Occam's Razor

• Sports Marketing Measurement Playbook.

  Last year, nearly all of the top 50 telecasts in the US were connected to sports. The 97th Oscars, SNL 50th, a 60 mins interview, an episode of The Floor and 67th Grammys were the exception. #27 was “NFL Weather Delay,” turns out we will watch anything with sports in the title. 😊 Sport is the last bastion of people watching live TV, and together. That is why streaming giants are getting into acquiring sports telecast rights. It is unsurprising then that marketers have been rushing into all sorts of sports advertising. Sponsorships of teams, dresses, balls/bats/cars/horses/sideboards/back panels for interviews. Running expensive paid media ads. Naming stadiums. And, more. At some point, a smart Sr. company leader asks: Look, I’ve enjoyed the hospitality suite, I’ve loved shaking hands with Tom Brady, and my mother-in-law thinks I’m da bomb for getting her into the clubhouse at Augusta National. But for $38 million dollars in marketing, is it doing anything for the business? Darn. Consequences. 😊 Let’s answer that question today. How to measure the impact of “Sports Marketing.” This blog post was published as Premium edition #497 of my newsletter. Each week, I share actionable insights and hidden patterns to stay at the bleeding edge of Marketing, Analytics, and AI. Sign up for TMAI Premium to accelerate your career trajectory. Prologue. It is critical to appreciate that all laws of marketing apply to sports marketing. If your paid media creative and “story” gets lost in the 1.2 seconds of attention on TikTok, the same happens to your logo on the sports car/t-shirt/back panel etc. In fact, in those situations since the purpose of watching is the game, you get even less attention. (Test: Super Bowl champion Seahawks have only one corporate logo on their uniform, and have had it for decades. Do you know which one? It’s like the most obvious slam dunk answer, yet I bet… you don’t.) If spike and silence is the kiss of death for your normal marketing, it is exponentially worse for sports – getting the halo from the sport / player being transferred to your brand takes 5x more spike and sustain (because, delicious irony, the sport is getting in the way). If frequency of 3/User/Week (NOT “avg freq”) is the sweet spot for normal marketing, with sports the “organic placement” is going to get ignored. You still need to get a frequency of 3/U/Wk to persuade for xy weeks. It will come from sports marketing with good old paid media (with the player and sport taking over your creative). If an occasional normal marketing post by your “massive global” influenceris a (colossal?) waste of money as it gets barely any reach via Organic Social, expect the same to occur with your sports stars carrying your water bottle on to the court or making an occasional post about their sponsor Atlassian. [Note: TMAI Premium subscribers, please see how to truly win Influencer/Organic Social Marketing: “TMAI #479: Organic Social: Operational Playbook for Winning.” It will save you a ton of money and stress. If you can’t find it, just email me.] If launching small pop-ups and boutique events has little business impact in normal marketing, beyond the few attendees, your hospitality tent at the Kentucky Derby or luxury suite at the SF Giants stadium will make your CXO and attending clients happy, those few clients might buy more/renew the SAAS contract, but there is little scale. (Though, I cannot thank John enough for the many invitations to the SF Giants suite! I am open to additional invites. 😊) If you follow my advice re optimal measurement below, you’ll discover this “dirty little secret” of sports marketing: Budgets allocated to sports marketing are best thought of as budgets allocated to developing creative. When sports marketing delivers business impact, it will all come from Paid Media Amplification of that creative – over years (with 5 being the minimum). Because many, many, many brands struggle with creative and storytelling. + Creative is responsible for 60% to 70% of all brand marketing success. = It can be super profitable to hand over your creative and storytelling to sports, inherit your brand values from the sport/athlete. Make this decision consciously. Understand the implications. Then.. MAKE SURE to ask for a ton of money to put paid media behind this creative – and the MMM will prove incremental profitability. I guarantee it. [Note: Premium subscribers, please activate the groundbreaking Brand Marketing framework in TMAI #496. It will change your professional compensation trajectory.] Higher Order Bits. My sports marketing measurement playbook consists of five distinct levels of sophistication: L5, pre-basic, to L1, gold standard. My “Impact Intelligence Score” (IIS) will rate how smart/informed the company is about business impact at each level. Scale: 1 to 10. I’m going to use a made-up company, CloudPixel sponsoring the San Jose Earthquakes (my local soccer team). Level 5: The Opening Gambit: Vanity. Speed of Impact: Superfast. Every brand starts here. Sadly, far too many stay here. L5 is the land of Big Numbers. They look very impressive on their slide decks (“450m IMPRESSIONS”). At this level, you are measuring activity, not outcomes. L5.1. Social Media Engagement. You’ll measure the best social media metrics: Conversation Rate. Applause Rate. Amplification Rate. Reported by each social platform. For your company’s organic posts or the sports team/athlete’s organic social posts. Bonus points for measuring sentiment in conversations. Unless you are doing something wrong, this activity should happen immediately, you can measure it just as fast, and often all the data is free. Tip: It is a good idea to establish measurement windows (0-24hrs, +72h, +7d) to start to get a sense of decay rates. Establish baselines, and see how the future is against that. L5.2. Sponsorship Recall. Nearly all decent sized sports sponsorships will come with my nemesis, the “Ad Recall” metric. Post-exposure surveys typically ask fans which brands they remember as sponsors – hopefully they say CloudPixel. Surveys could be at stadium exits, online on community sites, or via the Earthquakes CRM/newsletter sample. Tip: Often these surveys use an aided approach, which lowers signal quality. Are you aware the CloudPixel is a sponsor? Ask for unaided measurement. Split by “exposure intensity” (ex: watched full match, highlights only), and identify deltas. L5.3. Earned Media Value. EMV. Also called: Ad Value Equivalency. AVE. The biggest fav of agencies selling you sports marketing packages. The biggest “con.” Software counts every appearance of the CloudPixel logo/name anywhere, for any number of seconds. That “placement” gets multiplied by the most expensive ad you can buy. You get told: Hey, all those placements would have cost you $72 million if you bought ads. EMV assumes all exposure is good exposure, ignores the quality and relevance of the audience, the lack of a dedicated marketing message, and the hyper-cluttered environment. Hence, EMV is widely discredited by major marketing associations (and all causal marketing measurements). Tip: Stick to one vendor for consistency (rotten apples to rotten apples is sincerely a sound methodology), and, obviously, track trends over time and report deltas. [Note: Premium subscribers, please also see: TMAI #446: Brand Love Is Not A Helpful KPI. All three of the above will be reported to execs as “brand love.”] Level 5 Impact Intelligence Score: 1/10. No causality, no financial linkage, low signal quality, low decision readiness. We measure inputs, activity, are we visible, exposure, yada yada yada. We are literally measuring the noise. There is little to indicate if it is good noise, if there is any value. Certainly, measure it in the first few weeks of the campaign. In week five, move to L4. Level 4: Quest for Brand Impact. Speed of Impact: Months. First step toward understanding human impact, via a major upgrade… True test vs. control measurement of brand impact! No pre-post! For our CMO: Did sponsorship shift what people think and feel about CloudPixel? [Premium Sub Reminder: Must. Review. TMAI #496. For think and feel definitions.] L4.1. UBA Lift. Unaided Brand Awareness is a challenging metric to move. It is a long-term revenue positivity driver. An effective sports marketing program should be able to move it. Even if it takes time to move. [Never ever never do short-term sports marketing deals, that is flushing money down your you know what because of the long lag to even limited business impact.] L4.2. Consideration & (Purchase) Intent Lift. When you report this, your CFO will lean in! You are in the top three brands, next time I’m in the market for SASS software. You are my #1 choice, next time I make a purchase. Powerful, no? Hard to move. Deliciously proven medium-term revenue impact. The methodology to measure L4.1 and L4.2 are the same: Our traditional brand tracker type survey instruments (OTS, Panels, etc.). Exposed consists of people who demonstrably saw our sponsored content – either the broadcast itself, highlights, our paid ads amplifying the sports creative, etc. Control will be similar people who were ideally not exposed or only minimally exposed. Relo Metrics, Nielsen, Kantar, are among the vendors we can use. Tip: Use propensity score matching to ensure that the test and control groups are truly comparable. Instrument variables to account for external factors (like other company activity – from other advertising to product launches to competitor activity). Six to nine months in, start to segment by audience type (gen pop, soccer fan, decision maker, etc.). Leven 4 Impact Intelligence Score: 4/10. Causality some, financial linkage low, signal quality medium, decision readiness, low. Whie self-reported intent ≠ actual purchase behavior, still there is a ton of science in measuring perception shifts, and the causal impact specifically of sports marketing. [Premium Subscribers please see TMAI #426, for an excellent brand metrics deep dive.] L4 measurement is even more helpful for B2B companies, with extremely long or complex sales cycles. Start measuring in the first quarter of the sports marketing program and then keep a periodic pulse going for test of the (min) five years of investment. In a handful of quarters, you’ll be ready to move to L3. The Journey Continues. Achieving level 4 will get you into the game, but to truly get to proving to your CFO, your Board, that your Sports Marketing investment is delivering a profitable impact… There are three more levels to nirvana… Level 3, “The Heart & Mind Influence”, gets to insights into compounding brand advantage. Level 2, “The First Digital Trace”, helps you identify to financial outcomes driven. Level 1, “Show Me THE MONEY!,” ensures you prove the ultimate: Incrementality! L3, L2, L1, are covered in detail in TMAI #498. If you are a Premium Subscriber and can’t find that edition, or a new Premium Subscriber, please email me for a copy. Bottom line. Sports marketing can be immensely effective – even transformative. Ex: Rolex and tennis. With Level 5 measurement, you can tune creative and activation to ensure your $$$ are spent on getting you visibility. With Level 4, you get to prove to the CMO that her choice to invest in sports marketing is proving initial hints of something actually valuable: foundational shifts in the Big 3 Brand KPIs. Stop sponsoring in the dark. Turn the lights on. PS: Special Bonus: A podcast version of the Sports Marketing Playbook!

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• Bye, Bye Human-Powered Marketing Analytics.

  HiPPOs still hand down most decisions in a company – even when surrounded by piles and piles of reports with metrics galore. Marketers still use the “funnel” to imagine and allocate budget, people, actions – even when there is multi-decade data that the funnel is a lie. Company after company misses trends and goes kaput – even after investing in multi-million dollar data projects to build clean rooms, unified consumer view cloud-based business intelligence platforms. ☹️ The core challenge isn’t data scarcity; it is insights latency. Which impacts your ability to follow my advice to deliver IAbI, and not data. Here’s the traditional analytics workflow being practiced in your company: 1. Report Generation. Hours and hours of standard reports/dashboards. 2. Manual Analysis. Hopefully, laborious segmentation of known knowns missing subtle non-intuitive patterns. AKA High-dimensionality data challenge. 3. Insight Extraction. Identifying the most important findings, extracting context from Marketers, Finance, Sr. Leaders, creating presentations. 4. Exec Last-Mile Barrier. Data competing with other priorities, insights missed, misinterpreted, translation to action challenging – to say the least. A process that is fundamentally reactive and linear. It struggles with tracking hundreds of variables per user engagement, non-linear patterns (path analysis anyone?). Humans are ill-suited to find a needle in the haystack – identifying truly significant anomalies or emerging trends within massive datasets. ☹️ ☹️ It does not have to be this way. anymore. It is time to hand control of Marketing Analytics over to AI! Online, offline, digital, everything. 😊 AI can act as a force multiplier and overcome the above limitations. A. Pattern Recognition at Scale. Machine Learning (ML) algorithms are awesome at finding complex, non-linear relationships and hidden clusters within massive high-dimensionality datasets. B. Automating the Mundane. As I’m sure you’ve seen in your use of ChatGPT, Qwen, others, AI can automatically generate insights from routine data, it can flag anomalies instantly, and surface the most statistically significant changes. C. Predictive Power. Rather than reactive, what happened, AI is exceptional at what’s likely to happen, thus solving the insights latency. It is worth noting it can personalize this at a massive scale – dynamic segmentation, tailored experiences, value something impossible with manual rules. AND, do this uniquely for the needs for every human in your org! D. Continuous Learning. AI’s real superpower. AI models adapt as new data flows, they constantly refine their understanding of user behavior and system performance – at a massive scale. (It would be equivalent to the Analyst earning a Bachelor’s degree in a new field every few weeks!) Handing control of Digital Analytics over to AI achieves this profound shift: From Analyst-as-reporter to Analyst-as-strategist. From data puking and insights hunting to validation and activating action. 😊 😊 😊 It is time to rebuild analytics from the ground up. You’ll remember I originated the 10/90 rule of Analytics 20+ years ago. “If you have $100 to invest in smart decisions, invest $10 in tools and implementation, invest $90 in humans who will analyze the data!” Here’s my new 10/90 rule for success via investment in Analytics: “If you have $100 to invest in smart decisions, invest $10 in brilliant human analytical strategists, invest $90 in AI activation.” In fact, over time the $100 is likely to reduce to $80, then $70, and maybe less… While the quality of decisions, the scale of intelligence and automation, will exponentially increase. Incredible, no? Let’s learn how to activate this immense value. This blog post was originally published as Premium edition #492 of my newsletter. Weekly, I share actionable insights and hidden patterns to stay at the bleeding edge of Marketing, Analytics, and AI. Sign up for TMAI Premium to accelerate your career trajectory. Activating AI Power. AI is not yet AGI (Artificial General Intelligence), and certainly not SGI (Super General Intelligence). [Note: Premium subscribers dive into these key concepts in TMAI #457, learn how to apply them across all business functions. If you can’t locate it, please send me an email.] Today, activating the awesomeness above will take human grit, intelligence, and persistence. Things won’t be perfect. Your True North: Somewhat failing to activate my recommendations is 25x better than your present. And, as a bonus, you’ll be ready for AGI. I have ten specific implementation ideas for you to turn your digital analytics over to AI. I hope they’ll spark a dozen more in your team. 1. Predictive Analytics via Propensity Modeling. Impact Potential: Transformational. Human-powered digital analytics tells us who converted. AI can tell us who will convert! There are thousands to tens of thousands of humans on your site, using your apps today. Instead of spreading your budget, attention on all of them, you can focus on high-propensity humans. ML algorithms thrive on pattern recognition across hundreds of variables, and thus identify subtle combinations of behavior that signal conversion readiness (or whatever your digital objective is). IMPORTANT: Unlike rule-based systems (if user views pricing page three times, tag as hot or if a user has seen handbag 1, 2, 7, give them a discount), AI models consider non-linear relationships and interaction effects between dozens/hundreds of variables for a more brilliant understanding of human intent and what will happen next. Framed simply: What is the exact probability THIS human will convert/upgrade/churn in the next N days? AI Approaches and Algorithms to explore, stress test, and embrace: Gradient Boosted Machines (XGBoost, LightGBM). Currently, the gold standard for tabular data prediction. These algorithms excel at conversion prediction by combining many weak predictive models into a highly accurate ensemble. Random Forests. I have loved using RF when I have a need to understand feature importance. Ex: Which behaviors most strongly predict conversion? Neural Networks. The grandpa of AI. For massive datasets with complex, nonlinear relationships, deep learning architectures can uncover patterns other models miss. Survival Analysis. Good old statistics. Predicts not just if, but when a user will convert, enabling perfectly timed interventions. Each business is unique; you might use a couple from above, just one, or all of them to solve different propensity modeling opportunities (emails, B2B conversions, internal HR use for people who are likely to quit, etc.). One of them you should have activated in the next six months. Practical Example. From my experience: A propensity model using approx. 90 behavioral features (scroll depth, product views, cart additions, days & visits in experience, etc.). The model scored each user in real-time, allowing the ecom COE to: Serve dynamic offers to high propensity users. Adjust bid strategies for retargeting ads based on conversion probability. Identify “at risk” users who showed high intent but did not convert (so we proactively intervene vs. lose to competition). Potential Outcomes For You. Looking across my work on three continents, focusing on ecommerce: A. 35% – 60% improvement in Conversion Rates for the targeted segments. B. 20% – 35% reduction in acquisition costs due to more efficient ad spend. C. Not easily quantified qualitative impact of shifting from reactive to proactive marketing. Over the last three years, Propensity Modeling has been my most monetized, highest now-potential, game-changing action in handing over digital analytics to AI. Every quarter you don’t activate it, you are falling two to three quarters behind. [Note: TMAI Premium subscribers will recall three editions dedicated to sharing a roadmap for building AI-powered Propensity Models. TMAI 378, 379, 380. Email me if you can’t find them.] 2 Advanced Customer Segmentation. Impact potential: High. You should not be surprised that this is so important. My blog was born May 2006; this is from then: Excellent Analytics Tip#2: Segment Absolutely Everything. Most analytics teams segment users by demographics or broad behavioral categories (e.g., “mobile users,” “TikTok ad visits,” “logged in”). These segments are often too broad, and miss thousands of nuanced behavioral patterns. Creating more relevant, precise, sophisticated segments manually is extremely time-consuming and limited by human bias, human knowledge (little awareness of known unknowns, and none of the unknown unknowns). Unsupervised learning algorithms specialize in finding natural clusters in data without predefined categories. They can process dozens of behavioral dimensions simultaneously to identify segments that are statistically distinct rather than intuitively appealing. They can get to the unknown unknowns – hidden well below the human capability surfaces. AI Approaches and Algorithms to explore, stress test, and embrace: (This is less AI, a lot more algorithms and old school ML.) K-means Clustering. Thousand-year-old workhorse algorithm for segmentation and grouping users based on behavioral similarity across multiple dimensions. DBSCAN. Full, cute, name: Density-based spatial clustering for applications with noise. Instant love, no? 😊 Specifically awesome for identifying outlier segments or detecting novel user behavior patterns. (If your site gets more than 100k visits/day, there are thousands of novel user behavior patterns in there!) Gaussian Mixture Models. Few models handle ambiguity better than hard clustering approaches, when segments overlap probabilistically. Hierarchical Clustering. Fifteen times a day, Analysts have to drill from broad categories to highly specific micro-segments. Segmentation trees created by hierarchical clustering are a perfect solution. [Note: Premium newsletter subscribers for a practical application of using decision trees, please see TMAI #283. It shares how it helped 10x the impact of YouTube video campaigns with insights hidden in data (from Analysts!).] There is immense untapped potential to be extracted by applying unimaginable scale to segmentation via AI and algorithms. Practical Example. Tying this back to an example from my 2006 blog post, but applied recently to a B2B (SaaS) client. We applied clustering to session data across 28 behavioral dimensions. Instead of the standard free trial users segment, the algorithms identified: Segment A: Feature explorers, who try many features quickly. Segment B: Cautious adopters, who read documentation before acting. Segment C: Social validators, who always check testimonials first. Segment D: Price-sensitive evaluators, who immediately navigate to pricing. Tying these segments to outcomes, working backwards to influence them, allowed the team to customize onboarding of the trial experience in real time, the content and flow of the product, and, obviously, subsequent messaging to dramatically improve activation rates! Potential Outcomes For You. Reflecting on my clients and work: A. 25% – 50% improvement in Conversion Rates from behavioral targeting based on algorithmic segments. B. 60% to 75% reduction in analysis time dedicated to customer segmentation. C. Not easily quantified qualitative impact on customer joy and company revenue from a deeper understanding of potential customers and their behavior. Big picture: What took weeks of manual cohort analysis (assuming you could even guess them all right), now happens automatically (with dynamic segmentation, dynamically updated, at unimaginable scale and with improved precision). 3. Voice-of-Customer Integration with Behavioral Analytics. Impact Potential: High. Another one of my old web analytics dreams has come true. Early readers will remember my, at the time, revolutionary Trinity model for Analytics. 19-years later, I can AI it! Survey responses, support tickets, chat transcripts, call center voice recordings, and social media mentions live in separate systems from behavioral analytics. Analysts struggle to connect the why with the what (failing Trinity). This leads to incomplete understanding of user motivations, frustrations, and unmet needs. Solving this at scale with humans is futile. One of the key leaps of modern AI is multi-modality – the ability to understand text, images, voice, and video at unimaginable scale and incredible precision. Multimodal AI systems can process both structured behavioral data and unstructured text/voice data simultaneously. Advanced embedding techniques allow algorithms to find connections between language patterns and behavioral patterns at scale. Sentiment analysis has evolved beyond simple (and lame!) positive/negative classification to detect specific emotions, urgency, and intent that, a blessing for us, correlate with behavioral outcomes. AI Approaches and Algorithms to explore, stress test, and embrace: Multimodal Transformers. Great for processing text and behavioral data in a unified model architecture, with cohesive, understandable outputs – at scale. Cross-modal Retrieval. Helpful in finding behavioral sequences that correspond to specific feedback themes, assessing their quantitative relationship. Advanced Sentiment Analysis. Taking our positive/negative past approach to significantly higher accuracy and detailed why patterns by detecting frustration, confusion, excitement, and uncertainty. Applications across every part of the business (including annual employee surveys!). Topic Modeling with Behavioral Correlation. A data setup problem to overcome, but then at scale drive discovery of which discussion topics correlate with specific actions or drop-offs (which exist, ex, in your mobile and site quant data). Emotion-Action Mapping. Above hinted reflective analysis, now you switch the view to predictive analysis. Connect expressed emotions with subsequent behavioral patterns – driving proactive actions by ensuring you don’t lose a logistics provider or an employee quitting or a massive B2B client not renewing their contract. Practical Example. This one’s from a pal, in Canada. An ecommerce platform integrated the site’s chatbot data with behavioral data and put in place an AI model to analyze it. Discoveries: Positive mentions of sustainability in chats correspond with 3.4x higher lifetime value. Discovered emerging complaint patterns about a new feature, three months before negative NPS scores. Users expressing “size uncertainty” in chats have an 82% higher return rate. (OMG) Potential Outcomes For You. A. 8 – 12 points improvement in NPS scores. B. 20 – 25% reduction in fails (cart abandonment, returns, etc.) from real-time interventions put in place. C. Not easily quantified qualitative impact on product development from 360-degree customer understanding from connecting the why with the what systematically. [Note: With life experience, Trinity model became an even more sophisticated and modern Edge model for Analytics. Premium subscribers, please see TMAI #224. And then a refinement of it in TMAI #273: The Analytics Flywheel | Invent Once, Scale Infinitely.] The Profitable AI-Analytics Journey Continues. In TMAI #493 and #494, I’d shared additional super exciting ideas to deliver transformative profits via AI-Powered Analytics. Additional activations included: 4. Behavior Targeting & Intelligence (BTI). Impact potential: Transformational. 5. Natural Language Processing (NLP) for Unstructured Data. Impact potential: High. 6. Anomaly Detection and Automated Insight Generation. Impact potential: High. 7. Predictive (Whole Company) Customer Lifetime Value Modeling. Impact potential: Transformational. 8. Real-Time Pricing and Offer Optimization. Impact potential: High. 9. Intelligent “Liquid” Merchandising. Impact potential: Medium. Give all of the above is true today, I predict that the current type Analyst role will cease to exist over the next 18 or so month. In TMAI #495, I laid out a framework that outlines what the Analyst role will be in Jan 2028, and how you need to get ready for it starting now: TMAI #495: Analyst 2028: S.H.I.F.T For Relevance. If you are a new TMAI Premium member, please email me for the series above. If you are not, grab an annual Premium subscription here. Bottom line. The integration of AI into Analytics represents the most significant shift in our field since its birth as a science. The organizations that will thrive in the coming years aren’t those with the most data, most Analysts, most spending on Analytics. They will be the ones who can extract the most insight from their data with the greatest speed. i.e., reduce insights latency and increase automation. AI and advanced algorithms provide the tools to make this possible, transforming analytics from a practice of historical reporting to one of predictive intelligence and prescriptive optimization. Carpe diem. PS: It is only appropriate that I share with you an AI-generated summary visual of this blog post! For your slides…

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• The Best Marketing ROI Formula: Incremental Net Profit ROI!

  There’s a scary Giant hiding in your closet. It imposes hidden costs that, when accounted for, transform your claim that advertising is adding business profits. Short-term, long-term. The scary Giant changes your OMG! to omg? There’s an incredible return from investing time and love in identifying your Giant costs. Ex: Identifying non-working media costs, by calculating them for the core and sub-components. There is only one other thing more important in Advertising (incrementality). [Note:Newsletter Premium Subscribers: If you can’t locate TMAI #437: Compute Non-Working Media Costs and TMAI #411: Proving Marketing’s Incrementality, just hit reply.] This got me thinking about how frequently we throw around the key performance indicator (KPI), Return on Investment (ROI) – without being careful how they are computed or transparent about what they include or exclude. We simply claim: Our Performance Agency is delivering an ROI of 4! The claim’s implication: For every $1 our Agency spends on Ads, they are delivering $4 back. HURRAY!! So today… Let’s interrogate that 4. What is it? Can it be trusted to reward your Agency? A question to answer by the end: Does your Agency impact survive calculating ROI 4? This blog post was originally published as Premium edition #438 of my newsletter. Weekly, I share actionable insights and hidden patterns to stay at the bleeding edge of Marketing, Analytics, and AI. Sign up for TMAI Premium. 100% revenues are donated to charity. With the Real ROI, Please Stand Up? So, what’s ROI? The most common computation: ROI = [(Revenue – Media Costs)/(Media Costs)] Media Costs are typically the Dollars/Renminbi you paid to run ads – on Facebook, Magazines, CTV, Radio, Bing. Sometimes referred to as Advertising Costs. Revenue is the traceable sum of $$$ earned from running the aforementioned ads. ROI is often expressed at a campaign level – though you can obviously decompose it by an individual ad, a channel, a group of tactics, and on and on. [Premum Subscribers: This is when the Multi-touch Attribution methodology becomes super important – plese refer to TMAI #434.] I was reviewing a Client’s QBR for a recent Campaign and sure enough they’d computed ROI: [Privacy Note: Numbers are real, the visualization is mine. Any mistakes you catch are mine.] ROI = 4!! [Note: I’m not going to cover the commonly bandied about ROAS – Return on Ad Spend. While a close cousin of ROI, I consider ROAS to be emotionally sketchy.] The Agency did not know the Campaign’s overall budget, not unusual, as Agencies rarely do (though you should share with them). I’ve added that number to the table above. An ROI of 4 looks incredible, no? I offer that the 4 is unreal. It meets the classic definition of fake news. To sell the shoes / car parts / laptops / eyeglasses / Bluetooth adapters, you had to design them, manufacture them, ship them, store them, and wait for the order to come. Of all those costs, at the very minimum, you cannot ignore the cost to manufacture them. You sell a pair of eyeglasses for $50, you need to account for the $35 Cost to manufacture them. $35 is known as Cost of Goods Sold (COGS). Hence, this is a more real news formula of ROI: ROI 2 = [(Revenue – COGS – Media Costs)/(Media Costs)] OR ROI 2 = [(Gross Profit – Media Costs)/(Media Costs)] I call ROI 2: “Gross Profit ROI.” For the client above, this is a more real ROI the Agency delivered: For this company, the COGS was 70% of the sale price (expressed as Gross Margin above). After counting that, the amount the company made was $0.9 million, and not $3.2 million. The new, more real, ROI driven by advertising is 0.5. While heartbreaking, please learn to embrace the 0.5 – or you will never know how to be better. Wait, wait, there’s more. Remember, the total budget spent by the Marketing team was $1 million. It is not the $0.6 million being used in both the formulas above. The delta, $0.4 million, were non-working media costs. [Premium Subscribers: See TMAI Premium #437 for how.] IMPORTANT: Your Agency spent $0.6 mil, their calculation is right for what they know. You spent $1 mil, it is your job to account for this money. You must account for the Total Campaign Spend, by using this formula to compute ROI: ROI 3 = [(Revenue – Non-working Costs – COGS – Campaign Budget)/(Campaign Budget)] OR ROI 3 = [(Net Profit – Media Costs)/(Campaign Budget)] This helps us land even closer to the real ROI that your team (not Agency) delivered to the company: The Net Profit ROI 3? Minus 0.1. Your advertising campaign lost money. A shocking realization when you reported ROI as 4 to your CMO. No? We are not done getting to the business value of this campaign. There’s one more thing to get to the realest ROI from advertising. What would have happened if you did not execute this campaign?Would you have lost the entire $3.2 million in Revenue, if you had not spent the $1 million on advertising? Incrementality. Incrementality! We who are active practitioners of the art and science of incrementality know that you would have made a bunch of the $3.2 million even if you did not execute the campaign. I know, I know, it hurts our feelings as Marketers, but sadly, it is reality. Nearly all the sales that come into your company have nothing to do with Marketing! Let’s do one more computation of ROI, this time accounting for incrementality. In this case, the Agency did not practice incrementality for this Client, hence, for today, I’m going to assume it is a super high 30%. What does that number mean? 70% of the Claimed Sales by this campaign, would have occurred any way (store location, product features, seasonality, innovation, reviews on Amazon, whatever else). Here’s the final, closest to real, formula for ROI: ROI 4 = [(iRevenue – iNon-working Costs – iCOGS – Campaign Budget)/(Campaign Budget)] OR ROI 4 = [(Incremental Net Profit – Media Costs)/(Campaign Budget)] That yields the following Incremental Net Profit ROI (4) results: We really lost money. The Campaign’s incremental Net Profit ROI (iROI) is -0.7. A very different picture than the 4 the Agency presented at the start with ROI 1. Difficult Questions: What do you and your Agency compute today? ROI 3 at least? Perhaps, ROI 4?Does our journey today explain why the Marketing budget keeps getting cut by the CFO, despite Marketing’s protests that they are delivering 4x return on investment? Special Note | Brand Marketing ROI. The ROI computations above span a four to six month impact horizon. For brand marketing campaigns, the impact horizon, will stretch beyond six months. For such campaigns, we compute short-term ROI #4 using different KPIs (# People Lifted, Cost Per Individual Lifted – both vs. baselines), and different methodologies (true test-control surveys, not pre-post). And, we will hold Brand Marketing to account for delivering long-term profitability! For that, we will measure long-term ROI #4 with the same KPIs (incremental Profit), but different methodologies (longer impact horizon like advanced attribution modeling, ML-based mix models, and CausalAI). Radically improving Marketing’s ROI. Good Marketing can absolutely deliver a magnificent Return on Investment. But how? For my clients, I take a repeatedly tested in the real world four-step approach to deliver radically better ROI. I did a deep dive into each step, and actions you should take, in Premium edition #440. Here’s the summary: Step 1. The Marketing Team: Obsess about excessive non-working media costs. Step 2: The Agency: Obsess about highly incremental tactics. Step 3: The Commerce Team: Why is the Conversion Rate so low? Step 4: The Engineering Team: Product costs and process innovation The glorious profit-generating outcome my approach above looks like… You can replicate it in your company… [Higher resolution: Right Click, Open in a New Tab.] TMAI Premium subscriber? Please email me for the excel spreadsheet, and the deep dive details of the four step process above. Bottom line. Marketing tends to be the first budget to be cut in tough times. Two reasons: 1. No one at the top of the company quite believes any claim the CMO offers re impact of Marketing (see above). 2. Marketing competes with Engineering, Retail Stores, Customer Service, HR, Factories, Finance for budget – the short-term ROI from all of them is easier to see (and believe). This is our (Marketing’s) problem to understand, and fix. Here are your standards: ROI #3 is the minimum standard that’ll survive Board or CFO scrutiny. ROI #4 will ensure Marketing is among the last budgets to be cut. Carpe diem!

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• 2 Seconds to Brand Impact: A Modern Video Ads Playbook

  Would you believe it: Almost no one watches your video ads! Take your company employee hat off: Do you watch any other company’s video ads, if you have the choice to skip or swipe? Do you watch your company’s ads, if you have the choice to skip or swipe? The answer for you, me, our employee peers is likely no. Reason: Just as for our users… The ad’s in the way. When producing advertising, here’s the reality CMOs ignore: You are not competing against other ads. You are competing against the entire internet. All of it. If a human is actively watching your 60s ad on TV all the way to the end, the most likely reason is her phone’s battery is dead. Pause for reflection. This blog post was originally published as edition #489 of my newsletter TMAI Premium. Each week, I share strategic insights and actionable guidance on how to stay at the very bleeding edge of Marketing, Analytics, and AI-transformation. Sign up for TMAI Premium to accelerate your career trajectory. 100% of TMAI revenues are donated to charity. I am not advocating against video advertising. It is essential for effective and scalable brand marketing. I am advocating for ad creatives to embrace the decade-old reality of consumer behavior, media consumption, and attention fragmentation. I am for video advertising strategies that are built to recognize that attention is the most expensive currency on earth. To make the case for just how important this is… Here’s my synthesis of the data illustrating the average seconds of attention paid in each media channel, how much of that attention is with sound on (more effective!), and how much of your ad is watched all the way to the end… Video Ads: Attention Metrics [For a higher resolution image: Right mouse click > Open image in new tab.] Sobering, no? Big Insight: Active attention to an ad is contextual. And, brief. Increasingly: Just the first two seconds. Big Implication: A 60s TV ad is now, functionally, a 15-second ad with 45 seconds of background noise for most viewers. A 15s TikTok video ad is now, functionally, a 1s display ad view. Big Disappointment: Your Brand Marketing is largely delivering zero brand lift when measured with true test-control brand lift studies. If you are producing ads (“stories”) longer than 30 seconds – like the one- to five-minute sappy holiday creatives common this time of year – you are doing that purely for your own entertainment. Protect your career by not promising any business profits. The data above also explains why your TikTok / Reels / YT Shorts ad campaigns have almost never delivered brand lift with an above zero confidence interval – a massive waste of precious creative & Marketing budgets. [Note: TMAI Premium subscribers, carefully review TMAI #447: Confidence Intervals: A Brand Analytics MUST Have. Please email me if you do not have my awesome Excel model to compute your campaign’s real impact.] Why obsess about this? Effective Brand Marketing is the only way to grow Market Share over time. Video ads are a necessary tactic in that holy quest. Let’s embrace real consumer behavior, media consumption, and attention fragmentation. Shorter video ads. And, regardless of the ad length, front-loaded video ads with high-impact first two seconds. Wait, Wait, Wait… Loooong Ads Are Better! Like me, I’m confident you’ve heard a variation of this from your VP of Creative / Global Creative Director / CMO: Long creatives tell a better story, and people remember better stories. What does the data say? Data Fact One: Studies by Facebook’s Brand Lift team, Google/YT ABCDs find that shorter ads (6-15s) often drive equal or higher lifts in Ad Recall and Consideration than longer ads. (In part because they are less likely to be skipped or are unskippable.) Data Fact Two: Quantifying that… Research (Lumen/Teads) identifies that 15s ads drive 75-85% of the recall of a 30s ad – at half the media cost (Magna/IPG). Data Fact Three: If they hold attention throughout, longer ads (30s+) can drive higher emotional intensity and long-term brand affinity. Your VP, Director, CMO is right… Longer ads have additional value to offer! To deliver that special magic, long ad creatives have to solve three problems: 1. The long ad needs to be built to solve a different, long-term purpose. 2. If you just want to drive Unaided Brand Awareness, Consideration, or Purchase Intent, you can do so more efficiently with a shorter ad, while lowering resentment risk. The long ad creative needs to be super magnificently effective in the first 1-4 seconds. The creative has to be able to avoid the Skip / Swipe in skippable ad formats, and avoid the human looking away / going to the bathroom / looking down at their phone in the case of non-skippable formats. 3. The long ad creative needs to be supported by 3x – 6x additional media budget – when compared to the 15s ad media budget – to deliver the promised higher emotional intensity. Life Changing Insight: The modern battle for brand lift isn’t won by one long story; it is won by frequency of short, high-impact moments. No matter your ad length, if your ad is not seen x number of times over y weeks, it will not deliver impact. [Note: Premium subscribers deep dive and incorporate: TMAI #431: Impact of Ad Length on Campaign Cost.] It is difficult to meet these three magic-producing criteria, but it can be done. Use the ad length that is optimal for the business purpose you are solving for. Don’t use a jumbo jet to commute to Manhattan. Don’t try to cycle from NY to Chicago. Regardless of ad length/purpose, I’m confident you noticed that you really need to make the first two, three, seconds count. [Special Advice: The Ad Sales team at one particular ad platform aggressively champions the cause of looooooooooong ads. If you run into them, set all else aside and ask one question: How do we get distribution for the looooooooooong ads? If you get an affordable, scalable answer that spike and sustains, follow their advice.] An Ideal Video Ads Media Plan. Recognizing that effective Brand Marketing via video ads is not a one-size-fits-all, I want to sketch this starting point for your video ads strategy: Spark: 6s “Bumpers” / equivalent, will take a majority of your media budget (55-65%). They build frequency, recognition, and sustain your brand lift gains. Fuel: 15s / equivalent, will take nearly all of the rest of your media budget (25-35%). Ideally, sequenced with effective 6s ads so they would have gained interest to hear the rest of the story. Blaze: An occasional 30s (ideally non-skip) taking the remaining budget (5-8%), in big spike moments to support a specific brand feeling. Beacon: A rare, beautiful 60s film, not as an ad (0%), but organically seeded on social channels, shown in internal company meetings, submitted for industry awards. There can be small, occasional, variations. From my experience across industries and countries… For retail type companies, Spark takes up 70%. For B2B, Fuel can be up to 40%. For a revolutionary new product/company, Blaze temporarily can be 20%. Repetition: You will notice I’m consistently prioritizing frequency over length. Effective Brand Marketing is frequency-powered in an age where attention is the most expensive currency. Second repetition: Regardless of length, each type of video ad will have to start front-loaded, with a BANG. The first few seconds are critical to plant a memory, to generate interest in seeing rest of the story. Let’s learn how to do that. How to Be Creative: Zero 2 Interest in Two Seconds! Across all social video, users pay only 12 seconds of active ad attention for every hour(!!). Implication: Your share of voice is infinitesimal unless you disrupt their pattern. To do that, you have one to three seconds max. I’ll share data-identified effective creative tactics, for each channel. But first, there are five creative tactics that apply regardless of channel. Big 5 Universal Creative Effectiveness Truths. 1. Brand in 3. The brand must be recognizable within 3 seconds (logo, color, sonic signature, character). 2. Frame-One Impact. The first visual frame must tell a story or pose a question. (It is insanely difficult, that is what it takes to win.) 3. Sound as Lead, Not Support. Music, voice tone, and audio pacing drive emotional response faster than visual. 4. The “Why Now?” Answer the viewer’s unconscious question: “Why should I care about this right now?” (Reminder: Your ad’s competing against all the content on the internet.) 5. Creative Pre-Tested. The only way to win before you spend is to pre-test your creative – and ensure it passed in your ad’s media channel and your intended audience. For Concepts and high media weight Executions, use HMM Pro. For high volume, low media weight Executions, TikTok/Shorts/Reels, use HMM AI. These are super high standards for your creative teams to meet. In a world where you’ll get 12 seconds of ad attention per hour… Recommendations 1 – 5 above are mandatory. If you feel your video ads are falling short of the above truths: A. That explains why you can’t prove an iota of incremental impact from Brand Marketing on long-term Revenue. B. That should be a reason you pause your current video ad spend until your creative team/agency can deliver worthy creative. The Build Effective Creative Journey Continues. Every channel has its nuances. What works on TV rarely works on YouTube. What works on Reels often does not work for Facebook. Mobile video ads needs different Big Bang Two-Second start than if they are served on CTV. In TMAI #490 I’ve shared detailed best practices I’ve validated through testing and media tactics individually for Linear TV, CTV, YouTube Skippable, Facebook/Instagram Feed, TikTok/Reels/Shorts, and Snapchat. Lessons from approx. $10 bil in brand marketing spend analyzed. If you are a new TMAI Premium member, please email me if you can’t find edition 490 with detailed Part 2. If you are not, grab an annual Premium subscription here – the insights will transform your professional effectiveness! Bottom line. Our belief in the power of story is correct. Our canvas has changed. The 60-second spot is not dead, as illustrated above, it has a purpose in a Beacon strategy on free channels and for earning awards. The 60-second ad as interruption is dead. It does not perform as a media strategy. (Neither is there much inventory to buy. The platforms know it does not work!) Short-form creative is how we earn attention, and earn permission to tell our (slightly) longer, richer story. We are not abandoning our craft. Our quest remains legendary brand lift! The path we take to get there is new. Carpe diem. Avinash. PS: In the world of Chinese livestream sales, Zheng Xiang Xiang’s approach is super impressive. She sells 100 million Yuan ($19m) of products in a week. Don’t emulate it. Xiang Xiang operates within available attention. Appreciate that to become a better Marketer.

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• Loss Recovery Growth Model: Answer Engine Optimization (AEO)

  With the slow and steady evolution from keyword searching to resolution questions typed into Answer Engines, you are going to lose traffic (and revenue) somewhere in the rage of -18% to -64% during the course of the next calendar year. Today, our challenge is three-fold: A. How can you identify the size of your loss? B. What can you do to recover some losses? C. What actions can you take to take advantage of this shift and grow revenues? Every smart company is building a forecasting model to estimate these three life-critical dimensions. Let me make that important exercise a little easier for you. My model will help you estimate your losses, identify opportunities to recover, and share actions, and by how much each can drive growth. You can use the model to have strategic conversations with your CEO, CFO, and help your CMO create a clear, prioritized, list of actions (including hiring new staff with relevant expertise). This blog post was originally published as edition #482 of my newsletter, TMAI Premium. Each week, the newsletter shares strategic frameworks and practical here’s how to stay at the very bleeding edge of CFO-proof Marketing and Analytics. Sign up for TMAI Premium to accelerate your career trajectory. The Librarian to Grad Assistant Search Transformation. Past: When the User asked a question, traditional Google was a librarian. It did not know answers, it pointed us to a section of the library – four plus ten blue links – and said the answer is highly likely to be in these books, good luck. As a business, our job: Be the most attractive, relevant, authoritative book on the shelf, and/or pay to put our book on the special display by the front door of the library. Present: When the User asks a question, the new Answer Engine is the ruthlessly efficient grad research assistant. It goes to the library. It reads the 14 relevant books from a billion books, synthesizes the information, writes a perfect one-page summary directly answering the question. Most businesses lose the clicks, one or two get the click now – and even that we might not get as the Answer Engine absorbs ecommerce, and as AEs become Agentic eliminating the human step altogether. ChatGPT, DeepSeek, Claude are examples of Answer Engines. AI Mode in Google, now out in 40 countries, is also an example of an Answer Engine experience. ChatGPT Agent, Rufus from Amazon are examples of AE experiences that get close to Agentic search – humans need only express a wish; the agent does everything else. Learning, shopping, homework, will never be the same again. My recent AEO newsletter series, six editions (!), covered the changes underway, the actions you need to take on your digital experience, your organic and paid strategies, new measurement to embrace, and how you can prepare to live in an Agentic “Search” world. If you’ve not read that almost mini-book, please do. It is critical to understand the nuance and detail of this conversation. [Note: If you are a new Annual Premium Subscriber, please email me. I’ll be happy to share the six-part series with you.] Here’s a picture I sketched for a recent keynote on AEO, outlining the transformation underway… And, the implications… The completed forecast model will cover items 1 through 5 in the picture above, which are raised from these three questions for every business: 1. How much traffic will we lose? (Organic & Paid) 2. Given the librarian shift, how much of this loss can we recover? (Not all.) 3. Can I take advantage of this shift and grow new traffic? (Yes.) Let’s answer. The Business Losses Are Here (And, Accelerating). The change in user behavior above (in blue) is driving a change in the user experience (in black). Ex: Answers take up most of the real estate, with the answer often reducing the need to go to a downstream site. Ex: Paid ads might come, but for now there are either no ads or few ads (below the fold). Both create losses. My expectation is that the losses will accelerate in 2026. Important note: Losses, Recover & Grow will have different answers for different companies. Ex: For many publishers (news, magazine, content), the loss in traffic is already large, and permanent. These entities will need a phoenix type rebirth. I am going to focus on normal businesses: Ecommerce-type entities, B2B & B2C where the earning of revenue is a short, medium, long-term objective. 1. Factors driving losses in SEO Traffic. Informational queries are at the highest risk. These are the what is, how to, and best of queries. They represent, what some people call, top of the funnel. AEs are designed to directly answer these. Commercial queries are close to the highest risk. These are the vs queries, comparisons. Lenovo ThinkPad Snapdragon vs. Asus Zenbook A14. These are very high value queries, AEs will not generate an on-the-fly custom comparison table and even tell you to buy the Zenbook (btw, I have one and it is spectacular!). Non-brand High-Intent Transactional queries are at moderate risk. These are the user typing “buy men’s waterproof hiking shoes for an Alaska trip.” The current UX is that the AE will provide a handful of recommendations, with a summary of why. The user will click fewer times, and your organic category page will be ineffective. Branded & Navigational queries are at low risk. These are “Kate Spade Black Friday Sale.” Or “Coach Tabby in loved leather.” The AE will deliver the traffic to you, as that is the intent of the human. In future iterations, there might be insertion of things like “site links,” which might be a small risk. [Bigger lesson: Brand Marketing has never been more critical!] So, what’s the potential loss through the year 2026 of no AEO action by your company? Let us assume you currently get 5,000,000 visits from Organic Search, and the Revenue Per Visit (Total Organic Revenue/Total Visits) is $2.5. Based on my research, conversations with the top LLMs, competitive intelligence tool builders, and my judgment, here’s the section of the model that proposes the potential losses due to SEO (as Users shift from old-school searching to resolution queries in AE experiences): From an anticipated Revenue of $12.5m, a potential loss of $4m is coming your way. [Note: Annual Premium Subscribers received a full working Excel forecasting model, with losses, recovery, grow included. If you’ve misplace it, or are a new Subscriber, please email me.] Your to-do is to go to your Digital Analytics tool and take your current Organic Search traffic and compute the size of your Informational, Commercial, Non-Brand Transactional, and Branded & Navigational. Then, update column 2 (% of Current Visits). The rest of the cells will update and you’ll see your real losses. My assessment of Potential Loss in each row is conservative, and for “strong brand ecommerce” type business. If your business is different, please invest in research and update that column. With so much changing all at the same time, it is difficult to predict user behavior shifts with 100% accuracy. Consider the loss estimates as the best educated markers. They could happen much faster, could take a little bit of time, but if you stretch over the year 2026, you will broadly be in this range. Hence… Time. For. Action! 4. Factors driving losses in PPC Traffic. The threat to Paid Search is different, and just as scary. It is defined by two words: Transformation, and displacement. Remember, for now, none of the LLMs have advertising as a business model (though, it is too lucrative to resist ads for monetization). That’s displacement. Google, with the largest Search business to protect, will surely have ads in AI Mode, but for now they will look different and seem to be below the fold. Bing is trying new ad formats in its version of AI Mode. This is Transformation. Translation: Fewer options for you to pay to get clicks. Let’s use the same categorization as SEO to reflect on risks. Informational queries represent the same high risk of loss. Setting aside the no ads and below the fold ads issues, the entire purpose of Answer Engines is to answer. Hence, budget spent on informational questions will likely be completely wasted. The User is likely to ignore the ad, and certainly not scroll for ads below the fold. Commercial queries hold a giant point of friction. Our paid ads for, say, “best shoes for hiking,” will compete with the Answer Engine’s AI-analyzed answer for… best hiking shoes! Unless by the grace of God your brand was the AI’s assessment of the right answer, your text ad will lose the fight for attention. Non-Brand High-Intent Transactional queries are likely the bulk of your current ad spend. Ex: buy waterproof hiking shoes for a trip to Alaska. The risk here is not elimination, rather it is format shift. Ex: Text ad slots with breadcrumbs are being replaced with one (or two) AI-powered carousels. The four ad slots were clearly visible; in a carousel format that might not be the case. AEs are likely to have new approaches to Ad Quality Score (tied to now their superior understanding of the user’s intent), this might make it harder for your ad to show up. Other as yet unpredictable ad “innovations.” Branded & Navigational queries, as with SEO, are likely to have a lower risk of reduction. (But, as with your brand keyword ads today, when you measure them today on the basis of Incrementality, using CLS, you will find that they have very low incrementality. So… You should not be buying ads today. Definitely not in Answer Engines either – it is in their vested interest for them to send a user with your brand intent to you. [Note: There is another threat to Navigational and Branded ads. Buying seems to be moving directly into the Answer Engines. This might mean more wins for your competition, and certainly fewer cross and up-sell opportunities for you (lower AOV). Both, not fun.] So, what’s the potential PPC loss through the year 2026 of no AEO action by your company? Assuming $7m annual Visits, with Value Per Visit of $2.5… Here’s my model to help direct your strategic choices: From an anticipated Revenue of $17.5m, a potential loss of $4.6m coming your way over the course of CY 2026. As with SEO forecast, update your Traffic and Value Per Visit numbers from your Google Analytics data. Cluster the keywords you are buying into Informational, Commercial, Non-Brand Transactional, and Branded & Navigational. Update the column titled “% Current Visits.” The rest of the cells will fill in. Compute your potential losses. Take both of these tables to your next CxO strategic conversation. Have a good group cry. Then, start a little Marshall project to kick off urgent action. A Humbling Realization. Recognizing that every company is unique, even similar ones, and hence predicted outcomes will vary… My assessment here of the potential Loss for each query cluster is conservative. Additionally, I’ve assumed that you do everything I’ve urged you to do in TMAI #469: AEO Exp & PPC, TMAI #470: AEO Content FTW. If you have not, the non-positive impact will be significantly higher. So, pelase do not delay understanding and the urgent need to influence your company culture. The Modeling Journey Continues. Good news: You can recover some of your losses! Excellent news: You can take bold action right now, earlier than your competitors, to grow your traffic and revenue in an Answer Engine world!! The grow possibilities are substantial for some industries. TMAI Premium editions #483 and #484, outlined specific Recovery and Growth actions, and anticipated positive percentage impact of each action. They would specifically apply to ecommerce-type entities, B2B & B2C where the earning of revenue is a short-, medium-, and long-term objective. Using the format above, the model offers the same for Paid Ads. If you are a new Premium member, you can email me for the complete forecasting model. Bottom line. Every user behavior transition brings adjustments that businesses have to make. This is one such moment, happens to be the once-in-a-generation kind. Even when so much is unknown, you now have a clear and helpful model to start to quantify the size of the impact. That gives you power to take control of your destiny. Get your team ready to recover, and even grow. Carpe diem. PS: As you execute all of my recommeded actions, you’ll have an intelligent program of Answer Engine Analytics (AEA) to measure success, identify new opportunities. Here’s a helpful sketch, by our friend Gemini, of my Big 5 recommendations:

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