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Marketing has lost its creative edge, a casualty from a short-term, data-driven focus on the exploitation of metrics like ROAS (Return on Ad Spend). AIM offers a solution: a platform that marries data with intuition, enabling teams to leverage industry insights while grounding them in data. AIM’s AI integration with Google’s APIs for Ads and Analytics provides an interactive interface that supports personalized data exploration, helping organizations create impactful marketing strategies.&nbsp; AIM is more than just a data tool; it’s a thought partner that enables users to navigate structured data and unstructured ideas, creating room for innovation within data-driven strategies. The Challenge &nbsp;The digital marketing landscape has become dominated by short-term metrics. Metrics like ROAS provide immediate feedback, leading to an industry culture that prioritizes rapid, data-driven iterations over long-term strategy, creativity, and differentiation. As a result, marketing teams frequently default to repetitive messaging, creatives, and strategies—sacrificing innovation. The real challenge extends beyond advertising to the broader marketing sphere. Many companies struggle to navigate platforms like Google Ads and Analytics or connect various data points to form actionable insights. The typical solution is to outsource to agencies, yet these agencies often focus on scalable, one-size-fits-all strategies that fail to cater to unique organizational needs. AIM recognizes the need for a solution that allows companies to take control of their marketing data and turn their insights into distinctive, impactful strategies. Why This MattersCurrent marketing practices are hindered by a system that favors standardized approaches and quick returns, often at the expense of creativity and genuine differentiation. The obsession with ROAS has left marketing strategies generic and overly reliant on rigid value stacks, constraining the potential for innovation. Marketers are forced to validate their ideas immediately rather than exploring them in a space where hunches and insights can develop into impactful strategies. This issue is compounded by the lack of accessible, user-friendly data tools. For many companies, navigating digital platforms and understanding how metrics are interconnected remains challenging. Outsourcing to agencies, while convenient, often results in strategies that prioritize agency profitability over meaningful engagement with client data. Where Data and Intuition IntersectAIM addresses these challenges by providing a platform that bridges data with intuitive decision-making. Using Google’s APIs and advanced AI, AIM creates a space where marketers can leverage data without sacrificing creativity. Through a text and voice-enabled interface, users gain access to personalized dashboards that highlight essential paid and organic metrics. AIM enables &nbsp;marketers to explore insight-driven solutions tailored to their unique data and industry. AIM is more than just a data tool; it’s a thought partner that facilitates the synthesis of intuition and data. By turning gut instincts into actionable insights, AIM enables marketing professionals to navigate both structured data and unstructured ideas, creating room for innovation within data-driven strategies. Benefits and Impact &nbsp;AIM’s unique combination of data integration and interactive features offers multiple benefits, including:- Enhanced Strategic Depth: By aligning data insights with creative intuition, AIM fosters strategies that stand out in the marketplace.- Personalized Insights: Through customizable dashboards, marketers can focus on the metrics that matter most, avoiding one-size-fits-all strategies.- Improved Efficiency and Self-Sufficiency: AIM helps users navigate Google’s APIs independently, reducing reliance on agencies and encouraging a deeper connection with their own data.- Informed Decision-Making: AIM transforms hunches into data-backed insights, supporting innovation and creativity The MissionAIM’s mission is simple: create a safe space where creativity and data can coexist. It’s a place where hunches can grow into actionable insights. For marketers looking to innovate and differentiate, AIM is the practical next step, where the best of both worlds—intuition and data—come together to shape a new future for marketing.

AIM: Where Intuition & Data Intersect

Democratizing AI, Bit by Bit "Democratization of storytelling”, is how Co-Founder Alejandro Matamala, framed Runway AI’s vision&nbsp;“Democratization of technology”, is how Managing Director (North America) Darren Mowry, reframed it for Google Cloud&nbsp; Last year, Google made $652m/day aggregating, selling our data. This year, their focus has pivoted to the ‘democratizing of technology’, via open-source.&nbsp; Thomas Kurian (CEO of Google Cloud), shared the following in an interview on Stratechery, “we are seeing significant acceleration in how customers want to use digital tools and AI to transform their core business…. we have an open architecture, which gives them the ability to use these services, but across a range of different models. Some from Google, obviously Gemini, but also from partners.” Using the term, democratization, seems fitting for AI. Google aggregates data and automates workflows to maximize profit - AIM 2.0 (AI Marketing) is set up to democratize data via disruption of automation.A for-profit business’s goal is generally to maximize their profit against a given time horizon - spend efficiency by revenue over cost, also known as return-on-ad-spend (ROAS).&nbsp;Google Ads is set up as an auction. The advertiser sets up a campaign with a daily budget, said campaign then participates in real-time bidding (RTB) and the advertiser pays-per-click (PPC), also known as cost-per-click (CPC), regardless of the bid strategy (this is significant - Appendix). Bid strategies for campaigns focus towards maximizing or hitting a target CPC, ROAS, or CPA (cost-per-acquisition aka lead &amp; Appendix).The problem is that Google wants to maximize their revenue as well. Google does this by promoting automation through campaign types and bid strategies (Appendix). The DOJ has referred to the RTB framework as a “black-box”. Unsurprisingly, large-language models (LLMs) have also been described as a black-box.AIM 2.0 will be a free application released this quarter. The software will use an api call to fetch your data, run it against the trained model for industry/competitor specific context, and then output a bid target which will maximize your ROAS or conversion volume against given time-horizon.&nbsp;AIMs Framework The term AI is thrown around to the point that it has become laughable; it's provocative, gets the investors going. Every company is trying to automate workflows via fine-tuning a model, and then proudly share it with investors, clients, on earnings calls, etc&nbsp; I would compare this to figuring out how to dribble a basketball and then sharing that you can play the sport.&nbsp; Rather than fine-tuning (current competitor best practice), AIM leverages a custom, pre-processed, hybrid-LLM trained against datasets to properly compete with Google's blackbox, and which are complimented with Google's data-driven best practices, deep exploration via deep reinforcement learning and policy methods including RLHF, RLOO, etc (Appendix) - this enables robustness through reasoning, essential for game theory, allowing AIM to assign weights appropriately against the noise; dynamics which need to be understood in strategy. AIM 2.0 is engineered to extract pertinent historical Google Ads data, analyze seasonality, current-market dynamics, auction insights, demographic indicators, time of day and device efficiencies, etc - most importantly, with reason. Basically everything a strategist should do everyday for every keyword, product, ad group, etc (if it were humanly possible, while being profitable - Appendix). Simply put, when we fine-tune models, Gemini forgets which color vanilla ice cream is (Appendix).&nbsp; Why this is important: Value Stacks Rather than anthropomorphising, let's imagine we are a machine (weird) - our operating system (OS) is tied to an environment, bounded by its weighted values (Appendix). The human OS also is bound by its weighted values, which we can stack and prioritize when needed. For example, many Americans are single-issue voters. If you're pro-life, then a presidential candidate's stance on other issues becomes mostly irrelevant if they don't outweigh your pro-life positioning. The pro-life parameter takes priority in your value stack. Now let's apply the value stack framework to aggregators (Appendix) like Google and Meta. As mentioned earlier, for-profit organizations aim to generate profit. Historically, companies like Google earned most of their revenue from advertisers who have the technical expertise to use their ad platform. While billions of users indirectly support aggregators, advertisers and agencies have been their most important customers, implying that you are the product (Appendix).<figure id="figure-oceafbv1oluwmnies" class="selected"><img id="oceafbv1oluwmnies" src="https://cdn.durable.co/blocks/aQOwbfufVQBY8mytl0EgSyQ9sKF2ERTdOwuuXC2MLjWQE9ROmvc1Qdgma1VLeQT5.jpg" alt="" title="" class="object-cover bg-gray-100 " style="aspect-ratio: 1/1; object-position: 55% 100%;" data-corners="no-rounded" data-aspect="1:1" data-caption="Produced by Michael Parekh, managing director of MKP Group, previous to Goldman Sachs" data-positionx="55%" data-positiony="100%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/aQOwbfufVQBY8mytl0EgSyQ9sKF2ERTdOwuuXC2MLjWQE9ROmvc1Qdgma1VLeQT5.jpg" data-mce-style="aspect-ratio: 1/1; object-position: 55% 100%;"><figcaption class="body-normal text-gray-500 mt-2">Produced by Michael Parekh, managing director of MKP Group, previous to Goldman Sachs </figcaption>Another framework to consider is the AI Tech Value Stack (refer to the image by Michael Parekh). This stack depicts a shift from the traditional model of aggregating user data (Aggregation Theory - Appendix) to aggregating AI chips. This is evidenced by Project Stargate (the OpenAI &amp; Microsoft joint venture) and recent US executive orders (Appendix). This shift allows Google and Meta to emulate Amazon's approach, which is accredited with both integrating infrastructure, while maintaining aggregation of data, network effect, to maintain their moat (Appendix).&nbsp; Key Takeaway By prioritizing chips to support their infrastructure, along with their current aggregated frameworks (Aggregation theory - Appendix), Google and Meta are strategically focused with AI top-of-mind, and well placed to capture the disruption in data-driven industries (Appendix). </figure><figure id="figure-kablj2biriluwn04o4" class=""><img id="kablj2biriluwn04o4" src="https://cdn.durable.co/blocks/13wdMw9CE2CawixcqUDu6JU7Ve2sQrocjMtZmoyuaXyMrOPBFU55tqFSP3jRu1XR.jpg" alt="steering strategy through epistemicity&nbsp;" title="steering strategy through epistemicity&nbsp;" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 40% 15%;" data-corners="default" data-aspect="16:9" data-caption="steering strategy through epistemicity&nbsp;" data-positionx="40%" data-positiony="15%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/13wdMw9CE2CawixcqUDu6JU7Ve2sQrocjMtZmoyuaXyMrOPBFU55tqFSP3jRu1XR.jpg" data-mce-style="aspect-ratio: 16/9; object-position: 40% 15%;"><figcaption class="body-normal text-gray-500 mt-2">steering strategy through epistemicity&nbsp;</figcaption></figure>I shared this knowledge graph in mid-February. At the time, I painfully outlined how tech giants are monopolizing data and framing it as a privacy concern. On Tuesday (4/9/24), Meta closed up 46% YTD. This flywheel propelling data monopolization is essentially further increasing barriers to entry thresholds, or more simply, expanding tech giants moats (Appendix).ConclusionSam Altman, CEO of OpenAI, has been reported sharing, "95% of what marketers use agencies, strategists, and creative professionals for today will easily, nearly instantly and at almost no cost be handled by the AI.”&nbsp;Considering this is Google’s main customer-base (Appendix), Google is ironically searching, searching for new customers, and have placed their bits on AI start-ups, as clearly reflected through the theme of this year's Google Cloud Next.Google CEO Sindar Pichai kicked off the event by sharing, “ we’ve known for a while now that AI would transform every industry and company including our own. That’s why we've been building AI infrastructure for over a decade….”If the customer wants a faster horse, build them a car; a framework around innovation accredited to Henry Ford. The idea of a car was so foreign to us, to understand how to frame its output, we created the term horse-power.&nbsp; AI will allow people with agency (Appendix) to actualize their vision. Runway co-founder shared a surprise for the team when they were initially catching traction. Runway's ideal customer profile (ICP - Appendix) had been intended for enterprise-level creatives; rather though, saw their revenue being actualized by non-enterprise, non-creatives. The Google Managing Director responded to this with something I found even more intriguing.&nbsp; Historically, tech valuations were based on the software, &amp; more importantly, output capacity from the volume of employees who can build it out at scale. Today valuations for tech (AI) companies are based on vision, &amp; implications of the industry which is impacted by its disruption; video cameras were originally developed to film plays (paraphrasing). Perplexity is democratizing answers. Runway is democratizing storytelling. Google is democratizing technology. &amp; AIM intends to democratize data.&nbsp; Let’s make marketing great again.&nbsp;

Democratizing AI, Bit by Bit

Steering Strategy in the Era of Privacy

Audience strategy is intricate, &amp; AI, with DeepLimeSeg (DLS), is cracking its code. Explainable AI (XAI) isn’t a figment or segment of your imagination.DLS segments your data and gives explanations on which factors contributed to the given cluster (e.g. high-value customers: buying habits, demo/psychographic factors, etc). This crucial framework is based on the tradeoff (λ) of accurate and explainable data.- F(θ) = Accuracy(θ) − λ ∗ Explainability(θ)&nbsp; Here's an overview of how each contributes:AI in Uncovering Audience Insights&nbsp; &nbsp;General AI technology plays a crucial role in analyzing complex audience data, revealing intricate patterns that are essential for crafting targeted marketing strategies.DeepLimeSeg’s Role in Precision Segmentation&nbsp; &nbsp;DeepLimeSeg, leveraging DRL (Deep Reinforced Learning), XAI (Explainable AI), and LIME (Local Interpretable Model-agnostic Explanations), provides a detailed segmentation approach. In my opinion, the most valuable effect is identifying the superconsumers (SC) – a small yet incredibly influential audience segment.- XAI: Explainable AI ensures our strategies are not just effective, but also understandable, allowing us to trust the decisions made by AI - LIME: A technique we use to make our AI’s decision-making transparent and easy to interpret, ensuring we know exactly why certain marketing choices are being recommended - DRL: Deep reinforced learning helps the AI learn from vast amounts of data, much like a human learns from experiences, to make smarter marketing decisions &nbsp;<figure id="figure-yllj3wcmzwlsapcyni"><img id="yllj3wcmzwlsapcyni" src="https://cdn.durable.co/blocks/ceQblUF9T8eaePA4TThglk7ts14LoIR9A5OCoEYetKa7MlwMXKU7ctlN2mugTmcp.png" alt="woman wearing black top top holding black dumbbells standing in front of mirror" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 16/9; object-position: 100% 100%;" data-corners="default" data-aspect="16:9" data-caption="DeepLimeSeg Approach" data-positionx="100%" data-positiony="100%" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/ceQblUF9T8eaePA4TThglk7ts14LoIR9A5OCoEYetKa7MlwMXKU7ctlN2mugTmcp.png" data-mce-style="aspect-ratio: 16/9; object-position: 100% 100%;"><figcaption class="body-normal text-gray-500 mt-2">DeepLimeSeg Approach</figcaption></figure>Why Superconsumers Matter&nbsp; Here’s something interesting: 10% of your audience, the SCs, could account for up to 70% of sales. SCs play a critical role in driving 'share of growth'; understanding and engaging with this group can be transformative for brands. At Brainlabs, overlapping identifiers from SCs are often found in our most valuable audiences (MVAs).Knowledge Graphs for Deeper Insight with DLS&nbsp; &nbsp;Knowledge graphs help AI see and understand the complex relationships between different consumer behaviors, giving us a 360-degree view of our audience. They can also help humans understand complex relationships - the below illustrates how brands can utilize AI, as outlined in this article.<figure id="figure-yqzmjrxvx6lsapi3hr"><img id="yqzmjrxvx6lsapi3hr" src="https://cdn.durable.co/blocks/5Qv4WZ2ql693gD3v8FmBK8s7mxCBAegc3xoTrGIUs44QNG7pyLS39mKqQsEo15f1.jpg" alt="facebook button pins" class="object-cover bg-gray-100 rounded-sm md:rounded-md lg:rounded-lg" style="aspect-ratio: 1/1; object-position: undefined undefined;" data-corners="default" data-aspect="1:1" data-caption="Knowledge Graph Outlining Relationship of Concepts Shared" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/5Qv4WZ2ql693gD3v8FmBK8s7mxCBAegc3xoTrGIUs44QNG7pyLS39mKqQsEo15f1.jpg" data-mce-style="aspect-ratio: 1/1; object-position: undefined undefined;"><figcaption class="body-normal text-gray-500 mt-2">Knowledge Graph Outlining Relationship of Concepts Shared</figcaption></figure>&nbsp; &nbsp; Hypothetical Use Case Examples&nbsp;Banking and Financial Services:- Scenario: A bank wants to increase the uptake of its new investment product among millennials. - DLS Application: By analyzing transactional data, social media activity, and demographic information, DLS identifies a segment of millennial customers who are tech-savvy but financially conservative. - Strategy: The bank develops a digital marketing campaign targeting this segment with educational content about low-risk investment opportunities, utilizing platforms where these customers are most active. - SC Considerations: The identification of a segment of tech-savvy but financially conservative millennials is based on superconsumer behavior analysis. These individuals likely engage more with financial content online, indicating a deeper interest in financial products. The bank targets these superconsumers with educational content about low-risk investments, leveraging their existing interest to drive engagement. Health and Wellness Industry:- Scenario: A health and wellness brand aims to market a new range of organic supplements. - DLS Application: The brand uses DLS to sift through customer purchase histories and online behavior, identifying a segment deeply interested in organic and natural products. - Strategy: The company creates personalized email campaigns and social media content highlighting the organic nature and health benefits of their supplements, directly addressing the interests and values of this segment. - SC Considerations: The health and wellness brand targets superconsumers interested in organic and natural products. These customers likely exhibit behaviors such as actively searching for organic products and participating in health-related discussions online. The personalized campaigns are designed to resonate with these superconsumers' values and interests, thereby enhancing engagement. Education Technology:- Scenario: An EdTech company seeks to promote its new AI-based learning platform. - DLS Application: DLS analyzes user data from existing platforms, feedback forms, and educational forums, identifying a segment of learners who value personalized and flexible learning experiences. Strategy: The company targets this segment with a content marketing strategy, showcasing success stories and user testimonials about the platform's adaptability and personalization features. - SC Considerations: The EdTech company focuses on learners who value personalized and flexible learning experiences. These superconsumers are likely to be more engaged in online learning communities and platforms. By showcasing success stories and testimonials that align with their preferences, the company directly appeals to these superconsumers, increasing the likelihood of adoption. Final Thoughts and Considerations&nbsp;&nbsp; &nbsp;Outside of knowledge graphs, computation requirements can be mitigated through model optimizations, applying theories (Value Co-creation, TRA, TPB), time series forecasting models (ARIMA &amp; LSTM), &amp; customer journey mapping. In the process of AB testing which combination is most appropriate to reduce this - will share findings when this is completed!#ReframingFramework #ArtificialIntelligence #CustomerSegmentation #DataAnalytics #ExplainableAI #MachineLearning #MarketingStrategy #Superconsumers #AudienceStrategy #XAI #AI #OpenAI------------&nbsp; &nbsp; Further context on DeepLimeSeg can be found from an article which within a special issue of Mathematics, “Mathematical Optimization in Pattern Recognition, Machine learning and Data Mining”, titled: A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing&nbsp;----------- &nbsp;<a data-mce-href="https://chat.openai.com/g/g-0HFirG85r-aim-v1" href="https://chat.openai.com/g/g-0HFirG85r-aim-v1" target="_blank" rel="noopener">AIM</a> - AI Marketing - a strategic marketing analyst with expertise in data correlation and audience development &nbsp;On a separate note, I'd like to take a moment for a shameless plug about an tool I've developed – the AIM v1. This AI Marketing GPT, specializes in providing detailed, tailored marketing advice, identifying brand characteristics, analyzing market dynamics, and deeply understanding customer behavior. Whether you're crafting Ideal Customer Profiles or devising intricate marketing strategies, AIM v1 brings a unique blend of expertise in demographics, psychographics, shopper values, and media touchpoints. It's not just about suggesting effective marketing channels; AIM v1 dives into highly specific tactics within each channel, ensuring your approach is both strategic and practical. For businesses targeting specific segments, like B2B marketing to C-suite executives, AIM v1 is particularly adept.&nbsp;&nbsp; As always, the content promoting AIM is written by AIM.Any feedback for v2 is appreciated and will be considered.

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Blog

AIM: Where Intuition & Data Intersect

Democratizing AI, Bit by Bit

Steering Strategy in the Era of Privacy

Cracking the Consumer Code with AI