Tom Triscari

Adtech M&A is certainly heating up. Last week Digiday reported that Teads is exploring various sale options. This kind of news piques our Quo Vadis curiosity. So we did our best to puzzle together a fundamental valuation model. As imperfect as it may be, our model tells us that $2.2 billion is close to the magic number. Get the details and see how we get there: https://lnkd.in/ee6wEyXM Subscribe to Quo Vadis to follow this very interesting adtech valuation story! #adtech #programmatic #radicaltransparency

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🔍 New Article Alert: Setting Priors in Bayesian Marketing Mix Modeling (MMM) Bayesian MMM leverages prior distributions to embed domain knowledge, enhancing model robustness amidst noisy or uncertain data. In my latest Medium article, I delve into the importance of setting the right priors to expedite model convergence and yield actionable insights. 📌 What You'll Learn: - The role of priors in reflecting pre-existing beliefs about channel effectiveness, price elasticity, and more. - Practical steps to set priors for better budget optimization and stakeholder alignment. - How to choose between informative and uninformative priors based on available data. - Common distributions used in Bayesian modeling and their applications. Read the full article here to explore how integrating expert judgment through priors can transform your marketing analytics. #Bayesian #MarketingMixModeling #MMM #DataScience #Analytics #MarketingStrategy

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Advertisers everywhere are looking to make the most of AI. While those in AdTech look to make use of its data analysis capabilities, I find it interesting how advertisers themselves are looking to incorporate AI into the creative aspects of advertising, especially as an alternative to A/B testing. Things like dynamic personalization certainly seem interesting, but questions about how the cost inherent to these tools translates over to results still looms heavy over the industry as a whole. #AdTech #Innovation #Data #AI

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A majority of brands continue to under invest in YouTube because it shows badly on click based attribution. (Obviously, because nobody clicks on YT ads.) When measured properly YouTube is one of the most under valued inventory sources in the market. YouTube content is helping us drive strong user acquisition for a number of clients and consistently shows strong results (as long as you measure the job it is doing rather than trying to make people click on video ads)

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https://lnkd.in/dwBSuhU9 This week I look into the key attributes for Managed (Non-Programmatic) Pricing. This video is really just intended for people in Pricing and Yield Teams. In particular I take a pretty strong stance on a few attributes that I see teams trying to incorporate into their pricing decisions.   Spoiler Alert - If you are trying to price Cross Elastic effects and Time-Of-Day effects in a Managed Environment, don’t waste your time!   Controversial Take: You are probably spending too much time on incorporating Sell-Through-Rate and time-of-week or time-of-year into your pricing.

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You want to know the secret to creating killer campaigns? It's all about segmentation. Segmentation strategies are the key to standing out in a sea of noise. In this fast-paced digital age, customers want to feel SEEN and UNDERSTOOD...not just like another number. By catering to their specific needs and preferences, we can create a deeper connection with our audience and increase the chances of converting them into loyal customers. No-brainer right? I'll uncover everything you need to know about it here in this blog: https://lnkd.in/eWqb_h28 #Segmentation #Personalization #MarketingTips

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Thanks for raising the issues again Finn. One clear problem is that unfortunately too many researchers confuse making surveys or qual response less stressful with gamification. For a decade I've heard a lot about the latter Nd how we can learn from the gaming market. In reality the industry too often thinks adding characters or more fun answer options or the " chance to win if you complete the fun tasks" as gamification. It's not. Go look at successful games, from angry birds to world of warcraft. They create worlds where the player is there for the game, the data collected is huge but indirect. The last few years I've spent more time with esports and gaming companies. Companies that create "gamification" that people pay to play. When was the last time a market researcher was paid by respondents so they could play ? When was the last time market researchers paid gaming companies to develop ways to get the answers we seek inside their games ( and yes I know smarter MR people ate starting to use in game social like Discord)?. It's a big subject .. more dis ussion needed.thanks again for reminding us

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📺 📺 📺 75% of in-store retail media will be deployed in the next 18 months!!!! This Grocery Doppio research paper is a wake up call to grocery, big box and large #retail chains. The time is now to get to moving or be left behind on one of the greatest profit drivers for brick-and-mortar retail. Some takeaways: --- Retail media is good for shoppers, brands and retailers --- 🛍 There is a 38% increase in shopper engagement with in-store media with 61% of shoppers saying the media content was useful! 💚 27% increase in impulse buys at grocers who enable in-store media 🛒 97% of grocers intend to adopt white-label or thirds party rather than build 📊 93% of #CPG brands want to to tie digital and in-store engagement. 📈 The number of #retailmedia networks will double in next 18 months --- Challenges to scale --- 👩‍💼 79% of retailers say they don't have the talent to scale their #retailmedia biz 💰 Only 3% of CPGs have significant additional budget for grocery media in 2024, and 89% want greater data accuracy for that market spend. Download the report here --> https://lnkd.in/ejJQmeSu Thanks for sending my way Lisa Goller, MBA

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Anyone selling adtech/martech/data needs to remember: your prospects already have more of all that stuff than they can possibly manage. From the article: "AB InBev collects data from across more than 1,000 sources, including ecommerce, social and branded websites. It owns over 500 brands in 42 countries." AB InBev is a slightly extreme case, owning many of the world's largest consumer brands, but every company (at least the ones with any real budget to spend) has its own microcosm of this. The point is: the standalone value of your solution is not nearly as relevant as you might like, because no matter how performant it is, it's competing for finite mindshare and resources with literally thousands of other data sources, campaigns, channels, and technologies your client is already juggling. A few percentage points performance increase in one small corner of a client's digital marketing/advertising footprint often just doesn't matter very much. This is where it becomes CRITICALLY important to be able to zoom out on a client's business and understand all the ways your solution can add value and all the trade-offs and dependencies you need to help your client navigate. Every trade-off needs a rationale, and if you don't proactively help clients develop those rationales, they will just keep things status quo because it's a lot less work and a lot less risk. Great 1P data case study from Allison Schiff at AdExchanger. https://lnkd.in/gERHC3Rg

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New research highlights the decline of traditional tracking methods like coupon codes and unique phone numbers. With stricter privacy regulations, marketers are turning to advanced models, like Bayesian modeling, to better understand the full customer journey. This shift shows that focusing only on the last interaction ignores the impact of channels like offline media and PR. Brands embracing comprehensive measurement models can uncover hidden insights, boosting both brand awareness and sales conversions. How are you using advanced measurement models to capture the true value of your marketing efforts? Learn more: https://lnkd.in/gQRY-Cgz #MarketingTrends #CustomerJourney #AttributionModels #DataDrivenMarketing #MarketingLessons

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Ecom Brands: 10 Common Data Issues We Come Across 1/ Attribution Missteps Relying on outdated MTA tools or last/first touch attribution instead of contribution and incrementality models. This can lead to wasted ad spend. Start with simple geolift (MMT) and hold-out tests to measure incrementality. Understand that not all channels can be tracked w/ click-based tools. 2/ Pixel Problems Incorrect, duplicative, or missing ad pixels lead to inaccurate data collection. Audit your pixels monthly for accuracy. GA, Meta, Google, TikTok, Affiliate platforms, etc. 3/ Server-side Tracking Not using server-side tracking, resulting in signal loss and hindering ad optimization. You can be losing 20-30% of your conversions w/o proper SS setup. 4/ Excel Hell & Reporting Inefficiencies Manual and infrequent updating of reports, often trapped in spreadsheets. Automate common reports. Even basic KPIs updated daily for the team will be a big win. 5/ Product Catalogs are Stale Bloated or incorrect product feeds with outdated information and mismatched pixel data. Audit your catalogs frequently. Segment your feeds. Setup alerts. Use a centralized feed source or vendor to streamline updates. 6/ UTM Inconsistencies Poor campaign and UTM naming standards make performance breakdowns difficult. Create a standards document and ensure that all your marketers and agencies use it religiously. 7/ Customer Data Fragmentation Lack of a single source of customer truth, with customer and 1P data spread across various systems. Start to ingest your 1P data into a data warehouse. You should own and know how to govern your customer data. 8/ KPI Blindness Limited understanding of critical metrics like New Customers, LTV, MER, Contribution Margin, CAC... Knowing which to use and when. Understanding driver trees and ensuring your team knows the key metrics for their channel. 9/ Inaccurate Forecast Failures Poorly constructed forecasts or a complete lack of forward-looking projections. Even a basic forecast, shared across the team will help. 10/ Data Silos Isolated data and poor transparency across different teams and departments. Centralize datasets, KPIs and reports where possible. Better communication and data access. The good news is that fixing these issues is 25% technical cleanup and 75% education/process improvement. What other data challenges do you face in your e-commerce operations? #ecommerceanalytics #dtcmarketing #measure #ecommerce

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Insightful post on attribution models by Ruler Analytics! I find the U-shaped and Time Decay models especially reflective of consumer behavior from my own purchasing experiences. Curious to see if anyone has explored attribution models indexed to expected funnel journey times. Thoughts? #attribution #marketing #martech #data

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AI Video will change the way brands approach video marketing. My thoughts in the article below.

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Epic piece from Jon Bradshaw on just how ineffective targeting on digital platforms is. Simple broad reach conclusively beats targeting with third and second party data. First party does better, but contextual targeting (put your nappy ad in a parenting mag or your powertool ad on a home improvement TV show) is the best if you really want to target. A big load of evidence that the targeting promise sold by the platforms is a big load of rubbish.

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Adstock is an important concept in cross media attribution through marketing mix modelling. Here Winston Li explains in a very short video how it works. Arima #marketmixmodelling #attribution

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💡 5 Benefits of Consumer Sentiment Analysis with OneCliq Understanding how your customers feel about your brand, products, and services is more important than ever. OneCliq’s Consumer Sentiment Engine offers these 5 key benefits to help you stay on top of your game: 1️⃣ Understand Your Audience – Instantly see how consumers feel about your brand and tailor your messaging for better engagement. 2️⃣ Improve Customer Engagement – Address concerns and engage your audience at the right moments based on real-time feedback. 3️⃣ Optimize Marketing Campaigns – Use sentiment data to tweak your campaigns for better results. 4️⃣ Prevent PR Crises – Stay ahead of potential issues by forecasting consumer reactions and taking action early. 5️⃣ Gain a Competitive Edge – Leverage real-time insights to stay ahead of market trends and your competitors. 📈 Want to turn consumer sentiment into actionable insights? Let’s connect and discuss how OneCliq can help your brand! #ConsumerInsights #SentimentAnalysis #MarketingStrategy #PRTools #DataDrivenMarketing #OneCliq

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In this episode of the Revenue Engine Podcast, I talked with Mark McEachran, Senior Vice President of Product Management at Yieldmo, to discuss the evolving landscape of digital advertising. We explore the importance of first-party data, the role of contextual targeting in a post-cookie world, and future opportunities for privacy-focused startups. Check out the latest episode of the Revenue Engine Podcast. Link in the comments.

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This is an interesting case study on how one organization looks to use technology to make a substantial pass in data, advertising, and technology. Data efficiency tools are an interesting approach to making use of data, especially amid shifting advertising trends. When companies can make use of data granularly, they have a better chance of making an impact. #AdTech #Innovation #Technology

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Data is the biggest challenge most B2B CMOs are dealing with right now. There will be more changes to the main tools in the B2B marketing ecosystem in the next 12 months than in the last 12 years. - IP masking (already happening – will likely be systemic by the Fall – do you know which data sources and bidders are IP based? More than you’d think!) - Cookie deprecation (already happening – Google pushed till early next year because people just aren’t yet prepared – are you taking this reprieve to future-proof your business?) - Increasingly stringent privacy regulations (already happening – do you understand what will happen to data and your GTM motion when we rely on HEMs and MAIDs? Will the massive impact on our ability to scale media programs meaningfully impact your revenue?) These are questions many B2B CMOs are thinking about – but there’s a big issue. The only place to find answers is from data and platform vendors. These are good companies, and there are no bad actors, but they are also selling their own solutions in the best way possible. It’s extremely hard to navigate to the truth you need to be prepared for this emerging data environment. With that in mind, we’ve created The Data Dictionary for you to share with your team to do the basic job of codifying all the language and terms that matter when using data to execute an effective account-based (ABM) marketing strategy. It’s a living document that we will continue to update as the landscape evolves. It is the brain-child of the inimitable Mark Dye and I’m so proud to be able to share it. At The Imaginarium this is the work we do all day – Educate. Implement. Execute. We make sure our B2B clients identify the best possible system to drive growth, implement the tools and data that it needs to operate, and then support them through ongoing tactical execution. We do this for some of the best brands in the world. Let us know if you found the Data Dictionary helpful or if there are areas you think we should focus on as we continue to revise. Jonah Goodhart Toni Clayton-Hine Glenn Gaudet Dan Rosenberg Ali Manning Jessica Fernandez John Ruvolo Amanda Kahlow Patrick Reynolds Amanda St L Jobbins John McNeil Christine C. Cook Joe Zawadzki Putney Cloos Lynn Teo Gary Maggiolino Drew Neisser Gayle Meyers Christopher Burns Cynthia Gumbert Bernd Leger Hannah Budreski Scott Decker David Spitz Kate Ardini Frannie Danzinger Josh Golden Lynn Tornabene Janet Balis Jaime Punishill Anna Griffin Dan Petrossi Deidre Hudson Alicia Parker Jon Lombardo Jake Henry Jeremy Botwinick Jessica Koenig Jodi Lyons Miller @helen-shaw. https://lnkd.in/g5FGh6Es

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