Kelvin Claveria writes about marketing and consumer insights, including how AI affects marketing and what insights teams should consider when choosing research platforms.
6K followersAI in marketingBased on 9 posts · latest Sep 2026
Kelvin Claveria is most useful for thought leadership on AI in marketing and research platform selection.
Best for
A challenger insights technology or research software vendor wanting co-published thought leadership and a webinar segment.
Not for
A large incumbent research platform seeking independent comparison or AI cost-saving messaging.
Key takeaways
Attribution is messy, so customer conversations can explain what the numbers cannot.
A Rival survey found 74% of Gen Z reacted negatively to AI-made marketing.
Research platform checks include total cost, time to first real study, and participant experience.
Check first: Confirm deliverables, fees, disclosure, timing, and the Rival Group relationship in writing.
Main ideas
When attribution is messy, customer conversations can explain what the numbers cannot · AI efficiency can carry a cost if audiences see it as a sign of disregard · Evaluate research platforms by the work and experience they enable, not the headline price
Kelvin Claveria writes about marketing and consumer insights, especially where business decisions need more than dashboards: why customers buy, how brands should use AI, and what insights teams should weigh when choosing research platforms.
The posts reviewed show a practical, evidence-led voice that turns broad industry debates into questions readers can use.
Follow them for arguments that connect marketing choices to customer experience and the reasons behind the numbers.
Claveria’s distinctive move is to make abstract priorities concrete.
A post on brand building and attribution turns the measurement problem into five customer questions; a post on AI pairs survey figures with Gen Z’s own language, then distinguishes useful AI tasks from work such as strategy and creative direction.
The research-platform post likewise gives buyers five checks, including total cost, time to launch a real study, and whether participant experience could undermine data quality.
Written from inside Rival Group and drawing on what they say customers have told the company, the checklist is vendor-framed; buyers seeking an independent comparison should ask for one.
Across three LinkedIn posts, Kelvin Claveria returns to a practical idea: when marketing metrics or technology choices hide what matters, listen to customers and examine the real effects on people.
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They apply that idea to brand measurement, AI in marketing, and research-platform buying.
When attribution is messy, customer conversations can explain what the numbers cannot
Claveria argues that brand building, AI adoption, and customer experience can matter even when their effect on sales is hard to attribute.
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Rather than abandoning brand investment because of that measurement gap, they recommend asking customers directly about their priorities, buying motivations, discovery, purchase triggers, and alternatives.
In a post about expected 2026 CMO priorities, Claveria predicts attribution will remain opaque and messy, then lists five questions teams can ask customers to understand the “why” behind their behavior.
AI efficiency can carry a cost if audiences see it as a sign of disregard
Claveria does not argue that marketers should stop using AI.
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They say efficiency alone is an inadequate measure of success: audiences may read AI-made marketing as a signal about the company, not just judge the output.
They present task choice as the practical distinction, describing uses such as journey planning and CRM cleanup while reserving strategy, brand voice, and creativity for people.
The suggestion to keep listening as attitudes change is Paula Catoira’s view, quoted by Claveria, rather than a separate statement by Claveria.
Claveria cites a survey of 901 Gen Z consumers in the US and Canada: 74% reportedly react negatively on learning a brand’s marketing was made with AI, and 72% say they have punished a brand for it. They report words respondents used, including “lazy” and “soulless,” to explain the reaction.
Evaluate research platforms by the work and experience they enable, not the headline price
Claveria argues that a familiar, expensive enterprise platform is not automatically the safest or most valuable choice.
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Their checklist considers support, hands-on help, total cost, time to launch a real study, and the participant experience.
They connect poor survey usability to data quality: people may rush, disengage, or drop out.
The post is written from Rival Group’s commercial perspective; ask for independent evidence to validate its vendor comparisons.
Claveria contrasts a real study with a demo or sandbox, and warns that add-ons can raise the cost beyond the platform fee. They also describe long grids and desktop-first surveys squeezed onto phones as possible sources of participant frustration and weaker data.
Start here for the clearest statement of the argument Claveria returns to: measurement gaps are a reason to talk to customers, not a reason to stop investing in brand.
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Newcomers get both the 2026 priority list and the method suggested for addressing it.
Claveria gathers the priorities experts expect CMOs to carry into 2026 — brand building and awareness, AI adoption, and customer experience, particularly in B2B — then points to the awkward part: someone will still ask what the return is.
These are not performance-marketing line items, so their effect on sales is indirect and hard to attribute.
Rather than treat that as a reason to abandon them, they argue that attribution will stay opaque and messy, and that companies that keep investing in brand anyway will thrive.
When analytics and CRM cannot supply the answer, talking to customers can.
The post lists five questions a team can actually ask — what matters to customers now, what motivates them to buy, how they hear about products, what triggers a purchase, and who else they considered — and frames those conversations as a source of nuance and insight into the why.
For brands planning a campaign
A useful spine for a thought-leadership article or webinar on why insights teams should own the why behind the numbers. The five questions can be lifted into a session prompt or audience poll.
Shows whether AI in marketing carries a measurable brand cost, using specific numbers and the audience’s own words rather than a general warning.
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A good entry point for anyone assessing Claveria’s work on AI and research evidence.
Claveria starts with how leaders usually justify AI in marketing — more content, faster, cheaper — and asks what that focus on efficiency costs.
They cite a survey of 901 Gen Z consumers in the US and Canada: 74% react negatively when they learn a brand’s marketing was made with AI, and 72% have punished a brand for it, from unfollowing to refusing to buy.
The interesting move is in the interpretation.
Claveria argues Gen Z is not mainly rejecting AI because the output looks bad; they read its use as a statement about the company.
The words quoted back are blunt — lazy, soulless, gross.
The post avoids an anti-AI stance: it cites Gartner’s finding that CMOs now put an average of 15.3% of marketing budgets into AI, and quotes practitioners on which jobs should stay human, including strategy, brand voice, and creativity, versus tasks like building a customer journey plan, pressure-testing channel mix, or cleaning CRM data.
For brands planning a campaign
A hook for a webinar or article on AI governance in marketing. It also carries a caution for campaigns aimed at younger buyers: announcing efficiency gains can read to them as cutting creative jobs, so how a collaboration is framed matters as much as the numbers.
Shows how Claveria approaches research-platform selection, the closest thing in this material to a buyer’s checklist.
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The evaluation criteria are presented as a practitioner would actually use them.
This post turns a familiar phrase — nobody ever got fired for buying the big-name platform — into a buying checklist, arguing that the safe choice can be expensive.
The five questions cover service (some platforms treat you as a support ticket rather than a customer), whether the vendor does more than hand over software, total cost rather than headline platform fee, time to value, and participant experience.
The detail is concrete and quotable: Claveria asks how quickly a team can launch its first real study, insisting on real participants answering a real business question rather than a demo, a sandbox, or a portal.
The participant point gets the most weight: long grids, clunky interfaces, and desktop-first surveys squeezed onto a phone are a data quality problem.
People who find research tedious rush, disengage, or drop out, and the respondents a brand most wants may decide it is not worth their time.
Claveria writes from inside Rival Group and refers to Rival Technologies customers who previously used large enterprise insights platforms, so buyers should independently verify the checklist’s recommendations.
For brands planning a campaign
Maps directly onto a vendor-selection article or webinar segment. The source is vendor-framed, so pair these evaluative prompts with an independent comparison before presenting them as advice.
Recurring approach: research-anchored commentary with a promotional close3
Treating customer conversations as a standing research habit, not a campaign2
AI in marketing and the audience backlash against it1
How to evaluate and buy an enterprise research platform1
2026 CMO priorities and the measurement problem behind them1
AI in marketing and the audience backlash against it
Kelvin Claveria argues that the marketing industry's focus on efficiency — more content, faster output, lower cost — is colliding with how younger consumers view AI use.
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They draw on a Rival Technologies survey of 901 Gen Z consumers in the US and Canada: 74% said they react negatively on learning that a brand's marketing was AI-made, and 72% said they had punished a brand over it, from unfollowing to refusing to buy.
Their reading is that the objection is not mainly about quality; it is about what AI use signals about the company, with words like "lazy" and "soulless" attached to it.
They distinguish tasks AI can usefully do — journey planning, channel-mix pressure-testing, budget modelling, performance synthesis, CRM cleanup — from strategy, brand voice and creative, which they say should stay human.
Leads with first-party survey numbers, then explains the findings through named third-party commentary: Amy Rask on efficiency at the expense of authenticity, Scott Jacobs on task-level delegation, and Paula Catoira on keeping research ongoing. Closes by asking readers how they are handling the tension.
For brands planning a campaign
A technology brand seeking thought leadership on AI in marketing can start with consumer evidence rather than tooling.
The practical angle is a division of labour: which tasks AI takes on, which stay with people, and how brands can tell when AI use is costing them audience trust.
This is the strongest fit for a campaign about AI in marketing.
How to evaluate and buy an enterprise research platform
A buyer-side checklist for insights professionals deciding whether they are overpaying for a large enterprise research platform.
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Claveria lists five questions to ask before signing: what the service is actually like, whether the vendor goes beyond handing over software, the real cost once per-complete pricing, SMS distribution, video feedback and AI features are added, how quickly a first real study can launch, and what the participant experience is like. The cost and participant-experience points are the sharpest: the headline platform fee can hide the true cost of the research a team wants to run, while clunky, desktop-first survey design on phones can become a data-quality problem as disengaged participants rush or drop out.
Written as five numbered questions in a buyer's voice, with concrete failure modes — ticket-based support, renewal-only contact, hidden add-on pricing — rather than abstract criteria.
The post is written from Rival Technologies' commercial vantage point and refers to its own customers and Rival Group's support philosophy, so it reads partly as a sales argument against large incumbents. It also includes a pre-emptive note disclaiming any link to the Qualtrics layoffs.
For brands planning a campaign
This is the closest thing in these posts to vendor-selection content. A research or insights technology company could commission a buyer's-guide piece built around these five questions and the total-cost framing — useful for a webinar or downloadable evaluation checklist rather than a general trend article.
2026 CMO priorities and the measurement problem behind them
Claveria summarises expert predictions for CMO priorities in 2026 — brand building, AI adoption and, in B2B, customer experience — then highlights the challenge they share: their effect on sales is indirect, making ROI hard to answer.
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Their prediction is that attribution will stay messy and opaque, while companies that keep investing in brand will do well. They suggest teams talk directly to customers about what matters to them now, why they buy, how they hear about products, what triggers a purchase, and who else they considered, positioning insights work as the source of the "why" that analytics and CRM cannot supply.
For brands planning a campaign
Useful for a brand seeking annual-planning or thought-leadership material that connects brand investment to customer research. It offers a ready-made argument that surveys and customer conversations fill the gap left by attribution tools, which suits a vendor with an insights product.
Treating customer conversations as a standing research habit, not a campaign
Across these posts, the same recommendation recurs: keep listening to customers directly rather than assuming their current attitudes.
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In the 2026 priorities post, this takes the form of questions to ask when analytics cannot explain behaviour. In the AI post, Claveria warns that attitudes toward AI can shift quickly, quoting Rival Group's CMO on the need to keep listening instead of assuming where Gen Z stands.
For brands planning a campaign
A brand shaping a collaboration around continuous research rather than one-off studies could use this recurring argument as the spine of an article: the case for ongoing customer listening, illustrated by the AI-attitude example in which assumptions and survey results diverged.
Recurring approach: research-anchored commentary with a promotional close
The three posts share a recognisable shape.
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Each opens with a question or surprising claim aimed at marketers and insights practitioners, supports it with data or specific claims, and closes by inviting a response — an explicit question in two posts and a newsletter link in the third.
Two of the three reference Rival Technologies or Rival Group directly, and one cites an external research figure: Gartner's 2026 CMO Spend Survey puts AI at 15.3% of marketing budgets.
The material is written for peers in marketing and insights, not end consumers.
Posts are short and structured — a numbered list or short argument blocks — with links to a newsletter edition or research landing page rather than primary documents.
For brands planning a campaign
A brand choosing a collaboration format should expect a short, opinionated post built around a data point or practical list, with a call to discussion at the end. The format suits thought-leadership placements and LinkedIn co-publishing; long-form technical explanation would need an editing step.
Kelvin Claveria writes from inside Rival Group: the posts refer to "our CMO at Rival Group" and "our philosophy at Rival Group," and describe Rival Technologies as the insights platform whose customers previously used large enterprise research vendors.
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The posts mention Qualtrics, Gartner and other companies only as outside context; one explicitly says it has nothing to do with the Qualtrics layoffs. These mentions do not establish relationships.
Rival Group
Employment
Claveria's posts speak from inside the organization.
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One discusses what "Rival Technologies customers previously used," describes "our philosophy at Rival Group," and outlines five questions insights buyers should ask before signing with an enterprise research platform.
Another cites Rival Technologies survey research and quotes "our CMO at Rival Group."
The posts show Claveria speaking for the company on research-platform selection and AI in marketing, but do not state a job title.
Ask for their current title.
Rival Technologies is the insights platform named in the posts, with Rival Group as the parent organization.
Kelvin Claveria writes short, argument-led LinkedIn posts for marketing and insights professionals, usually built around one number or one list.
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Start with the post on AI in marketing and Gen Z reactions, which captures the core tension: audiences can read efficiency gains as a signal about the company behind them.
The research-platform buying checklist is the most concrete piece for anyone choosing tools, with five questions to ask before signing.
The 2026 priorities post links the two, arguing that when attribution remains messy, asking customers directly gives teams the "why" that analytics and CRM cannot supply.
Together, the posts show a writer who works from survey evidence, identifies what data cannot settle and usually ends by asking readers how they are handling the same problem.
They cover customer listening, AI adoption and vendor evaluation.
AI in marketingMarket researchCustomer insightsBrand measurement and attributionResearch platform selectionLinkedIn postsB2B marketers and insights leaders
Both write for senior marketing leaders about how to organise marketing, and both build posts around outside authority rather than personal anecdote.
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Sallata quotes McKinsey on the CMO owning the customer journey and Deloitte survey perspectives on go-to-market alignment; Claveria cites named researchers and a Gartner spend figure. Both argue that fragmented customer ownership creates silos, competing agendas and redundant tools, and urge leaders to diagnose the problem before choosing new systems.
Sallata focuses on the enterprise operating model: who owns the customer inside a large company, how sales, marketing and service share data, and how to rebuild a go-to-market foundation.
Claveria stays closer to the research itself: which questions to ask customers, how to buy a platform and what AI does to audience trust.
Sallata makes the case for organisational change through analyst and consultancy framing; Claveria draws on survey findings to argue for better listening practice.
Both are research practitioners who write about how the work gets done and where AI belongs.
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González describes drafting discussion guides and survey questions, then using an agent as a second editor while keeping synthesis and critical thinking human.
Claveria draws a similar line, assigning journey planning and CRM cleanup to AI while reserving strategy, brand voice and creativity for people.
Both use a specific personal experience—a college professor's feedback in one case, Gen Z survey responses in the other—to make the argument land.
González writes about user experience research and research careers: role-fit mismatches, interview questions and what a researcher should accept in a job.
Claveria writes for marketing and insights buyers; two of the three posts reviewed tie to a commercial research-technology position rather than personal career reflection.
González's audience is practitioners building a career; Claveria's is mostly teams making marketing and vendor decisions.
Laja surveys 100 B2B marketing leaders to show that most big marketing decisions are gut calls, Voice of Customer work is common, and research into non-customers is rare.
Claveria uses a 901-person Gen Z survey to show what brands assume about AI versus what audiences say.
Both write short, data-led LinkedIn posts that end on a pointed line, and argue that teams fill the gap with internal lore instead of asking the people they want to reach.
Laja focuses on research methodology and market research as a repeatable practice: the distinction between customers and the wider market, and why conference conversations and sales calls do not scale.
Claveria focuses on how to use that listening: AI in marketing, brand investment when attribution is opaque, and how to evaluate and buy a research platform.
Laja is more prescriptive about how to run research; Claveria more often explains what findings mean for a marketing choice.
The only measured audience figure cited is a LinkedIn follower count of 6,033, observed 8 October 2026.
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No platform demographics or audience survey are mentioned, so geography, age, gender, job function, seniority, and company size remain unconfirmed.
The writing is aimed at insights and marketing practitioners: all three posts address them directly (“Insights pros”, “Marketing and insights pros”) and cover decisions those teams own, including research-platform selection and total cost, AI adoption in marketing, brand-building measurement, and customer-journey research.
One post reports a Rival Technologies survey of 901 Gen Z consumers in the US and Canada: 74% reacted negatively to marketing they believed was AI-made, and 72% had punished a brand over it.
These figures describe the surveyed consumers, not the account’s followers.
The account is a reasonable channel for thought leadership on AI in marketing and vendor selection, but buyers cannot yet confirm the seniority, employer type, or location of the people reached.
Ask for a recent LinkedIn follower export covering geography, age, and gender; audience survey or first-party data covering job function, seniority, and company size; and post-level impressions, reactions, and comments for comparable topics and formats.
Kelvin Claveria writes from inside Rival Group: their posts refer to Rival Technologies customers, describe “our philosophy at Rival Group,” and quote the Rival Group CMO.
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They promote Rival’s research and link to a Rival report, but none of the three posts provides a sponsorship menu, rate card, audience package, lead time, or partnership contact.
Buyers would need to propose and scope a collaboration directly, confirming deliverables, fees, disclosure, and timing in writing before committing spend.
Competing insights or research-technology vendors should weigh the practical conflict: Claveria’s recent published work argues for one platform and against overpaying for large enterprise alternatives, which may make an independent or comparative campaign difficult to place with them.
An insights-technology or research-software vendor wants a co-published thought-leadership article and a webinar segment on AI in marketing and how insights teams should choose a research platform. The vendor is a challenger rather than a large incumbent, and it is willing to supply independent data or a customer story alongside Claveria's material.
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Reasons
Claveria already writes about both required topics. One post is a five-question buyer's checklist for enterprise research platforms: service quality, whether the vendor does more than hand over software, total cost once per-complete pricing, SMS distribution, video feedback and AI features are added, time to first real study, and participant experience. The other draws on a Rival Technologies survey of 901 Gen Z consumers in the US and Canada — 74% react negatively on learning a brand's marketing was AI-made, and 72% have punished a brand — to make a governance argument about which tasks AI should take and which should stay human. A vendor does not need to start from scratch to commission content on either subject.
The formats match the ask. Each post is short, opens with a question or a counter-intuitive claim, carries a numbered list or a bounded argument, and closes by inviting reader response. That shape adapts to a co-published article or a webinar poll or discussion prompt with little rewriting. Claveria's five questions work as a session outline or audience checklist.
The audience alignment is plausible but unmeasured. All three posts address insights and marketing practitioners directly and work through decisions those teams own. The only firm audience figure available is a LinkedIn follower count of 6,033 observed 8 October 2026. Follower data, engagement per post and an audience survey were not provided. A vendor testing a small placement can proceed; one budgeting on claimed penetration should ask for audience and engagement data first.
Buyer note
No sponsorship menu, rate card, lead time or partnership contact route is published. Agree deliverables, fees, disclosure and timing directly and in writing. Expect the offer to be framed around Rival Group's work, and disclose that relationship prominently rather than presenting the collaboration as neutral.
Verdict
Strong fit on subject, format and audience. Two of the three posts address these questions directly, and the third supplies the measurement argument that connects them.
When to choose a specialist
A large incumbent enterprise research platform wants an independent category comparison placing its product against named competitors, with campaign messaging leading on AI-driven cost savings and reduced marketing headcount.
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Reasons
One post argues directly against the safe large-vendor choice: the headline platform fee hides the true cost of the research a team wants to run; some enterprise platforms treat a customer as a support ticket; and time to first real study is the question to ask. Commissioning a comparison from Claveria would ask them to argue against their own published position.
The AI-efficiency framing is the specific thing the other post pushes back on. Advertising more content, faster and cheaper, to an audience that reads AI use as a statement about the company puts the campaign message in tension with the numbers Claveria cites: 74% react negatively and 72% report having punished a brand.
Claveria's current published work is written from inside Rival Group: they refer to Rival Technologies customers, describe 'our philosophy at Rival Group' and quote the Rival Group CMO. A rival platform asking them to assess competing products creates a disclosure problem that no amount of framing removes. If a vendor in this position still wants to work with Claveria, the workable version is narrow — a closed roundtable or a non-comparative piece on participant experience, where the argument is not about which platform wins.
Buyer note
Rule this brief out before spending on scoping, rather than negotiating it and discovering the conflict after a draft. A general-reader reader should also not take the absence of a documented sponsorship offer as evidence that Claveria never accepts outside work.
Verdict
Poor fit. The conflict is substantive and visible in the published work, not merely a matter of tone.
A worked campaign example
Deliverable order
Agree on the article brief and disclosure wording first; draft the article from Claveria's existing argument plus one new data point; then run the webinar from the published article so the two reinforce each other instead of duplicating.
Format
One co-published LinkedIn article and one 30-minute webinar segment, delivered on the vendor's own site and channels rather than as a standalone page.
Next steps
Confirm in writing that Claveria can take an outside collaboration, the disclosure wording for both the article and webinar, whether the Rival survey data may be reused, and a realistic launch date.
Risks
Audience reach is unverified beyond the follower count, so set a modest first-placement target. The publisher description of the Rival survey establishes its existence and stated findings, not independent confirmation of its methodology. Any vendor reusing those figures should cite the original rather than re-reported versions.
Second angle
For the vendor-selection half, adapt the five-question checklist into a downloadable evaluation prompt. Because the source post is vendor-framed, pair the questions with independent comparison rather than presenting them as neutral advice, and label the vendor relationship.
Shape
Open on the survey numbers — 74% of surveyed Gen Z consumers react negatively to AI-made marketing, and 72% report having punished a brand — then move to Claveria's reading that the objection is about what AI use signals about a company, not output quality. Use their own quotes ('lazy,' 'soulless') rather than paraphrasing them. Follow with the practical split they already draw: AI for journey planning, channel-mix pressure-testing, budget modelling, performance synthesis and CRM cleanup; humans for strategy, brand voice and creative. Close with a question to the audience, as Claveria's posts do, and use it as the webinar poll.
Title
The efficiency trap: what Gen Z's reaction to AI marketing means for insights teams
What the vendor supplies
Its own independent data or a named customer story, and any product claim it wants tested. The campaign should not rely on the vendor's assertion alone; the collaboration's value is an argument backed by evidence.
Choose Laja when the campaign’s main decision is whether B2B marketing teams are hearing from buyers outside their customer base.
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Their posts cite a survey of 100 B2B marketing leaders and contrast the use of customer interviews with the much less frequent study of noncustomers. That makes Laja a closer match for a campaign about market research and buyer evidence.
The examples focus on market research habits, not AI in marketing or evaluating research platforms. The posts present survey findings from a sample of 100 and describe the questions asked.
Choose Diego Mas González when the campaign is about practical AI boundaries in research work.
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Their post describes using AI to edit and pressure-test discussion guides and survey questions, while keeping synthesis, critical thinking and final delivery human-led. That offers a more specific practitioner-led angle than a broad marketing-efficiency discussion.
The example concerns UX research practice, not Gen Z responses to AI-made marketing or how insights teams should select an enterprise research platform. Ask for examples on those topics before considering this a fit.
Choose Lauren Sallata when the campaign is chiefly about aligning marketing, sales and service around a shared go-to-market plan.
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Her post offers diagnostic questions on the ideal customer profile and pipeline slowdowns, supporting a strategy-focused discussion for revenue leaders.
The available examples are about go-to-market alignment and customer ownership, rather than AI in marketing or research-vendor selection. Her work is a less direct match for a campaign about AI in marketing or research-vendor selection than Claveria’s posts on those two subjects.
This page draws on three LinkedIn posts from Kelvin Claveria: one on expected 2026 CMO priorities and the difficulty of attributing brand spend, one on AI in marketing that reports a Rival Technologies survey of 901 Gen Z consumers in the US and Canada, and one offering a five-question checklist for evaluating an enterprise research platform.
These three posts define the scope.
The posts have no publication dates, so they are treated as a set rather than placed on a timeline, and only LinkedIn posts were available.
Newsletter editions, video, longer-form writing and any books, shows or earlier work were not covered, and no claim is made about what Claveria is best known for beyond these posts.
Claveria's relationship with the Rival Group is described through first-person references in the posts; no company pages or profile-side documents were available, and the posts do not state a job title, so none is given here.
Outside companies named in the posts, including Qualtrics and Gartner, are treated as context rather than relationships.
The only audience figure available was a LinkedIn follower count observed 8 October 2026; no follower data, engagement data or audience survey was available.
Comparisons to other creators draw on their posts dated August 2025 through September 2026.
That short sample is the main gap: targeted name-search research into signature work and earlier material is still needed before any broader claim about Claveria's body of work.
