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

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.

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

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? 👀

No alternative text description for this image

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.

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.

I'm Dan, a marketing measurement and analytics specialist with over a decade of experience in the data trenches, turning complex data into clear action for brands that want more than just a good-looking dashboard. I work as a freelancer/contractor under Kickflip Analytics, and as part of a senior team of specialists at Data to Value.

Daniel Perry-Reed

UTMs are the undisputed gold standard for tracking attribution across almost every marketing platform today.

Because your CRM, CRO and analytics platforms all speak the language of UTMs, every single channel needs to use them. No exceptions. If there is a gap, your data is wrong.

Yet, many marketing teams don't use them in Google Ads, thinking auto-tagging (GCLID) does the same job. It doesn't, and creates a massive blind spot in your data ecosystem.

There are three main scenarios where a robust UTM strategy is non-negotiable in Google Ads:

🔸Your Google Ads traffic is appearing in GA4 as (organic) or (direct).

🔸You need to pass verifiable attribution data into your CRM, CDP or analytics tool.

🔸You want to personalise landing pages in your CRO tool based on the specific Google Ads Campaign or Ad group.

Annoyingly, Google Ads does not have a way to apply UTMs automatically. So it is up to us to do it. To help, I have just updated my Google Ads script that automatically applys UTMs to all ads.

It now supports PMax, Video and Shopping campaigns, it also adds support for Ad group names too. Plus some efficiencies to speed up the script for larger accounts.

You can find the updated guide and the script here: https://lnkd.in/e5aU2RVE

Now go forth, and UTM EVERYTHING! 🖖

Marketing measurement and analytics @ Kickflip Analytics

Open article: How to add UTMs in Google Ads for better attribution by kickflipanalytics.com

How to add UTMs in Google Ads for better attribution

AI is not a job.

​Google Ads is not a job.

​GA and GTM are not jobs.

They are tools, pure and simple.

Sometimes, you need to swap out old tools for better ones - a hammer is brilliant until you need a drill.

Don't panic over the tools, things in marketing change all the time.

Focus on the job.

All marketers know the value of optimising to CPA, but only a fraction actually agrees on what "CPA" means.

This friction often does not stem from a lack of skill, but from a lack of shared definitions. When a CMO asks for "the CPA", they assume it is a single, objective number.

It isn't. As my example data shows (see image), CPA can be calculated in at least three ways, each serving a different strategic purpose:

🔸Target CPA: This measures the cost to acquire a specific product you were actually advertising. If you spend £100 on shoe ads and sell 10 pairs of shoes, your "target" CPA is £10.00.

🔸Total CPA: This accounts for the fact that humans are unpredictable. Someone clicks a shoe ad but buys a dress or hat instead. Total CPA divides your spend by every single conversion from that campaign. So the "total" CPA for shoes is £7.69 in our example.

🔸Blended CPA: This is the big picture view, looking at the total sales of a product across all campaigns. Your hat ads might have a high target CPA, but if your shoe and dress ads are also driving hat sales, your "blended" CPA for hats might actually be lower (£8.82) than you think.

And just to keep things interesting, this is before we even introduce attribution models. Depending on whether you use first click, last click, or data-driven attribution, you have got a whole new set of CPA calculations to navigate.

There is no single objectively 'correct' calculation for CPA. The right CPA depends entirely on the question you are trying to answer.

The danger lies in letting black-box marketing tools decide for you.

There is massive value hidden in simple data like this. You do not need to be a data scientist or run machine learning models to make a difference. You just need to agree on the definitions.

Data table and stacked bar chart showing how three ad campaigns targeting Shoes, Dresses and Hats can convert for any of these three products. It shows three CPA calculations for target CPA, total CPA and blended CPA.

Most marketing reports don't resonate with stakeholders because they try to do three different jobs.

To win stakeholder trust, we need to separate our communication into three distinct layers:

1️⃣ Narration (the "what"): 100% automated - this is the data. "CPCs are up 10%." This belongs in a self-serve dashboard or Slack message. If a machine can say it, don’t waste your time repeating it in a meeting.

2️⃣ Commentary (the "why"): 50/50 human and machine - this is the context. "CPCs rose because a new competitor entered the auction." Your tools provide the signals (change logs, algo updates, etc.) but we filter them.

3️⃣ Insight (the "so what"): 100% human - this is the strategic pivot. "Because of the auction heat, we’re shifting 20% of the budget to long-tail terms." This is human-driven descision making. It belongs in a call, meeting or a QBR, not a report.

The trap? Trying to automate the insight or manually narrating the data. One wastes your authority, the other wastes your time.

Build reports to handle the narration so you have the headspace to deliver the insights. A report won't do that for you.

1) naration, the what - fully robot. 2) commentary, the why - half robot, half human. 3) insight, the so what - fully human. By Daniel Perry-Reed @ Kickflip Analytics, Generated via Nano Banana 2.

Well, I suppose they did what they were told 😂

{{replace this with a keyword-stuffed call to action that the LinkedIn algorithm will like - for reach}}

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