# Diego Mas González

Diego Mas González writes about UX research, including how to connect research with customer experience, improve research practice, and make research useful to product teams.

- Profile: https://b2binfluencer.marketing/creators/diego-mas-gonz-lez/
- LinkedIn: https://www.linkedin.com/in/dmasgonzalez
- LinkedIn followers: 1,807
- Topics: artificial-intelligence, ux
- Based on 13 dated posts, latest 2026-08-18

## Summary

Diego Mas González is most useful for written LinkedIn posts that connect UX, customer experience, and market research for practitioner audiences.

- Best for: A B2B research and insights platform or research operations vendor for practitioner content.
- Not for: Consumer brands or mass market advertisers needing broad reach or video first sponsored content.
- Check first: Confirm if they accept sponsorships and request rates, formats, and audience figures.

### Takeaways

- Customer experience research found drop-off after support contact; UX research found self-service pages hid support articles.
- Involve product managers, designers and engineers early; translate one finding for each audience; find internal champions.
- AI can edit research questions, but researchers keep synthesis and reporting under human control.

## Main ideas

- **Research does not carry itself; people do**: Strong data and clean methods are not enough to change a product, Diego Mas González argues. Stakeholders who help shape a study are more likely to trust it; findings need to be translated into what each audience already cares about; and a project needs internal champions to push it forward. Adoption is part of the research job, not something that happens after the report is delivered.
- **UX research and market or customer experience research need each other**: Diego Mas González distinguishes between research traditions: market research uncovers what people do, while UX research explains why they do it. Customer experience research follows journeys across channels such as support calls or bill payment, while UX research examines how people navigate, struggle, or succeed inside a digital product. Combining them gives a fuller view of how customers perceive a product and whether that perception holds up in real interactions.
- **AI can be a second editor, but not the researcher**: Diego Mas González makes a bounded case for AI in research. After drafting and editing discussion guides or survey questions themselves, they use an agent to tighten wording, check the flow and logic of questions, and suggest anything missing. They review each suggested question to confirm it is open-ended and not leading. Synthesis, the final report, and delivery stay with the researcher. They say AI use should be transparent and human expertise should remain at the centre.
- **Check job titles against the actual work and your skill mix**: Diego Mas González argues that titles such as UX Researcher, UX Designer, or Product Designer do not define the range of work a person can do, and that a role can be advertised under one label while the daily work sits somewhere else. Their advice is to test a role against a deliberate mix: roughly 60% skills you know deeply, 25% you have some experience in, and 15% you want to grow into. In interviews, they suggest asking what a typical week looks like, which tools and methods will actually be used, and whether you can speak with someone currently in the role. Unclear expectations during hiring are a signal, and walking away is a legitimate option.

## Work to read

- [Where customer experience research and UX research meet](https://www.linkedin.com/posts/dmasgonzalez_uxresearch-cxresearch-customerexperience-activity-7333118656937156609-JFx0)
- [Making UX research stick through allies and partnerships](https://www.linkedin.com/posts/dmasgonzalez_userresearch-uxresearch-stakeholdermanagement-activity-7300521243278016513-FvnC)
- [Why market research needs UX research](https://www.linkedin.com/posts/dmasgonzalez_why-market-research-needs-ux-research-activity-7308180277753729024-MLwh)
- [A role titled UX researcher that wasn’t one](https://www.linkedin.com/posts/dmasgonzalez_uxresearch-userresearch-uxcareers-activity-7421991458230079489-0_Pj)
- [Where AI fits in a research practice—and where it stops](https://www.linkedin.com/posts/dmasgonzalez_uxresearch-userresearch-uxr-activity-7422289011383242753-vLeE)
- [Your job title isn’t your ceiling](https://www.linkedin.com/posts/dmasgonzalez_ux-careergrowth-jobsearch-activity-7394395563787599872-Rnoj)

## Partnerships

Diego Mas González's posts describe UX and CX research practice—study design, stakeholder partnerships, research careers, and the responsible use of AI in research—rather than a commercial offering. Across the six posts, none states a sponsorship format, rate, media kit, brand-collaboration policy, or way to inquire about working together. The closest adjacent item is a research-industry topic discussion, not a paid placement. They do not say whether they accept sponsorships or partnerships, so buyers should confirm directly.

## Alternatives

- Richard Best, MBA/MA: Better when the same tech-company campaign is about design-tool strategy and AI’s effect on design roles rather than research operations. Richard’s two posts make concrete claims a campaign can use: a six-year wait for a variant-bloat fix, AI prototyping capped around 60 prompts a month, an MCP server that strips semantic tokens into raw hex values, a five-person team paying $1,500 to $2,000 a year before AI credits, and a named field of challengers including Pencil.dev, Paper, Google Stitch, Penpot, Subframe, Framer, v0, and Lovable. The career post forecasts role shifts such as product designer to full-stack product designer and UX researcher to UX research automation lead. If the buyer task is arguing that design and research teams must adapt to AI-native tooling, Richard offers a sharper industry-change thesis than Diego’s research-practice posts.
- Christophe Jammet: Better when the campaign is for a tech company selling AI or automation implementation and needs a human-centered design argument backed by enterprise failure cases. Christophe’s white-paper post states that enterprise AI pilots collapse not because of technical limits but because teams skipped the UX research that would show how people actually navigate decisions. It names two implementation patterns: privacy automation that scaled by preserving human expertise at critical decision points, and platform migrations that succeeded by mapping mental models before moving workflows. The content-marketing post adds a distribution argument about targeted paid amplification and underused budget for the right audience. If the buyer task is AI adoption strategy or enterprise transformation, Christophe is a stronger fit than Diego’s narrower research-practice posts.
- Daryn Edelman, M.A., C.P.R.W: Better when the campaign decision shifts to recruiting, interview preparation, candidate experience, or research-career content. Daryn’s two posts give a ready question bank for candidates interviewing companies: recruiter questions, job questions, team insights, manager perspectives, and company vision. For a tech company building a talent-brand or research-career campaign alongside the research-lead brief, these posts supply practical interview prompts for a clear job-seeker audience.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/