I help companies measure and take action on their marketing data.

We both know that being online takes a lot of work. I help eliminate the "Guess Work". 

Using Google Marketing Cloud we are able to predictably increase MRR while measuring each channel's step in the marketing journey.  

We take all of your Data from your CRM, Google Analytics, and cart platform, then turn it into a  real-time data dashboard for you to take action.

What most digital businesses need is someone to provide reliable consistent data to take action. You have a team and a plan, but want to know "Is it working?" & "Where can we cut the fat?".

Head to Mediauthentic.com and apply to work with us. 

THE COMPANIES THAT WE WORK WITH! 
✅ Care about your customers! 
✅ Want to grow with as little wasted marketing efforts as possible.  

WHAT WE DO! 
We offer custom solutions that help you: 

☑︎Grow your MRR (Monthly Recurring Revenue).

☑︎Increase the velocity of return (Aware -> Revenue). 

☑︎Measure your current system efficiency. 

☑︎Pivot into more reliable traffic solutions.

☑︎Alway improve your measurement systems.


WHAT OUR CLIENTS ARE SAYING
“Accountable, knowledge, and able to deliver on time. You won’t regret working with JJ.” Russell Kim, DDS - Windward Smiles

“So amazing to work with! it seems like they get stuff done 10x faster then I ever would have thought!” Shilhi Seibel - Passion Roots 

“Mediauthentic helped me create an online medical education platform. They helped me feel proficient with the technology applications I needed for my projects. What I appreciated most was that JJ took the stress out of the process (actually made it fun!). He was easy to interact with and kept me on track to reach my goals” - Darin Gould, MD


JJ Reynolds

5 tools to do one job.

That's the average data stack in B2B SaaS today.

PostHog bet on convergence. And it shows:

➡️ One single event schema for everything

➡️ Analytics and routing from the same source

➡️ ~50k MAU / 5M events: $615/mo vs $3k-$8k+ with Amplitude + Segment

➡️ No connection fees, no license Tetris

Where does it fall short? Segment is still better with 50+ destinations and complex transformations.

But for most teams: fewer tools = less money = one single source of truth.

Is your stack still fighting itself? 👀

Helping marketers make better use of their data | custom tracking, visualization & forecasting

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AI makes data analysis and activation way faster.

But if your data foundation is broken, you're just automating bad decisions faster.

And most companies have a broken foundation.

Before you touch AI in your stack, you need to have this:

➡️ Governed event taxonomy

page_viewed vs Page Viewed vs pageView across three SDKs = data that looks complete but can't be joined.


➡️ Identity resolution across sources

Without a layer that unifies anonymous ID, email, and domain, your AI is analyzing ghost users counted three times.

➡️ Data contracts and schema enforcement

When a dev renames a property, your pipeline should break loudly. Not silently pass malformed data for weeks.

➡️ Modeled entities in your warehouse

AI querying raw events is going to hallucinate joins and miss business logic every single time.

➡️ Quality monitoring

Anomaly detection, freshness checks, drift alerts. If you don't know your data is broken within hours, nothing downstream is reliable.

Get this right and AI becomes a multiplier.

Skip it and you're just automating bad decisions faster.

Imagine this world -
Product & Marketing analytics in the same location 
&  
engineering teams aren't bogged down from marketing asking for " one more thing"... 

Check this out 
https://lnkd.in/g7AR8zES 

PostHog endpoints fueling a web analytics custom UI. 

Time to action is getting shorter & shorter for marketing & product teams.

Thanks for all the DMs with suggestions! 
Now you can easily filter all metrics & dimensions in real-time :) 

Using the PostHog endpoint. 

Also;
Pricing is unknown 
Response time is debatable 
Coolness is great!

A lot of teams aren’t staying on GA4 because it’s working especially well. They’re staying because ripping it out feels like a pain, and nobody wants to be the person who creates even more tracking chaos.

Meanwhile the chaos is already there.

Purchase events don’t quite line up with actual revenue. One country domain tracks one way, another breaks in a totally different way. Someone tries to fix it server-side and suddenly one market looks cleaner and another one looks worse. And after a while nobody really says it out loud, but nobody fully trusts the numbers either.

So the team just... lives with it.

Keeps patching. Keeps adjusting. Keeps looking at dashboards with that quiet feeling of “this is probably directionally useful, I guess.”

That’s usually the point where I think the GA4 vs PostHog conversation gets framed wrong.

PostHog isn’t just “another analytics tool.”

It changes the shape of the setup.

With GA4, you’re mostly working inside one system and trying to make your needs fit around it.

With PostHog, the data can move where you need it to move.

Sessions, personas, events. Into BigQuery. Into Slack. Into HubSpot. Into feature flags. Into your warehouse. Into the rest of the stack you actually use.

So it’s not really about “starting over.”

It’s more like finally setting things up in a way that matches how your team already works.

And honestly, for most teams, the real blocker isn’t love for GA4.

It’s fear of the mess of switching.

Which is fair.

But staying in a system nobody fully trusts is its own kind of mess too.

What’s actually keeping your team on GA4?

Actionable Insights come at the intersection of expectation x outcome x ability to act. 

Useless scenarios: 
You do not have an expectation & know the outcome
You do not have the ability to act & have an expecation 
You know the outcome & have the ability to act, but there was not expectation.

There are three games to win. 

Game 1: Own the underlying data at a cost everyone supports 

Game 2:  Own the interaction of the data 

Game 3: Own the opinion on how to action the data. 

Each game increases in complexity and downside if done wrong.

Juan Bello

Co-founder at Porter (No-code marketing reporting for teams and agencies) 📊

I'm co-founder at Porter Metrics, a startup that automates cross-channel marketing reporting for teams and agencies.

We've +1,500+ customers in 60 countries including Oracle, Johns Hopkins University, Visual Capitalist, Globant, and Havas. 

We're building agents on top of this data—that we've built for years—to automate marketing operations tasks. Think of an n8n for marketing. 

I'm always looking to talk to great marketers who are both technical and creative.

Co-Founder

At Porter, we connect apps to reporting tools. Think of Zapier for data reporting.

We started by automating marketing agencies reporting on Google Data Studio and Google Sheets.

¿Quieres crear tus propios dashboards y gráficos con AI? 

Por ejemplo, crear tu propio reporte de marketing con Claude Code. 

Quizás, con algo de amor, crees tus propios dashboards con vibe coding desde '—pero mantener eso se te va a llevar una buena cantidad de tokens. 

Andrey Vinitsky de Graphy acaba de sacar la infraestructura (APIs, skills, SDK)  para que puedas hacer vibe coding de tus dashboards y reportes. 

Si quieres crear tu propia herramienta de reportes—y dejar de usar cosas como Looker Studio— es un buen momento para hacerlo. 

Si quieres acceso...no voy a pedir comentarios. 

Solo escríbele o ve a graphy(.)dev

Co-Founder @ Porter Metrics | Marketing Operations Agents

AI no va a matar al SaaS...por ahora. 

Yo he estado paranóico con Claude Code. Cualquiera puede crear cualquier producto de software.

Tanto así, que nosotros creamos un "Claude Code para marketing" ya con usuarios pagando para que puedan crear sus propias apps y hacer su marketing. 

Ya tenemos usuarios corriendo sus ads, sitios web y outbound con esta aplicación. 

Pero con los mismos usuarios he visto la fricción que existe que va más allá de programar. 

⚠️ Uno tumbó su sitio de WordPress por dar mal su instrucción (se recuperará)
⚠️ El otro no pudo hacer el user management (o no sabía que podía usar un tecero) 
⚠️ A otro lo bloqueó Meta por conectar su cuenta via API y hacer acciones a escala. 
⚠️ La gestión de costos es un tema todavía. 

Estos y muchos otros problemas no se resuelven con un mejor modelo de AI o mejor código. 

Se resuelven con ingeniería, con experiencia, con política, con gestión—con comer 💩.  

Con todas esas cosas aburridas que implican tener una empresa de software. 

Por una parte, es triste que hayan tenido estos problemas usando nuestro nuevo producto. 

Pero también me da un respiro de que las empresas 100% basadas en software viviremos un poco más (hasta que salga otra cosa que "lo cambie todo"). 

Un consejo: hacer caso a quienes usan AI y crean productos de AI de verdad. No a creadores dedicados a hablar de AI. 

Ambos tenemos incentivos diferentes: unos necesitan engagement; los otros vender un producto que la gente quiera usar cada día. 

A uno de estos grupos le toca hablar con la verdad.

Así se siente manejar tu CRM con AI desde un chat

En Porter tenemos más de 200k contactos en ActiveCampaign. Pagamos más de $1,000/mes. 

No lo había priorizado por 1) pereza, 2) es demorado, 3) no he hecho funcionar email marketing. 

Hoy saqué unas horas y lo hice todo desde el chat:

✅ Eliminó contactos desuscritos e inactivos +50,000 emails inactivos removidos +9,000 contactos que no deberían estar en la lista

✅ Guardó un backup antes de borrar

✅ Identificó 100 agencias de marketing entre mis clientes Con nombre del dueño y sitio web de cada una

✅ Creó una campaña de email Copy redactado y enviado desde el mismo chat

La API de ActiveCampaign tiene un límite de 5 requests por segundo.

Con 200k contactos eso tarda varias horas. Pero él mismo gestionó esos límites, esperó, reintentó, y lo resolvió solo. 

Hoy es más útil para mí que una herramienta tenga una buena API a que tenga una buena interfaz, así puedo controlarla desde el chat. 

Esta imagen es del test del email que creé.

Some players in the marketing analytics space claim to build agents trained on top of $X Billions on ad spend data. 

I can concede that I'm not an AI expert, but really don't know how $50k, $500k, or $5m on training data make literally any difference. 

It's marginal at best. 

I used to think such fine tunning would be very important as early AI models sounded generic. 

Now I think you just need some great texts with your way of thinking and analysying data. 

That's 2 pages. Not 1 petabyte of info. 

It's a great narrative though. 

But speaks volume about defensibility and product differentiation. 

If you think that it sounds amazing, here I go:

Our Slack notifications at Porter Metrics are trained on $1 trillion on ad spend. (100% verified, trust me).

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The Battle

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