Tom Miller

A compelling argument for the importance of experiments and the use of Bayesian modeling. The objection/notion of isolated, walled garden experiments is slowly becoming obsolete. As advertisers increasingly leverage open-source or proprietary geo/user-based experimentation on their own internal metrics, the need for incrementality testing as the gold standard for establishing media causality becomes undeniable. Incorporating experimental data into a Bayesian framework should enable more accurate and actionable insights in this regard.

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Bonus point for the F*ck Survey Fraud shirt, Roddy! Good challenge to the industry about what we have done to create the current situation we are in where data quality is still an issue. First, he unpacked the MRX monster: - constrained budgets - pricing pressure - disengaged participants - survey farms and more sophisticated organized fraud - more bots (non-human) “participants” - AI being used for open-ends The answer is that instead of focusing on CLEANING the data we should be getting focused on it being real, representative and reliable data by not allowing fraud to even get into the survey. It’s a commitment to addressing the root of the problem and not spending money just mopping things up. There’s the dtect differentiation for sure. He had some really great figures to understand the actual hard and soft costs of fraud. Why do we find this totally acceptable??? Also fun scratch ticket below. Great presentation. At Quirk's Media here in New York. Little Bird Marketing #quirks #mrx

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READ OF THE DAY! Insights on #AI impact on #search from Yoast's Carolyn Shelby Want to future-proof your SEO strategy? Focus on being the most trusted answer on the web. E-E-A-T and QC are just labels – the real job is to show your work.

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🚀 One Platform, Infinite Value! We’re revolutionizing the way brands connect with their audiences. By seamlessly integrating content, community insights, and creators, we empower you to: 🎯 Speak to the right audience 📝 Deliver the right content ⏱️ Engage at the right time 💡 Imagine a world where every audience in your funnel enjoys a personalized, effortless experience with your brand. We’re making it possible. 🌟 Check out this video to learn more about how we’re shaping the future of audience engagement. #Innovation #Marketing #CommunityInsights #Creators #AudienceEngagement #ArtificialIntelligence #MarketingTechnology #LLM #DataScience #GoogleCloudPlatform

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For over a decade, Caleb Benningfield has been at the forefront of developing customer data solutions. His post details the ongoing struggle to dismantle the longstanding silos in enterprise data management and describes how the lakehouse model is a significant advancement, combining the benefits of data lakes and warehouses while adding seamless data sharing capabilities. This integration allows each application to optimize its compute capabilities and features, while all data can be accessed through a single, unified interface. This not only simplifies data governance but also enhances flexibility, freeing up data engineering teams to refine and expand the data ecosystem rather than being bogged down with integrations. Do take a moment to read Caleb's post and explore how these insights could influence your strategies and solutions.

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What if messy data isn’t the problem we think it is? Earlier this year, I was with a brand director, and the both of us were staring at data and reports from 14 different agencies—each speaking their own "data language": Different conversion definitions Varying standards for qualified leads Unique counting methods for events "Should we just standardize everything?" he asked, clearly frustrated. "Have them all do it the same way." It’s the obvious answer. The safe answer. But maybe...it’s the wrong answer. Imagine getting 14 agencies to change their processes. This would: - Take at least 6-8 months - Cost $500K+ - Risk breaking their existing processes - Create new inconsistencies So I asked a different question: “....what if we didn’t try to fix it?” So we built the team a translator, an AI-powered “Rosetta Stone” 🗝️ for all those different data languages that: → Learns each agency’s unique "language" → Adapts to new data patterns → Gets smarter with each inconsistency The results were surprising. Within 3 months, we had: ✅ 86% data consistency across vendors ✅ Reports in days, not 4-6 weeks ✅ 60% less time spent fixing errors ✅ $300K+ saved in standardization costs And the best part? The system kept getting smarter, learning from every inconsistency and unique data quirk.  4 months in, we hit 88% consistency.  By 6 months, 92%. And we didn't ask a single person to change how they worked.  Not one process needed updating.  Not one spreadsheet needed reformatting. The Takeaway: In a world where everyone has access to the same tools, tech, and capabilities, the competitive edge isn’t in having perfect data—it’s in how you handle imperfect data. Think about it. While others spend months cleaning data, what if you could start extracting value from it today? If you're leading a team juggling multiple data sources and need faster, smarter insights without the cleanup chaos— Lets chat. #DataStrategy #AIinBusiness, #DigitalTransformation, #Analytics #Innovation, #BusinessIntelligence Repost for others ♻️ Thanks!

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Listen to Statistician and Data Science expert, Allison Olson break down for you how to leverage existing data and move beyond generalized "personas" to hyper-target your specific ideal audience ---- to save money, increase quality lead generation and conversion rates! She spoke last year at SMASH and she is here again to dive into this with me today! Watch the latest episode of The Drake Insights Podcast by Drake Strategic The Drake Insights Podcast Ep 14 - Master Audience Hyper-Targeting with Data Science & Analytics, Allison Olson https://lnkd.in/d5X9Pgnj #seniorlivingmarketing #digitalmarketing #TheDrakeInsightsPodcast #datascience #audiencetargeting #marketingdata

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Google has delayed cookie deprecation by another year 👀 While it does feel like we're chasing a mirage, future-proofing your measurement framework shouldn’t be sidelined. 🔍 𝐒𝐡𝐢𝐟𝐭 𝐘𝐨𝐮𝐫 𝐅𝐨𝐜𝐮𝐬: Move away from relying on attribution and embrace solutions like Marketing Mix Modeling (MMM) and Geo-Lift testing. Focusing on incrementality leads to better business outcomes from media. ⏰ 𝐔𝐬𝐞 𝐭𝐡𝐞 𝐓𝐢𝐦𝐞 𝐖𝐢𝐬𝐞𝐥𝐲: This extra year is a gift - use it to fine-tune and test your measurement framework. 🚫 𝐅𝐮𝐭𝐮𝐫𝐞-𝐏𝐫𝐨𝐨𝐟: 2024 won’t be the death of the cookie, but it should be the end of over-reliance on attribution. 📆 Don’t push improving your measurement framework to 2025! https://lnkd.in/eWQnnqkB https://lnkd.in/eSqQeBSY

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Results from EMARKETER around MMM and measurement that I find VERY interesting and that our teams are solving every day for clients. Our Rapid MMM can often be turned around within 2-3 weeks (sometimes less) once clean data is ingested. And it's not just fast, it's comprehensive and accurate. We incorporate competitor data, macroeconomic data (like S&P500, Unemployment Rate, etc.), holiday and promotional data, non-media spend data like email, sms, etc. and more. Statistically the average R-squared of our models is above 87% with a MAPE below 10%. All of this is agnostic and fully transparent to all brands that engage with us. We share the data, the model outputs and more if a brand wants to validate. We are about growing brands and sometimes that means investing LESS in media spend that we manage.

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Frequency Cap AMC-Driven Optimizations 🔶 Data provided by: Dustin Wassner / btr media Let's talk about AMC, but first, imagine you're playing with LEGOs. Do you consider yourself a Master Builder? Well, think of AMC as having tons of LEGO pieces, each representing data, information, and facts about the individuals who buy stuff on Amazon. For instance, AMC can help us understand how often someone sees an ad on Amazon. It's super important because we want people to see the ad just enough times, not too many or too few. In this case, this AMC lil feature is one of those "pieces" I mentioned above. It's like making sure you show your incredible LEGO creation to your friends just the right amount of times so they think it's cool but not annoying. 😂 With AMC, we can see exactly how often someone sees an ad and if they buy something. 👊🏽 It's top-notch data that reveals who's viewing our ads and their actions afterward. 🤯 By leveraging AMC, we optimize our ads to attract more shoppers to your brand on Amazon. Our goal is for them to interact with your brand just enough to convert them into loyal customers! So, if you're interested in how we use AMC to make our ads awesome or want to chat, feel free to shoot a message to Dustin Wassner or me! 😊 #adtech #amazonppc #retailmedia #btrmedia

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Have any of you seen an online experimentation case study that can pass statistical scrutiny? 10+ years and I’m yet to see one. - Insufficient data - Cherry picking metrics - Flat out misleading conclusions - Post test “massaging” of the numbers If you’ve seen one published publicly please share it. #experimentation #abtesting #cro #optimization

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Are we talking about First Party Data the wrong way? In the old world, marketers mostly relied on pseudonymous data such as click IDs and device identifiers. In the new world, marketers are shifting to personal data such as email addresses and phone numbers. But too many stop here. At the traditional definition of 1P data. Limiting themselves to audience identifiers - just of a different type. But what if we took a broader definition of 1P data and included any data that is either proprietary to a company or generated/gathered internally? This way, we could include operational data that's been directly collected through their own processes, such as inventory levels or profit margins. And include category data such as market trends and own customer research. Reminding us that - in the age of platforms - businesses do not win with more tech, but with how they use it. And that each of us should be leveraging all the data assets they have at their disposal to build a strategic advantage. After all, what matters is how the data it's used, rather than its type and origin.

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Yikes! -- Upwards of 35% of media productivity lost via the combination of IVT, MFA and non-viewable/measurable impressions. Day 2 kickoff insight from Association of National Advertisers In-House Conference in Carlsbad, CA. (May 14-16). Stat is from ANA's December '23 study on Supply Chain Transparency on Programmatic Media. Great reason to incorporate high speed MMM to complement a marketer's existing ROAS toolset to understand marketing's full contribution to achieving a brand's business goals (including programmatic). Josh Casarona, Chris Potts, Federico Salvitti #ANAInHouse, #MMM, #ROAS, #arima

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Instacart not having negative keywords as an option is the best example of what "machine learning" actually costs brands to support. The best solution I've found? Take their auto applied keywords and make their bid the lowest possible (30 cents). I built a really solid ads structure supporting each of our Target sku's and woke up to us bidding on "Ben and Jerry's" as a "optimized auto applied suggesition" for our Vanilla Ice Cream Whey. Gee, I wonder what sophisticated dev team decided "auto bid on product title". Yeah, in the world of CPG there could neeevvverrr be issues with that. Who leads this team and how much do you make. (Hint: too much). Happy Friday to everyone except overpaid leadership that don't actually understand what they're building and get paid boat loads of money for it.

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Do yourself a favour - learn how the Google Tag Manager implementation script works. Learn what an IIFE is. Understand anti-patterns, and how easy it is to break analytics. All in Chapter 4 of The Little Book of GTM v2.0: https://lnkd.in/eySvnc9s #GTM #GoogleAnalytics

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GA4 demographic and interest data isn't even directionally accurate. I work with a lot of brands that make decisions based off of the "best data they have available." It sounds like sage advice, except for when you confuse "best" with "available." Just because something is reported doesn't mean it is accurate, or useful. We have clients who frequently look at the information available in GA4 and make IMPORTANT business decisions like: • Customer strategy • Product strategy • Marketing strategy • Logistics strategy It's tempting, I get it. Google is just so gosh darn convincing. And look, to be fair, a good chunk of the time their data is close enough. But, it's very hard to know if your business is one of the ones collecting a bunch of nonsense. I'll give you a recent example: We had a client with GA4 data that suggested: • Their average age was 25-34 • Heavily skewed male (65%) • Interested in technology, video games, etc We then plugged in their Shopify first party data into nova -- where we actually used their encrypted PII to append verified credit card and demographic details -- and you know what we found? Their actual customer was: • 55+ • Slightly skewed female (55%) • Focused on family, vacations, coupons This made sense with their product, which intuitively seemed to cater more to a retiree focused on their family. This caused us to significantly change our messaging, creative, media, and experiments we were running on CRO. Why is GA4 so frequently off? Outside of the handful of people who actually tell Google the truth when they get those little YouTube popup surveys, the vast majority of their data comes from probabilistic modeling. You searched for knitting so you love knitting. You looked at colleges so you're college age. You watch monster trucks getting crushed so you're male. Etc. For bigger brands that cater to more of the mass market and have huge amounts of data are less affected. But if you're a sub $10m/yr brand, especially one that caters to a specific niche? Fair warning. #strategy #marketing #persona #ga4

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Budget ← [ EXPERIMENTATION TALENT GAP ] → Need There’s a HUGE gap in experimentation & optimization adoption → On one side you have your FAANG + another 50 or so organizations Running 100s to 10s of 1000s of experiments each year. Employing an army of analysts, researchers, engineers, designers, managers & economists. How do you compete with businesses who are so large they need economists to better understand the impact they are having on their users & customers?  (but that’s a whole different topic for another day) → On the other side you have small marketing teams (1-5 people) Barely trying to grasp the complexity of running experiments (let alone at scale) Fighting tooth and nail for budget & headcount Trying to fill the missing expertise with UNICORN 🦄 talent. → Right smack in the middle are all the rest They can’t compete with the giants So they end up levitating towards the bottom of the scale On the hunt for Unicorns as well. Individual employees who can: -Identify (research, analysis)  -Execute (engineering, design, QA) -Deliver (present, document, brief, educate) If you are thinking… “I know there are no Unicorns but I still need to find one or something close to it” You are not alone. And it shows. This week I’m tackling this gap. My goal(s) for this week: - Why it’s dangerous to hunt for experimentation unicorns - What you can do today to try and fill this talent gap - How to compete and win even against giants Happy Monday & have a wonderful week! — — — Still reading… 1. Follow me Hristian 2. DONATE SOCIAL DOPAMINE to a worthy cause: EXPERIMENTATION* *→→ like, comment or share — — — P.S. “Experimentation Confidential | Off the Record” ↳ Private conversations. Actionable insights. → Let’s talk → #experimentation #gtm #b2bmarketing #cro #hiring #employees

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One client using 5 different analytics tools. Imo, that's 4 too many. I met w/ a new client yesterday who has a bunch of analytics tools being used for marketing reporting. 1. ERP as the financial source of truth 2. Adobe Analytics for site performance and attribution 3. Agency using a different attribution tool 4. Quantum Metric in place 5. Ad platform data... Having multiple tools is a great thing. They often have different specialties and uses. Some are more holistic, others more tactical. But I get scared when I hear things like: - "Adobe doesn't match the ERP." - "The agency attribution doesn't match Adobe." - "Reports are shared broadly from ALL these systems..." Confusion and chaos. If you're going to use multiple tools be sure to: - Ensure accuracy across all tools - Clearly define your source-of-truth for each metric - Limit access to multi-source data - Educate teams on data differences before sharing - Curate vs. Open-access ...and this gets harder as your company and team grow. What's your take? How many analytics sources are you using at your brand? Do you allow access to all or are you more selective? #analytics #singlesourceoftruth #marketinganalytics

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