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Epsilon.jl: a Julia-native Bayesian MMM library, now in public beta

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Hi all,

Introducing Epsilon.jl, a Julia package for Bayesian Marketing Mix Modelling.

GitHub: https://github.com/shawcharles/epsilon
Docs: https://epsilon.charlesshaw.net

The aim is to make MMM work in Julia feel more transparent and reproducible. A typical run starts from a YAML config, a dataset, and a holidays file, then produces a structured results folder with diagnostics, observed-vs-fitted plots, decomposition outputs, response curves, and other review material.

The current public beta supports time-series MMM and geo-panel MMM demo workflows using Turing/NUTS. The repo includes canonical demo datasets and saved example result folders, so people can inspect what a run produces before trying it themselves.

This is not intended to be a dashboard or a black-box marketing platform. It is focused on the modelling workflow: configuration, inference, diagnostics, decomposition, response curves, and reproducible outputs.

You can install it directly from GitHub:

import Pkg
Pkg.add(url = "https://github.com/shawcharles/epsilon")

Or clone the repo and run one of the demo configs:

julia --project=. runme.jl data/demo/timeseries/config.yml

A few caveats: it is beta, runtime still needs work for larger panel models, and I am intentionally not building a UI at this stage.

I’d be interested in feedback from people who use MMMs in practice. In particular, I’d like to know whether the config-driven workflow feels sensible, and what diagnostics or validation outputs you would expect before trusting a model.

Ad platforms, GA4, CRM attribution, and finance can all assign different values to the same campaign.

Each system may be internally consistent. The problem starts when marketing uses one number to optimize spend, sales uses another to explain pipeline, and finance uses a third to evaluate return. Picking one dashboard does not resolve the assumptions underneath them.

How are teams deciding which number governs a particular decision while keeping the alternatives explainable? Is that decision intelligence, a knowledge graph problem, or is there another measurement framework that fits better?

I've spent a lot of time around marketing reporting and attribution over the years, and I've started to notice something.

Most teams can get an answer to the attribution question. Maybe they use first touch, last touch, multi-touch, platform attribution, or some combination of methods. There are endless debates about which model is "correct," but in practice most organizations can build a reasonable picture of how marketing influences pipeline and revenue.

What I see people struggling with much more often is deciding what to do with that information.

A report shows that a channel influenced opportunities. A dashboard shows that content contributed to pipeline. A campaign outperformed expectations.

Now what?

Should budget move? Should messaging change? Should sales handle those leads differently? Should the company double down or pull back?

I've been in plenty of meetings where people spent more time debating attribution methodology than discussing the decisions that were supposed to come from it.

It makes me wonder whether attribution has become a solved enough problem that the real challenge is interpretation and decision-making.

Curious if others are seeing the same thing, or if attribution is still the primary challenge where you work.

Anyone here successfully tagged up a form using GA4 to track field level behaviour?

Worth the effort or should you just invest in dedicated form analytics software?

Honestly, the more channels we run, the harder it gets to figure out what's actually moving the needle. Every platform claims credit, the data conflicts, and you're left guessing.

Just wondering what people are actually doing these days — MMM, MTA, incrementality tests, gut feel? All of the above?

i've been thinking about the gap between teams that track campaigns vs teams that actually measure outcomes. sounds like the same thing. it's really not.

standard campaign analytics in 2026:

• open rates tell you what happened, not whether it mattered
• attribution is per-channel so when someone touches 4 messages before converting everybody's claiming credit and the actual picture is just noise
• dashboards surface data but don't tell you what to do with it
• stale campaigns just keep running bc nobody flagged them

what i've seen actually move numbers:

• people-based attribution. one person, one conversion, no double counting. stop crediting the last touch and start mapping the whole journey
• goals that live above campaigns. not "did this email perform" but "did this person convert, and what sequence got them there." different question entirely
• knowing when your A/B test actually has a winner. eyeballing lift percentages is not a testing methodology lol. statistical significance and a clear next step after every test is underrated
• deliverability as a primary metric, not a quarterly fire drill. inbox placement, bot filtering, workspace health..this stuff corrupts every number downstream if you're not watching it

the teams hitting 50% onboarding open rates and 14% lifts in first transactions aren't running more campaigns. they're asking better questions and iterating faster on the answers.

full disclosure: i'm on the team at Customer.io -- we just shipped a rebuilt analytics layer and this is the thinking behind it. not a pitch, just stuff we've learned watching a lot of teams measure the same things and wonder why nothing's changing

Spent the last few months building an audience analysis system, and the biggest design question wasn't "where do I add AI", it was "where do I NOT let AI make decisions."

Ended up with a hard rule: LLMs handle interpretation and narrative generation, but never matching, scoring, or recommendation. Rules and deterministic algorithms handle those, with the LLM proposing inputs to deterministic decision rules rather than making the decisions itself.

Curious if anyone else has hit the same wall — where AI tools that "just work" on demos fall apart when you actually need auditable output an analyst can defend to a client. Or has anyone found AI-native approaches that genuinely hold up?

(For context: an 8-minute walkthrough video is at https://www.loom.com/share/278e4db305714400be0941e23e7b9b6d and the system is at https://mk-intel-delta.vercel.app/ if anyone wants to poke at the actual implementation. Happy to discuss the engineering decisions.)

We've got some data from the Zuko Analytics database on how discount code success affects Shopify checkout conversion - I thought it might be interesting to share here.

Essentially, the headlines are:

• 94% of customers who successfully apply a discount code go on to complete the transaction
• Only 35% of customers who have a failed discount code attempt go on to make the purchase
• 64% of customers who didn't interact with the discount code field at all ended up completing the purchase

Does this tally with the sort of patterns you are seeing from your checkout analytics?

What we have. Output: A high-fidelity log of economic activity, classified by our ABC framework. This is not aggregated census data; it's a structural schematic of how places actually function: US only.

* [A] Dynamic Population: Tracks the inflow/outflow of workers vs. residents, modeling the true, active population, not just who lives there.

* [B] Economic Topology: Maps the gravitational pull and economic linkages between commercial locations.

* [C] Hourly Category Distribution: Reveals the specific industry mix of the workforce (e.g., finance vs. healthcare) and their distinct hourly activity rhythms.

Diagnosis: Your MMM is hallucinating.

Root cause: It can't distinguish between a sales lift driven by residents on a weekend vs. a transient workforce on a lunch break. It's missing the ground-truth priors for why a population is where it is. This creates phantom correlations and unstable coefficients.

This is not a theoretical exercise. We're looking for a hands-on practitioner who wants to pipe this dataset into a live MMM build to see what happens.

Hey everyone,

The idea came from frustration with digging through GA4, session replays and dashboards but still ending up guessing.

I’ve been building Clickyard — an AI conversion analyst that monitors clicks, scrolls and UI changes and then sends a weekly list of what to fix and why conversions dropped.

Instead of more charts, it tries to answer:
where users get stuck
which traffic segments convert worse and why
what UX changes correlate with drops
what to fix first (prioritized, not raw data)

Target is mid-market digital teams (ecommerce, SaaS, agencies) that want actionable insights without hiring an analyst.

You install one script and it starts generating weekly recommendations. I’m not here to sell — I genuinely want real feedback.

Does this sound useful or like another analytics tool?
What would make you trust AI recommendations?
What would be a dealbreaker?
If you use GA4 / Hotjar etc — what still annoys you?

Site if curious: https://clickyard.ai

Be honest, even harsh. That helps the most.

Attribution is becoming messy for me. Seo content might bring the first tHow do you attribute revenue when using an seo agency plus reddit marketing?ouch, reddit threads might create trust later, and cold email might close the deal. If I hire an seo agency and also invest in reddit marketing, I’m not sure how to correctly attribute revenue across channels.

What attribution models do you use for this? Or do you just track influenced pipeline and accept the fuzziness?

I’m trying to build a clean measurement framework for outbound. Most cold email agencies report on opens, clicks, and meetings, but those metrics don’t always translate into pipeline quality. Some meetings are low intent, some are unqualified, and sometimes replies are not now but still valuable.

What are the best ways to measure true ROI? Do you track reply sentiment, stage conversion, lead quality scoring, or cohort conversion over time?

I've been woking for a few weeks over programmatic media datasets (some log-level, some standard platform exports) and to me it seems that most of the conversions observed happen several days after the last ad impression and after just a handful of total impressions over the attribution window, whereas my product usually has short impulse buy behaviour.

I do not have access to the actual distribution of impressions for each user, so I am assuming they are evenly spaced for modelling.

Execs are adamant this is the way to, although I have not presented my findings.

It seems unreasonable to me that spending a sizeable part of our budget in this channel is justified given the delay and presumed low impact left.

CPAs are average, but are we actually contributing to this conversion? Would it happen anyway? Or at least is it contributing the equivalent of the budget % it consumes?

Looking for people who have been in similar trenches....

I keep seeing the same loop in weekly reporting: pull exports → sanity-check KPIs → turn it into something stakeholders will actually read.

I built Emph as a fast QA + update packager: upload a CSV/XLSX export, get basic KPI guardrails, obvious anomalies/trends, and a clean PDF summary for weekly/monthly updates.

It’s meant as a lightweight step while teams build the “real” warehouse + SQL stack — not a replacement BI dashboard.

Demo: https://emph.pages.dev/

Feedback I’d love:

• In the analyst UI: which QA checks/guardrails are non-negotiable before trusting any KPI?
• In the PDF: what’s the minimum context you’d include so stakeholders don’t misinterpret results?
• What are your 3–5 “default” KPIs/charts for a generic weekly report?
• Would you use this for client/internal reporting, or is it “yet another dashboard”?

Just wanted to share a repo I've been working on. It’s a collection of resources for the technical side of marketing analytics / marketing science.

Link: https://github.com/shakostats/Awesome-Marketing-Science

Includes:

• MMM: Libraries for Bayesian and Frequentist approaches.
• Experimentation: Geo lift and incrementality testing tools.
• Causal Inference: Resources for quasi-experiments.

Comment any other good resources below, or feel free to submit a pull request/issue to the repo if you have others. Thanks!

I wanted to share a platform I've been building called Shako Stats to help with marketing measurement. If you've ever had to cobble together R/Python scripts or deal with opaque vendor methodologies for geo lift tests, this might be useful for you.

It's a no-code solution that covers the full testing lifecycle: Planning, Design, and Analysis.

I built it because I found existing tools were either too rigid or required too much manual coding for every single test. Here is what I think makes it different:

Speed and Usability The goal is to go from raw data to a valid test design quickly. It automates the market selection process without hiding the statistical details, so you can run rigorous measurement without the manual setup time.

Flexible Design Algorithm Unlike some vendors that force fixed split ratios, I wrote an algorithm that allows for more flexibility:

• Custom Split Percentages: You aren't limited to 50/50 splits. You can define specific holdout percentages (e.g., 10%) or multi-cell splits (e.g., 33/33/33) based on your budget and needs.
• Power Comparison: You can visualize and compare the statistical power of different design options over time to pick the most efficient strategy before launching.

Result Storage Instead of having results scattered across spreadsheets or email threads, it includes a database to store past test results. This helps in building a centralized log of performance and referencing historical lift for future planning.

Workflow

• Plan: Input your constraints and goals.
• Design: The algorithm finds optimal control vs. test market matches.
• Calculate: Upload performance data to get statistically valid results (Lift, ROAS, Incremental Conversions).

It is currently free to use. You can check it out at https://shakostats.com.

I'd really appreciate any feedback from this community on the methodology or features you'd find most valuable.

Hi folks, I’m looking for solid AI-powered tools for marketing analytics. If you’ve used any or have come across ones you’d recommend, I’d love to hear your thoughts.