Gérard S.’s Post

Last-click attribution is still an option in many platforms, but it rarely provides a true picture of ad effectiveness. While it may be comforting, relying on it is unlikely to drive actual growth. Incrementality tests, in particular AB tests, randomised experiments and geo-holdouts, may not show you large "claimed conversion" numbers. The true incremental uplift is often only between 1-10%. However, this is the truth. Think about this from a sensitivity point of view: If the last-click (or last-touch) attribution numbers were accurate (often suggesting 50-80% sales are driven by one campaign), every company running multiple ad campaigns would need to grow as fast as NVIDIA over the past year. The good news: The same platforms that offer last-click or last-touch attribution also tend to provide various incremental lift tests in many regions. For example: Meta - Conversion Lift and AB Testing https://lnkd.in/gr6nyg7m and https://lnkd.in/gTip8uJ9 TikTok - Split Test https://lnkd.in/gaCiZ-G7 Amazon - AB Test and Brand Lift https://lnkd.in/gF2YBmsN and https://lnkd.in/g5KDzgek Google - Conversion Lift https://lnkd.in/gWCYnwsu Trade Desk - Conversion Lift https://lnkd.in/gWBaaGdS Otherwise, there are also great third-party tools that can be used for incrementality tests. Additional comment 1: The comparison of last-click/ touch attribution versus incrementality tests mainly refers to within-channel/within-platform optimisation. Additional comment 2: Not all split or AB test tools will assign users randomly to different ads or test conditions. Sometimes a targeting algorithm decides on the selection of users too. This means we cannot speak of pure causal incremental effects. Unfortunately, often tutorials and explainers are not clear on the settings and manipulations. That's why it's super important to always check this. One great research study outlining the differences in types of experiments is the following study (link below): Braun, Michael, et al. "Leveraging digital advertising platforms for consumer research." Journal of Consumer Research 51.1 (2024): 119-128.

This is the first-ever display ad on the internet. A fun fact I discovered when putting together my talk for Global Sellers sp. z o.o. event in Warsaw 🇵🇱 later this week. Despite being from 1994, it looks immediately familiar as a display ad. A rectangular format with an eye-catching message that sparks curiosity and interest. Clicking through took the user to an informative landing page for AT&T's "You Will" ad campaign that was optimized for fast loading times. It all sounds pretty familiar, doesn't it? Consider what has changed: ▶ Targeting - Category, product, demographic, interest, in-market, remarketing for views and purchases to name a few. The list grows much longer if you include ADSP and the ability to upload your datasets. ▶ Creative - We have dozens of display ad formats, some of which dynamically adapt to who is viewing the ad and what product detail page conditions are (coupon, LD, deal of the day). They work across all kinds of devices with different screen sizes including TVs. We now benefit from AI-generated creative. Display ads can be downright beautiful if done right. ▶ Reporting - In Amazon's closed-loop marketplace the reporting is fairly comprehensive. Sure there are a few gaps we'd still like to see filled (click vs view attribution, off-Amazon placements) but we can understand our impressions, NTB purchases, and optimize campaigns depending on our bidding type. With the arrival of AMC, our ability to analyze the customer journey and their interaction with our display ads has seen an exponential increase. Advertisers from 1994 could scarcely imagine the sophistication we benefit from today. They would be excited, or maybe a bit shocked and even scared to dive in and use these features, depending on their attitudes toward privacy. At least we don't have to optimize landing pages for 1994 internet speeds 😁

What's the difference between click vs. view attribution? Is view attribution the same as view metrics? Why does it even matter? Well, let me break it down. As mentioned in the previous post, attribution is who (what advertising platform) gets credit for a sale/lead within a given amount of time. Then there's two types of attribution - the first is CLICK - the second is VIEW. In other words, 7 day click 1 day view means, if a customer clicked on an ad within 7 days or viewed it within 1 day, that platform will claim credit for the sale/lead. Attribution is credit - and proof of that credit. For example, in AdBeacon, we can show you that a customer clicked on an ad for META, Google, Klayvio etc. all before purchasing your product. That click is tangible and validated with a touch. This proves to be extremely valuable when allocating advertising dollars to impactful campaigns that drive customers to buy with data that is no longer accessible in META etc. With view, things are a bit trickier. There are metrics in advertising platforms like META that tell you how long someone watched a video, however, we don't know what is considered a viable "view" that attributes to a sale without a click. What is considered a viable view that validates attributed credit? Would you put weight behind someone who viewed your ad but didn't click? How much weight? This is the ongoing debate and depending on your media buying style can vastly effect your decision making. At AdBeacon we decided that while view is valuable, we wouldn't give it credit towards a sale without a click. Why? Because we can't prove it. What're your thoughts? What is considered a view? How do we quantify it and standardize that across all channels?

The Attribution Models: When to Use 3rd Party Tools? As a media buyer for DTC Brands, I've had my fair share of conversations about attribution tools. Recently, One founder asked about when to use third-party attribution tools for tracking data. I responded on how many platforms he was advertising, He said he is mainly running ads on a single platform i.e. Meta. But he wanted to use the tools like Triple Whale or Northbeam. It got me thinking - Does he have clarity on when to use these tools? Well, I guided him, the third-party attribution tools are fantastic, but you need to know exactly when to use them. If you're advertising on multiple channels like Meta, TikTok, and Google etc it's worth investing in these tools. But if you're only advertising on one platform, you might not need them. Here's the thing: if you rely solely on in-platform conversion reporting, you'll see a lot of double or triple reporting. Each ad channel will claim the same conversion, making it difficult to understand the true impact of your ads. 𝘓𝘦𝘵 𝘮𝘦 𝘤𝘭𝘢𝘳𝘪𝘧𝘺 𝘪𝘵 𝘸𝘪𝘵𝘩 𝘢𝘯 𝘦𝘹𝘢𝘮𝘱𝘭𝘦. 𝘐𝘮𝘢𝘨𝘪𝘯𝘦 𝘢 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘸𝘩𝘰 𝘤𝘭𝘪𝘤𝘬𝘴 𝘰𝘯 𝘢 𝘍𝘢𝘤𝘦𝘣𝘰𝘰𝘬 𝘢𝘥, 𝘵𝘩𝘦𝘯 𝘰𝘯 𝘢 𝘛𝘪𝘬𝘛𝘰𝘬 𝘢𝘥 𝘰𝘧 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘣𝘳𝘢𝘯𝘥, 𝘢𝘯𝘥 𝘧𝘪𝘯𝘢𝘭𝘭𝘺 𝘮𝘢𝘬𝘦𝘴 𝘢 𝘱𝘶𝘳𝘤𝘩𝘢𝘴𝘦. 𝘐𝘯 𝘢 3𝘳𝘥 𝘱𝘢𝘳𝘵𝘺 𝘢𝘵𝘵𝘳𝘪𝘣𝘶𝘵𝘪𝘰𝘯 𝘵𝘰𝘰𝘭, 𝘵𝘩𝘦𝘴𝘦 𝘢𝘥𝘴 𝘸𝘰𝘶𝘭𝘥 𝘳𝘦𝘤𝘦𝘪𝘷𝘦 𝘱𝘢𝘳𝘵𝘪𝘢𝘭 𝘤𝘳𝘦𝘥𝘪𝘵, 𝘮𝘦𝘢𝘯𝘪𝘯𝘨 𝘵𝘩𝘦 𝘤𝘳𝘦𝘥𝘪𝘵 𝘪𝘴 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘦𝘥 50% 𝘵𝘰 𝘣𝘰𝘵𝘩 𝘢𝘥𝘴. 𝘞𝘩𝘦𝘳𝘦𝘢𝘴 𝘪𝘧 𝘺𝘰𝘶 𝘶𝘴𝘦 𝘪𝘯-𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮 𝘳𝘦𝘱𝘰𝘳𝘵𝘪𝘯𝘨 𝘣𝘰𝘵𝘩 𝘱𝘭𝘢𝘵𝘧𝘰𝘳𝘮𝘴 𝘸𝘪𝘭𝘭 𝘵𝘢𝘬𝘦 𝘵𝘩𝘦 𝘤𝘳𝘦𝘥𝘪𝘵 𝘰𝘧 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯 𝘴𝘰 𝘪𝘵 𝘸𝘪𝘭𝘭 𝘣𝘦 2 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯𝘴 𝘣𝘶𝘵 𝘪𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘪𝘵𝘺, 𝘵𝘩𝘦𝘳𝘦 𝘪𝘴 𝘰𝘯𝘭𝘺 𝘢 𝘴𝘪𝘯𝘨𝘭𝘦 𝘤𝘰𝘯𝘷𝘦𝘳𝘴𝘪𝘰𝘯. So, when do you use 3rd party attribution tools like Northbeam & TrippleWhale? In my POV -> when you're advertising on multiple channels. These tools help you understand the true impact of your ads and allocate your budget more effectively. Are you using 3rd party attribution tools? would like to know your thoughts #adtracking #attributiontool

#AdtechTrendsToPayAttention: New advertising technology is rising, more unique up to date Adtech solutions are created and trends are formed. Let's look at the trends that are emerging where we need to pay attention. #Programmatic: The use of Omni channel programmatic advertising will rise. To reach customers across various platforms, brands will use a variety of channels including mobile, video content, display and voice ads. #Geotargeting: Depending on persons present location in the world, geotargeting is a series of coordinated procedures used by platforms to manage which ads the user will see. Advertisers can geotarget their ads based on a users country, state, city or pincode. #AIAndDigitalMarketingMerge: Marketers utilise AI to estimate client demand, create customer profiles, carry out programmatic ad buying and other backup tasks. The other is the customer facing aspect in which marketers employ AI to enhance the customer experience, strengthen the brand, increase sales etc. #MetaverseAds: is definitely a perspective for the next 5 years. Major brands are actively experimenting with the possibilities of this channel, Metaverse is replacing classic gamification with their interaction mechanism and creative possibilities. #InGameAdvertising: with the introduction of immensive gaming, metaverse and 5G, there are a ton of untapped prospects in the gaming business for advertising. In game advertising has become profitable for advertisers due to high engagement rates. #HyperPersonalisation: The Adtech trend that will persist is hyper personalization and data analytics. AI and chat, metaverse and other disruptive tech are helping advertisers create tailored experiences through setting smart goal, better audience management. #InAppAdvertising: Advertisers continue to increase in app budgets. Smartphones are the primary source of attraction for the audience attention, where the dominant space is mobile apps in which users spend most of their time making in app communication channels is a must have for brands. #BigBrandedData: All information owners began to engage in data science, sell data outside or use it internally, creating their own advertising products. The demand for quality data from branded vendors continues to grow strongly #CookielessApproach: The next ten years will be a turning point for data market, with huge number of advertisers moving to a cookieless approach which will completely change the existing rules. One of the alternatives to cookieless is contextual targeting, allowing you to display ads in the context of a perticular page, catching the user reading a perticular article. #ContextualTargeting: Ads are tied to the content and platforms where they are really displayed thanks to semantic contextual targeting which connects them to the intended audience using areas such as NLP, CP and other algorithmic process. If you resonate with my views, please like and comment. Thank you and wish you a great day ahead!!!

In the ever-evolving world of digital advertising, Meta and Google are revolutionizing the game with their machine learning campaigns: Meta's Advantage+ and Google's PMAX. 🔷 Meta's Advantage+: This campaign taps into the metaverse, delivering immersive, personalized experiences for users while maximizing ROI for advertisers. 🔶 Google's PMAX: Google's PMAX enhances ad placement, targeting, and performance using machine learning, promising precise targeting, ad relevance, and better conversions. GMW has embraced these innovations to help our clients grow! Check out the article below to dive deeper into these game-changers shaping the future of advertising. https://loom.ly/xXzJUHg #DigitalAdvertising #MachineLearning #Meta #Google #AdvantagePlus #PMAX #MarketingInnovation

Popular digital advertising techniques keep changing to keep up with evolving technology and shifting market trends. Buckle up as we spill the secret of the most popular digital advertising trends. 1. AI and machine learning are a big part of digital advertising. Marketers use solutions like programmatic advertising for efficient resource use and optimal ad placement. 2. Generative AI and AI & ML-powered data analytics allow advertisers to effectively segment audiences and offer personalization at scale. 3. Video advertising is a popular and effective medium to engage audiences, combine that with immersive advertising and the hype goes through the roof. 4. The multichannel approach is the standard now, with marketers adapting uniform messaging across the channels. 5. Privacy and data protection are dealt with by using transparent data practices and resorting to privacy-compliant solutions like contextual marketing. 6. Influencer marketing is growing impressively as a means to engage niche audiences and build credibility. 7. Emerging technologies like AI-driven analytics, automation, and augmented and virtual reality go hand-in-hand with digital advertising now. 8. Voice and AI assistants take up a considerable volume of online searches and marketers address it by optimizing their ad campaigns and websites for these technologies. To gain a deeper insight into digital advertising trends, read our full blog here https://lnkd.in/dHPf9vAn #digitaladvertisigntrends #digitaladvertising

Day 14 Still on Performance Ads, I learnt about Performance Ads type/objective on X and Ad format. Performance Ads is a type of ads that seeks users to complete an action. In this type of ads, advertisers will only pay when a user complete a specific predefined action. Performance Ads is a conversion ads which is at the bottom of the funnel. The aim of advertisers is to get large audience to perform a specific task. The major KPI for performance Ads is Return on Ads Spend (ROAS) and in this case, the ads budget is an investment. The ROI of the ads is tied to the number of people that performed the action. Other Performance metrics used for Performance Ads as it pertains to X ads include; - Click-through rate (CTR) - Cost per clicks (CPC) - Conversion rate (CVR) There are 3 Performance Ads type and 3 ads Format you launch on X. The Ads type are; - App download - App user re-engagement or retargeting and - Website visit. The Ads formats are; - Carousel - Still image - Video Performance ads on X are also placed on X Audience Platform (XAP) although X advices that only App download ads should be place on there Audience Network.

🚀 Digital Advertising Trends for 2024: Insights from StackAdapt Experts In our recent dynamic session with StackAdapt experts, key digital advertising trends for 2024 were unveiled, offering a roadmap for navigating the evolving landscape. 🌐 Global Programmatic Growth: Khaled Alfangary and Chelsea Johnston from StackAdapt highlighted the surge in global programmatic advertising and social media's pivotal role in shaping ad spending across industries. ✈️ Travel Sector Resurgence: A notable comeback in digital ad spending targeting UK travellers has reached pre-pandemic levels, signalling robust sector recovery. 🔑 Key Strategies: The importance of retention, reactivation, first-party data activation, and AI-powered marketing were underscored as critical components for future success. 📺 Video Streaming's Rise: With user engagement shifting towards platforms like TikTok and Netflix, advertising strategies are adapting to this new reality. 🍪 Navigating Post-Cookie Era: The upcoming deprecation of third-party cookies calls for a shift towards contextual targeting and robust data strategies. 💸 Economic Impacts on Consumer Behavior: The cost of living crisis and inflation are reshaping consumer habits, yet interest in luxury items persists, underscoring the need for personalisation in marketing approaches. 👥 Targeted B2B and AI Integration: 2024 will see a heightened focus on B2B marketing, leveraging AI for personalised messaging and strategic customer acquisition. 📊 Evaluating Ad Impact: Innovative measurement solutions, like football traffic measurements and attention metrics, are crucial in assessing digital ad campaign effectiveness. 🔍 Programmatic Platforms & New Channels: The role of programmatic platforms in optimising multi-channel campaigns and the exploration of new channels, such as in-game advertising, are key trends to watch. Stay ahead in the digital advertising game with these insights from StackAdapt's session! #IndependentAgency #StackAdapt #ProgrammaticAdvertising

Is the end of third-party cookies really so scary? 😱 Earlier this year, studies showed that #cookieless users are 30% less profitable. This represents a loss of revenue for media publishers, but also for advertisers, who find it harder to reach their targets. However, this loss of revenue can be overcome by a monetization strategy tailored for the cookie-free era. What does this mean? That many opportunities lie ahead for publishers in 2024. Whether it's setting up the right metrics to attract advertisers, providing them with targeting capabilities or optimizing its entire advertising stack. Find out more in the article written by my colleague Chloé Couannet: https://bit.ly/3OXskm7 #adtech #publishers #onlinemedia