Google Ads Specialist for DTC eCommerce Brands | PPC, Paid Search, SEM

Hi, I'm Shimon, a Google Ads & Meta Ads specialist with over 10 years of experience dedicated to helping eCommerce DTC brands get new customers and increase their monthly revenue with Google Ads.

For instance, over the last few years, I'm proud to have generated the following results below for DTC brands:

✔️ Scaled a women’s beauty accessories brand to a 12x Return on Ad Spend (ROAS)
✔️ Tripled ROAS for an organic tea brand
✔️ In the past 18 months, I've responsibly managed over £1.2 million in Ad Spend, generating an impressive £8.8 million in Revenue.

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Client Testimonials 

⭐⭐⭐⭐⭐
"I highly recommend Adtent Media for eCommerce brands seeking Google Ads management support. Their exceptional communication, data-driven advice, and quick turnaround make them invaluable partners for achieving great results." Kendra DeYoung - Director of Growth, FlutterHabit

⭐⭐⭐⭐⭐
"Working with Shimon and his agency for the past nine years has been an exceptional experience. Not only have we seen a significant increase in sales and profit, but their open and honest communication has made collaborating with them a breeze." James Slade - Managing Director, Nordic Supplements

⭐ How Can I Help You? ⭐

My primary mission is to amplify your product sales and boost your monthly revenue through strategic Google Ads & Meta Ads campaigns. I apply an individualised approach, tailoring every strategy to fit each brand's unique objectives and market position.

If you're a DTC brand with monthly revenues surpassing $50,000 and have a vision for significant growth in the forthcoming year, let's collaborate. 

Send me a DM and together we'll craft a custom Google Ads or Meta Ads Profit Roadmap specifically to your brand's growth.

Shimon Miah

Strong data-driven marketing and analytics professional who has a penchant for the creative & business side of advertising as well.
Well-versed in BigQuery, Looker Studio, Marketing Mix Modeling, Incrementality Testing, AB testing
I bring a healthy mix of research orientation & a client-focused approach to the table. 
Been 8+ years working with Google Ads, Facebook Ads, Bing Ads, Linkedin Ads, Google Analytics, Google Tag Manager, Google Search Console and other digital marketing channels.
Having managed over $1M a month in ad spend for clients across different sectors and countries and having scaled dozens of them, I have learnt a thing or two about growth hacking. 
Experience working with:
BI using Power BI/Looker Studio & SQL & Python for exploratory and predictive data analytics.
Meridian, Lightweight MMM, Robyn, PYMC (open-source MMM tools)
CausalPy, GeoLift, CausalImpact, MatchedMarkets for incrementality testing/geo-experimentation 
Causal Inference, Causal Impact, Difference-in-Difference, Synthetic Controls, Interrupted Time Series (ITSA), Regression Discontinuity Design (RDD)

Kisholoy Mukherjee

You don't need any third party tools for calculating Share of Search! 

You can just use Google Ads API to build a Share of Search Tool that can give you the average as well as evolution over time of the share of search of your brand.

This is how it works: 

✅ You first have to provide important credentials like Developer Token and GCP credentials 
✅ Then it establishes the connection with the specific account ID from Google Ads (Pro Tip: you need the right level of account in API Center!)
✅ Then you simply input your target brand, target location, language and a list of competitor brands to define the overall market 
✅ And that's it - within seconds, the tool will make API calls and fetch search volume for all related branded terms for the brands in question and give the shares of search for each brand 
✅ A nice time series visualization as well as aggregated bar and pie charts and the raw table will be provided too 
✅ It can be easily extended into other things like geo-mapping it over different locations to see the extent of penetration of the brand compared to competitors in each geo 

What you do next with this information matters the most - you can use upper funnel media channels like Meta awareness campaigns or offline branding via print or OOH or YouTube Ads to improve brand penetration in the markets that you wish to expand your brand's base.

One last thing before I conclude this post - while it is a good thing that more and more brand managers/CMOs/growth strategists are becoming aware of Share of Search (SoS) as a proxy measure for growth, the metric or the concept itself is hardly new. 

Ask any seasoned Google Ads marketer who has spent at least $500K/month and they will tell you that Google has been providing this information - sometimes directly via its dedicated account managers or indirectly through the Keyword Planner - for a long time now.

It's probably for brands that have been primarily marketing offline that this will be new because they haven't tried Search Engine Marketing or SEM like Google Ads before.

So the approach is hardly new - it's what you do with it using modern marketing strategies that will make all the difference. 

I have attached the full video walkthrough - if you are interested in trying out the app, drop a comment or DM me.

#marketinganalytics #performancemarketing #marketingmeasurement #marketingscience #marketingstrategy #googleads #googleanalytics #datascience

Kishalaya Mukhopadhyay

This is why performance marketers, growth strategists and CMO's should keep Simpson's Paradox in mind whenever they take budget reallocation decisions. 

In a nutshell, it means that when you look at a breakdown, things can look very different from how they look like at an aggregate level. 

Let's take a simple example: suppose you are a media buyer and you're looking to reduce budgets in a Google Ads account because efficiency seems low this month. This is overall or aggregate account level efficiency.

So you end up turning down the budgets of campaigns you think are not doing well. But more importantly, you don't increase the budgets of hitherto lower spend campaigns with good returns/ROI. Because you have already made up your mind that efficiency can improve by controlling spends. 

But if you drill down deeper, you may find a surprising trend at times - for each of the constituent geos (replace this with any breakdown, like campaigns) you actually have an improvement in ROI (Revenue/Spend). But the aggregate efficiency has been going down in the same period!

How can that be possible? 

Enter Simpson's Paradox - this paradoxical situation arises because even though performance has been improving for all the geos, the average ROI has been lower for the highest spending, larger geos. And guess what, because they are bigger markets, you may have been pushing spends there in the hope of squeezing out more, and the spends have been decreasing in the smaller geos (because you have already met your target spends, so why bother?!). 

End result? More saturation and diminishing return setting in and overall performance drops.

Whereas you could have tried increasing budgets in the smaller geos, which had much better efficiency. 

Indeed they would reach saturation faster (possibly), but you're most likely far away from that stage. 

#marketinganalytics #branding #growthmarketing #growthstrategy #branddevelopment #marketingscience #marketingmeasurement #googleads #performancemarketing

One of the things about measurement approaches is that new things always look shiny.

Share of search (SoS) isn't one of them. Les Binet famously showed a few years back that SoS can be used as a proxy for Share of Market (SoM). He found, using econometrics methods, that the two are not just highly correlated but also that SoS is actually a leading indicator of SoM. 

It is this latter finding that is far more interesting that just the concept of SoS itself. 

The core idea behind SoS is extremely simple - it uses organic searches (not paid searches) for all related terms of a brand relative to searches for all brands in that category, as a proxy for understanding brand popularity. SEM or Google Ads marketers have actually used this concept for a long time - to gauge penetration and potential for growth of a brand. 

They have unknowingly often done the extrapolation as well - that it correlates with SoM, without actually establishing it scientifically. They mostly used it as a proxy for ecommerce or digital-first brands who operate in markets with almost no available data on market share. 

Now they can be certain after Les Binet's pathbreaking work that their intuition has been right all along!

That said, Share of Search (SoS) isn't some magical pill. There are limitations to the concept. Just because searches are higher for a brand, doesn't necessarily mean purchase intent is higher.

On the other hand, it is equally important to understand what exactly causes an increase of Share of Search? One of the findings of Les Binet's research was this: Share of Search is also strongly correlated with Share of Voice (SoV), which is basically ad spend. 

But what kind of ad spend exactly? This is where things might be different considerably from brand to brand and requires custom analysis. 

Stay tuned for more in-depth takes on 
#marketinganalytics #marketingmeasurement #marketingscience #performancemarketing #marketingeffectiveness #growth

Just launched an update in my Decision Flow Facilitator app to incorporate the change history of your paid ads channels like Google Ads automatically to create what I'd like to call the Decision Flow Plot. 

Pretty neat way of visualizing how outcomes vary based on optimization inputs. 

Allowing for more sophisticated analysis than just passive ones like time series forecasting.

Want to give it a shot? Just Comment "decision" below and I will share it with you
#marketinganalytics #performancemarketing #datavisualization #googleads #marketingmeasurement #dataanalytics #marketingautomation

Scientific Budget optimization is one of the biggest advantages of MMM. Some may rightly argue - that is it's main purpose, not forecasting, not explaining the past. But giving a clear direction for the future.

This is where MMM dominates over MTA - because MTA-based budget allocation does not take into account proper marginal analysis. It's based on heuristics alone, which is a fancy way of saying "guesswork". 

I explained the whole budget optimization process in MMM's (taking example of a frequentist approach) starting from saturation curves to the different optimization solvers/algorithms and the appropriate choice depending on the shape of the response curve after saturation transformation. 

Take the guesswork out of the budget allocation/media planning process:
https://lnkd.in/grp4Ytjn
#marketinganalytics #performancemarketing #marketingscience #marketingstrategy #mediaplanning #budgetallocation #marketingmixmodeling #marketingmeasurement #mmm #mta

Open video: Budget Optimization Using MMM | MMM vs MTA | CVXPY vs SCIPY on youtube.com

Budget Optimization Using MMM | MMM vs MTA | CVXPY vs SCIPY

A nice new addition to my growing list of books on marketing science, marketing measurement and marketing analytics.

I was inspired to make this post after recently getting a lot of questions on good resources for developing your knowledge/skills in these domains. 

This one is a classic as you can figure out from the authors' names. While a bit dated (originally printed in 1992), it contains some really valuable material on topics like some legacy market response models like BRANDAID, which though a bit old, still help clarify important concepts. 

Plus there are valuable discussions on things like carryover effects and mathematical optimization too. 

The best parts are however, the appendices. The discussions on mathematics of marketing response models, including different distributions like gamma, Weibull and log-normal distributions, provide a nice refresher. The section on mathematical optimization is also pretty neat if you want to quickly refresh the mathematical foundations. 
#marketingbook #marketingstrategy #marketinganalytics #marketingscience #mmm #marketingmixmodeling #marketingmeasurement

The biggest advantage of MMM's (when they are feasible to run) is the capability to arrive at the most optimal budget shares for channels and individual campaigns.

This is based on saturation curves and marginal analysis and other modeling parameters that are integral to MMM's. Based on the marginal ROI of each channel, the most optimal allocation of budget among different channels/strategies/campaigns can be ascertained. 

I have described the whole process from first principles to the full math as well as mathematical optimizers that do the heavy lifting in a quick video. 

In this breakdown, I explain: 
✅ Why attribution is the wrong goal 
✅ How saturation curves enable optimization 
✅ Why (& How) CVXPY/SCIPY's mathematical budget optimization works 

If you want to quit the guessing game and start allocating budget scientifically, just drop a comment saying "budget" or "optimization" and I will share the link with you. 

#marketinganalytics #marketingmixmodeling #marketingscience #marketingmeasurement #performancemarketing #marketingstrategy #budgetallocation #growth #mmm

A common question in MMM is: how many data points is enough? 
This question can be broken down into 3 parts: 

Number of data points vs number of variables 
Number of variables vs number of parameters (coefficients, adstock, saturation etc)
Number of data points vs number of parameters

This is just a thumb rule. It will differ from dataset to dataset but these thresholds are useful to understand if you have enough data with respect to the number of channels and the complexity you are adding into the model. 

I have literally seen people trying to model for 15 variables, 45 parameters and just 100 data points! That is highly unlikely to work. 

Keep this benchmark cheatsheet handy. 

#marketinganalytics #marketingmeasurement #mmm #marketingmixmodeling #marketingscience #advertisingeffectiveness #datascience

Update on the reporting dashboard: 
Now it pulls Change History. For now, I am only looking at bid strategy and budget changes in Google Ads, as they are the most important/significant ones. 
More cool updates coming soon - incorporating the change history into charts as annotations and decision making flows. 
If you are interested in the app, just drop a comment, I will share the link - you will automatically get future updates. 
#googleads #performancemarketing #marketinganalytics #marketingautomation #ai #AIinMarketing #marketingautomation #dashboards #reportingtool

If you work with data or analytics, you must have already tried LLM's or GenAI tools like Claude and ChatGPT. They can be immensely helpful, as long as you know exactly what you're looking for.

But if you are not careful, your project is done for. 

I show through a live example - asking Claude about the algorithm for budget optimization in Meridian. What happened next, well, that's in the video...

Do let me know if you have faced something similar with the GenAI tools like ChatGPT or Claude or Gemini.  

#marketingautomation #AI #marketingstaregy #branding #growthmarketing #growthhacking #AItools .#marketingscience #marketingmeasurement #marketinganalytics

📢 Here's update 3 to my decision flow facilitator dashboard: now change history is incorporated into campaign level plots 🤟 

💡 This means as a marketer, you now have contextual understanding at your fingertips - this makes it less of just a dashboard but a decision flow enabler. 

So far I have restricted this to only campaign level changes and that too only bid strategy & budget changes. Depending on the complexity and size of the accounts in question, too many changes getting visualized may make the chart look clumsy. 

🤔 More information doesn't necessarily lead to better visualization. 

So far it is only Google Ads, but more integrations are coming soon to facilitate synergy between granular intra-channel & holistic cross-channel  optimization decisions. 

📊 Other useful additions are decision-focused ratios like SoC/SoR (Share of cost / share of revenue) and deltas like del(L3D Spend)/del(L3D Revenue). 

This can be useful in deciding which one to scale, which one to cut back spends on, for example. 

The last one or its close variants, a kind of elasticity metric, has been quite useful in granular decision-making. 

If interested in trying out this app, do drop a message and I will share the link with you. 

#performancemarketing #marketinganalytics #googleads #marketingmeasurement #automation #AI #reporting #dashboard #marketingtools #businessanalytics

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