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Technology is pushing the boundaries of commerce like never before. Artificial intelligence (AI) is one of the primary driving technologies at the forefront of the commerce evolution, using advanced algorithms to revolutionize marketing and personalize customer experiences. As of 2024, AI adoption in e-commerce is skyrocketing, with 84% of brands already using it or gearing up to do so.
This article explores the AI revolution coming to commerce, focusing on what makes AI a driving force for e-commerce in particular, and the ways it’s reshaping how businesses engage with consumers.
Understanding the AI revolution in commerce
AI is quickly reshaping commerce as we know it by democratizing access to sophisticated tools once reserved for large corporations, breaking down functional silos within organizations, and integrating data from multiple sources to achieve deeper customer understanding. It’s paving the way for a future where every brand interaction is uniquely crafted for the individual, powered by AI systems that anticipate preferences proactively.
AI is a broad term that encompasses:
- Data mining: The gathering of current and historical data on which to base predictions
- Natural language processing (NLP): The interpretation of human language by computers
- Machine learning: The use of algorithms to learn from past experiences or examples to enhance data understanding
The capabilities of AI have significantly matured into powerful tools that can improve operational efficiency and boost sales, even for smaller businesses. They have also fundamentally changed how businesses interact with customers and handle operations. As AI continues to develop, it has the potential to provide even more seamless, personalized, and ethically informed commerce experiences and establish new benchmarks for engagement and efficiency in the marketplace.
Four benefits of the AI revolution coming to commerce
Major commerce players like Amazon have benefited from AI and related technologies for a while. Through machine learning, they’ve optimized logistics, curated their product selection, and improved the user experience. As this technology quickly expands, businesses have unlimited opportunities to see the same efficiency, growth, and customer satisfaction as Amazon. Here are four primary benefits of AI adoption in commerce.
1. Data-driven decision making
AI gives businesses powerful tools to analyze large amounts of data more quickly and accurately than a person. Through advanced algorithms and machine learning, AI can sift through historical sales data, customer behavior patterns, and market trends to uncover insights and suggest actions that might not be immediately obvious to human analysts. By transforming raw data into actionable insights, AI empowers businesses to make more informed decisions, reduce risks, and capitalize on opportunities.
As a real-world example, Foxconn, the largest electronics contract manufacturer worldwide, worked with Amazon Machine Learning Solutions Lab to implement AI-enhanced business analytics for more accurate forecasting. This move improved forecasting accuracy by 8%, saved $533,000 annually, reduced labor waste, and improved customer satisfaction through data-driven decisions.
2. A better customer experience
AI is set to make customer interactions smoother, faster, and more personalized by recommending products based on preferences and behaviors, making it easier for customers to find what they need.
When consumers visit an online store, AI also provides instantaneous help via a chatbot that knows their order history and preferences. These AI-powered assistants offer real-time help like a knowledgeable store clerk. They give the appearance of higher-touch support and can answer basic questions at any hour, provide personalized product recommendations, and even troubleshoot issues. Chatbots free up human customer service agents for more complicated matters, and these agents can then use AI to obtain relevant information and suggestions for the customer during an interaction.
3. Personalized marketing
Data-driven personalization of the customer journey has been shown to generate up to eight times the ROI, as data shows 71% of consumers now expect personalized brand interactions. Until AI came around, personalization at scale was complex to achieve. Now, gathering and processing data about a customer’s shopping experience is easier than ever based on lookalike customers and past behavior.
Many businesses have adopted AI to glean deeper insights into purchase history, web browsing, and social media interactions to drive better segmentation and targeting. With AI, advertisers can analyze behavioral and demographic data to suggest products someone is likely to love. Consumers can now browse many of their favorite online stores and see product recommendations that perfectly match their tastes and needs.
AI can also offer special discounts based on purchasing habits, and send personalized emails with products and content that interest customers to make their shopping experience more engaging and relevant. This personalization helps businesses forge stronger customer relationships.
Personalization across digital storefronts
Retail media involves placing advertisements within a retailer’s website, app, or other digital platform to help brands target consumers based on their behavior and preferences within that environment. Retail media networks (RMNs) expand this capability across multiple retail platforms to create seamless advertising opportunities throughout the customer journey. Integrating AI into RMNs can improve personalization across digital storefronts with personalized, relevant ads and custom offers in real time that improve the customer experience.
4. Operational efficiency
AI can also be beneficial on the back end, enabling more efficient resource allocation, pricing optimization, efficiency, and productivity.
Customers can be frustrated when they visit a store for a specific product only to find it out of stock or unavailable in a particular size. With AI, these situations can be prevented through algorithms that forecast demand for certain items. Retailers like Amazon and Walmart both use AI to predict demand, with Walmart even tracking inventory in real time so managers can restock items as soon as they run out.
AI can automate and streamline operational tasks to help businesses run smoother, faster, and more cost-effective operations. It can:
- Offload tedious data entry, scheduling, and order processing tasks for greater fulfillment accuracy.
- Analyze historical data and market trends, predicting demand to help businesses optimize inventory, reduce waste, track online and in-store sales, and prevent shortages.
- Forecast demand levels, transit times, and shipment delays to make better predictions about logistics and supply chains.
- Improve data quality using machine learning algorithms that find and correct product information errors, duplicates, and inconsistencies.
- Adjust prices based on competitor pricing, seasonal fluctuations, and market conditions to maximize profits.
- Pinpoint bottlenecks, identify issues before they escalate, and provide improvements for suggestions.
Future trends and predictions
If you want to stay ahead in e-commerce, it’s just as important to know what’s coming as it is to understand where things are today. Here are some of the trends expected to shape the rest of 2024 and beyond.
Conversational commerce
Conversational commerce allows real-time, two-way communication through AI-based text and voice assistants, social messaging apps, and chatbots. Generative AI advancements may soon enable more seamless, personalized interactions between customers and online retailers. This technology can improve customer engagement and satisfaction while providing helpful insights into preferences and behaviors for better personalization and targeting.
Delivery optimization
AI-driven delivery optimization uses AI to predict ideal routes for each individual delivery, boosting efficiency, reducing costs, promoting sustainability, and improving customer satisfaction throughout the delivery process.
Visual search
AI-driven visual search is quickly improving in accuracy, speed, and contextual understanding. Future developments may integrate seamlessly with augmented reality (AR) so shoppers can search for products by pointing their devices at physical objects. Social media and e-commerce platforms may soon incorporate visual search more prominently, allowing users to find products directly from images.
AI content creation
AI is already automating and optimizing aspects of content production:
- Algorithms can generate product descriptions, blog posts, and social media captions personalized to specific customer segments.
- AI tools also enable the creation of high-quality visuals and videos.
- NLP advancements ensure content is compelling and grammatically correct.
- AI-driven content strategies analyze consumer behavior and refine messaging to meet changing preferences and trends.
This automation speeds up content creation while freeing resources for strategic planning and customer interaction.
IoT integration
Integrating AI with Internet of Things (IoT) devices could help make the ecosystem more interconnected in the future. AI algorithms can use data from IoT devices like smart appliances, wearables, and sensors to gather real-time insights into consumer behavior, preferences, and product usage patterns. This data enables personalized marketing strategies, predictive maintenance for products, and optimized inventory management. AI-driven IoT data analytics can also streamline supply chain operations to reduce costs and inefficiencies.
Fraud detection and security
There will likely be an increased focus on the ethical use of AI and data privacy regulations to strengthen consumer trust and transparency. AI-powered systems will get better at detecting and preventing fraud in e-commerce transactions, which will heighten security measures for both businesses and consumers.
Chart the future of commerce with Experian
AI has changed how marketers approach e-commerce in 2024. With AI-driven analytics and predictive capabilities, marketers can extract deeper insights from extensive data sets to gain a clearer understanding of consumer behavior. This enables refined segmentation, precise targeting, and real-time customization of messages and content to fit individual preferences.
Beyond insights, AI automates routine tasks like ad placement, content creation, and customer service responses, freeing marketers to concentrate on strategic planning and creativity. Through machine learning, marketers can predict trends, optimize budgets, and fine-tune strategies faster and more accurately than ever. The time to embrace AI is now.
At Experian, we’re here to help you make more data-driven decisions, deliver more relevant content, and reach the right audience at the right time. Using AI in your commerce marketing strategy with our Consumer View and Consumer Sync solutions can help you stay competitive with effective, engaging campaigns.
Contact us to learn how we can empower your commerce advertising strategy today.
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Retail media networks (RMNs) are on the brink of a major shift. While they are poised to capture over 20% of ad spend in 2025, on-site monetization won\’t be the growth driver it once was. With advertisers consolidating spend among just six or seven RMNs on average, including giants like Amazon and Walmart, it’s hard for smaller RMNs to compete. Off-site retail media ad spend is projected to grow 42.1% in 2025—nearly three times the rate of on-site growth (15.1%), according to eMarketer\’s November 2024 forecast. This dramatic shift underscores that while on-site placements are maturing, off-site is where the momentum (and money) is heading. To remain competitive, RMNs must move beyond traditional, on-site placements and embrace a broader, more integrated approach to media activation. The future of retail media is about utilizing enriched first-party data to drive performance across the open web, connected TV (CTV), and other digital channels. Break free from your owned and operated properties Historically, RMNs have limited ad placements to their own digital properties. While this approach has delivered high-margin returns—on-site ad margins can reach 70-90%, compared to 20-40% for off-site—it’s also inherently limiting. Retailers only have so much owned inventory to sell, and advertisers demand greater scale and flexibility. As brands push for more reach, RMNs must extend their impact beyond owned-and-operated (O&O) properties. Omnichannel retail media ad spending is forecast to hit $61.2 billion in 2025. Brands are looking beyond retail sites to build integrated, multi-channel strategies that drive results across the funnel.eMarketer Off-site doesn’t just mean digital. Walmart’s recent expansion of its Fuel and Convenience stations – planning to open or remodel 45 in 2025, bringing the total to 450 – shows how physical spaces are also becoming extensions of a retailer’s media network. These locations create new touchpoints where advertisers can engage shoppers with timely, context-aware messaging while they fuel up or grab a snack. These quick-stop environments are ideal for limited-time offers or impulse-triggering messages—especially since 68% of U.S. adults say discounts contribute to their latest in-store impulse purchase. Maximize the value of first-party data One of retail media’s biggest promises is the power of first-party data for precision targeting. While on-site ads are inherently lower-funnel, off-site activation allows advertisers to move up the funnel and apply retailer customer data holistically across the open web. For example, DoorDash and Macy’s now offer self-service audience data to advertisers via The Trade Desk, allowing brands to target consumers programmatically. Meanwhile, Walmart is taking a different approach—cloning The Trade Desk’s technology to maintain its walled garden. These moves demonstrate how retailers are rethinking data monetization strategies to scale beyond O&O limitations. Drive new revenue streams with off-site activation Off-site activation enables RMNs to drive incremental reach on channels where audiences are actively engaging, including CTV, programmatic display, and social media. This expansion allows brands to connect with consumers beyond retail websites. Retailers are also utilizing non-endemic advertising opportunities in environments like gas stations and kiosks Unlike traditional grocery or apparel aisles, these spaces are brand-neutral, allowing advertisers who don’t sell products in-store to still activate campaigns using retailer data. In fact, 53% of brands have already partnered with a retailer that doesn’t carry their product, and that number is expected to grow as advertisers seek new ways to tap into retail media’s rich targeting capabilities. Retailers looking to extend the value of their data beyond O&O inventory have two primary off-site opportunities: First, they can use an identity graph to resolve customer identifiers into addressable IDs that can be enriched with additional attributes and activated across channels like the open web and CTV. This allows retailers to find and reach known customers with relevant messaging outside of their owned platforms. For example, a grocery RMN can identify lapsed snack buyers and deliver streaming TV ads that reengage them on CTV platforms. CTV retail media ad spending alone is expected to grow 43.1% this year, reaching $4.86 billion, highlighting the appetite for video-based upper-funnel strategies. Second, RMNs can broaden reach by activating first-party audiences, syndicated segments, or custom-built audiences through onboarding capabilities. These audiences can be sent to a variety of programmatic and CTV destinations, enabling advertisers to engage shoppers in high-impact environments. For example, a home improvement retailer can send its audience segments to programmatic ad exchanges, ensuring DIY shoppers see relevant offers even while browsing unrelated sites. Together, these approaches allow retailers to monetize their data more effectively while giving brands the ability to reach consumers in moments that matter beyond just retail websites and apps. Scale and measure success with data partnerships For smaller RMNs to compete with larger players, they need more than just inventory—they need the ability to scale campaigns and prove performance. Data partnerships play a critical role in both expansion and measurement. Measurement remains one of the biggest challenges for RMNs moving off-site. On-site retail media offers closed-loop attribution, but off-site activations introduce complexity. Retailers can work with an identity resolution partner like Experian to connect ad exposures to actual retail outcomes, such as store visits or purchases, across digital and physical environments. Whether it\’s through pixels placed on campaign ads or TV impression logs, these connections help RMNs demonstrate real impact. This approach helps unify disparate data—such as a CTV ad exposure and a subsequent online or in-store purchase—into a clear, measurable outcome. These insights not only show what’s working, but help RMNs optimize future campaigns and provide advertisers with transparent, third-party-validated reporting. As retailers like Walmart integrate loyalty programs like Walmart+ into their physical extensions, they gain valuable behavioral insights into how customers shop across formats—from fueling up to filling carts. These data signals help refine identity graphs and improve measurement across increasingly hybrid consumer journeys. Beyond ads: The data monetization opportunity Smaller RMNs may struggle to scale ad-supported revenue, but there’s another path forward: Data-as-a-Service (DaaS). Providing anonymized, privacy-compliant audience insights to brands offers a high-margin, scalable revenue stream. In fact, some retailers are already embracing this model by licensing their data to programmatic platforms. A playbook for smaller RMNs to win off-site The future of retail media belongs to those who harness data to influence consumer behavior across all digital marketing channels. To succeed, RMNs should focus on: Moving beyond owned inventory: Activate first-party data across CTV, social, and programmatic channels to meet advertisers where their audiences are. Expanding reach through partnerships: Collaborate with identity resolution providers to maximize match rates and campaign effectiveness. Building a full-funnel offering: Position off-site retail media as a brand-building play, tapping into ad budgets that traditionally fund upper-funnel campaigns. Monetizing data, not just ads: Explore DaaS models to generate passive revenue. The time to move off-site is now Retailers that wait too long to embrace off-site activation risk falling behind. Those that expand beyond their owned inventory, invest in off-site data strategies, and build strategic partnerships will be the ones that shape the future of retail media. Experian isn’t just part of the RMN conversation. We’re driving it. Let’s talk. Connect with our team Latest posts

Not all customers are the same, so why waste your budget marketing to them like they are? McKinsey research shows that 71% of consumers want personalized shopping experiences, and 76% get frustrated when they don’t have them. That’s where demographic segmentation comes in. But what is demographic segmentation, exactly? We define it as a process that helps you categorize your audience into meaningful demographic groups so you can reach the right people with impactful custom messages. Businesses across industries are partnering with Experian to power smarter decisions and better results through solutions like demographic segmentation — but what does this look like in action? This article breaks down five real-world demographic segmentation examples, showing how businesses have worked with us to drive measurable success so you can see exactly how it can work for you. What is demographic segmentation? Demographic segmentation involves dividing your audience into smaller, more specific groups based on shared demographics like income, education, gender, job, family status, and more to gain a more granular understanding of your brand’s target segments. The better you know your audience, the better you speak to their unique needs — and the more effective your campaigns will be, as you’ll be able to target each segment with highly personalized content that resonates. For instance, a company might market a new tech gadget to young adults in one way while promoting the same product to families with young children in a completely different way, ensuring the message speaks to each group’s lifestyle and priorities. Demographic segmentation attributes Some of the most common attributes used in demographic segmentation include: Age Each age group has different wants and needs. A new video game might catch the eye of teenagers, while a retirement plan is more likely to appeal to someone in their 50s or 60s. Gender Gender impacts preference for certain products, from fashion to gadgets, so knowing who you’re talking to helps make your marketing more relevant. Income Someone with a higher income might be more likely to purchase premium products, while someone on a budget will respond better to discounts or value-based offers. Education The level of education a person has can influence what kind of messaging will resonate with them, whether it’s complex or more straightforward. Occupation A marketing message targeting busy professionals might differ from one aimed at students or retirees. Occupation can tell you what’s important to a person in terms of their needs and lifestyle. Family Status A family with young kids likely has different priorities than a single person or a couple without children. You can adapt your messaging to be more relevant to what matters most to them, like convenience or value. Benefits of using demographic segmentation Demographic segmentation offers several valuable benefits for marketers. Here’s why it’s one of the most commonly used and effective ways to target audiences: Improved targeting and personalization: Demographic segmentation powers highly customized campaigns so you can cater to different income levels, family structures, job types, and so forth. B2C brands can provide offers based on factors like age, income, and gender, while B2B brands can target by occupation to reach decision-makers. Better product and service development: Understanding which demographics use your product or service is a great way to inform future improvements. Higher engagement: With highly customized content, you can speak directly to specific demographic groups and increase engagement. Cost efficiency: As you target the most relevant segments, you optimize your spending around the most likely buyers and will see better returns. Increased conversion and retention: Relevant, targeted messaging leads to higher conversion rates, and when people feel understood, they’ll want to keep coming back. Clearer customer insights: Demographic data provides precise, actionable insights for refining your marketing strategy. Simplicity and effectiveness: Demographic insights are immediately actionable and easy to implement, which gives you a great starting point for focused campaigns. When to use other segmentation types While demographic segmentation provides valuable consumer insights, there are times when other approaches may offer a more effective strategy: Your business provides location-dependent services. If you strictly serve a local area, geographic segmentation would be more effective in targeting customers based on location. Your business offers hobby-centric products or services. Psychographic segmentation (based on interests, lifestyle, or values) may be more relevant than demographics alone for products related to specific interests or hobbies. You have access to detailed behavioral data. If you collect data on customer behavior (like browsing history or purchase patterns), behavioral segmentation would allow for more personalized targeting than demographics. You\’re selling high-end luxury products. While income is a useful demographic variable, psychographic factors like values, aspirations, and lifestyle better capture the desires of luxury consumers. Your target audience shares similar behaviors, regardless of demographic factors. Behavioral or psychographic segmentation might offer more insight if your customers engage with your product or service based on shared behaviors rather than demographic traits. Your product or service targets specific needs or pain points. Segmenting by need or issue rather than traditional demographic variables would likely yield better results if you\’re offering a solution to a particular problem (like a health-related product). How our customers are using demographic segmentation to produce tangible results Demographic segmentation is about knowing your audience and using data to create marketing strategies that drive measurable outcomes. Let’s look at some real-world use cases from brands like yours that have been successful in this effort, working with Experian to translate demographic insights into significant business growth. Use case #1: Identifying customer spending potential to boost growth for a retail chain Objective A large retail chain wanted to understand the spending potential of each customer in their stores. Their goal was to uncover and maximize untapped spending potential. Solution Experian conducted an analysis to identify the top demographic factors that drove spending in the retail store the previous year. Our consultants found the four key drivers were: Age Income Family structure (household composition) Location/region Results By combining these attributes to create segments, we uncovered two valuable annual estimates: Potential spend: A conservative estimate of how much a customer could spend if they reached the top 20% of spenders within their specific demographic segment (based on data from the highest spenders). Unrealized spend: The difference between a customer\’s annual potential spend and their current spend. An estimate of how much more they could be spending each year. These demographic segments provided the marketing strategy the retail chain used to target $1.1 billion in unrealized spend. This revealed how much additional revenue could be captured by targeting the right customers with tailored marketing and offers through demographic segmentation. Use case #2: Helping a financial institution identify regional DE&I opportunities Objective A large financial institution needed help identifying regional diversity, equity, and inclusion (DE&I) opportunities. They wanted to better prioritize their outreach to underserved communities in the Los Angeles area. Solution Experian\’s Custom Analytics team provided the data and insights to pinpoint specific areas needing attention. We used three key indices to analyze the region: Income index: Measured each underserved economic group by comparing the percentage of low-to-moderate income consumers against the entire L.A. area. Ethnicity index: Measured the percentage of consumers by ethnicity, such as African-American, Hispanic, Asian, and others, against the entire L.A. area. Credit index: Identified potential credit disparities by looking at the average FICO score and the percentage of customers with credit accounts against the entire L.A. area. Results Our client received an analytics dashboard to track and report these metrics, providing clear, traceable data to prioritize DE&I outreach. This dashboard helped them measure progress toward more inclusive practices. Use case #3: Segmenting a health supplement ambassador program for enhanced engagement Objective A health supplement company wanted to identify specific segments within their ambassador program to provide better support and increase engagement. Solution Experian’s Custom Analytics team developed tailored customer segments to address specific needs and behaviors. These segments included: Young and independent: Younger, lower-income singles or starter households who are just beginning to establish their own lives. Families with ends to meet: Young and middle-aged families with kids who are budget-conscious, often using coupons and enjoying fast food. High-end families: Middle-aged families with kids and high incomes, financially secure big spenders who also give to charities. Empty nesters: Older households with no kids who focus on cooking at home and may have more disposable income. Results Segmenting at registration allowed for more effective communication and engagement with prospects. Customized messaging, guided by customer demographics and purchasing behaviors, improved acquisition and retention by helping the right messages reach the appropriate individuals through their preferred channels. Use case #4: Comparing customer bases: Insights for a retailer across two cities Objective A national retailer with locations in two major cities (their home base city and a recent expansion city) wanted to understand how different their customer base was in each city. They aimed to uncover key demographic and behavioral differences to refine their marketing strategies and ensure each location received the most relevant messaging and promotions. Solution Experian’s Custom Analytics team analyzed each city’s customers across a wide range of characteristics:. Demographics: The expansion city had a younger population with more families, while the home base city had an older and more established customer base. Purchasing behavior: Customers in the expansion city spent more per transaction than those in the home base city. Preferred marketing approach: Customers in the home base city were likelier to be Brand Loyalists, responding well to familiar, trust-driven messaging. Shoppers in the expansion city were Savvy Researchers who responded better to value-based content and product comparisons. Results Using these insights, the retailer tailored its marketing approach to align with each location’s customer base: Home base city: Focused on maintaining loyalty by emphasizing brand trust and highlighting long-term customer benefits. Expansion city: Positioned marketing to appeal to younger, family-focused consumers to showcase high-value purchases and competitive pricing These adjustments led to improved engagement and higher sales in both cities. Use case #5: Optimizing direct mail to help a nationwide retailer maximize impact on a limited budget Objective Facing a shrinking marketing budget, a nationwide retailer needed to refine their direct mail strategy to reach the right customers while reducing costs. Solution Experian’s Custom Analytics team developed a comprehensive dashboard summarizing two dozen recent direct mail campaigns, which allowed the retailer to: Understand the demographic composition of high-response customers across different regions. Identify key patterns in response rates, helping them pinpoint the most receptive audiences. Discover that the Power Elite Mosaic Group representing affluent, high-spending households comprised only 17% of their mailed audience but accounted for 47% of responses. Results With these insights, the retailer restructured their direct mail strategy to target the highest-performing segments. Changes like these led to a 30% reduction in mailing costs while retaining 92% of sales, proving that strategic segmentation can drive efficiency without sacrificing revenue. Explore demographic segmentation with Experian’s Analytics Consulting team Now that we’ve provided a demographic segmentation definition and given you some real-world demographic segmentation examples, it’s time to get a deeper understanding of your audience. Our experienced consultants utilize Experian Marketing Data to create the demographic segments you need to enhance targeting and uncover hidden opportunities. Key features Expertise and experience: Our consultants bring a wealth of analytic knowledge and industry expertise, so you can utilize the latest techniques and best practices. Customized solutions: We provide tailored, custom solutions like demographic segmentation to address your specific needs and challenges. Data-driven insights: Our expertise in Experian Marketing Data quickly delivers valuable insights to optimize operations and identify new opportunities. Industry perspective: As external experts, our consultants offer the unbiased perspective necessary to see beyond internal biases and make objective decisions. Competitive advantage: Utilizing our analytic consultants gives your business a competitive edge by understanding market trends, customer behavior, and potential risks faster than your competitors. Connect with us today to see how our data and expertise can improve your targeting, personalization, and campaign performance. Connect with us Latest posts

In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Brian Mandelbaum, CEO and Co-Founder at Attain. About Attain Built for privacy — with visibility across all retailers, verticals and purchases — Attain provides solutions for the modern marketer. Its real-time measurement and optimization solutions coupled with high-fidelity audiences and proprietary insights enable marketers to drive valuable business outcomes. The power of transaction-based audiences Attain’s real-time transaction data provides a 360-degree view of consumer behavior. What makes this approach more effective than traditional demographic or behavioral targeting? Attain is the industry’s most trusted source of live purchase data, powered by a robust panel of 8 million fully permissioned consumers. Our platform delivers unmatched, real-time visibility into consumer purchase behavior across retailers, industries, and payment methods. Marketers gain deep insights — such as in-store vs. online purchases, payment methods, purchase frequency, cart contents, and average transaction value — enabling more precise audience targeting and media strategies. With Attain’s rich, transaction-based data, marketers can optimize campaigns with direct, actionable sales signals. Ensuring data accuracy and relevance Attain curates audiences using real-time transaction data, but advertisers often ask whether this data is deterministic or probabilistic. Can you clarify your methodology, and if probabilistic, how do you ensure accuracy and representation across the entire US population? Our transaction data comes directly from the largest live purchase data panel in the U.S. Covering over 10,000+ merchants and $600B in cumulative spend, our dataset offers a complete and dynamic view of real-world purchase behavior. Using advanced machine learning, we scale this data to represent the entire U.S. population with unmatched accuracy, ensuring a balanced and unbiased reflection of consumer spending patterns. Our rigorous methodology eliminates outliers, continuously optimizing for precision and stability, so marketers can trust our insights for better targeting, measurement, and optimization. Privacy-first data practices Attain is built on a privacy-first, consumer-permissioned model. There are many ways to capture purchase data—why did Attain choose a panel-based approach, and how does this method compare to other collection strategies in terms of accuracy, scale, and compliance? Attain’s panel-based approach is the foundation of our privacy-first, consumer-permissioned model. By capturing real-time transaction data directly from our opted-in consumer panel, we ensure unmatched accuracy and ethical data sourcing — paramount in today’s privacy-conscious world. In exchange for sharing their data, consumers receive valuable benefits like early wages, savings tools, and shopping rewards, with no hidden fees. Unlike legacy third party data providers, our directly sourced transaction data provides deeper, more precise insights, enabling highly granular and actionable audience segments. Our continuously growing panel reflects a broad cross-section of U.S. consumers while maintaining strict privacy and compliance standards. We fully adhere to regulations like CCPA and GDPR, giving both consumers and advertisers confidence in the responsible use of data. Attain’s approach delivers the ideal balance of accuracy, scale, and compliance—while prioritizing consumer trust. Cross-channel addressability With brands activating audiences across display, mobile, and CTV, how does Attain’s purchase data help advertisers refine their cross-channel strategies? Attain’s purchase data empowers advertisers to refine cross-channel strategies with smarter, data-driven insights. Our real-time transaction-based audiences enable scalable activation across display, social, online video, and addressable TV — ensuring campaigns reach high-intent buyers more likely to convert. By applying purchase-based audiences across all channels, marketers are utilizing the strongest signals possible, which enables a more effective holistic strategy to drive to that ultimate sales outcome. Whether through social media, TV/CTV, mobile, or programmatic platforms, Attain helps brands connect with consumers at key moments in their buying journey, maximizing media impact with real behavioral insights instead of proxies. With an expansive and growing network of media partners, Attain ensures brands reach their audiences wherever they are, delivering consistent, high-impact messaging. Whether optimizing for brand awareness or performance, our data helps marketers make smarter decisions to drive superior results. Proven performance with live purchase feedback Attain moves beyond traditional proxy metrics by providing live purchase data. How does this help advertisers optimize campaigns while they’re still running? What sets Attain’s audiences apart isn’t just the data fidelity and holistic coverage of consumer behavior, it\’s that they’re built and validated using live, privacy-safe purchase signals. Advertisers can execute campaigns confidently, knowing that they’re reaching real consumers based on recent, real-world transactions, not outdated models or inferred, probabilistic behaviors. Attain’s ability to measure sales lift across a wide range of inputs means that marketers can easily understand which audiences are driving actual sales outcomes during flight. This unlocks smarter mid-campaign optimizations, discovering new audiences, and fine-tuning targeting — to ensure audience performance continually improves against real revenue goals. Attain’s closed-loop approach gives advertisers a faster path from targeting to transaction, helping brands maximize the value of every impression. Industry-specific use cases Beyond CPG, Attain supports industries like QSR, retail, and financial services. Can you share a compelling example of how brands in these verticals are utilizing your audiences? Attain’s audiences provide a comprehensive view of the consumer, capturing all aspects of their purchase behaviors — from travel and dining to TV content consumption and shopping habits. This broad perspective offers brands a far richer set of buying signals than ever before, enabling them to make more informed decisions across the entire consumer journey. Quick service restaurants (QSR): With a comprehensive view across all transaction types (cash, credit, debit) – Attain enables QSRs to capture a full picture of customer spend at their nationwide locations. Ensuring these brands have holistic coverage across all sales channels, powered by a direct relationship with the consumer, Attain captures transactions both in-store, online, and through 3P delivery apps like UberEats and Grubhub. This powers Attain’s deep insights, which QSRs can use for intelligent, precise targeting- including frequent visitors, competitive share, products purchased, and more. QSRs can use this data to solve a variety of business objectives, like retention/growth, competitive conquesting, and more. Retail: In retail, Attain provides a wide range of audience segments, including loyalty shoppers, in-market buyers, competitive shoppers, and even adjacent buyers who may be interested in similar products. By combining these segments, retailers can optimize their campaigns to target real-time shoppers with the highest intent, rather than relying on outdated or generalized profiles that other providers might offer. Additionally, with our industry-leading refresh rate, brands benefit from the most up-to-date data, ensuring their campaigns are always aligned with the latest consumer behaviors. Financial services: In the financial services sector, Attain’s purchase data helps identify consumers who are actively considering financial products such as credit cards or loans. By understanding their purchasing behaviors, marketers can deliver highly personalized and relevant offers to those already displaying intent, leading to better conversion rates and more effective acquisition strategies. Integration with Experian\’s marketplace Attain is now available through the Experian marketplace. How does this integration make it easier for advertisers to activate and scale your audiences? Attain’s integration with Experian marketplace makes it easier than ever for advertisers to activate our purchase-based audiences across TV, social, and programmatic. This partnership makes Attain’s data even more accessible, supporting our mission to build the most comprehensive and trusted consumer data ecosystem. With direct access to our real-time audiences within Experian’s marketplace, advertisers can more efficiently launch campaigns at scale and make more precise, data-driven decisions. As one of Experian’s inaugural partners, we’ve already seen strong adoption and demand, reinforcing the value of this partnership. The future of transaction-based targeting As the use of transaction data in advertising continues to grow, what changes do you anticipate in how brands will apply it for targeting and measurement? And how is Attain evolving its approach to support those shifts? As transaction data reshapes advertising, brands can shift from targeting probabilistic audiences to reaching high-intent consumers for greater ad relevance and conversions. Purchase data also unlocks highly accurate incrementality measurement, closing the loop and revealing which tactics and channels drive true incremental sales. Attain’s platform is built for outcomes-driven advertising, capturing data across the entire media cycle to continuously optimize performance. As we continue to make investments in AI and machine learning into our platform, our insights will become even more actionable and efficient — helping brands maximize impact, drive incrementality, and fuel long-term growth. Thanks for the interview. Any recommendations for our readers if they want to learn more? To explore our audience segments, visit the Attain website or contact your Experian account representative to schedule your free match test. Contact us About our expert Brian Mandelbaum, CEO and Co-Founder, Attain Brian Mandelbaum, a veteran entrepreneur and investor, is the co-founder and CEO of Attain, North America’s largest opt-in purchase platform. Prior to Attain, Brian founded Clearstream TV, a data-enabled video distribution platform acquired by Engine Group in 2015. He brings over 20 years of experience in data-driven digital media, collaborating with top agencies and major brands. Latest posts