Simon Daniels

Google is a world leader in both search advertising and #AI, but as its traditional search business transitions to AI mode, which typically results in less clicks, does Google need to change its business model? This morning I discussed this question with Sally Bundock on BBC News.

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Agencies continue to rapidly adopt AI technologies, and it's critical for clients to understand how those tools are being used on their behalf. Gartner clients who want to seize the opportunity presented by agency AI tools while avoiding risk can read our new insights - "What CMOs Must Know About How Their Agencies Use GenAI" co-authored by my awesome colleagues Julie Reeves and Nicole Greene https://lnkd.in/er9eDJFd #agencies #cmo #marketing

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The age of the agent is ending. Marketing agencies are no longer just service providers — they’re becoming purveyors of solutions, platforms, capabilities, and outcomes. As AI, consolidation, procurement pressures and the rise of new independent competitors reshape the industry, agencies pivot from selling time to selling products, technology, data, and consulting along with traditional services. In 2026, expect this to accelerate among enterprise and large PE-backed firms, while specialists double down on a pure agent proposition. CMOs and heads of media must reimagine partner rosters, redefine how to attain and measure value, and reignite growth using the multiple business modes agencies offer. Welcome to the age of the purveyor. Read my latest blog to better understand this shift beyond agency. #ForresterPredictions #Marketing #AgencyTransformation #CMO Mike Proulx Keith Johnston Emily Collins Kelsey Chickering Chuck Gahun Shynise McElveen https://lnkd.in/eH3Qz-KV

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Twir, baby, twirrr! As usual, lots happened in the insights space this week. Here's what you might've missed: 🚀 AI & Tech: - Conjointly rolls out “Deep probe” for automated survey analysis. - Salesforce unveils CRMArena‑Pro to test AI agents. - Pureprofile adds AI coding with Inca. - Discuss debuts “vibe coding”; Parrot launches Sports Demand. 📊 B2B MR: - UserEvidence buys Zealot + raises $7m. - BoardClic funding + new CEO. - Optable names co‑founder Vlad Stesin as CEO. - Aurasell AI emerges with $30m for an AI‑native CRM. 💰 M&A & Partnerships: - Thoma Bravo to acquire Verint (~$2B). - Circana completes Nielsen MMM acquisition. - Accenture to buy Japan’s SI&Co. - Behavix × ShareParty Insights in APAC; FreeWheel × Experian in UK. 🔄 Org Changes: - Tobii CEO Anand Srivatsa to depart. - Kantar appoints Finland MD Christoffer Hultin. - Cartology expands Mike Tyquin’s remit; UK agencies add senior hires. 🎧 Expert takes: - APAC spotlight on attention metrics; tech transforming qual. 🤖 Did the robots and I miss anything big? Share it below. 🔗 Read the full twir recap in the comments. 📬 Want twir sent straight to your inbox? Sign up for our newsletter. 📣 Got upcoming industry news? DM to be included in a future episode. 💡 About twir: twir is your weekly digest of market research news, insights, and emerging trends. We scan leading industry sources to distill the essential developments shaping the world of research—globally and across methodologies.

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Third-party data promises precision. In practice, it's sometimes too expensive and inaccurate to justify it. In our latest episode of The Marketing Architects Podcast, we discuss the study “How Effective Is Third-Party Consumer Profiling and Audience Delivery?” by Nico Neumann, Catherine Tucker, and Timothy Whitfield. The researchers ran three large-scale field tests across 19 data brokers and more than 90 audience segments to test how well third-party targeting actually works. Study One simulated a typical programmatic campaign targeting men aged 25-54. Using DSPs and Nielsen Digital Ad Ratings, they found that on average only 59% of impressions reached the intended audience. That’s better than random targeting (which would have hit 26.5% of the time), but it came at a high price and accuracy across DSPs varied widely. Study Two removed the DSP layer and looked at the raw accuracy of the audience data itself. The results were worse. Across 11 data brokers, the average accuracy for identifying men 25 to 54 dropped to just 24%. Gender classification averaged 42%. Age accuracy ranged from 10% to 32%. The study found in homes with kids, targeting accuracy declined significantly. This is likely due to shared devices. Study Three tested interest-based segments like “sports,” “fitness,” and “travel.” These performed slightly better, with accuracy between 72.8% and 87.4%. Still, there was wide variation by provider. The authors also ran a cost-benefit analysis. For high-CPM placements, the cost of targeting might be justified. But for lower-cost placements (like display), third-party data often adds significant cost without enough improvement in performance to justify it. This line sums it up perfectly: third-party audiences are often economically unattractive. So if you're investing heavily in hyper-targeted media plans, it's worth asking: Are you actually reaching the people you think you are? Or just paying for the illusion of precision? Links in the comments to the podcast episode + full study. 👇

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7 years ago I went to my first Forrester CX conference – got the photo below and the T-shirt 😎   Back then, “CX” was all new to me. I didn’t arrive knowing what Qualtrics, Medallia or InMoment did. I just knew:   👉 As a consumer, I’d never fill in a long survey after the fact. 👉 From psychology, I knew feedback gathered later is 40% less accurate than in-the-moment. 👉 From statistics, I knew insights from a tiny, self-selecting group aren’t representative.   And from my dad’s 30 years in retail, I knew: 👉If data isn’t timely, it’s not actionable. 👉If insights aren’t tied to financial results, they don’t get attention or buy-in.   So to me, change felt obvious. And judging by the enthusiasm on my face in that photo, I thought it would come sooner…   Today feels like a historic moment.   Forrester – the leaders in CX – have just issued a “sobering” report (link in my comments):   “CX programs still don’t excel where it matters most” – driving impact, action, and confidence.   The root cause? “Outdated practices and missed opportunities.”   TruRating fixes this – hearing from 80% of customers in-the-moment, tied to what they spend and how often they return. Data you can trust, act on immediately, and prove the financial impact of. 🎯 💪   Because if feedback is too slow, too narrow, or disconnected from results – it’s just another long survey no one fills in.   Our time is now 💙

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Are you working on user needs for your media organisation? These are the articles you NEED to read on The Audiencers... > The game-changing 6-week user needs course (sent by email) from Khalil A. Cassimally to get internal buy-in, prepare for implementation, run an audit, build a cross-functional team and scale your user needs operation: https://lnkd.in/eCs7CAqn > Understand Dmitry Shishkin & smartocto's User Needs 2.0 plug-and-play model https://lnkd.in/eNwuUtV4 > Benchmarks on how other media organisations have adapted the User Needs Model to their content and audience, including examples from Ringier, Berlingske, Vox and TBIJ https://lnkd.in/eYzY5S9q > If you're in need of data to convince stakeholders of the value of user needs, Stefan ten Teije shares how the user needs approach boosts engagement here https://lnkd.in/e7v4_jMs > And we couldn't talk about user needs without Lars K Jensen - here he discusses how implementing user needs is cultural change and how you can successfully implement user needs with this in mind https://lnkd.in/eK4-Gp-a

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I’ve heard this too many times: “We can’t analyse anything until we have the GA4 raw data in BigQuery.” In reality, most of the value can be created with aggregate and sampled data. As long as you have relevant questions. And act on recommendations. https://bit.ly/4flPkXI

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𝗜𝗧/𝗢𝗧 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 —𝗧𝗵𝗲 𝟱-𝗕𝗹𝗼𝗰𝗸 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗗𝗲𝗳𝗶𝗻𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗜𝗧 𝘔𝘰𝘴𝘵 𝘱𝘭𝘢𝘯𝘵𝘴 𝘢𝘭𝘳𝘦𝘢𝘥𝘺 𝘳𝘶𝘯 𝘱𝘪𝘦𝘤𝘦𝘴 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘴𝘵𝘢𝘤𝘬. 𝘛𝘩𝘦 𝘸𝘪𝘯𝘴 𝘤𝘰𝘮𝘱𝘰𝘶𝘯𝘥 𝘸𝘩𝘦𝘯 𝘔𝘘𝘛𝘛, 𝘤𝘰𝘯𝘵𝘢𝘪𝘯𝘦𝘳𝘴, 𝘚𝘋𝘈, 𝘋𝘢𝘵𝘢𝘖𝘱𝘴, 𝘢𝘯𝘥 𝘋𝘦𝘷𝘖𝘱𝘴 𝘳𝘶𝘯 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳. Shop floors are increasingly becoming software-defined. A successful approach is built on five foundational blocks: open messaging, portable runtimes, control-as-code, governed data, and disciplined release practices. #IT and #OT leaders are aligning on a common set of strategies to successfully integrate these technologies for full-scale factory implementation. 𝗠𝗤𝗧𝗧—𝗔 𝗟𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 The Pub/Sub protocol minimizes bandwidth use while supporting reliable communication between devices. With well-defined Quality of Service (QoS) levels, it supports delivery even in unstable networks. Factory floors use #MQTT to standardize machine-to-machine and machine-to-system messaging, cutting integration lag and supporting near-real-time visibility. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 & 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲-𝗗𝗲𝗳𝗶𝗻𝗲𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 #Containers let engineers package automation applications in a consistent, portable form. #SoftwareDefinedAutomation (SDA) extends this through the combination of hardware/software decoupling, virtualization, IDEaaS, version control, automated backups, and centralized orchestration. The benefit is reproducibility: logic tested in a Dev/QA environment behaves the same on the shop floor. Rollbacks and updates can be done without halting production for long stretches. 𝗗𝗮𝘁𝗮𝗢𝗽𝘀—𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 Industrial #DataOps focuses on building pipelines that treat machine data as governed assets. Using a Unified Namespace (UNS) structure, DataOps guarantees data is contextualized and time-aligned as it flows from sensors to enterprise systems. Clean, contextual data makes KPIs (think of OEE) more accurate and actionable. 𝗗𝗲𝘃𝗢𝗽𝘀—𝗥𝗲𝗽𝗿𝗼𝗱𝘂𝗰𝗶𝗯𝗹𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 Bringing #DevOps methods into factory automation means the application of automated testing, infrastructure-as-code, and version control. This way, control engineers can push changes with audit trails, rollback capability, and consistent deployment processes—key aspects for minimizing manual errors during commissioning and shortening the time it takes to move from code development to production use. Together, MQTT, containers, SDA, DataOps, and DevOps form the tech foundation for modern IT-driven operations, creating shop floors that are more predictable, flexible, and measurable. 💡Which of these technologies feels most urgent on your shop floor?—𝗟𝗲𝘁'𝘀 𝗖𝗼𝗻𝗻𝗲𝗰𝘁! ***** ▪ Enjoy? Save 💾 ➞ Like 👍 ➞ Comment 💬 ➞ Share ♻️. ▪ Follow me and ring the 🔔 to stay current on Industrial Automation & Software insights!

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