Observed Signal · Feb 17, 2026 · Best Practice / Strategy · Source: https://martech.org/feed/ · Impact: 2/5 · Sentiment: Positive
Maximize B2B CTV Impact with Smart CRM Targeting
This MarTech how-to explains how B2B marketers can use CRM data to reach relevant audiences on connected TV (CTV). It argues that CRM quality is essential for B2B targeting because buyer groups are small and purchase windows narrow; only ~5% of a B2B audience is in-market at any time, so CTV is best used for brand lift, education and awareness rather than direct response. Practical tactics include matching business emails to home emails or IPs via data partners (the article names RevOptimal), building device-graph and firmographic audiences, creating lookalike company lists, and retargeting based on high-intent site behavior or contract renewal timing. The author notes CRM readiness is the gatekeeper for testing CTV and recommends allocating at least $10,000 for a test campaign with appropriate expectations.
Provides practical, actionable guidance for B2B advertisers on using CRM and identity data to target audiences on CTV — relevant to audience/identity strategies but not a platform-level or regulatory change.
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Key Takeaways & Evidence Grounding
- Article recommends using CRM data to build CTV audiences by matching business emails to home emails or IP addresses via data partners (mentions RevOptimal).
- Only about 5% of a B2B audience is in-market at any one time; CTV is recommended for brand lift, awareness and education rather than direct response.
- Tactics include using a device graph to connect work and home identities, layering firmographic details, building lookalike company lists, and retargeting based on high-intent pages or renewal timing.
- The author advises CRM readiness before testing CTV and suggests a minimum test budget of $10,000.
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Unlock B2B Potential with Connected TV Advertising
MarTech published a March 4, 2026 interview episode with Chris Modzelewski, EVP of ad solutions and data monetization at Intentsify, that examines how connected TV (CTV) is opening new targeting and reach opportunities for B2B marketers. The piece outlines the evolution of CTV—bringing online-style targeting to television—while noting barriers such as creative differences for TV spots and opaque measurement. The episode covers practical topics including current CTV state, measurement approaches, where CTV fits in B2B strategies, hiring trends, and anticipated CTV topics for the coming year. The article is hosted on MarTech (owned by Semrush) and authored by Mike Pastore of Third Door Media.
B2B Marketing Stack's Blind Spot: TV Screen
The article argues that modern B2B marketing stacks — account-based marketing, intent data, LinkedIn targeting, enrichment and AI — lack a layer: TV (especially streaming/CTV). The author explains that decision-makers watch TV and that CTV brings guaranteed, full-screen attention, measurement mechanics familiar to performance marketers, and tactics like CTV retargeting that can close the loop with digital ABM motions. The piece cites IAB data showing CTV ad spend growth, gives customer examples (Relay, Gusto, Otter) that saw demand or engagement lifts from TV, and positions TV as a complementary layer that builds credibility, awareness and pipeline for B2B brands. The author is Amit Sharan, SVP of Marketing at Tatari, a company building infrastructure to modernize TV advertising.
Nadella's AI Pivot: Can He Reinvent Microsoft Again?
This article examines CEO Satya Nadella's efforts to transform Microsoft for the AI era. Despite a near $4 trillion market cap and early investment in OpenAI, Microsoft trails rivals like Nvidia, Anthropic, and Meta in key AI segments. The company is shifting from subscription-based pricing to consumption-based billing (UBB) for its AI products, starting with Copilot Cowork. GitHub Copilot has seen growth to 50 million users, but Microsoft lost its exclusive cloud relationship with OpenAI. Nadella promotes building a 'frontier ecosystem' rather than a single frontier model, integrating various AI models into Copilot. Challenges include sluggish Copilot adoption, competition from AI-native startups, and uncertainty among enterprise customers about usage-based costs.
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