### Retailer First-Party Data as the Emerging Core of Insights PlatformsThe rapid expansion of retail media over the past several years has triggered a reevaluation of how data and insights are produced and monetized in the commerce ecosystem. Retailers, by virtue of the digital transformation of transactions and loyalty programs, now possess vast repositories of first-party shopper data—arguably the most direct and actionable signals of consumer intent available in the digital age. This development has set the stage for the emergence of insights and analytics platforms rooted in retailer-controlled data rather than relying on third-party aggregators or traditional market research players.The news discourse revolves around the potential for these new retailer-driven platforms to disrupt established insights providers, giving rise to what has been provocatively termed a "Kantar Killer"—a nod to the possible displacement of traditional firms, whose business models have historically relied on surveys, panels, and aggregated sales data. While the phrase is intentionally hyperbolic given the considerable scope and capability of legacy institutions, it signals a genuine inflection point in the industry.### The Value of Retailer First-Party Data in e-CommerceFirst-party data refers to information collected directly from customers or audiences through a retailer’s proprietary digital infrastructure—websites, loyalty card programs, purchase histories, and omnichannel interactions. This data is distinct from third-party cookie-based tracking or syndicated datasets because it is both intention-rich and tied unequivocally to transactional behavior.The evolution of retail insights platforms built on first-party data brings several advantages to the table:- Precise audience targeting rooted in actual purchase behavior.- Closed-loop attribution, allowing brands to tie advertising impressions directly to sales in near real-time.- Granular segmentation capabilities, enabling the construction and activation of highly specific shopper cohorts.Key retailers have already advanced in this domain. Tesco, via its partnership with Dunnhumby, has built one of the richest transactional datasets in the UK. Kroger’s 84.51° and Ocado’s Beet platform exemplify new frameworks for integrating media, loyalty, and insight functions. Internationally, players such as Profi in Romania and The Warehouse Group in New Zealand are also building out their analytics ecosystems.### Implications for Content Infrastructure#### Product Data Feeds and Cataloguing StandardsThe shift toward first-party data-driven insights directly impacts how **product feeds** are constructed and managed within e-commerce platforms:- Retailers can dynamically update product attributes, promotions, and inventory status based on real-time demand signals observed in their ecosystem.- Enhanced segmentation and propensity modeling enable more intelligent and responsive assortment planning, feeding back into the structure and completeness of product catalogs.- New cataloguing standards are likely to evolve to accommodate increased granularity (e.g., behavioral micro-segments, purchase propensity tags) and the operational needs of AI-driven recommendation engines.These changes compel content teams to rethink the architecture and taxonomy of product data, prioritizing flexibility, interoperability, and enrichment to support rapid insight-to-action cycles.#### Quality and Completeness of Product ContentFirst-party data–empowered analytics platforms can directly inform the optimization of product cards (PDPs):- By tracing the actual consumer path from ad impression to in-cart purchase, retailers gain actionable knowledge about which product features, images, or content variants are most effective for conversion within specific segments.- This insight allows for the iterative improvement of content quality, moving away from generic templates toward highly contextualized, data-informed content strategies.- No-code and low-code solutions, increasingly layered with generative AI, make it possible for non-technical teams to rapidly experiment with and deploy content variants in response to live data signals.#### Assortment Speed-to-MarketThe increasing ability to model the impact of pricing or promotional adjustments in real-time streamlines the process of assortment optimization:- Merchants can forecast demand with greater accuracy, reducing the friction associated with introducing new products or adjusting existing assortments.- Automated feedback loops accelerate the identification of white spaces and opportunities, supporting a more dynamic and competitive approach to inventory management.### The Role of AI and No-Code in Democratizing AccessModern analytics platforms are rapidly integrating conversational AI "co-pilots" and no-code interfaces. This trend reduces dependency on dedicated data science resources and enables brand and agency teams to self-serve insights:- Teams can inquire, for instance, about the likely effects of a 10% price adjustment on a specific shopper cohort, receive prescriptive recommendations, and deploy campaigns or content updates without lag time.- This democratization of insight execution collapses traditional silos between analytics, merchandising, and content functions, enabling a more holistic and responsive e-commerce operation.### Structural Barriers and the Complexity DilemmaDespite technology readiness and data richness, widespread adoption faces persistent challenges:- Legacy habits remain entrenched among brands and agencies, with many still embedded in traditional measurement paradigms. There is a significant awareness and education gap around the advanced, live capabilities now available via retail partners.- Retailers’ primary motivation is often asset monetization rather than the pursuit of objective, market-leading measurement frameworks. This can result in fragmented offerings and a lack of standardized metrics, complicating cross-channel optimization.- The most sophisticated platforms, such as Amazon Marketing Cloud, bring immense potential but are often hampered by operational complexity, deterring adoption among less data-mature organizations. This complexity gap offers fertile ground for streamlined, user-friendly alternatives.### The Outlook for Traditional Third-Party Insights ProvidersWhile first-party data platforms hold the promise of transforming the insights industry landscape, it is premature to forecast the complete disintermediation of established players. The ongoing need for objective, market-wide measurement and expertise—especially in environments characterized by investment fragmentation and variable analytic sophistication—suggests a continued, though perhaps evolving, relevance for third-party insights organizations.Initial adoption may remain uneven, driven by the leading capabilities of advanced retail players and the willingness of brands to transform their internal workflows and content infrastructure. As data interoperability standards mature and **AI-powered tools** become more accessible, the gap between traditional and retailer-led analytics will continue to blur.### Additional Industry ContextRecent reports indicate a surge in investment by global retailers in proprietary analytics platforms and monetization of first-party data through retail media networks. Leaders are experimenting with AI-powered segmentation, prescriptive analytics for assortment, and real-time feedback mechanisms for content asset optimization. However, market-wide standards for interoperability and unbiased measurement are lacking, prompting experts to view this as a transformative but not yet fully mature phase for the sector.For further reading on the sector’s evolution and the tension between proprietary and third-party data regimes, see the latest features in InternetRetailing and Retail Dive.In sum, the rise of retailer first-party data platforms marks a fundamental recalibration of e-commerce content and analytics processes. While their ability to supplant legacy insights giants remains an open question, their influence is already forcing both brands and technology teams to revisit how product content is structured, optimized, and brought to market—placing data-derived agility at the heart of future commerce infrastructure.From a NotPIM perspective, this trend clearly signals the growing importance of high-quality, adaptable product data. The ability to quickly enrich, catalog, and transform product information becomes crucial for leveraging the insights derived from first-party data platforms. Our SaaS solution, designed for e-commerce teams, streamlines this process without requiring specialized technical skills, which helps businesses adapt to the rapidly evolving data landscapes and content demands. This agility is an enabler.