{"id":9675,"date":"2026-02-17T01:37:42","date_gmt":"2026-02-17T09:37:42","guid":{"rendered":"https:\/\/www.kaushik.net\/avinash\/bye-human-powered-marketing-analytics-hello-ai-powered-analytics\/"},"modified":"2026-02-17T01:37:42","modified_gmt":"2026-02-17T09:37:42","slug":"bye-human-powered-marketing-analytics-hello-ai-powered-analytics","status":"publish","type":"post","link":"https:\/\/www.kaushik.net\/avinash\/bye-human-powered-marketing-analytics-hello-ai-powered-analytics\/","title":{"rendered":"Bye, Bye Human-Powered Marketing Analytics."},"content":{"rendered":"

HiPPOs<\/a> still hand down most decisions in a company – even when surrounded by piles and piles of reports with metrics galore.<\/p>\n

Marketers still use the \u201cfunnel\u201d to imagine and allocate budget, people, actions \u2013 even when there is multi-decade data that the funnel is a lie.<\/p>\n

Company after company misses trends and goes kaput \u2013 even after investing in multi-million dollar data projects to build clean rooms, unified consumer view cloud-based business intelligence platforms.<\/p>\n

☹️<\/p>\n

The core challenge isn\u2019t data scarcity; it is insights latency.<\/strong><\/em><\/p>\n

Which impacts your ability to follow my advice to deliver IAbI<\/a>, and not data. <\/p>\n

Here\u2019s the traditional analytics workflow being practiced in your company:<\/p>\n

\n

1. Report Generation.<\/strong> Hours and hours of standard reports\/dashboards.<\/p>\n

2. Manual Analysis.<\/strong> Hopefully, laborious segmentation of known knowns missing subtle non-intuitive patterns. AKA High-dimensionality data challenge.<\/p>\n

3. Insight Extraction. <\/strong>Identifying the most important findings, extracting context from Marketers, Finance, Sr. Leaders, creating presentations.<\/p>\n

4. Exec Last-Mile Barrier.<\/strong> Data competing with other priorities, insights missed, misinterpreted, translation to action challenging \u2013 to say the least.\n<\/div>\n

A process that is fundamentally reactive and linear. It struggles with tracking hundreds of variables per user engagement, non-linear patterns (path analysis anyone?<\/a>). Humans are ill-suited to find a needle in the haystack – identifying truly significant anomalies or emerging trends within massive datasets.<\/p>\n

☹️ ☹️<\/p>\n

It does not have to be this way. anymore.<\/p>\n

It is time to hand control of Marketing Analytics over to AI! Online, offline, digital, everything.<\/p>\n

😊<\/p>\n

AI can act as a force multiplier and overcome the above limitations.<\/p>\n

\n

A. Pattern Recognition<\/strong> at Scale. Machine Learning (ML) algorithms are awesome at finding complex, non-linear relationships and hidden clusters within massive high-dimensionality datasets.<\/p>\n

B. Automating the Mundane.<\/strong> As I\u2019m sure you\u2019ve 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.<\/p>\n

C. Predictive Power.<\/strong> Rather than reactive, what happened, AI is exceptional at what\u2019s likely to happen, thus solving the insights latency. It is worth noting it can personalize this at a massive scale \u2013 dynamic segmentation, tailored experiences, value something impossible with manual rules. AND, do this uniquely for the needs for every human in your org!<\/p>\n

D. Continuous Learning.<\/strong> AI\u2019s real superpower. AI models adapt as new data flows, they constantly refine their understanding of user behavior and system performance \u2013 at a massive scale. (It would be equivalent to the Analyst earning a Bachelor\u2019s degree in a new field every few weeks!)\n<\/div>\n

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.<\/p>\n

😊 😊 😊<\/p>\n

It is time to rebuild analytics from the ground up.<\/p>\n

You’ll remember I originated the 10\/90 rule of Analytics<\/a> 20+ years ago.<\/p>\n

\n

\\”If you have $100 to invest in smart decisions, invest $10 in tools and implementation, invest $90 in humans who will analyze the data!\\”\n<\/p><\/div>\n

Here’s my new 10\/90 rule for success via investment in Analytics:<\/p>\n

\n

\\”If you have $100 to invest in smart decisions, invest $10 in brilliant human analytical strategists, invest $90 in AI activation.\\”\n<\/p><\/div>\n

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.<\/p>\n

Incredible, no?<\/p>\n

Let’s learn how to activate this immense value.<\/p>\n

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<\/p>\n

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<\/a> to accelerate your career trajectory.<\/div>\n

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<\/p>\n

Activating AI Power.<\/font><\/strong><\/p>\n

AI is not yet AGI (Artificial General Intelligence), and certainly not SGI (Super General Intelligence). <\/p>\n

[Note:<\/strong> 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.]<\/strong><\/p>\n

Today, activating the awesomeness above will take human grit, intelligence, and persistence. Things won\u2019t be perfect. <\/p>\n

Your True North: Somewhat failing to activate my recommendations is 25x better than your present. And, as a bonus, you\u2019ll be ready for AGI.<\/p>\n

I have ten specific implementation ideas for you to turn your digital analytics over to AI. I hope they\u2019ll spark a dozen more in your team.<\/p>\n

1. Predictive Analytics via Propensity Modeling.<\/span><\/p>\n

Impact Potential:<\/strong> Transformational.<\/p>\n

Human-powered digital analytics tells us who<\/em> converted. AI can tell us who will convert!<\/em><\/p>\n

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.<\/p>\n

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<\/em>), 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.<\/p>\n

Framed simply: What is the exact probability THIS human will convert\/upgrade\/churn in the next N days?<\/em><\/p>\n

AI Approaches and Algorithms<\/strong> to explore, stress test, and embrace:<\/p>\n

\n

  • Gradient Boosted Machines (XGBoost, LightGBM).<\/strong> 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.<\/p>\n

  • Random Forests.<\/strong> I have loved using RF when I have a need to understand feature importance. Ex: Which behaviors most strongly predict conversion?<\/p>\n

  • Neural Networks.<\/strong> The grandpa of AI. For massive datasets with complex, nonlinear relationships, deep learning architectures can uncover patterns other models miss.<\/p>\n

  • Survival Analysis.<\/strong> Good old statistics. Predicts not just if, but when a user will convert, enabling perfectly timed interventions.<\/p>\n<\/div>\n

    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.<\/p>\n

    Practical Example.<\/strong><\/p>\n

    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: <\/p>\n

    \n

  • Serve dynamic offers to high propensity users. <\/p>\n

  • Adjust bid strategies for retargeting ads based on conversion probability.<\/p>\n

  • Identify \u201cat risk\u201d users who showed high intent but did not convert (so we proactively intervene vs. lose to competition).<\/p>\n<\/div>\n

    Potential Outcomes For You.<\/strong><\/p>\n

    Looking across my work on three continents, focusing on ecommerce: <\/p>\n

    \n

    A. <\/strong>35% – 60% improvement in Conversion Rates for the targeted segments. <\/p>\n

    B. <\/strong>20% – 35% reduction in acquisition costs due to more efficient ad spend. <\/p>\n

    C. <\/strong>Not easily quantified qualitative impact of shifting from reactive to proactive marketing. <\/p>\n<\/div>\n

    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\u2019t activate it, you are falling two to three quarters behind.<\/p>\n

    [Note: <\/strong>TMAI Premium subscribers will recall three editions dedicated to sharing a roadmap for building AI-powered Propensity Models. TMAI 378, 379, 380<\/strong>. Email me if you can\u2019t find them.]<\/strong><\/p>\n

    2 Advanced Customer Segmentation.<\/span><\/p>\n

    Impact potential:<\/strong> High.<\/p>\n

    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.<\/a><\/p>\n

    Most analytics teams segment users by demographics or broad behavioral categories (e.g., \\”mobile users,\\” \\”TikTok ad visits,\\” \u201clogged in\u201d). 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<\/em>, and none of the unknown unknowns<\/em>).<\/p>\n

    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<\/em> \u2013 hidden well below the human capability surfaces.<\/p>\n

    AI Approaches and Algorithms<\/strong> to explore, stress test, and embrace:<\/p>\n

    (This is less AI, a lot more algorithms and old school ML.)<\/p>\n

  • K-means Clustering.<\/strong> Thousand-year-old workhorse algorithm for segmentation and grouping users based on behavioral similarity across multiple dimensions.<\/p>\n

  • DBSCAN.<\/strong> 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!)<\/p>\n

  • Gaussian Mixture Models.<\/strong> Few models handle ambiguity better than hard clustering approaches, when segments overlap probabilistically.<\/p>\n

  • Hierarchical Clustering.<\/strong> 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.<\/p>\n

    [Note:<\/strong> 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!).]<\/strong><\/p>\n<\/div>\n

    There is immense untapped potential to be extracted by applying unimaginable scale to segmentation via AI and algorithms.<\/p>\n

    Practical Example.<\/strong><\/p>\n

    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:<\/p>\n

    \n

  • Segment A: Feature explorers, who try many features quickly.<\/p>\n

  • Segment B: Cautious adopters, who read documentation before acting.<\/p>\n

  • Segment C: Social validators, who always check testimonials first.<\/p>\n

  • Segment D: Price-sensitive evaluators, who immediately navigate to pricing.<\/p>\n<\/div>\n

    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!<\/p>\n

    Potential Outcomes For You.<\/strong><\/p>\n

    Reflecting on my clients and work: <\/p>\n

    \n

    A.<\/strong> 25% – 50% improvement in Conversion Rates from behavioral targeting based on algorithmic segments. <\/p>\n

    B.<\/strong> 60% to 75% reduction in analysis time dedicated to customer segmentation. <\/p>\n

    C.<\/strong> Not easily quantified qualitative impact on customer joy and company revenue from a deeper understanding of potential customers and their behavior.<\/p>\n<\/div>\n

    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).<\/p>\n

    3. Voice-of-Customer Integration with Behavioral Analytics.<\/span><\/p>\n

    Impact Potential: <\/strong>High.<\/p>\n

    Another one of my old web analytics dreams has come true. Early readers will remember my, at the time, revolutionary <\/em>Trinity model for Analytics<\/a>. 19-years later, I can AI it!<\/p>\n

    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 <\/em>with the what <\/em>(failing Trinity). This leads to incomplete understanding of user motivations, frustrations, and unmet needs. Solving this at scale with humans is futile.<\/p>\n

    One of the key leaps of modern AI is multi-modality \u2013 the ability to understand text, images, voice, and video at unimaginable scale and incredible precision. <\/p>\n

    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. <\/p>\n

    AI Approaches and Algorithms<\/strong> to explore, stress test, and embrace:<\/p>\n

  • Multimodal Transformers.<\/strong> Great for processing text and behavioral data in a unified model architecture, with cohesive, understandable outputs \u2013 at scale.<\/p>\n

  • Cross-modal Retrieval. <\/strong>Helpful in finding behavioral sequences that correspond to specific feedback themes, assessing their quantitative relationship. <\/p>\n

  • Advanced Sentiment Analysis.<\/strong> 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!).<\/p>\n

  • Topic Modeling with Behavioral Correlation.<\/strong> 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).<\/p>\n

  • Emotion-Action Mapping.<\/strong> Above hinted reflective analysis, now you switch the view to predictive analysis. Connect expressed emotions with subsequent behavioral patterns \u2013 driving proactive actions by ensuring you don\u2019t lose a logistics provider or an employee quitting or a massive B2B client not renewing their contract.<\/p>\n<\/div>\n

    Practical Example.<\/strong><\/p>\n

    This one\u2019s from a pal, in Canada. An ecommerce platform integrated the site\u2019s chatbot data with behavioral data and put in place an AI model to analyze it. Discoveries:<\/p>\n

    \n

  • Positive mentions of sustainability in chats correspond with 3.4x higher lifetime value.<\/p>\n

  • Discovered emerging complaint patterns about a new feature, three months before negative NPS scores.<\/p>\n

  • Users expressing \u201csize uncertainty\u201d in chats have an 82% higher return rate. (OMG)<\/p>\n<\/div>\n

    Potential Outcomes For You.<\/strong><\/p>\n

    \n

    A.<\/strong> 8 \u2013 12 points improvement in NPS scores.<\/p>\n

    B.<\/strong> 20 \u2013 25% reduction in fails (cart abandonment, returns, etc.) from real-time interventions put in place.<\/p>\n

    C.<\/strong> Not easily quantified qualitative impact on product development from 360-degree customer understanding from connecting the why<\/em> with the what<\/em> systematically.\n<\/div>\n

    [Note:<\/strong> With life experience, Trinity model became an even more sophisticated and modern Edge model for Analytics<\/strong>. Premium subscribers, please see TMAI #224. And then a refinement of it in TMAI #273: The Analytics Flywheel | Invent Once, Scale Infinitely.<\/em>]<\/strong><\/p>\n

    The Profitable AI-Analytics Journey Continues.<\/strong><\/font><\/p>\n

    In TMAI #493 and #494, I’d shared additional super exciting ideas to deliver transformative profits via AI-Powered Analytics. Additional activations included:<\/p>\n

    \n

    4. <\/strong>Behavior Targeting & Intelligence (BTI).
    \nImpact potential: Transformational.<\/p>\n

    5.<\/strong> Natural Language Processing (NLP) for Unstructured Data.
    \nImpact potential: High.<\/p>\n

    6.<\/strong> Anomaly Detection and Automated Insight Generation.
    \nImpact potential: High.<\/p>\n

    7. <\/strong>Predictive (Whole Company) Customer Lifetime Value Modeling.
    \nImpact potential: Transformational.<\/p>\n

    8.<\/strong> Real-Time Pricing and Offer Optimization.
    \nImpact potential: High.<\/p>\n

    9.<\/strong> Intelligent \u201cLiquid\u201d Merchandising.
    \nImpact potential: Medium.<\/p>\n<\/div>\n

    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.<\/em><\/p>\n

    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<\/a>.<\/p>\n

    Bottom line.<\/strong><\/font><\/p>\n

    The integration of AI into Analytics represents the most significant shift in our field since its birth as a science. <\/p>\n

    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.<\/p>\n

    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.<\/p>\n

    Carpe diem.<\/p>\n

    PS: It is only appropriate that I share with you an AI-generated summary visual of this blog post! For your slides…<\/p>\n

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