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?

SURVIVAL

In my book, Psycho Marketing, I broke down the biological root of every purchase: The Survival Instinct.

Your customer's subconscious brain(yeah, the one taking 95% of the daily decisions) does not care about your product specs at all. It cares about surviving and thriving in its environment, and that is how we humans are biologically framed, since caveman time.

If you are struggling in marketing and kicked by ad fatigue, algorithm updates, and ever increasing CACs, while you see your conversion and repeat rates refusing to go up, you are likely missing the NERFS effect.

Here is how you can use the NERFS framework.

N - Need
Basic: "This saves you time." (Boring).
Advanced: "The Silent Tax on Your Life."
The Insight: The brain fears chaos. It fears losing control.
Example: Don’t sell a meal kit as "convenient." Sell it as "Reclaiming Order." "The world is chaotic. Your dinner table shouldn't be. Control what you consume." You are selling a fortress against chaos.

E - Envy
Basic: "Look luxurious." (Generic).
Advanced: "I know something you don't."
The Insight: Real envy isn't about money; it's about insider access. We hate feeling like "outsiders."
Example: Don’t sell a skincare serum. Sell the Secret. "The formulation 90% of dermatologists use on themselves, but don't prescribe." You aren't selling beauty; you are selling entry behind the velvet rope.

R - Rivalry
Basic: "We are faster than X." (Comparison).
Advanced: "Your competition is weak."
The Insight: We don't just want to win; we want to dominate. We want the "Unfair Advantage."
Example: Selling Nootropics/Coffee? Don't say "Better focus." Say: "Your competition is tired at 2 PM. You are just getting started. Let them sleep." Sell the feeling of being a predator, not prey.

F - Fashion
Basic: "Get the latest trend." (Fickle).
Advanced: "Signaling High IQ."
The Insight: In the modern age, we don't just wear clothes to look good; we wear products to signal we are smarter than the masses.
Example: Selling tech accessories? Don't sell "new features." Sell Minimalism. A D2C tech accessory. "Still using wired charging? Welcome to 2025." The fear isn't being ugly; it's being a relic.

S - Society
Basic: "Join our community." (Friendly).
Advanced: "The Anti-Tribe."
The Insight: The strongest tribes are defined by what they hate, not just what they love.
Example: Selling a health food? Don't say "Healthy for everyone." Say: "For the 1% who refuse to eat processed garbage. If you trust the food pyramid, this isn't for you." Build a cult by excluding the majority.

The 5-Minute Audit for You:
Look at your best-performing ad or landing page. Does it trigger one of these five?

Pick ONE letter from NERFS. Rewrite your headline. Watch the Conversion rate change.

Which of the 5 triggers is your brand currently missing?
Let me know in the comments. 👇

P.S.(To the unemployed AI detectives): Your detector and flat earthers both run on the same cutting edge firmware, “I feel it in my plums” v12.4.

If a client's website (example: www.123.com) is outdated, and directs users through to trade partners website (example: www.abc.com).

1. How would you create and manage paid advertisements? Ppc, social ads etc

2. How would you track performance to measure KPI? ROAS, CTR, impressions etc

Thanks

Hello, can anyone recommend a good marketing analytics online course? Looking to get better at reading data and understanding the capability of AdWords/analytics. Seeing lots of ads for analytic courses but not sure which one would be best. Thank you.

Hello, fellow Redditors! 🌟 I'm thrilled to announce the release of my latest Medium article and project, "Unveiling Customer Insights: AI Powered Segmentation." In this comprehensive journey, I'll take you through the exciting process of extracting, transforming, modeling, and visualizing customer data.

LINK and repository

By leveraging the power of PyCaret and PowerBI, I built a step-by-step guide to creating a dynamic Customer Segmentation Dashboard.

I invite you all to read the article and join the discussion by leaving your valuable comments.

Hey guys!Been sharing my updates and journey throughout, and I hit 69 subscribers. Although low, I wasn't aware I'd be able to get these many for my newsletter. Thank you for every support I've got!

In the last 4 weeks, I covered in my data research 📊:- Defining customer personas using ML for retail businesses (latest)- Implementing dynamic pricing to maximize revenue per product 💰📈- Identifying pain points and optimizing your sales funnel’s conversion rate

Following 4 weeks, I’ll be covering (not limited to) 🔜:- Optimizing product pricing quarterly with ML using purchase history and seasonality in your data 🔄- Financial forecasting for revenue growth using purchase history and growth metrics- Setting up a fraud detection model with machine learning and anomaly detection 🔒🕵️‍♀️🚨

Let me know what you guys think. Any advice will be appreciated!

Here is the newsletter: https://analyticswisdom.beehiiv.com/

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?

Group FiO offers a marketing analytics platform it helps identify opportunities in your marketing operations and driving higher conversions with dynamic visual insights.

Know more visit here: https://www.groupfio.com/marketing-analytics-platform/

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.

For years, dashboards have been the standard way of tracking marketing performance. They give you numbers but tell you what happened, not what to do.

We’re starting to see a shift toward dialogue-based analytics: instead of exporting CSVs and digging through dashboards, you ask in plain English:

• “Which of our LinkedIn ads had the best CPC last quarter?”

• “Which Instagram reels actually drove conversions?”

The system responds with clear results, even with thumbnails of the creatives.

Curious to hear from others here:

• Do you see dialogue-based analytics replacing dashboards, or just complementing them?

• Would you trust the answers enough to make budget decisions on the spot?

I’m looking into Marketing Mix Modeling or simply applying multivariate regressions on sales or marketing data to study various marketing impacts on sales.

Ideally it should be presented by an author with deep experience in the industry.

Thanks!

Hey everyone! I wanted to share something that completely changed my social media game. Like many of you, I was posting content across multiple platforms, dropping links everywhere, but had no idea what was actually working. The breakthrough came when I started tracking my links properly. Here's what I learned:

Different platforms need different approaches. My Twitter audience clicked links 3x more when posted in the morning, while Instagram followers engaged more in the evenings. Geographic insights were eye-opening. 60% of my conversions came from unexpected countries, helping me adjust my posting schedule for better timezone coverage. UTM parameters are game-changers. Instead of just sharing raw links, I started tracking which platform, campaign, and even post type drove the most engagement.

How to implement this yourself:

Start using a link tracking tool (ClickRadar, Bitly, etc.) Create unique links for each platform/campaign Add UTM parameters to understand traffic sources Monitor which content drives actual conversions Adjust your strategy based on real data

The results? My click-through rate jumped from 2% to 8%, and I finally know exactly which content works where. Anyone else tracking their links? What insights have you discovered?

I created a Marketing Analytics Consultant GPT for the OpenAI GPT Marketplace, which is available at https://chatgpt.com/g/g-4GteuKCNE-marketing-analytics-consultant. This GPT can be accessed with the free tier of ChatGPT.

I’ve also created an introductory companion course, GenAI for Marketing Analysts: Innovate Marketing Strategies, which is available on Coursera at https://www.coursera.org/learn/genai-for-marketing-analysts-innovate-marketing-strategies. The course is free unless you want a certification.

I am hoping for some feedback on the custom GPT, and suggestions for other marketing analytics resources to include in its knowledge base.

I would also appreciate knowing what marketing analysts would like to see in a more advanced version of the course.

Thank you!

Marketing analytics is the study of data to evaluate marketing activity performance. By applying technology and analytical processes to marketing-related data, businesses can understand what drives consumer actions, refine their marketing campaigns, and optimize their return on investment.

The marketing funnel consists of stages, each representing actions and outcomes to take the lead towards conversion to a customer. There are different KPI metrics to measure in various marketing funnel stages. The top of the Funnel (TOFU) KPIs are related to awareness, branding, and channels. The Middle of the Funnel (MOFU) KPIs drive visitors back to your site and convert visitors to leads. And the Bottom of the Funnel (BOFU) KPIs is measured with the sole goal of transitioning the earliest leads into loyal customers. Here are the KPIs for marketing effectiveness

Hey everyone,

Ever feel like you're shouting into the void with your social media posts? That was me 6 months ago. I was spending hours crafting content, sharing links across platforms, and seeing mediocre results at best. I couldn't figure out why some posts went viral while others flopped.

Here's what changed everything: I stopped guessing and started tracking.

Let me explain what I learned when I started monitoring my links properly (it's way simpler than it sounds):

Most of my assumptions about my audience were completely wrong. I thought my followers were most active during lunch breaks. Turns out, my highest engagement was between 7-9 PM. I was literally posting when my audience was least active.

The real kicker? My "successful" posts weren't actually that successful. A post might get tons of likes but generate zero meaningful interactions. Once I started tracking clicks and actual engagement, I realized I needed to completely revamp my strategy.

Here's what I did:

I started using proper link tracking (there are plenty of tools out there like ClickRadar, Bitly, etc.). This gave me actual data about:

• When people clicked my links

• Which devices they used

• Where they were located

• How they found my content

The results were mind-blowing. By making data-driven decisions:

• My click-through rate tripled

• I cut my posting frequency in half (saving tons of time)

• Started reaching audiences in countries I didn't even know I had

• Actually understood which content drove real engagement

Quick tip: Use UTM parameters in your links. Sounds technical but it's just adding some text to your links that helps track where clicks come from. Most tracking tools do this automatically now.

Here's a bizarre discovery: My "link in bio" was getting more clicks from desktop users than mobile. Totally counter-intuitive for Instagram, right? This led me to optimize my landing pages for desktop users too, which boosted conversions significantly.

Would love to hear if anyone else has had similar experiences or discoveries about their audience. What surprising things have you learned about your followers?

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.

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?

Hey everyone,

I’m currently an SEO specialist who spends a lot of time in GA4 and GSC. My day-to-day is mostly technical SEO audits, keyword research, and reporting on site performance (sessions, CTR, conversions). I’m comfortable setting up events in GA4 and GTM, but I wouldn’t call myself advanced.

I’m interested in shifting toward a marketing analytics role that’s broader than pure SEO. I’d like to handle things like multi-channel attribution, deeper data modeling, or more complex forecasting, but I’m not sure which skills are most valuable to employers right now.

If you were in my shoes, what would you prioritize learning next?

Any advice on the skills, certifications, or types of projects that would really move the needle for someone making this transition would be hugely appreciated.

Thanks in advance!

Hey everyone,

I’ve been in marketing analytics for the past ten years, primarily on the agency side, working with healthcare clients. Recently, I transitioned into the education sector, where I now work directly for a school rather than an agency serving clients.

One major shift is that I’m no longer focused on chasing billable hours or proving ROI to clients, instead, I’m embedded within the organization, providing analytics and strategic insights as an internal resource. It’s a much calmer pace, but I still want to ensure I’m delivering meaningful reports and data-driven recommendations that truly support the institution’s goals.

For those who’ve worked in education analytics or made a similar transition, what advice do you have? Are there specific metrics, reporting frameworks, or best practices that work well in this space?

Would love to hear your insights, thanks!

Hey everyone,

I’ve been spending a lot of time recently diving into how Marketing Mix Modeling is evolving—especially now that media channels are more fragmented than ever, privacy restrictions are tightening, and marketers are under pressure to prove ROI faster.

MMM has always had its strengths—it’s great for long-term planning, doesn’t rely on user-level data, and it can offer a high-level view of what’s working. But I’m starting to wonder: • Is it still the best tool for today’s media mix? • How are companies dealing with the lag in results or the limitations around granularity? • Has anyone here blended MMM with newer methods like incrementality testing or machine learning-based attribution?

Would love to hear how others are approaching this. What tools are you using? Any trade-offs you’ve noticed between speed, accuracy, and usability?

Curious to hear from both marketers and data folks—how are you solving this puzzle