# Matt Scott

Matt Scott works in paid media and marketing, sharing practical explanations of measurement methods such as marketing mix modelling and multi-touch attribution. They also describe how their team uses client case studies to shape its approach to paid media.

- Profile: https://b2binfluencer.marketing/creators/matt-scott/
- LinkedIn: https://www.linkedin.com/in/matt-scott-uk
- LinkedIn followers: 4,581
- Topics: artificial-intelligence, paid-ads, marketing-measurement
- Based on 10 dated posts, latest 2026-10-06

## Summary

Matt Scott is most useful for plain-language B2B paid media measurement explainers on MMM, MTA and incrementality testing.

- Best for: A B2B marketing agency building authority in paid media measurement through educational, case-backed content.
- Not for: Product endorsement, guaranteed-performance campaigns, or consumer entertainment awareness and ticket-sales work.
- Check first: Confirm audience demographics and the claimed +60% median KPI uplift before reuse.

### Takeaways

- MMM uses aggregate sales and channel spend for broader budget planning.
- MTA tracks digital customer journeys for granular optimisation, not incrementality testing.
- AI helps find patterns in agency case studies and procedures, not a universal playbook.

## Main ideas

- **Use different measurement methods for different decisions**: Scott argues that marketing mix modelling (MMM) and multi-touch attribution (MTA) are not competing ways to produce one answer. MMM uses aggregate sales and channel-spend data to inform broader planning; MTA follows trackable digital journeys to support more granular optimisation. Their numbers may not match. Incrementality testing is different again: it asks what happens when a channel is removed.
- **Use AI to find patterns in experience, not to promise a universal playbook**: Scott describes AI as a way to examine the agency’s case studies and standard operating procedures, identify patterns in how the team works, and shape a repeatable methodology. They stress that businesses differ and the framework is not a guaranteed straight line to success.
- **Adapt the strategy when the audience and campaign need change**: Scott’s Wake The Tiger account shows the value of applying past learning without assuming it can simply be reused unchanged. They contrast a long-standing client relationship with the need to compete again and build a strategy for a different audience and campaign need.

## Work to read

- [MMM vs MTA: a plain-language split for budget planning and daily optimisation](https://www.linkedin.com/posts/matt-scott-uk_mmm-vs-mta-marketing-departments-are-under-activity-7508104969800257536-c9b7)
- [AI as a lens on the agency's own case studies: the MAPS Framework](https://www.linkedin.com/posts/matt-scott-uk_before-ai-takes-my-job-or-kills-me-or-whatever-activity-7507755403020034049-FBTM)
- [Wake The Tiger: retaining a client through a separate pitch for a new market](https://www.linkedin.com/posts/matt-scott-uk_most-new-clients-come-to-us-when-somethings-activity-7457709023392382976-8Bts)
- [A senior marketing role post that shows the network around the paid media work](https://www.linkedin.com/posts/matt-scott-uk_south-west-heads-of-marketing-or-those-ready-activity-7448303637723959297-yMDU)

## Brand relationships

- **Advantage Paid Media** (disclosed_partnership)
- **Wake The Tiger** (client)

## Partnerships

Matt Scott’s posts describe client work through the paid media business—a long-running client campaign and an internal framework built from past case studies—along with a senior marketing vacancy they were helping a contact fill. They do not state partner terms, deliverables, rates or a public way to apply. For a B2B marketing agency interested in collaborating on measurement content such as MMM, MTA and incrementality testing, the posts point to a direct conversation about a specific scope of work, not a published sponsorship menu. Ask directly whether they accept collaborations and what terms apply.

## Alternatives

- James Congdon: Choose James Congdon when the agency's measurement authority goal depends on talent-market credibility: explaining how paid media measurement roles are defined, what a Head of Digital versus a Performance Manager owns, and how job titles shape who gets hired into measurement work. Congdon recruits for search, growth and performance marketing roles across agencies, eCommerce and SaaS businesses. They argue that inflated titles such as Senior Performance Manager or Head of Digital often do not match the scope underneath them, stopping good people from taking sensible career moves. That suits an agency campaign aimed at hiring managers, candidates or clients asking who should own measurement and at what level.
- Oliver Yuwen: Choose Oliver Yuwen when the collaboration is about reconciling attribution numbers that disagree for an eCommerce or DTC brand. The $14M outdoor apparel example walks through a Klaviyo dashboard claiming $1.8M in email revenue against $1.1M of email-originated Shopify orders, then a warehouse-built deduplication model using last-touch as the baseline and assisted touches layered on top. Email came out at about $803K under that model, and some paid channels that looked weak showed a 4.2x blended return once attribution overlap was removed. The companion retention post applies the same predictive logic to churn, detecting a ten-day inactivity gap in month four that predicts non-renewal by month six. That addresses an operator's specific task: understanding why platform dashboards disagree and producing one number the exec team agrees on.
- Abdo Mazloum: Choose Abdo Mazloum when the buyer task is tracking and data-quality plumbing rather than measurement strategy. The Meta post explains that activity businesses already share (website visits, product views, purchases, app activity, offline and CRM events) now feeds personalisation and AI responses as well as ads, and gives a five-point audit: pixel and CAPI deduplication, purchase values and currencies matching Shopify revenue, event coverage across funnel stages, consistent event matching, and documented consent and governance. The Shopify post covers making product, collection and policy data readable to AI agents through structured data, schema, feeds and theme data. That is the stronger choice if the agency needs to teach brands how to make the underlying signal trustworthy before anyone models, attributes or personalises from it.

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