Generative Engine Optimization (GEO) as an evolution in digital marketing, is distinguishing it from traditional Search Engine Optimization (SEO) and Answer Engine Optimization (AEO). GEO focuses on optimising content for AI-driven search engines like ChatGPT and Google's AI Overviews, aiming for content to be cited and synthesised into comprehensive AI responses rather than merely ranking in search results. The podcast explains how GEO differs from AEO, highlighting GEO's broader goal of influencing AI-generated narratives through authoritative and well-structured content. It also provides strategies for integrating GEO into marketing, emphasising quality, E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), conversational language, and regular updates, alongside case studies illustrating its benefits for increased reach and brand authority in an evolving AI-centric search landscape.
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Why is Agentic Commerce gaining such traction at this specific moment? The recent, mind-blowing leaps in “GenerativeAI” and “Large Language Models (LLMs)” have dramatically improved AI's ability to understand natural human language. This sophistication means AI can now "get" what we truly want, even if our instructions are nuanced or incomplete. Agentic commerce is the ultimate answer to this craving, promisingto make shopping practically effortless. Add to this the increasing interconnectivity of digital applications and a maturing data infrastructure, and you have the fertile ground necessary for these intelligent agents to thrive.
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Interview -Podcast
Optimising for AI searchHow to master the shift from SEO to Answer Engine Optimisation (AEO)…Interviewed by Rodrigo Wielhouwer, Founder, Datafeed.
Topic: How AI is changing consumer search behaviour: with Nishith Srivastava
Consumer behaviour is undergoing a significant transformation, particularly in terms of how people discover products and services. Gartner forecasts a 25% decrease in traditional search engine volume by 2026, largely driven by the rise of AI chatbots. This shift in searches towards AI-powered platforms, demands a new approach to visibility and engagement from marketers.
To gain a deeper understanding of these changes, we asked Nishith Srivastava, VP, Europe - BORN (Digital, CX, Martech, Data and eCommerce Transformation) at Tech Mahindra, with over 25 years of experience in omnichannel customer experience, MarTech architecture, data-driven growth and marketing strategy across global markets, about the implications of this shift for marketers, he emphasised the critical need for adapting to new consumer interactions shaped by AI:
“Users no longer 'search' in the traditional sense, they 'ask.' And that pivot demands a new kind of strategy i.e. Answer Engine Optimisation (AEO). It’s not just about ranking anymore; it’s about responding. ”
In practical terms, Nishith suggests: “Marketers must shift from 'ranking' to 'resolving' because if you’re not optimising for answers, you’re invisible. AEO flips the SEO playbook. Instead of optimising for keywords, we must optimise for answers. You must rethink how content is structured, semantically tagged, and contextually served to voice assistants, generative AI models, and zero-click platforms. This means investing in structured data, schema markup, and a laser-sharp focus on entity-based content modelling.”
A critical step, Nishith advises, is to “own the question space.” This means thinking from the user's perspective rather than the marketers. Instead of assuming consumers will ask explicitly about a brand or its offerings, Nishith notes that “the majority of users have an information need, and that's the sweet spot.” Marketers should thus shift the narrative from brand-centric to user-centric, highlighting benefits and purposeful information tailored to the consumer's intent.
“Change your AEO narrative from ‘About your Brand’ to ‘Benefits and Purpose for the User.”
Additionally, Nishith stresses, “If your content isn’t built to answer, it’s built to be ignored.”
To effectively compete in this new landscape, marketers should regularly audit their content for clarity and relevance in question-and-answer formats. Tools like AlsoAsked or AnswerThePublic can be valuable to uncover and identify latent semantic queries relevant to industry-specific topics. Accordingly, structure FAQ’s and responses in concise, scannable formats (bullet points, tables, or step-by-step guides) to align with answer engines' preference for snippet-ready content.
To ensure comprehensive coverage, marketers should also map audience intent across all stages of the consumer journey, aligning content closely with conversational queries. Nishith recommends prioritising conversational phrases such as 'how', 'why' and 'when', rather than limiting oneself to just 'what'. By integrating FAQ schema, leveraging knowledge graph relationships and employing natural language generation (NLG), businesses can efficiently scale authoritative answers that AI tools trust and value.
Finally, Nishith emphasises the importance of adopting a conversational tone. He points out that voice search and AI assistants prioritise natural language and suggests claiming your knowledge graph real estate with structured data (Schema markup) to reinforce entity-based authority.
By proactively embracing these strategies, marketers can successfully navigate and thrive in the evolving landscape of AI-assisted consumer discovery.
To read more: https://www.datafeed.website/post/optimising-for-ai-search
This podcast introduces Answer Engine Optimization (AEO) as a critical digital strategy for the age of AI-driven search. It explains that unlike traditional SEO focusing on broad keyword ranking, AEO aims to position content to provide direct answers to user queries through platforms like Google SGE, Microsoft Copilot, and AI chatbots.
The podcast outlines key strategies for implementing AEO, including optimising content for voice search, using structured data markup, and creating comprehensive FAQ pages, emphasizing that this approach enhances online visibility, user engagement, and organic traffic by establishing brand authority in the evolving search landscape.
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This podcast discusses Vibe Marketing, a modern approach that leverages AI tools and automation to significantly enhance marketing efficiency and effectiveness. It explains that vibe marketing allows a small team, or even a single marketer, to perform complex tasks like rapid ad testing, multi-channel campaign deployment, and data analysis, which traditionally required numerous specialists.
The podcast contrasts vibe marketing with traditional methods, highlighting its speed and emphasis on AI handling execution while humans focus on strategy and brand voice. Several examples of brands successfully using vibe marketing are provided, alongside actionable steps and specific AI tools to help businesses implement this approach. Ultimately, the source posits that vibe marketing is a crucial evolution in the industry, offering cost savings, increased output, and a stronger focus on building authentic brand connections.
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AI agents are software tools that act autonomously to perform tasks, leveraging AI models, particularly large language models, to understand language and interact with systems. These agents can automate workflows, augment individual productivity, and even form complex, collaborative systems within organisations.
Recent advancements have enabled agents to move beyond knowledge retrieval to action-oriented capabilities, leading to their potential widespread adoption across various industries. The podcast covers different types of AI agents, their operational processes, and their relationship with LLMs, further exploring their potential to drive business growth by reimagining processes and modernising infrastructure.
Organisations face hurdles in adoption, including building trust, managing change, and ensuring data protection, while their tech architectures will likely evolve towards multiagent models. Ultimately, the implementation of AI agents requires strategic planning in technology, talent, and operating models to unlock their transformative potential.
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What vibe marketing adds is a framework to harness this automation not just for efficiency, but for exponential growth.Vibe marketing is a significant evolution in marketing, driven by artificial intelligence and accessible automation, promising faster campaign cycles and greater efficiency. This new approach empowers marketers to achieve exponential growth by automating repetitive tasks and enabling rapid, large-scale testing.
The podcast contrast traditional, siloed marketing teams with the agility offered by vibe marketing, where a single marketer can leverage AI tools to manage complex campaigns.
Real-world examples illustrate its application, and the text suggests that embracing vibe marketingoffers a crucial competitive advantage in the rapidly evolving digital landscape.
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Agentic AI, a form of artificial intelligence capable of autonomous action and decision-making, is poised to significantly reshape the healthcare industry. This podcast highlights how Agentic AI can enhance diagnostic accuracy, personalise treatment plans, and streamline administrative tasks, ultimately leading to improved patient outcomes and reduced costs.
Furthermore, it discusses the potential of agentic AI to accelerate drug discovery and improve access to healthcare, particularly in underserved areas. The podcast also addresses crucial challenges for successful implementation, such as data quality and the need for strategic integration, offering solutions like centralised governance and a focus on measurable value.
Agentic AI: Transforming B2B Ecosystems in Key Industries
This podcast explores the burgeoning field of agentic artificial intelligence (AgenticAI) and its transformative impact on business-to-business (B2B) ecosystems, particularly within pharmaceuticals, industrial manufacturing, chemicals, and oil & gas.
It highlights the projected substantial growth of this market and contrasts agentic AI with generative AI, emphasising its specialisation in vertical-specific applications using proprietary data.
It discusses various use cases across these industries, illustrating how agentic AI enhances efficiency, optimises workflows, and improves decision-making through technologies like AI, IoT, and digital twins, ultimately leading to more proactive and personalised B2B interactions.
By exploring real-world use cases, it demonstrates how agentic AI drives efficiency, innovation, and transformation in these sectors. The discussion highlights the strategic integration of intelligent systems to optimize B2B workflows and enhancedecision-making by providing the overall technology stack that is required to integrate Agentics AI in existing B2B ecosystem.
This podcast examines the challenges and strategies for deriving return on investment (ROI) from generative AI (GenAI) investments. It then highlights the significant economic potential of GenAI, citing projections of trillions of dollars inadded value, while also cautioning that enthusiasm currently outpaces understanding.
The podcast then delves deeper into specific challenges in measuring GenAI ROI, such as the lack of standardised metrics, the complexity of attributing impact, and the difficulty in quantifying intangible benefits. It advocates for a balancedapproach to key performance indicators (KPIs), incorporating both financial and non-financial metrics.
Furthermore, it provides a balanced approach to key performance indicators (KPIs), incorporating both financial and non-financial metrics. Furthermore, the podcast outlines a framework for setting up and measuring ROI, including defining objectives, establishing performance baselines, and continuously improving implementations; as well as the importance of addressing data complexity, ethical considerations and business environment changes.
The challenges are evident: a lack of standardization, complexities in attribution, and benefits that are often hard to quantify. However, a practical and iterative approach—guided by clear objectives, human oversight, and data-driveninsights—can unlock the full potential of generative AI.
Organizations that perceive ROI as an ongoing process, continuously refining their strategies and metrics, will be best positioned to transform AI investments into measurableimpacts.
The true value of generative AI goes beyond cost savings and efficiency gains—it lies in its ability to transform processes, stimulate innovation, and empower better decision-making. In this rapidly evolving landscape, those who succeed will bethose who redefine ROI, striking a balance between quantifiable financial outcomes and strategic, long-term contributions.
This podcast examines the progression of artificial intelligence (AI) from its current narrow applications (ANI) to the theoretical realms of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI). It defines AGI as AI mirroring human cognitive abilities and ASI as AI surpassing human intelligence in every aspect.
It further explores the potential benefits of AGI and ASI, including advancements in healthcare, science, and problem-solving, as well as associated risks, such as loss of human control and ethical dilemmas.
It identifies key technologies that may drive the evolution towards ASI and presents existing AI applications as foundational building blocks.
Finally, it advises business leaders to proactively prepare for the disruptive potential of AGI and ASI by investing in AI literacy and embracing ethical experimentation and agile adaptation.
The era of autonomous, AI-driven commerce is here.
The question isn’t whether AI will redefine eCommerce—it already has.
The real challenge is: Are you ready to embrace it?
This podcast explores the rise of "Agentic Commerce" and its potential to transform the eCommerce landscape.
Section 1: How Agentic AI is transforming the eCommerceLandscape
- Highlights significant investments in Agentic AI by techgiants like Microsoft, Tesla, Amazon, and Google.
- Diverse applications of Agentic AI across B2B and B2Csectors, including autonomous procurement, personalised shopping assistants, and optimised fulfilment.
Section 2: How to build Agentic AI-powered eCommerceEcosystems
This podcast introduces the concept of digital twins and their growing importance in eCommerce and retail. They define digital twins as virtual representations of physical products, customers, or operational processes, and highlight their capacity to simulate behaviours and improve decision-making.
Applications span personalised shopping experiences through interactive product visualisation, enhanced product development via real-time consumer insights, and optimised supply chain management.
The authors stress digital twins' role in predictive consumer insights, sustainable practices, and potential advancements through AI, AR, 5G, and expansion into new retail sectors.
Adopting digital twins can give retail firms an early-mover advantage if implemented alongside an established digital culture to keep the twins reliable and up to date.
Agentic AI, unlike traditional AI, operates autonomously, making decisions and acting independently within defined parameters. The podcast covers:
The podcast outlines a modern approach to omnichannel customer loyalty programs. It emphasizes shifting from transactional loyalty based on rewards to cultivating deeper, emotional connections with customers. The approach uses a layered technological architecture, incorporating AI and machine learning, to personalize customer experiences across all touchpoints (online, in-store, mobile). This system facilitates real-time data analysis and integration to enable hyper-personalised campaigns, improved customer engagement, and increased lifetime value. Ultimately, the goal is to transform loyalty from a retention strategy into a core driver of sustainable business growth.
This podcast on Digital Transformation Priorities for 2025 outlines key digital transformation priorities for CIOs in 2025. A shift in focus from rapid AI adoption to a more measured approach, prioritising employee AI readiness, robust data governance, and comprehensive security training is happening. It furthers identifies key areas for increased investment, including upskilling initiatives, improved data quality, and cloud migration, while simultaneously advising against rushed AI projects lacking clear business value, inefficient migrations, and assuming employee self-sufficiency in adopting new technologies. The podcast highlights the importance of a holistic, data-driven approach to digital transformation that improves business outcomes and enhances customer experiences. Ultimately, success hinges on building a strong, adaptable culture and fostering a data-informed decision-making process.
Nishith Srivastava's report explores the significant hurdles preventing widespread adoption of AI-driven hyper-personalised customer experiences. Key challenges highlighted include data privacy concerns limiting data collection, data silos hindering insightful analysis, high implementation costs, consumer scepticism towards AI, and the rapid evolution of customer preferences outpacing algorithmic adaptation. The report emphasises the need for robust data management, ethical AI practices, and skilled personnel to overcome these obstacles and successfully integrate AI for enhanced customer experiences. Ultimately, the author argues that while the potential benefits are substantial, realising the full potential of AI in customer experience requires addressing these crucial challenges.
In summary, achieving AI-driven hyper-personalization is not solely a matter of technology, but requires organisations to address complex issues relating to data privacy, quality and accessibility, financial feasibility, customer trust, talent acquisition, and ethical considerations.
Agentic AI - Predictive Personas: Holy Combo of Data, Machine Learning, Predictive Analytics and AI
The article explores #PredictivePersonas," a marketing technique using #data, #machinelearning, and #AI to anticipate customer behaviour and preferences. It details how top companies like Netflix and Spotify utilise this approach, providing case studies across various sectors demonstrating how businesses predict and adapt to evolving customer personas.
STEP 1: Get the data foundation in place with your Customer Data Platforms (#CDPs)
STEP 2: Derive insights on an auto mode with Machine Learning and #PredictiveAnalytics
STEP 3: Transition from Hyper-Personalised to Hyper-Innovative with #GenAI and Predictive Personas
Predictive analytics involves analyzing historical and current data to forecast future outcomes, behaviors, and trends. By applying this technique to customer personas, marketers can gain a profound understanding of their target audience, anticipate their needs and preferences, and tailor their campaigns effectively.
In a nutshell, businesses of any vertical or scale can adopt and leverage ‘Predictive Personas’ approach to find their next best set of prospective customer segment. Since its purely based on the analysis and prediction of how a human would or wouldn’t take a certain action, it opens up lot of possibilities for companies to experiment.
Its a win-win!
To read the article: https://medium.com/@28thjun/predictive-personas-holy-combo-of-data-machine-learning-predictive-analytics-and-ai-cf904ac40a6f
This podcast explores the application of artificial intelligence (AI) and generative AI (GenAI) to enhance omnichannel customer experience (CX).
It details several use cases, ranging from personalised content creation and predictive customer support to in-store analytics and campaign optimisation.
It assesses the current adoption and demand for these AI-driven solutions, categorising them by their level of implementation and discussion within businesses. The ultimate goal is to create a seamless, data-driven omnichannel CX that improves customer satisfaction, boosts revenue, and fosters long-term loyalty. Several examples are used to illustrate successful AI implementations.
For detailed reading: https://medium.com/@28thjun/ai-driven-omnichannel-cx-new-use-cases-maturity-matrix-89e93be804f8
Creating exceptional omnichannel consumer experiences often necessitates brands to adopt a data-driven approach to their digital transformation initiatives. Each component of their IT ecosystem, from establishing a foundation with data platforms to effectively integrating data and analytics across all components, plays a crucial role. By ensuring strong data & analytics connections among these interconnected blocks, brands can ultimately construct & enable an ecosystem that facilitates seamless omnichannel consumer experience. This strategy relies on a three-layer interconnected framework:
LAYER 1: Data Strategy → Digital Transformation
LAYER 2: Digital Transformation → MarTech Transformation
LAYER 3: MarTech Transformation → AI driven Omnichannel CX
This podcast explains how and where ‘DATA’ needs to be integrated and leveraged within each of these layers.
Look forward to your feedback and suggestions.
Source Article: https://medium.com/@28thjun/how-to-use-data-to-transform-omnichannel-consumer-experience-cx-4582ea232305
