# Thomas Vladeck

Thomas Vladeck writes about Recast’s marketing mix modeling work, explaining how models can account for changing ad performance, channel reach, and uncertainty—and how marketers can use better data to make more reliable decisions.

- Profile: https://b2binfluencer.marketing/creators/thomas-vladeck/
- LinkedIn: https://www.linkedin.com/in/tomvladeck
- LinkedIn followers: 7,376
- Based on 11 dated posts, latest 2026-10-07

## Summary

They are most useful for technical thought leadership on marketing mix modeling methods, data quality, and vendor claims.

- Best for: A marketing analytics firm seeking technical thought leadership on advanced marketing mix modeling.
- Not for: A consumer brand seeking broad consumer reach or guaranteed distribution.
- Check first: Ask about audience roles, engagement, sponsorship formats, inquiry route, and terms before planning a paid placement.

### Takeaways

- When Meta and YouTube spend rise together, an MMM may not distinguish their effects.
- Channel performance changes over time, so models need time structure rather than one constant.
- Saturation ceilings should grow with a channel's reach, not penalize it for entering bigger markets.

## Main ideas

- **When two models disagree about a channel, the problem is usually the data, not the algorithm**: Vladeck argues that models often cannot separate channel effects because the spend data does not contain enough independent variation. When Meta and YouTube scale at the same time, or a brand scales all paid channels together, the math fits "Meta did everything," "YouTube did everything," and "both contributed equally" equally well. Vladeck calls this unidentifiability and treats the model's answer as correct behavior: it is reporting that the data does not support a confident claim. The fix Vladeck recommends is not a better algorithm but creating variation — changing spend deliberately, running holdout tests, and making other planned changes to the marketing program.
- **Channel performance changes over time, so models need time structure rather than a single constant**: Vladeck argues that most MMMs assume channel performance stays constant across the modeling period, which doesn’t match how paid media behaves. Recast encodes two assumptions using Gaussian processes with a locally periodic kernel: days close together should perform similarly, as should days exactly one year apart, while far-apart days can differ more. The intuition is that time has structure. They say this lets seasonality emerge naturally and keeps results stable week over week. Without it, a model might report that a top channel suddenly became the worst overnight, making confident decisions difficult.
- **Saturation ceilings should grow with a channel’s reach, not penalize it for entering a bigger market**: Vladeck identifies a specific failure mode: a paid media channel that once reached 30% of a country now targets 80%, and both sales and spend climb. A model that overweights past data treats the new spend as climbing an old, low-ceiling response curve and under-reports recent performance. One that overweights recent data overwrites the old curve and over-reports past performance. Either mistake can lead a brand to pull back spend on a channel that’s working as intended. Recast’s Channel Footprint Scaling uses a channel’s coverage history so the saturation ceiling grows with its footprint.
- **Recast’s models are slow on purpose; making them fast was an engineering choice, not a simplification**: Vladeck argues against fitting a simple regression because it would assert things about marketing that Recast does not believe: that a channel’s ROI is exactly the same today as in 2024, that one number captures ad decay and saturation, or that point estimates without uncertainty are enough to decide with. In Recast’s model, ROIs move over time, each paid channel gets its own saturation curve, holidays and promos take their own shapes, and every estimate carries uncertainty. A 20-channel model carries roughly 30,000 parameters and evaluates itself against the data more than 250,000 times. The research team then made it 5–20x faster without simplifying the model, loosening priors or dropping parameters. Vikadeck frames the payoff as iteration speed: rerunning when something looks off, testing a different assumption before committing, and eventually letting customers change their own assumptions and watch forecasts update in near real time.

## Work to read

- [Why two MMMs can give different answers about the same channel](https://www.linkedin.com/posts/tomvladeck_how-can-two-mmms-give-completely-different-activity-7493320669364076544-4XKl)
- [Modeling channel performance that changes over time](https://www.linkedin.com/posts/tomvladeck_most-mmms-assume-channel-performance-stays-activity-7493675303878291456-YIES)
- [Channel Footprint Scaling: when a channel’s reach expands](https://www.linkedin.com/posts/tomvladeck_were-full-steam-ahead-in-the-recast-research-activity-7487892376061943808-fX__)
- [Why Recast’s Bayesian models were slow, and how the research team made them faster](https://www.linkedin.com/posts/tomvladeck_bayesian-models-dont-have-to-be-slow-and-activity-7495464746339561472-Xffm)

## Brand relationships

- **Recast** (employment)

## Partnerships

The material reviewed for Thomas Vladeck documents no sponsorship or partnership format. The posts are technical and educational, covering marketing mix modeling choices, how Recast’s models are built and sped up, and how data structure affects what a model can identify. They describe no creator sponsorship offering, deliverable, rate, exclusivity term, or booking process. The only contact route mentioned is for existing customers to reach their account team to turn on a product feature. The posts are written from inside Recast’s modeling and research team, so a commercial conversation would likely run through the company, not a personal creator partnership. Before planning a sponsored engagement, ask Recast who handles partnerships and confirm the available formats, pricing, and availability.

## Alternatives

- Gavin Trippe: Choose Gavin Trippe when an analytics firm's clients question model accuracy and the first step is checking whether media partners spent the plan. Trippe recommends comparing each partner's actual spend with plan before blaming the model, and treating a forecast miss as a possible adherence problem. That practical framing suits firms helping clients distinguish execution failures from modeling failures. Trippe also offers concrete channel timing guidance, such as starting long-lag TV and audio well before Black Friday so purchases land during the sale window.
- Cynthia Sandoval: Choose Cynthia Sandoval when the audience is earlier in measurement maturity and needs a plain-language reason to move beyond platform-reported ROAS. Her incrementality post explains holdout tests, geo-split experiments, and modeled attribution as practical responses to broken attribution—a useful starting point for media buyers, CMOs, and founders who are not yet asking about model parameters. Her platform-change roundup suits campaigns needing a quick digest of current paid media shifts across Meta, Google, YouTube, privacy, and AI tools.
- Sean Scogin: Choose Sean Scogin when the campaign goal is DTC growth execution rather than measurement methodology. Their marketing-function post lays out a concrete hiring and resource plan for a low eight-figure brand, while their creative-strategy post argues that more ad variations will not fix rising Meta CPA if every ad makes the same argument. For buyers helping brands interview customers, mine reviews, and build genuinely different creative arguments, Scogin's work is a better fit than MMM model internals. They also offer to audit a brand's reviews and Meta ad library and return one underused customer segment.

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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/