your team build the foundation necessary to write and execute a successful AI strategy. By leveraging this process, brands can move toward scalable, data-driven personalization, while avoiding the costs and risks associated with 1:1 personalization. Here’s how strategic data segmentation can help guide your approach:
Legal and Brand Safety: By using strategic data segmentation, your team can focus on personalizing content for specific segments instead of millions of individual profiles. This significantly simplifies the legal review process, making it easier to ensure compliance with privacy and advertising standards while maintaining brand consistency.
Cost Efficiency and Scalability: With strategic data segmentation, your marketing team can focus on high-value segments, reducing the need to generate millions of personalized messages. This streamlines your infrastructure and helps you work more efficiently by targeting strategic segments that drive higher ROI.
Creating Meaningful Customer Journeys: Strategic data segmentation enables you to create personalized experiences for customer groups who share similar behaviors and preferences. For example, Nike could use this process to design a customer journey for basketball enthusiasts, offering relevant promotions, products, and content that align with their interests, without needing to generate millions of individualized messages.
What is Strategic Data Segmentation?
Strategic data segmentation is Silverbullet’s approach to organizing and utilizing customer data. Instead of overwhelming brands with data from millions of profiles, strategic data segmentation identifies high-impact customer segments and generates tailored content for each segment. Here’s why it works:
- Relevance: Strategic data segmentation focuses on creating segments that are large enough to be profitable but specific enough to feel personal.
- Actionability: With fewer segments to manage, your team can quickly launch targeted campaigns that are highly actionable and easy to track.
- Efficiency: Strategic data segmentation reduces the burden of managing millions of data points, making it easier to extract meaningful insights and optimize your campaigns.
Why Strategic Data Segmentation is the Future of Personalization
The future of AI-driven personalization isn’t about trying to create millions of individual customer journeys. It’s about making the most of strategic data segmentation to deliver personalized experiences at scale. By using strategic data segmentation, brands can:
- Deliver Personalized Content More Effectively: Rather than overwhelming their systems with millions of unique messages, strategic data segmentation allows brands to focus on high-value segments that deliver personalized content efficiently and at scale.
- Reduce Data Chaos: Strategic data segmentation simplifies the data collection process, allowing brands to manage their data effectively and uncover actionable insights without being bogged down by an overload of information.
- Improve Speed to Market: With fewer segments to manage, teams can launch personalized content faster, improving marketing agility and driving better results.
Why Silverbullet’s Strategic Data Segmentation Sets You Up for AI Success
At Silverbullet, our focus is not on creating your AI strategy but on building the data strategy that will enable your team to implement AI personalization successfully. We help brands build a foundation for effective AI use by ensuring their data is clean, segmented, and ready to fuel AI-driven personalization.
Through strategic data segmentation, we set you up to manage your customer data in a way that supports scalable AI-driven personalization. Your team can then use this data to generate personalized content for key audience segments, driving better results without the chaos of 1:1 personalization.
Build Your Data Strategy with Silverbullet
Your CDP has the potential to be a powerful engine for AI-driven personalization—but only if it’s built on a strong data foundation. At Silverbullet, we help you transform your data strategy into a scalable solution through strategic data segmentation.
Reach out to us today to learn how we can help transform your data strategy and ensure that your CDP becomes the powerful engine for personalized digital customer experiences that drive both engagement and profit.
Keep It Simple, Stupid: Paving a Better Future for AdTech
Q&A with Charlie Pinkney, Global Business Development Director at 4D and Rhys Denny, Co-founder and CEO at @curate
Many of you will have seen and heard of curation. Throughout the AdTech landscape, it has become one of the hottest trends set to establish and transform Programmatic Marketplace (PMP) deals. Many advanced curation platforms are playing a critical role in integrating inventory and leveraging signals to optimize these transactions. And, among the various signals, ‘attention’ has surfaced as a particularly important metric, driving the relevance and effectiveness of curated ads.
‘Attention’ has become a real interest of mine over recent years, especially through my lens here at Silverbullet. To help me better understand how curation is impacting the (m)adtech landscape, I sat down with Rhys Denny, co-founder and CEO @curate.
Before I even started the questions, Rhys gave me a little insight into his new company, @curate.
“@curate has been designed to enhance existing trading strategies. Through our offering, businesses can achieve more precise and efficient ad placements and curate relevant and high-performing inventory, benefiting both advertisers and publishers. This strategic shift highlights the very future of our landscape” said Rhys.
Impressive stuff.
Alongside understanding curation a little better, I am also super interested in understanding how curation – when driven by AI and underpinned by contextual – can offer new ways to both advertisers and publishers, to capture ‘attention’ in those crucial moments, all the while supporting the navigation surrounding the challenges of privacy and data regulation.
I quizzed Rhys on the benefits that curation will present to the industry.
So Rhys, there seems to be an abundance of efficiencies that can be achieved through curation; whether that be closer relationships and transparency across deals, or through optimization and bidding – can you help us understand this better?
“Of course. The great thing about curation is that it can automate many aspects of the ad buying and placement process. By automating tasks like audience segmentation, ad creation, and bidding, curation allows advertisers to scale their campaigns more effectively. This is particularly important in programmatic advertising, where speed and scale are critical.
Innovative curation solutions can also help in curating vast amounts of content and inventory, making it easier for advertisers to find the most relevant opportunities in an increasingly complex digital landscape.
When we look more at optimization of ad placements and bidding strategies, curation can also play a leading role. Through machine learning, AI can analyze historical data, predict outcomes, and adjust bidding strategies in real time to ensure that ads are shown to the most relevant audiences at the optimal cost. This can lead to better ROI for advertisers and more efficient use of ad spend.
What’s more, curation can analyze the quality and relevance of inventory, helping advertisers choose the best placements for their campaigns. This ensures that ads are not just reaching any audience but the right audience, in the right environment, at the right moment.”
Amazing. Relevancy is huge right now isn’t it, and something we are massively passionate about here at 4D. There’s also a huge privacy play with curation isn’t there?
“As third-party cookies become less and less desired, curation can help navigate the shift toward privacy-centric advertising.
As you know more than most, first-party data, collected directly from users with their consent, is becoming increasingly important. We can essentially process this data to derive insights and curate personalized ad experiences without relying on invasive tracking methods.
Curation solutions like ours, supported by AI, ensure that ads are relevant and respectful of user privacy, aligning with new regulations like GDPR and CCPA. This builds trust with consumers, who are more likely to engage with brands that prioritize their privacy.”
You mentioned the ‘right person, place and time’ earlier, which is the long-standing goal for advertisers and publishers alike. Can you take us through how curation impacts personalized experiences online?
“Of course! In short, curation involves carefully selecting and organizing content to meet the specific needs and preferences of an audience. When combined with AI, curation can achieve an unprecedented level of personalization, through the analysis of first-party data to understand individual user behaviors, preferences, and intents. This allows for the creation of highly curated ad experiences tailored to the unique profiles of each user, delivering the right message at the right time.
With these technical advancements, we can ensure that the ads served resonate more deeply and drive higher engagement. This level of personalization not only improves the user experience but also increases the likelihood of conversion, making advertising more effective. It’s a win-win really, and something I am really excited about in the future of @curate.”
And finally, when it comes to curation and contextual intelligence working closer together – what gets you excited about those types of partnerships?
“These are the partnerships that future-proof and offer credible alternatives to the current buying status quo. Coupling the relevancy of contextual alongside the outcome-driven optimization of curation with the ability to overlay data through a deal ID offers an exciting and impactful future to how media can be traded.”
My final thoughts:
I spoke a little about curation and contextual intelligence a few weeks back (you can read the full article here!) but this chat with Rhys really did remind me of how exciting these partnerships will have on the future of AdTech.
My take is that curation is set to revolutionize online advertising and programmatic exponentially. And, when you overlay that strategy with the contextually driven data and insights, you end up with a very attractive and privacy-safe strategy that not only solves for the cookieless future (despite the fact third-party cookies are here to stay), but it creates a positive future. One that is simple, bright, and better. Much, much better.
If you want to meet up with myself and Rhys for a coffee, then get in touch today! Either head to our Contact Us form, or DM me here.
Contextual, Actually.
By Andrew Sims, Senior Strategic Account Manager, 4D
As the glorious days of summer begin to wane, colder weather and the hustle of the holiday season are quickly approaching. I know I know – we’ve not even hit Halloween yet! However, we are just under four months away from the festive celebrations of Christmas, Hanukkah, and the broader holiday season.
While it may feel too soon to mention, the reality is that many consumers are already shifting their focus towards holiday spending. In fact, 34% of U.S. shoppers started their holiday shopping as early as July this year, an increase from 28% in 2023.
Thanksgiving falls later this year, shortening the peak holiday spending season by nearly a full week. This gives brands less time to connect with consumers, making it crucial to act swiftly. To maximize opportunities, businesses should target early shoppers and ensure they’re strategically positioned at key points in the consumer’s shopping journey.
Did you know that retail e-commerce is projected to grow to $6.3 trillion this year, with 91% of online shopping taking place on mobile devices?
There is a BIG question many brands will be asking themselves over the coming weeks –‘how can we hit the mark with our holiday strategies and truly reach consumers in the moments that matter?
Don’t fret. 4D is here to save the day (and spread the festive cheer)!
The year’s final quarter is packed with key seasonal events, from Thanksgiving to Black Friday, Cyber Monday, and Christmas. For brands, the time to start planning is now. A deep understanding of consumer behavior and online habits is essential to identify where they’ll be spending their time.
- Will the “Miracle Workers” be turning to their laptops in October to research Thanksgiving meal prep ideas?
- Will the “Early Elves” be seeking Christmas and Hanukkah gift ideas as early as late September?
- Will the “Rapid Robins” be scanning online gift recommendations and Christmas blogs for inspiration before it’s too late?
- And, will most consumers be holding out for Black Friday sales, making purchasing decisions under the shadow of economic uncertainty?
Regardless of when or how you aim to capture your audience’s attention, it’s crucial to first understand their mindsets and the online spaces they trust most.
The good news is, 4D’s advanced contextual targeting and insights solution can support you in capturing audiences in the perfect moments this holiday season.
If you want to know how best to build your seasonal strategy this coming quarter, download our Q4 Seasonal Playbook today.
Once downloaded, feel free to get in touch with me, and we’ll start you on your contextual journey.
Related Documentation
4D Q4 Holiday Season Playbook
1. Fill out the form below
2. Click the “Download” button
3. Receive instant access to whitepaper
Do the Right Thing: Putting Google’s Motto into Practice
By Kristen Cure, Senior Account Executive, 4D
Did you know Google once had a tagline, “Don’t be evil”? This phrase was a key part of its corporate code of conduct, symbolizing its commitment to ethical and transparent business practices. And, in 2015, the tech giant replaced it with “Do the right thing.”
The word ‘irony’ comes to mind.
Recent headlines have accused Google of a “clear abuse of privilege,” according to the judge overseeing the Justice Department’s antitrust trial concerning its alleged digital advertising monopoly. The corporation’s ethos now feels like one hefty smoke screen in this context.
Now look, I don’t for one second want this article to be a point-blank Google bashing. We’re in a world where we need Google as much as we don’t. But the point which I am trying to make is, that we rely too heavily on these tech giants at times, and in many instances, we could – and should – look to defend ourselves.
As many of you will know, this summer Google backtracked on its long-standing promise to block third-party cookies in Chrome. Despite this being a step back for many in the pursuit towards a privacy-centric era, it’s a pretty good outcome for Google. The core reasoning behind this decision was time; time to address some of the key issues that data privacy regulators have raised about its well-discussed solution, Privacy Sandbox.
Another awkward realization in recent times is that Google’s Privacy Sandbox terms may, ironically, violate privacy law, according to new research by the Movement for an Open Web (MOW), a nonprofit interest group that advocates against restrictive digital walled gardens.
Again, let me preface this with: I am NOT bashing Google. I am merely shining a light on the huge transition our industry is witnessing right now. No one is going to get it right the first time. And, we all have to learn together on this crazy roller-coaster ride. The question is, can we do things right? Can we be better? And if so, how?
Doing the right thing. Actually.
Many of us are still trying to determine what the best next steps are. How can we be part of a future that places privacy right at the centre of everything we do – whilst reaching scale and growing revenue? Is there a world where scale and trust exist together in tandem?
You wanna know my take? Yes. Yes, we can – and it might not be as difficult as you think.
Most readers will know that contextual targeting’s market share has soared in recent years. The Global market for Contextual Advertising is projected to reach $562.1 Billion by 2030. By its very nature, contextual advertising is inherently 100% privacy-safe and cookieless. Instead of targeting individuals, this technology focuses on context and relevance as its core methodology. This approach not only alleviates concerns associated with third-party cookies and outdated practices but also offers a smarter way to reach new, unknown audiences by targeting the environments that consumers love.
This isn’t to say targeting users on a one-to-one level is bad. Quite the contrary. One-to-one marketing and engagement between a consumer and brand is still the holy grail for brands looking to keep and retain loyal customers. But while first-party data and smart CX strategies can help activate personalized, meaningful connections with known audiences, contextual intelligence can support businesses in attracting new audiences. The two together create an incredible use case for the future of brand storytelling.
So, whether or not Google decides to ‘do the right thing’, there are incredible options available to advertisers who want to thrive in the new marketing era. And fortunately for you, Silverbullet can do both.
If you want to find out more and see how we can help you, then get in touch today! You can find me on LinkedIn here.
The Impactful Impact of First-Party Data and AI: A Glimpse into the Future of Digital
By Fi Taylor, Strategic Global Marketing, Silverbullet
Two major forces have taken the (M)adTech landscape by storm over recent years: first-party data and artificial intelligence (AI). Both are set to revolutionize the way brands connect with their audiences. Not only are they transforming marketing strategies, but together they are paving the way for more personalized, effective, and privacy-conscious marketing campaigns. And if that sentence didn’t have enough industry buzz words within it, then keep reading – there’s plenty more to come!
What is First-Party Data?
Ok, I don’t want to teach you to suck eggs, but in case there are a few readers who need a refresher, here we go. First-party data refers to the information a company collects directly from its audience, customers, or users. This can include data from website interactions, email subscriptions, purchase histories, customer feedback, social media engagement, and more. Unlike third-party data, which is gathered by an outside entity and sold to marketers, first-party data is unique to the organization that collects it. This makes it more reliable, relevant, and valuable.
Why is First-Party Data So Important?
After years of consumer push-back and data breaches, privacy regulations such as GDPR, CCPA, and many more, were put into action as the industry looked towards a first-party data-centric world. Google’s cookie deprecation promise, although now cancelled, was a direct response to consumer backlash and governmental pressure to change how online data and consumer insights are handled. With or without Google’s support, the facts remain the same – old ways of working are no longer applicable for today, or the future. And first-party data is very much part of that future.
Here’s why first-party data is becoming the cornerstone of digital marketing:
1. Enhanced Privacy Compliance: First-party data collection aligns with global privacy standards, ensuring that businesses can build trust with their customers while complying with regulations.
2. Accuracy and Relevance: First-party data is directly sourced from your audience, making it highly accurate and relevant. This enables marketers to create more targeted and personalized campaigns.
3. Customer Relationship Building: By leveraging first-party data, brands can gain deeper insights into their customers’ preferences, behaviours, and needs, fostering stronger relationships and loyalty.
4. Cost-Effectiveness: Unlike third-party data, which often comes at a high cost and can be of dubious quality, first-party data is more cost-effective and provides better ROI for marketing campaigns.
AI’s Role in Amplifying the Power of First-Party Data
So, how does AI fit into all this? Well, AI is a huge topic within itself, but for the purpose of this article, it is the catalyst that unlocks the full potential of first-party data. Here’s how AI is set to transform digital marketing by leveraging first-party data:
1. Hyper-Personalization: AI can analyze vast amounts of first-party data to deliver personalized experiences at scale. By understanding individual customer preferences and behaviours, AI-driven systems can tailor content, product recommendations, and messaging to each user, enhancing engagement and conversion rates.
2. Predictive Analytics: AI can predict future customer behaviours based on past interactions, allowing marketers to anticipate needs and deliver proactive solutions. This predictive power helps in optimizing marketing strategies, from content creation to ad targeting, ensuring that resources are used efficiently.
3. Automation and Efficiency: AI streamlines marketing processes by automating tasks such as data analysis, customer segmentation, and campaign management. This not only saves time and resources but also allows marketers to focus on strategic decision-making and creative aspects of their campaigns.
4. Improved Customer Insights: AI-driven analytics provide deeper insights into customer journeys, identifying key touchpoints and areas for improvement. By understanding these insights, marketers can refine their strategies to better meet customer expectations and enhance overall satisfaction.
5. Enhanced Ad Targeting: With the decline of third-party cookies, AI can help bridge the gap by using first-party data to create more accurate audience segments. This leads to more effective ad targeting, reducing ad spend waste and increasing ROI.
A match made in heaven you might say?
As first-party data and AI become more integral to digital marketing, we can expect several key trends to emerge:
- Increased Focus on Data Collection and Management: Businesses will invest more in technologies and strategies that enhance first-party data collection and management. This will include the use of customer data platforms (CDPs) and advanced analytics tools to ensure data is organized, accessible, and actionable.
- Shift Towards Consumer-Centric Marketing: With AI’s ability to analyze and utilize first-party data, marketing will become more consumer-centric. Brands will prioritize understanding and meeting the needs of individual customers rather than relying on broad, impersonal campaigns.
- Integration of AI Across Marketing Channels: AI will be increasingly integrated across all marketing channels, from social media and email marketing to content creation and customer service. This will create a seamless, cohesive brand experience for consumers.
The combination of first-party data and AI represents a powerful shift in the future of digital marketing and online storytelling. As these technologies continue to evolve, they will enable marketers to create more personalized, efficient, and effective campaigns that resonate with their audiences on a deeper level – which is the modern-day holy grail of marketing.
Future success will be defined by those who can harness the power of these tools to not only meet but exceed customer expectations in a privacy-conscious world.
What are your predictions about the future of AI and first-party data?
Three’s (Not) A Crowd - Contextual, Curation, and First-Party Data
By Charlie Pinkney, Global Business Development Director
Customer experience is undeniably one of the hottest topics in the (M)adTech industry right now. Whether your focus is on obtaining quality consumer data to drive personalization or delivering premium advertising in relevant environments to capture attention – the mission remains the same: to provide excellent customer experiences in the moments that matter.
Despite Google’s recent reversal on their 3p deprecation stance, there has been a significant shift in the marketplace towards striving for a privacy-first, consent-driven future. For some time now, the resurgence of contextual targeting has been touted as a clear strategy for those looking to reach unknown audiences in the contexts they resonate with the most. Why? Well, it offers an innovative solution for the privacy-first era, allowing advertisers to reach unknown audiences through relevant, quality content.
However, contextual targeting is just one piece of the puzzle. The real excitement begins when you blend contextual targeting and insights with other innovations.
A Contextual Refresh:
In its simplest form, contextual advertising is a targeted marketing approach that places ads based on the content of a web page or video, rather than user data. In a privacy-first era, contextual advertising provides a solid alternative to leveraging 3p data. By relying on the context of the page or video, the technology analyses and classifies this content as targetable keywords, and topics, and understands the deeper semantic meaning of said content to deliver relevant ads at the moment the media is being consumed.
By its very nature, contextual advertising respects user privacy while providing a targeted advertising experience focused on audience interests and intent, ensuring a more transparent and respectful online experience. It’s a win-win.
The Next-Gen of Contextual:
Advanced contextual advertising solutions are built on vast data foundations and can also integrate directly with thousands of diverse publishers. Some solutions enable brands to curate their own content environments according to their campaign KPIs for the ultimate user experience. This is where programmatic media curation comes in.
Due to its very nature, contextual advertising emerges as a privacy-first and brand-safe method for advertisers to collaborate with publishers in reaching targeted audiences within premium and relevant environments. And what’s really exciting is that curation can promote more effective and efficient ad campaigns, allowing for increased relevance and scale.
Curation provides advertisers and publishers with a more efficient way of conducting the ad buying and selling process. Brands, retailers, publishers, and data providers can combine their first-party data with inventory sourced from third-party publishers within PMPs, which can then be delivered to demand-side platforms (DSPs) as easy-to-activate Deal IDs.
A Deeper Dive:
Curation, like contextual targeting, is AI-powered, cookieless, and privacy-centric. For advertisers, it offers improved scale, performance, measurement, and greater control and flexibility, increasing transparency across the entire supply chain, giving those using curation platforms the confidence to transact within a brand-safe environment.
The benefits don’t stop there. For publishers, curation provides a privacy-conscious approach to monetizing first-party data and offers new revenue streams as demand for curation accelerates. It also offers a solution for inventory commoditization and heightened demand.
The New King & Queen (and room for one extra?):
Many have cited contextual targeting combined with first-party data as the next king and queen of (M)adTech—and rightly so. Combining those two worlds creates a fascinating future for truly personalized and relevant experiences for users online.
With curation in the mix, the outlook of these two worlds colliding is positive. Instead of “two’s company, threes a crowd,” we might be entering a world where the three biggest trends in the industry work together for a better future.
How it all works together:
According to ExchangeWire, leveraging predictive audience data for campaign planning was the leading priority for curated media buys across European brands and agency marketers, cited by 43% of respondents.
Curation is acting like the glue that brings together all the component parts to create greater efficiency and effectiveness. In essence, curation can be driven by both contextual data – used to predict the environments that are most likely to drive campaign outcomes – with earned first-party data (such as spending power, interests, or life stages) – applied on the supply side with greater addressability for predictive audiences.
Further, curation does not necessarily need to be limited to content, but can also further be refined by using predicted metrics around viewability, view-through rates, expected completion rates, and other relevant campaign metrics to provide the best possible environment for a campaign to run in.
In summary, by combining contextual targeting, ad curation, and first-party data, advertisers can deliver highly relevant, engaging, and compliant ads that enhance the user experience, improve engagement, and drive better results. This synergy ensures that ads are not just placed based on context but are also continuously optimized for maximum effectiveness.
Delivering Personalized Experiences in a Distracted Digital World
In the era of digital saturation, capturing and maintaining consumer attention has become increasingly challenging for advertisers and publishers alike.
The average consumer is bombarded with countless advertisements and marketing communications daily, leading to a phenomenon known as “banner blindness,” where users consciously or unconsciously ignore online ads. Despite this, digital advertisers have a powerful tool at their disposal: personalization.
By delivering tailored experiences, advertisers can cut through the noise and engage consumers more effectively. Here’s how they can achieve this.
1. Leverage Data and Analytics
Data is the cornerstone of personalization, with a strong focus on first-party data. By collecting and analyzing consented data, advertisers can gain a comprehensive understanding of individual consumer preferences and behaviors. Advanced analytics and AI tools can help segment audiences based on this data, allowing advertisers to create highly targeted campaigns.
Actionable Steps:
- Understand Your Audience: Intrinsically understand your audiences across digital platforms to tap into their interests and preferences.
- Segment Your Audience: Use smart technology to create detailed customer segments. This could be based on demographics, psychographics, past purchases, or browsing habits.
- Predictive Analytics: Employ predictive analytics to forecast future behavior and needs, allowing for preemptive personalization.
2. Create Dynamic and Contextual Content
Personalization extends beyond simply addressing the consumer by name. It involves creating dynamic content that changes based on the viewer’s context, such as their location, time of day, or recent interactions with the brand. Contextual advertising ensures that the content is relevant to the consumer’s immediate needs and environment, thereby increasing the likelihood of engagement.
Actionable Steps:
- Dynamic Creative Optimization (DCO): Use DCO tools to automatically generate and serve personalized ad creatives in real-time.
- Contextual Triggers: Set up triggers that tailor ad content based on real-time data such as weather conditions, local events, or trending topics.
- Personalized Recommendations: Implement recommendation engines that offer personalized products or content suggestions based on user behavior.
3. Embrace Multi-Channel Personalization
Consumers interact with brands across multiple channels, including social media, email, websites, and mobile apps. To deliver a cohesive personalized experience, advertisers must ensure consistency across all these touchpoints. An omnichannel strategy allows for seamless transitions and interactions, enhancing the overall user experience.
Actionable Steps:
- Unified Customer Profiles: Develop a single view of the customer by integrating data from all channels into a unified profile.
- Consistent Messaging: Ensure that personalized messages are consistent and synchronized across all platforms.
- Cross-Channel Campaigns: Design campaigns that consider the user journey across multiple channels, ensuring that each interaction adds value and drives engagement.
4. Prioritize Privacy and Transparency
As personalization becomes more sophisticated, concerns about privacy and data security have grown. Consumers are increasingly aware of how their data is being used and expect transparency and control over their personal information. Building trust is crucial for successful personalized advertising.
Actionable Steps:
- Clear Privacy Policies: Communicate your data collection and usage policies clearly and transparently.
- Consent Management: Implement robust consent management systems that allow users to control their data preferences.
- Secure Data Practices: Invest in advanced security measures to protect consumer data from breaches and misuse.
5. Measure and Optimize Continuously
Personalization is not a one-time effort but an ongoing process that requires constant measurement and optimization. By regularly analyzing the performance of personalized campaigns, advertisers can identify what works and what doesn’t, making necessary adjustments to improve effectiveness.
Actionable Steps:
- Performance Metrics: Track key performance indicators (KPIs) such as click-through rates, conversion rates, and customer engagement levels.
- A/B Testing: Conduct A/B testing to compare different versions of personalized content and determine which performs better.
- Feedback Loops: Establish feedback mechanisms to gather user input and continuously refine personalization strategies.
- Contextual insights & Optimisation: When utilizing contextual tools such as 4D, advertisers can unlock incredible insights to support the optimisation of campaigns.
In a world where consumer attention is a scarce commodity, personalized advertising offers a powerful solution to capture and retain interest. By leveraging data and analytics, creating dynamic content, embracing multi-channel personalization, prioritizing privacy, and continuously optimizing efforts, digital advertisers can deliver relevant, engaging experiences that resonate with their audience. As technology and consumer expectations evolve, so too must the strategies for personalization, ensuring that advertisers stay ahead in the competitive digital landscape.
What Does Google’s Announcement Mean?
Google announced last week that it will not be deprecating third-party cookies, despite years of delays and speculation. Instead, the tech giant is shifting strategies to meet privacy requirements, such as GDPR, by pursuing “a new path for privacy.” This new approach will prompt users to choose whether to enable or disable cookies, ultimately retaining them within Chrome.
“We recognize this transition requires significant work by many participants and will impact publishers, advertisers, and everyone involved in online advertising,” said Anthony Chavez, Google’s manager in charge of the Privacy Sandbox project. “In light of this, we are proposing an updated approach that elevates user choice. We’re discussing this new path with regulators and will engage with the industry as we roll this out.” (source, FT)
Many in the industry are frustrated after years of preparing for a cookieless future. Stephen Bonner of the Information Commissioner’s Office commented, “It has been our view that blocking third-party cookies would be a positive step for consumers. The new plan set out by Google is a significant change and we will reflect on this new course of action when more detail is available.”
Despite the headlines and panicked boardrooms, the underlying reason for the cookie’s deprecation remains the same: consumers are pushing for a safer, more privacy-focused future that empowers rather than hinders their journey. Even with the option to opt out of third-party cookie tracking, the significance of third-party cookies will diminish.
First-party data and other trusted key data sources will become the new holy grail of advertising and marketing. The move toward privacy is unstoppable, and Google will eventually have to follow Apple’s model, which gives consumers full choice.
The Opportunity
While many will be feeling frustrated, Google’s announcement moves the market forward. The focus on delivering unparalleled customer experiences that empathize with customers in the moments that matter still holds strong. Cookies are just one small part of a much larger, ambitious journey to a privacy-centric world. In fact, there may be some advantages to cookies remaining within the (M)adTech wheelhouse, opening opportunities for increased customer experiences and measurement touch points in programmatic media.
Whatever the future holds, we know that old ways of working are no longer applicable.
To achieve CX transformation in the new marketing age, combining human expertise with innovative, next-generation products will be crucial. Budgets will be directed toward driving better ROI for marketing spend using all consented first-party data and contextual data available to clients.
At Silverbullet, we are perfectly positioned to support global enterprise businesses as they step confidently into the privacy-first era.
First-Party Data + Contextual Data = Powerful Business Outcomes
By Ted Vernon, VP of Sales at 4D
2024 seems to be the year of privacy and trust. This comes as no surprise, due to the ever-growing global data regulation enforcement, combined with the ongoing push from leading browsers to lockdown on privacy-centric initiatives aimed at protecting the consumer. Governments all across the world are making the right kind of noise around stricter legislation, urging marketers to let go of their deeply rooted habits of third-party data usage for behavioural targeting.
And, it is in this pursuit for a better future, that advertisers are returning their efforts to the holy grail of advertising and marketing – first-party data – the gold dust of the AdTech landscape, where information about consumers is used to enable relevant, empathetic and personalized advertising. For those unsure, first-party data presents some of the most valuable insights a client will gain on its ‘known audiences’. For years first-party data has been used to tailor brand messages to recent, loyal and lapsed consumers, acting as an extremely powerful data set to build brand trust and long-lasting relationships.
Up until recently, first-party data has long been used in combination with the not-so-sought-after third-party data set, and with third-party cookies decreasing in popularity, businesses are seeking new data sets that can enrich their first-party data, adding needed scale and a deeper understanding of the environments in which consumers spend time.
The answer? Contextual data. Privacy-safe by its very nature, contextual data can enhance first-party data to align brand messaging in appropriate and meaningful contexts, tapping into new audiences to bridge the gap between ‘known’ and ‘unknown’. It’s no surprise that contextual advertising spend was estimated at $227.38 billion on the global level at the end of 2023.
In fact, a WARC study reviewed 76 campaigns across eight different verticals, providing a comprehensive view of the effectiveness of contextual targeting. It revealed that contextually targeted solutions consistently outperformed industry benchmarks, delivering nearly 70% more attention for skippable ads. It’s clearly a promising future. Particularly when used with first-party data.
A Match Made in Heaven.
Let’s explore an example of how the two can work to enhance the end customer experience. Using first-party data alone, a brand’s Diaper advertising campaign may appear in written or video content that could span any topic from football to the weather. Arguably, you’re reaching a wide-scale audience through this method, but the relevancy will likely diminish due to the unknown insights surrounding the environment.
By simply blending contextual data into the mix, that brand is now able to deliver Diaper advertising, in content that is specific to newborns, new parents, and more. The brand message is tailored by first-party data, and the delivery is tailored by context. It’s a match made in heaven.
How about that for an advertising strategy that combines consensus, transparency, trust and context to create an immersive customer experience? It’s clear to see why more than 6 in 10 advertisers expect to increase their use of contextual data this year – according to a ComScore report.
Welcome to one of the most efficient and advanced methodologies for advertising online in the post-cookie world – First-party data + Contextual Data.
The Equation That Matters.
Contact me today to find out how you can tap into the new, privacy-first era with Silverbullet and 4D.