Digital Advertising Metrics

Both AI and neuromarketing are playing transformative roles in the world of advertising, reshaping strategies and enhancing the effectiveness of campaigns. What do you think about this Ad? Here's how they contribute: Personalization: AI algorithms analyze vast amounts of data to understand individual preferences, behaviors, and demographics. This information allows advertisers to create highly personalized and targeted campaigns, delivering content that is more likely to resonate with specific audiences. Predictive Analytics: AI can predict consumer behavior and trends based on historical data. Advertisers leverage predictive analytics to identify potential customers, optimize ad placements, and allocate resources more effectively. Chatbots and Virtual Assistants: AI-powered chatbots and virtual assistants provide personalized interactions with consumers. They can answer queries, recommend products, and guide users through the purchasing process, enhancing customer engagement. Content Creation and Optimization: AI tools can generate and optimize content for advertising. From writing ad copy to creating visuals, AI algorithms analyze data to determine what elements are most effective in capturing audience attention and driving conversions. Programmatic Advertising: AI-driven programmatic advertising automates the buying of ad space in real-time. This allows advertisers to target specific audiences across various channels and optimize campaigns for better performance. Emotion Analysis: Neuromarketing, particularly through the use of neuroimaging techniques, helps advertisers understand how consumers emotionally respond to advertisements. This insight enables the creation of emotionally resonant content that has a stronger impact on the audience. Eye-Tracking Technology: Neuromarketing studies often involve eye-tracking technology to understand where individuals focus their attention in an advertisement. Advertisers can use this information to design layouts that draw attention to key elements. Neurofeedback for Ad Testing: Neuromarketing techniques, such as neurofeedback, are used to assess the neurological responses of individuals to advertisements. This data helps in refining and optimizing campaigns by understanding which elements evoke positive or negative reactions. Voice and Visual Search Optimization: AI is integral in optimizing advertising for voice and visual search. As more consumers use voice-activated devices and visual search tools, advertisers need to adapt their strategies to be discoverable through these mediums. Dynamic Pricing and Offers: AI algorithms can analyze market conditions, demand, and competitor pricing to dynamically adjust product prices or offers. This dynamic pricing strategy can be implemented in real-time to maximize revenue. #ai #marketing #technology #innovation via @ marketing.scientist

10 Ways to Use ChatGPT to Improve Your Copy: (With Simple Copy-and-Paste Examples) 1) Trimming Down Goal: Condense your copy for clarity and impact. Focus on: Complex sentences Redundant phrases Long paragraphs Example prompt: "Trim down this [phrase/sentence/paragraph] of my copy." 2) Finding Word Alternatives Goal: Find better synonyms for certain words to enhance readability and engagement. Look to replace: Fillers Jargon Clichés Adverbs Buzzwords Example prompt: "Provide [adjective] alternatives for the word [word] in this copy." 3) Doing Research Goal: Gather detailed information about your target audience to tailor your copy. Consider: Likes Habits Values Dislikes Interests Behaviors Challenges Pain points Aspirations Demographics Example prompt: "Create an ideal customer profile for [target audience]." 4) Generating Ideas Goal: Brainstorm multiple copy elements to keep your content fresh and engaging. Do this for: CTAs Stories Leads Angles Headlines Example prompt: "Generate multiple [element] ideas for this copy." 5) Fixing Errors Goal: Identify and correct any errors in your copy to maintain professionalism. Check for: Spelling mistakes Grammatical errors Punctuation issues Example prompt: "Check this copy for any [type] errors and suggest corrections." 6) Improving CTAs Goal: Make your call-to-actions more compelling and click-worthy. Play around with: Benefits Urgency Scarcity Objections Power words Example prompt: "Give me [number] variations for this CTA: [original CTA]." 7) Studying Competitors Goal: Gain insights from your competitors' copy to improve your own. Analyze their: CTAs USPs Offers Leads Hooks Headlines Example prompt: "Provide a breakdown of [competitor]'s latest [ad/email/sales page]." 8) Nailing the Voice Goal: Refine the tone and voice of your copy to align with your brand and audience. Consider: Target audience Brand guidelines Advertising channel Example prompt: "Make this copy [adjectives] to suit [target audience]." 9) Addressing Objections Goal: Anticipate and address potential customer objections to increase conversion rates. These could be about: Price Quality Usability Durability Compatibility Example prompt: "Analyze this copy to find and address potential objections." 10) A/B Testing Goal: Create variations of your copy's elements to determine what works best. Try different: CTAs Hooks Angles Closings Headlines Headings Frameworks Example prompt: "Generate variations of this [element] for A/B testing: [original element]."

Programmatic ads used to take 6 months to optimize. AI cut that down to 6 weeks. Programmatic advertising is smarter and faster than ever with the help of AI. Unlike Meta and Google (which have built-in algorithms that optimize ads automatically)... The Trade Desk and other programmatic platforms require manual optimization. That’s where AI is changing the game. Historically, optimizing programmatic campaigns took months. ~Data had to be manually analyzed. ~Adjustments were slow and reactive. ~Performance trends had to be spotted by humans. Now, AI is cutting that process down from 6 months to just 6 weeks. Here’s how: 👉 AI-Powered Trend Analysis Instead of sifting through raw data manually, AI scans performance trends and suggests optimizations. It identifies what’s working, what’s not, and where budgets should be shifted. 👉 Faster Decision-Making AI helps parse data at scale, allowing teams to make real-time adjustments instead of waiting weeks to react. 👉 More Time for Strategy AI has eliminated almost all the busywork that goes into running profitable campaigns. By handling large-scale data analysis, it frees up teams to focus on: ~Creative strategy. ~Consumer psychology. ~Campaign messaging. What This Means for Brands & Agencies Programmatic is QUICKLY catching up with Meta and Google in terms of efficiency. And a lot of that has to do with new AI-driven optimizations. For brands, this means: ~Faster learning cycles. ~More precise targeting. ~Better performance with less waste.

🚨Attribution is getting a major upgrade, and Meta is leading the way. Meta has just introduced one of the biggest advancements in measuring true ad impact. I really wish Google Ads would follow suit. They’ve started rolling out a new feature called incremental attribution. What this means is that Meta will now optimize for the incremental impact of ads. In short, they aim to filter out conversions from people who saw the ads but would have purchased anyway. As marketers, this is always the big question when we look at ROAS, POAS, and conversions in specific channels. What about the other channels, and would I have gotten some of these conversions even if I hadn’t spent this budget? That’s exactly what Meta is trying to solve by leveraging their lift algorithms, using both conversion lift and geo lift. Here’s how it works: they hold back ads from a portion of the audience that should have seen them, and then analyze the incremental lift by comparing with those who did. Of course, this opens up broader discussions around transparency, such as which geos are included in the holdout, whether there’s enough data in smaller campaigns, and so on. But that’s not the main point. The key takeaway is this: Meta is taking a huge step toward helping advertisers understand and act on incremental conversions. Yes, we’ve been able to run incrementality tests in different channels for a while, but let’s be honest, few marketers actually use them. Now, Meta is making it easier by automating the process. I really welcome this move from Meta, and I hope Google Ads launches something similar. It would make it much easier for marketers to spend their budgets more effectively.

Epic new dashboard: Google just created a new Looker template that allows you to analyze Google Analytics + Search Console data for SEO: This is a new resource that's available on Google Search Central that was created by Daniel Waisberg and Cherry Sireetorn Prommawin. In this article they link to a template that SEOs can use to analyze both GA4 and GSC data in one place. In order to use the dashboard you'll simply: 1. Open up the Dashboard link 2. Connect your Google Analytics 4 data 3. Connect your Search Console data (URL Impressions) The report will then auto-populate with some interesting visualizations of your SEO data such as: 1. Organic sessions and engagement rate over time: This maps your organic sessions against your engagement rate to see if there have been changes to your site engagement that align with your total traffic. 2. Percentage of organic traffic over time: This will give you an idea of how much of your total traffic is attributed to organic search and how that changes. 3. Clicks and CTR over time: This maps your website's total clicks and the CTR you're getting from search over a timeline view. You can see if a drop or increase in clicks is related to CTR changes. 4. Top pages and queries by clicks and click through rate: Shows you your pages and queries that have the strongest clicks or CTR. It can be filtered by either. Super useful for doing analysis and analyzing both Search Console and Google Analytics data in one report.

One of the critical reasons for Display ads' lower click-through rate(CTR) today is low viewability. It goes as low as 30% on mobile devices these days. This means our CTRs would be 3X better if the audience had just seen our ads. Even the word 'seeing' has a trick here. An ad impression is counted when the ad loads on the user screen. Not necessarily seen by the users. For example, Let's say you open your Facebook app. Initially, the app loads 3 to 4 folds of your feed while the rest(below the 4th fold) loads as you scroll. This is called lazy loading. Here, an impression is counted when an ad is pushed to your feed. An impression becomes a view if at least 50% of the ad appears on the user's active screen for one second or more. Just 50% of the ad, and that too for 1 second. Due to this minimum requirement, a view is also insufficient to quantify whether the customer sees the communication. This limitation makes evaluating the creative performance based only on the CTR ineffective today.

How do you know if your video ads are costing too much on #LinkedInAds? I've got a formula that I think you'll like. When you're running video ads, LinkedIn will give you the default Cost Per View (CPV), but I think most will agree that since a "View" just means it sat on someone's screen for 2 seconds, that's not a great metric for showing interest in your brand. So is there a better metric to track? I think so! My team started basing video costs on a Cost Per 50% View. If your videos are longer, you'll naturally see a higher cost. If your videos are boring you'll definitely see a higher cost. But we've found this to be a much better measure. If you export to Excel, this is easy to calculate (Total Spent / Views @ 50%) but if you stay in Campaign Manager, you have to do this calculation manually. We've found averages to sit around $2-4, but have seen as low as $.21 when the video creative is amazing! Have you found a metric that you like to use to evaluate video consumption? 📈 Happy #MetricMonday! What metric do you want me to tackle next? #b2bmarketing

Core Web Vitals should matter to not just SEO experts, but anyone interested in scaling D2C Often your product is great, and your ads are getting people to your site, but good chunk of them bounce before even seeing the second image Or worse, your D2C site ranks below third-rate aggregators on Google despite having a better brand, better product, and better reviews on keywords you should own One big reason could be that your site experience sucks. And that’s exactly what Core Web Vitals is trying to measure. These aren’t vanity metrics. They’re Google’s way of telling you that your user experience is frustrating. And we won’t reward it with visibility Here’s what Core Web Vitals actually mean (without jargon): •LCP (Largest Contentful Paint) = How long your main visual/image takes to load. If it’s >2.5s, it’s a problem •CLS (Cumulative Layout Shift) = Does the screen jump around while loading? If buttons shift while the user tries to click, it’s hurting conversions •INP (Interaction to Next Paint) = How fast your site reacts to taps/clicks. If there’s a delay when they hit “Add to Cart”-bad news. Think of it this way: •LCP = First impression •CLS = Does the page feel stable? •INP = Is it snappy? Why should founders/CMOs care? Because these metrics directly affect 3 things: 1. Organic traffic (SEO): Google demotes slow and clunky sites. Doesn’t matter how good your content or backlinks are 2. Conversion rate: People bounce when images load late, or buttons move as they click 3. Ad ROAS: Your performance marketing team is paying to drive traffic to a broken experience. You lose money before the user even evaluates the product How to check your Core Web Vitals: Free Tools: •PageSpeed Insights •Lighthouse Ideal Benchmarks in my opinion: •LCP < 2.5s •CLS < 0.1 •INP < 200ms If you are doing everything else right- good products, good marketing, good creatives etc, don’t let slow LCP and messy CLS undo your good work

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

ANA: Just 36% of programmatic spend reaches consumers due to ‘cost waterfall. #Adtech transaction costs eat up 29% of that spend, while 35% is wasted on unmeasurable or low-value environments like made for advertising (MFA) sites. Working with trusted sellers, optimizing supply-side partnerships and mastering log-level data could save marketers billions of #advertising dollars, according to the Association of National Advertisers. The report also states that working with just 75-100 trusted sellers that reach a few thousand quality #media websites would achieve the desired outcomes while cutting down on fraudulent or nonviewable traffic. The ANA’s complete findings suggest that marketers could realize $22 billion in efficiency gains if they adopt more hygienic programmatic strategies. The ANA worked with 21 marketers from a variety of industry verticals to uncover these insights. Mondelēz International, Molson Coors Beverage Company, Walgreens, State Farm, Shell and Nissan Motor Corporation were among the participants.