Observed Signal · Jun 10, 2026 · Report Publication · Source: https://martechseries.com/feed/ · Impact: 3/5 · Sentiment: Positive
Braze: Unified Data and Human-Led AI Drive Personalisation
Braze published the Media & Entertainment Personalisation Report, finding that media, streaming, sports and gaming brands that achieve the strongest returns from AI-led personalisation combine clean, connected data foundations, clear commercial use cases and disciplined measurement with human oversight. Drawing on practitioners across Asia, ANZ and the GCC, the report warns that fragmented point solutions and channel-specific tools hinder scalable, revenue-linked personalisation. It recommends centralised data warehousing, open architectures that permit best-of-breed modules, stronger testing cultures using control groups, and using behavioural outcomes tied to retention and revenue rather than vanity metrics. The report highlights self-learning reinforcement AI as effective when applied to defined use cases and stresses AI’s role as decision support with human-led guardrails.
The report links AI-driven personalisation outcomes to foundational data, measurement and organisational practices—insights that affect MarTech strategy, CDP/warehouse adoption, testing and ROI measurement across media and streaming businesses.
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Key Takeaways & Evidence Grounding
- Braze published the Media & Entertainment Personalisation Report focused on streaming, sports, gaming and entertainment.
- The report draws on insights from practitioners across Asia, ANZ and the GCC.
- Close to a third of media and entertainment marketers still use channel-specific solutions that fragment the customer experience.
- Kayo Sports reported an incrementality improvement example: initial incremental rate of 50% for a reactivation offer rose to 80% after two months and to consistently over 95% after six months using self-learning AI.
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Related Market Signals & Shifts
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Bridging the Trust Gap: AI in Customer Engagement
Braze published its 2026 Global Customer Engagement Review (CER), reporting a widening “Trust Gap” between marketing leaders’ expectations of AI and consumer experiences. The survey finds 93% of marketing leaders believe AI helps them understand customer needs, while only 53% of consumers agree. It forecasts AI agent adoption in brand interactions rising from 19% today to 46% by end of 2026, with Gen Z notably more comfortable. The report highlights data-sharing resistance—27% of consumers refuse to share any data with AI agents and 43% would disengage if data were misused—and shows top-performing Braze customers use AI more to anticipate purchase intent, correlating with higher loyalty and recommendation rates. The CER frames AI agents, LLMs, and real-time orchestration as strategic priorities for brands seeking human-centric personalization.
Rethink Personalization: Unite Audiences Through Shared Experiences
Marketers are urged to rethink hyper-personalization: AI enables precise 1:1 messaging, but the article argues that constant tailoring can fragment audiences and overlook universal human signals. It calls for a smarter balance where personalization serves resonance and belonging, not just segmentation. Examples include Amazon’s 'Joy Ride' and Coca-Cola’s holiday campaigns, which aim for shared emotion rather than one-to-one targeting. The piece also highlights CRM and loyalty programs—like Marriott Bonvoy and 7-Eleven’s rewards—that align real-time behavior with brand identity and core emotional drivers such as convenience and recognition. It cautions against universal ultra-granular targeting and emphasizes knowing when to personalize versus when to unify. As brands explore AI-driven experiences, the message is to blend data insight with creativity to evoke emotional responses and bring people together. Data-Driven Thinking is a Razorfish/AdExchanger column by Dani Mariano, promoting nuanced, human-centered marketing.
Master AI in Marketing: Build Stronger Customer Connections
An OMR article presents a four-step roadmap for using AI in marketing to build closer customer relationships through personalization. The steps are: 1) create a clean, unified data foundation focused on behavioral and contextual signals; 2) close the relevance gap by using AI to deliver timely, non-repetitive personalized content; 3) use AI as an accelerator by linking measurable channels and leveraging tools such as Performance Max, Demand Gen and AI Max for search and multi-channel placement; 4) make trust a competitive advantage through transparency, user control and privacy-respecting measurement (e.g., Google Consent Mode and cookie-independent modelling). The piece uses Tchibo as a case study (noting its long-running loyalty card and data base) and cites a Google–Kantar study on personalization preferences in German retail.
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