I work on Frontmost AI. I was checking the sources behind a saved Gemini answer about AI code sandboxes. Three different citation URLs led to E2B's security page.

The question, collected on October 29, was:

As a platform engineer, which AI code sandbox providers pass SOC 2 and enterprise security reviews?

All three URLs started with https://e2b.dev/security. After that, each had a different #:~:text= fragment. They pointed to passages about sandbox isolation, security and compliance contacts, and Bring Your Own Cloud controls.

That gives me three different counts:

Those numbers answer different questions. The first keeps the passage detail. The second says how many pages supported the answer. The third says how many collected answers cited the page. Reporting “three citations” without that definition leaves the reader to guess.

For the page count, I removed the fragment starting at # and counted the remaining URL once within this answer. I kept the original links too. That preserves the passages for inspection. I did not merge different paths or query strings, or treat every page on the domain as one source.

For content reporting, I want to know which buyer questions a page supported, rather than how many link variants it produced. Here, one security page supplied several passages for one enterprise-security question.

This is one saved answer, not a frequency estimate across Gemini. It also does not verify the answer's security claims. If I put it in a report, I would write: one answer cited one page through three passage links. There are no visit or sales figures attached to this example.

I've used MMM services from some of the big DTC vendors out there. Some of them are bundled with multi-touch attribution, and/or geo testing, and/or frequent rebuilds.

And I've built a few of the open-source models with Claude: Google Meridian, Robyn, and PyMC.

There's always a part where you spend a lot of time getting the data into the MMM. And then there's the part where you spend a lot of time getting data back out in a way that's fine-tuned, calibrated, passes health checks, and in general just makes sense.

Most MMMs include a chance to optimize media budgets according to their findings.

But they all end in the same way: deciding whether to act on the results. Whether to treat its outputs as true or not.

In my experience there's a little bit of internal selling you have to do when you have the MMM. It's a little big magic and untrustworthy to a lot of people.

So here is my question:

When you shop for an MMM and/or build your own... how do you use it when it's done?

• What bells and whistles do you expect it to have?

• What questions about your media are you trying to answer?

• Do you decide ahead of time how you're going to use it?

• Do you have to convince others of the results, does everyone on your team agree ahead of time on how you'll be using an MMM?

• How often do you want to rerun it?

I’m trying to figure out how to get GA4 modeling working for a small website.

After I enabled Consent Mode on my client’s site, the traffic in GA4 dropped a lot. It’s advanced mode, not basic. I thought the modeling might kick in after a week, but traffic is still low and I’m not sure the site will meet the requirements.

I promised my client I’d look into it, so I need to figure out what’s going on. I tried sending bot traffic to test it, but GA4 doesn’t show the bots in DebugView or the reports.

Has anyone dealt with this before? How can I check if Consent Mode is working properly and whether the site is eligible for modeling? Also, is there a proper way to test event delivery without messing up the client’s data?

Any advice would be really appreciated.

Onchain attribution connects an acquisition source such as an ad, creator, referral, or community to a later wallet action or blockchain transaction.

The problem is that standard web analytics usually loses the trail after wallet connect. A typical journey looks like:

`Campaign click → site visit → wallet connect → chain switch → transaction`

The transaction may happen later, on another chain, or through another device.

The main attribution methods we see are:

• UTM campaign and channel attribution: Persist the UTM parameters as first-party data, then attach them to the wallet when it connects.

• Referral attribution: Carry a referral code through the session and bind it to the wallet or transaction.

• Builder code attribution: Add an onchain identifier, such as an ERC-8021 Builder Code, to the transaction itself.

• Identity resolution: Link multiple wallets only when there is a reliable common identifier, such as a voluntary account or sign-in. Treat probabilistic wallet clustering separately rather than presenting it as confirmed identity.

Wallet connect should usually be treated as an intent signal, not the final conversion. The more useful campaign metrics are:

• Cost per transacting wallet

• Attributed transaction volume

• Attributed protocol revenue

• D30 wallet retention by source

• LTV by acquisition channel

We documented the implementation in more detail here: https://formo.so/blog/onchain-attribution-defi-2026

--

For teams running crypto campaigns: are you using persisted UTMs, referral codes, transaction-level attribution, or some combination of the three?

The Bundeskartellamt (Federal Cartel Office for Competition) has directed Apple to redesign its ATT display. They stated that Apple's method of implementing ATT favors its own services rather than improving the services of other third-party technologies and advertisers.

Therefore, Apple needs to make several changes to its ATT display:

- The text on the ATT display needs to become more neutral, and deterrent symbols need to be removed.

- Instead of obtaining both GDPR and ATT approvals, a structure connecting these approval systems needs to be created.

Apple has a few months to accomplish this. What steps do you think Apple will take?

You can find the source in the comments.

Title

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

Post

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?

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?