Marketing Information Systems
Introduction
Philip Kotler, ‘Father of Modern Marketing’, MIT PhD Economist, and Kellogg School of Management Professor Emeritus, Distinguished Professor of International Marketing, and educator of over 50 years at Northwestern University, offers the most concise definition of the marketing information system, which is as relevant as it was almost thirty years ago: “A marketing information system is a continuing and interacting system of people, equipment, and procedures to gather, sort, analyse, evaluate, and distribute the pertinent, timely, and accurate information for use by marketing decision-makers to improve their marketing planning, implementation, and control” (Kotler, 1997).
It’s the humble, understated marketing system that provides the foundation for the striking, colourful, exciting advertising that latches onto the hearts and minds of television viewers, video and audio streamers, shoppers, and blog readers around the world, creating hype, igniting trends, and sparking conversation, leading to interest, purchase, and sometimes lifelong relationships with brands, occasionally extending beyond the individual consumer to friends and family through referral, or online communities and vloggers sharing a common brand or product obsession, with their tips, tutorials, hacks, and other UGC.
Consider Apple and its ‘1984’ ad at the NFL Super Bowl XVIII, heralding the launch of the Macintosh computer two days later. This singular 60-second spot that never even showed the product (BBC, 2024), that would have been pulled by the board if it wasn’t for the belief, expert intuition, and gumption of the Chiat/Day team led by creative director, Lee Clow (see CNN, 2024), was produced by Fairbanks Films, and directed by no less than Ridley Scott, who started his career in advertising before achieving fame with Alien (1979) and Blade Runner (1982), and drew a poignant and persuasive parallel between George Orwell’s 1949 novel of the same name and the personal computer market at the time, planted the flag for Apple’s positioning in that market, garnered millions of dollars’ worth of free publicity through rebroadcasts in the news (Friedman, 2005), won a plethora of awards, “changed the Super Bowl forever” (New York Times, 2024), and forty years later is still considered one of the best ads of all time (see, for example, CNN, 2024).
Today, the NFL Super Bowl is considered one of the biggest events for advertisers in any year and companies pay around USD$7 million (Kantar, 2023) just to snatch a 30-second slot, let alone pay for production, agency, celebrities, soundtrack, and airtime, with total costs that can amount to USD$40 million dollars (Fast Company, 2025). Nevertheless, the impact of an NFL Super Bowl ad is estimated at twenty times that of an ordinary advertisement (AdNews, 2024), with average return on investment calculated at 4.6 (Kantar, 2023), and the best efforts achieving ROI of ten or more (Netchoice, 2025). The most successful ads are considered to be those who can create the deepest cultural resonance by capturing and incorporating the zeitgeist most effectively (Kantar, 2023).
And it takes more than expert intuition to tap into whatever is most salient to global audiences at the time. Testing such intuition against data, and accurately identifying consumer sentiment, trends, and behavioural patterns in such a way to reflect it back so clearly and concisely that consumers feel instantly recognised, can relate to the advertising brand, and be motivated to connect to it through information-seeking, interaction, or purchase, takes research and analysis of relevant, current, and accurate data, and that requires an effective, high-performing, marketing information system.
Beginnings
Bournemouth University (Defining Marketing: A brief history of marketing, n.d.) describes three main periods in the development of modern marketing practices: production orientation, sales orientation, and marketing orientation. Production orientation is said to have taken place during the Industrial Revolution to the turn of the twentieth century, when goods were largely made and distributed locally, and while opportunity for growth was limited, so was competition and the emphasis was on producing and supplying the goods at the lowest possible cost. From this time through to the boom years following the end of WWII in the mid to late 1950s, mass production emerged and markets became more competitive, leading to the sales orientation which was focused on developing new products, selling them, and achieving broader distribution, with practices such as door-to-door selling, telemarketing, and the introduction of television allowing new and more personable forms of advertising. With markets becoming saturated from the 1960s, competition intensified and the marketing orientation era began, where the marketing department was formed and priority was placed on identifying and meeting consumer needs. Authors such as Grundey (2010) have identified later stages or sub-stages in marketing practice, including societal marketing, relationship marketing, and interfunctional marketing.
During the sales orientation era the first significant paper-based tool was developed to manage marketing-related information: the Rolodex (a portmanteau of ‘rolling’ and ‘index’). The Rolodex is a device comprised of rotating card files that allows contacts to be stored and accessed in alphabetical order, with space for a short note to be added. It was invented in 1953 and patented in 1956 by Danish engineer, Hildaur Neilsen (Google Patents, n.d.), who was at that time the Chief Engineer at Zephyr American, an office product manufacturing company in New York (Columbia Journalism Review, 2020). The manager of Zephyr American, Arnold Neustadter, shared Neilsen’s passion for productivity and efficiency and was an inventor himself, supporting the manufacture and marketing of the Rolodex in 1958 (Cooper Hewitt, 2014), and is often credited with its invention, even changing the company name to Rolodex Incorporated (ibid.), although Neilsen held the original patent (Google Patents, n.d.), and was named as its ‘Inventor’ on subsequent patents assigned to Rolodex Incorporated (Justia Patents, n.d.).
Subsequently, three key characteristics of the marketing-oriented era - the drive to generate greater market demand for existing products and to develop new offerings for competitive advantage, the more complex and far-reaching distribution channels available with modern transportation, and the focus on customer satisfaction - perhaps combined to highlight the need for effective collection, storage, and analysis of relevant information in order to achieve and sustain profitability, and the need for a marketing information system was soon identified. Researchers at the time such as Richard Brien and James Stafford (1968) observed that, “Business enterprise in the United States is caught in an ironic dilemma: our economic system generates a massive volume of data daily, and the rate of information generation appears to be increasing exponentially; yet most managers continue to complain that they have insufficient, inappropriate, or untimely information on which to base operating decisions“ (Brien and Stafford, 1968, p.19). The authors further noted that, “The process of developing timely, pertinent decision data for marketing management can now be characterised more meaningfully, even if somewhat prematurely, as the functioning of a ‘marketing information system’ rather than simply as ‘marketing research’“ (ibid.)
Philip Kotler not only defined the marketing information system, he recommended a framework for such a system in his seminal publication, Marketing Management: Analysis, Planning, and Control (1967), possibly the most popular marketing textbook of all time, which is now in its 16th edition and forms part of the core marketing program in vocational and tertiary institutions around the world. Within this book, Kotler sets out guidance principles and requirements for an effective marketing information system, including four components: Internal Records System; Marketing Intelligence System; Marketing Research System; and Marketing Decision Support System (DSS). Although Kotler’s Marketing Management was written before business computing became popular, we can see the components of his marketing information system reflected in the software categories developed over the forthcoming decades, each at a different pace, starting with the Internal Records System.
Development
Within the Internal Records System component, two different domains of marketing information systems initially evolved to overlap with the DSS: Sales Force Automation (SFA) managing lead generation and customer acquisition, and Customer Relationship Management (CRM) managing customer engagement and retention. Though today both domains are increasingly integrated into one solution, both deserve their own dedicated spotlight, and as the ‘sales orientation’ era preceded the ‘marketing orientation’ or customer-focused era in marketing history, this article will look at SFA, and the CRM will be covered in an upcoming article.
Following Kotler’s first setting out of an MIS framework, business computing took hold across most industries during the 1970s and 1980s and marketing departments and managers adopted generic software such as databases, spreadsheets, and documents to perform SFA activities such as recording and analysing client, sales, and advertising data, and comparing results against set goals (see, for example, OpenFlow, n.d.). Over time, those undertaking these activities on a daily basis started to see the challenges presented by this approach, and that there were better ways of managing marketing information (see Salesforce, n.d.).
In the 1990s through to the early 2000s, some of the major marketing software solutions that we know today were launched. The first software application to be introduced for this purpose is said to be Unica’s Enterprise Marketing Management in 1992 (Kane, 2024), designed for marketing campaign management which focused on lead generation (ibid.). Around the same time, SAP R/3 ERP integrated marketing and sales into its software. This was followed by Siebel Sales Enterprise, launched in 1995 by Siebel Systems (Business Insider, 2020), founded by former Oracle executive, Tom Siebel (ibid.); Omniture web analytics in 1996 (Crunchbase, Organisation - Omniture), Salesforce SFA and then CRM in 1999 (Salesforce, 2025); Eloqua marketing automation in 1999 (Kane, 2024); ExactTarget email marketing in 2000 (Salesforce, 2013); Datalogix digital advertising platform in 2002 (Crunchbase, Organisation - Datalogix), Marketo and Pardot marketing automation software in 2006 and 2007, respectively; and finally, Microsoft launched Dynamics NAV in 2002 upon acquiring Danish startup, Navision (Microsoft 2023; Microsoft, 2002), later to become Dynamics 365 in 2016 and then Dynamics 365 Business Central in 2018 (Microsoft 2023).
If ever an industry offered an illustration of market consolidation, it is that of marketing system software in the early to mid-2000s, when the popularisation of the Internet was swiftly followed by social media innovation, before the introduction of the mobile phone, with each of the smaller players in this pioneering set eventually acquired by larger companies. Marketing information systems became increasingly integrated and offered additional features in web analytics and digital marketing utilising the new digital platforms available for advertising to and engaging with customers, generating new data to be tracked and analysed.
Their acquisition typically marked the entry or expansion into broader marketing software by a major company with an established offering in an affiliated domain such as a CRM or in a specific area of sales, leading to success for some and later second thoughts for others. ExactTarget acquired Pardot in 2012 (TechCrunch, 2012) and was itself acquired by Salesforce in 2013 to power its Marketing Cloud (Salesforce, 2013), Adobe (in the creative software market since 1982) acquired Omniture in 2009 (see CNBC, 2009), unveiling its Marketing Cloud in 2012 (Adobe, n.d.), later renamed Experience Cloud (ibid.) and incorporating newly acquired Marketo from 2018 (Adobe, 2018); Oracle CRM acquired Siebel Systems in 2005 (see CNET, 2005), then Datalogix in 2014 (Oracle, 2014), launching its Oracle Sales Cloud and Oracle Advertising, respectively; and IBM acquired Unica in 2010 (PR Newswire, 2010), introducing its Digital Marketing Cloud in 2013 (PR Newswire, 2013). Though IBM later divested Unica and its other Digital Marketing Cloud applications to HCL in 2018 (IBM, 2018) and Oracle would offload its Advertising business in 2024 (Oracle, 2024), both parent companies still thrive in the areas of sales and CRM.
One last notable development for marketing information systems in the early 2000s was the trend in marketing software targeted specifically to address the needs of the smaller business, rather than the large, established enterprise. In the SFA domain, one of the first to arrive and still seen to be the longstanding major player has been HubSpot, founded in 2006 with the mission of “Helping millions of organisations to grow better“ (HubSpot, n.d.), to address changing consumer needs in advertising, and now serving over 248,000 business customers around the world (ibid.). It offers six Hubs across marketing, sales, service, content, operations, and commerce, as well as a wide range of self-help resources on different areas of SME marketing, and its clever strategising and staying ahead of consumer and market trends over the ensuing years meant that HubSpot was able to avoid the fate of other marketing software startups of the past in being acquired and instead become an acquirer itself. HubSpot, along with the other major marketing software players in Salesforce, Microsoft, SAP, Oracle, Adobe, and IBM, use their decades of industry experience to continue to forge a path into the age of AI today.
State of the Art
Amongst this group, Salesforce and Microsoft compete closely to take the lead in the area of Sales Force Automation, the CRM, and staying ahead of technological developments, while Adobe strengthens its own positioning in the experience domain, SAP and Oracle sharpen their Sales Cloud offering with intelligent features, and HubSpot continues to develop tools and features for smaller business.
Salesforce
Salesforce CEO, Marc Benioff, declared in 2014 that, “Salesforce will become an AI-first company” (Salesforce, 2023), with its aim of developing the existing Einstein CRM and other products into intelligent solutions so that every company and employee could harness the power of AI (ibid.). This was seen as a natural evolution of the company’s history in innovating sales and marketing solutions, and soon a development team was formed with the mission of “democratising AI” (ibid.).
In its most recent efforts, Salesforce launched Agentforce and Agentforce Atlas Reasoning Engine in September 2024 (Salesforce, 2024), and AgentExchange on 4 March 2025 (Salesforce, 2025b), which respectively allows organisations to build, customise, and deploy their own agents easily; autonomously analyses data, makes decisions, and completes tasks reliably and accurately (Salesforce, 2024); and provides a marketplace for developers to monetise their agentic AI components and for businesses to select and deploy any of these agents within their own work environment (Salesforce, 2025b). Other AI and Gen AI-driven features of Salesforce SFA products today include automated research and data entry, personalised content creation, lead qualification and opportunity assessment, sales forecasting and pipeline analysis, dynamic presentation assistance, data analysis and deal insights, personalised email generation, call transcriptions and analysis, and predictive insights.
As to the future of AI in Salesforce, in an interview with Bloomberg at Davos in February, Benioff explained that, where work can be performed by an AI agent, it will essentially mean a redeployment of existing human resources rather than their redundancy (Bloomberg Live, 2025), and this is suggested to be illustrated in the company’s recent letting go of around 1000 workers before hiring 2000 new staff to sell Agentforce tools to clients (see Inc. 2025). “So, look, I want an unlimited workforce. I think everybody does Agentforce agents. That’s the beginning of an unlimited workforce“ (Bloomberg Live, 2025). In the company’s subsequent Q4 FY2025 earnings call, Benioff issued a summative statement of Salesforce’s position on AI in the enterprise: “My message to CEOs right now is that we are the last generation to manage only humans” (Inc. 2025).
Microsoft
Microsoft is also moving fast with incorporating AI into its software, including Dynamics 365, and with new features developed almost daily, its vision and implementation might be best conveyed through a few quotes from key figures at the company. First, when Microsoft CEO Satya Nadella announced the launch of Copilot in 2023 he reflected that, “Today marks a significant milestone in our journey to empower every person and every organisation on the planet to achieve more. With Copilot, we are bringing the power of next-generation AI to the tools millions of people use every day” (Microsoft, 2025a). At a later Microsoft Ignite keynote, Microsoft corporate vice president for AI at Work, Jared Spataro, suggested, “Agents are the new apps for an AI-powered world. Every organisation will have a constellation of agents, ranging from simple prompt-and-response to fully autonomous” (ibid.).
Lastly, on the near future of AI agents and similar developments at Microsoft, principal program manager, Amy Rosencranz, who works on Agents in Microsoft Digital, observed, “We are so at the tip of the iceberg, and the pace at which the product is developing is unlike anything I’ve seen in my tenure at Microsoft, and I’ve been here a while. Agents are already so powerful, but they’re only going to get more powerful. More ‘wow’ moments are coming” (ibid.) A few of these ‘wow’ moments might include the launching of 10 autonomous agents for Dynamics Business Central 365 in 2024 (Microsoft, 2024), and on 5 March 2025 announcing two new agents - Sales and Sales Chat - and Microsoft AI Accelerator for Sales, to help businesses more easily and quickly transform their sales organisation with Microsoft Copilot and agents (Microsoft, 2025b).
Adobe
In March 2023 Adobe introduced Adobe Sensei GenAI for Experience Cloud (Adobe, 2023), which aims to act as a “marketer’s copilot” (ibid.) to “deliver personalised experiences at scale” (ibid.) through integrating AI into Experience Cloud, with the promised benefits of transforming data into deep and actionable insights using advanced marketing measurements, predicting customer behaviour, generating and personalising content, making campaigns more meaningful, and automating digital workflows, resulting in greater overall return on marketing investment (Adobe, 2025).
The successful integration of AI and GenAI into Adobe Experience features, including content, asset, and journey management, mean that repetitious tasks in these areas can be streamlined and automated across teams, content can be repurposed, recommendations and experiences personalised, and advanced predictive insights are enabled to allow the marketer to design journeys based on customer behaviours and engagement preferences (Adobe, 2025). Further, the marketer is given guidance by embedded smart features on designing the best experience and optimising delivery for individual customers at all stages of the journey (ibid.).
SAP
SAP launched its generative AI assistant, Joule, in September 2023 (SAP, 2023), which includes an agent-builder that a business can use to create and deploy its own agents to power workflows and outcomes (SAP, 2025a). Joule is described as an AI agent resource with strong business process expertise and deep knowledge of the user’s business, grounded in comprehensive data through SAP Knowledge Graph and SAP Business Cloud, enabling seamless connection and cross-function collaboration between each agent to supercharge human capital (ibid.).
Introduction of Joule brings intelligent features to SAP Sales Cloud (SAP, 2025b), including guided sales execution with prescriptive workflows that reinforce positive behaviour, next-best action recommendations, AI-assisted customer communications, 360-degree customer views, optimised outreach, intelligent insights, intelligent mobile selling, scalable sales automation across pricing, invoicing, available to promise, retail execution, and field sales (SAP, 2025b). These features offer claimed benefits such as 80 per cent faster completion of everyday sales tasks, lower customer acquisition cost, optimised sales cycle, and increased customer loyalty and retention (ibid.).
Oracle
Oracle Sales Cloud takes a slightly different approach to its AI innovation than the other major sales software vendors, in that it avoids creating a chat entity and instead works on infusing intelligence in impactful ways throughout its existing Sales Cloud product, aiming to boost sales productivity, drive user adoption, deliver a connected customer experience, uncover more revenue opportunities, and help businesses remove the guesswork from AI adoption into their sales function.
The AI-driven Sales Cloud offers six main Sales Force Automation capabilities through ML-based selling tools based on complete and verified customer data from internal and external data sources: guided selling; complete customer data; user experience; sales productivity; sales performance management; and sales planning. Here, the three capabilities most relevant to the sales function will be considered further: guided selling, sales productivity, and sales planning.
The guided selling capability helps sellers with strategically deployed, personalised, and holistic sales guidance to support representatives in selling faster, smarter, and with the right focus. Two additional features offered by Sales Cloud that assist towards this end are automatic data verification of customer address, email, and phone contact details and simplified machine learning implementation for fine-tuning of local models or Oracle’s native ML by the business according to its circumstances.
The sales productivity capability is created through the Oracle Sales Mobile app, Oracle Sales Assistant, and integrated email. The mobile app facilitates sales professionals to be able to complete daily sales tasks in the field, from home, or otherwise remotely; Oracle Sales Assistant helps the sales team access critical records and update records such as opportunities, schedule meetings, and log notes easily; and with the CRM integrated into email, the sales team can use it without leaving the system to edit or update information changed as a result of interacting with a contact via email.
The purpose of the Sales Planning capability is to drive sales alignment and performance to plan. It enables quota planning to optimise human capital within the sales team and maximise its agility and motivation, while deploying predictive analytics for modelling and territory assignment. Account planning is enhanced with a data-driven tool to improve promotion strategies, collaboration, and what-if scenarios; while the accuracy of forecasting is boosted with advanced statistical predictions and sales pipeline data.
HubSpot
HubSpot launched their AI assistant, Breeze, comprised of a Copilot and Agent, with over 80 AI features embedded across the customer platform, on 18 September 2024 at their Fall INBOUND conference (HubSpot, 2024). Breeze is integrated into HubSpot’s existing products with the new Copilot and agents, as well as Breeze Intelligence for data enrichment and buyer intent. These agents add to the existing six AI assistants offered by HubSpot: Blog Post Generator, Social Post Generator, Web Builder Assistant, Email Content Assistant, Reporting Assistant, and AI Chatbot.
HubSpot started working on its own AI innovation with the aim of supporting Go-to-market teams in marketing, sales, and service, towards achieving customer happiness, revenue generation, and business growth (HubSpot, 2024) by making its platform more unified, fast, and easy, after finding through proprietary research and customer data that most GTM teams are switching between an average 15 different solutions during the day to manage customer interactions, with 80 per cent not getting value from their tech stacks, and 70 per cent working with disconnected data (HubSpot proprietary research, and HubSpot customer data, 2024).
HubSpot’s Breeze AI Agents are provided in four domains aligned with existing features: Content Agent, Social Media Agent, Prospecting Agent, and Customer Agent. Breeze Content Agent assists the marketing team to create blogs, landing pages, podcasts, and case studies in a brand voice. Breeze Social Media Agent can help with analysing performance, industry data, and best practices to create compelling social content. Breeze Prospecting Agent aides in crafting and implementing tailored outreach for CRM prospects to enhance the sales pipeline. Breeze Customer Agent responds quickly to site visitors based on its training from the website, blog, and knowledge base.
Breeze Intelligence has three main capabilities: data enrichment, where it uses over 200 million buyer and company profiles to help business improve form conversion rates, identify buyer intent from companies visiting the website, and keep the database fresh with regular updates; buyer intent, which it uses to help business discover which companies in a target market are showing intent on the website; and form shortening, which can build optimised forms that autofill information behind the scenes boosting conversion rates. Together, these platforms reinforce a quiet truth: there’s no single path to marketing system excellence. Whether built atop ERP infrastructure, bolted onto a CRM, or grown from the ground up with a founder-friendly UX, the common thread is the drive to make marketing more targeted, timely, and tightly aligned with revenue.
Possibilities
Gaarlandt et. al (2025), writing for the Harvard Business Review on the impact of ongoing AI developments on marketing and retail, suggest that gen AI could “fundamentally transform the landscape, much like ecommerce before it.” McKinsey Digital (2024), the arm of the renowned consultancy that helps business with AI integration and technology modernisation, and MarTech (2025), a content producer within the portfolio of leading visibility management SaaS platform, Semrush, that focuses on the intersection of marketing and technology, make similar observations on how marketing practices will be comprehensively shaped by gen AI and AI agents in the near future.
With AI agents capable of quick and comprehensive consumer search, a shift in power could take place from retailers and influencers to brands and AI agents, acting as gatekeepers for the end customer (Gaarlandt et. al, 2025), starting from the most commonplace standardised consumer goods through to those that are highly differentiated. This could mean that the small set of factors which were once strengths for brands in search marketing like pricing, location, and content become less important to conversion and brand success than a broader set of more holistic factors including pricing, availability, reliability, service, and partnerships, within quality of the overall customer journey, uniqueness or differentiation, and trustworthiness based on sentiment analysis. Essentially, Gaarlandt et. al (2025) propose the new ‘winners’ of marketing and retail to be those who can provide the best service or product at the lowest cost, with Amazon well-positioned to take the lead in this race, at least in its early stages.
McKinsey Digital (2024) describe that the value of AI agents stems from their ability to automate complex tasks comprised of highly variable inputs and outputs, which can be challenging for a business to address effectively, through three capabilities: managing multiplicity and adapting to dynamicity in workflows and processes which are not easily codified and easily broken in conventional automation; being directed by natural language rather than programming, so that even the most complex workflows can be encoded quickly and without technical expertise; and their ability to work with existing software tools and platforms to communicate across the digital ecosystem in carrying out tasks. In marketing, they suggest this could enable advanced integration and coordination between the software tools, applications, and platforms involved in marketing campaign management; a conversational collaboration with an agent system to develop, test, and iterate marketing campaign ideas, as well as generating content, copy, and design, essentially automating tasks that would otherwise be tedious and time-consuming; and a digital marketing strategy agent to analyse data, surveys, and activity recorded in CRMs and market research platforms to craft schemes using multimodal foundational models; with each of these agents working together toward continuous improvement.
MarTech (2025) went so far as to declare 2025 to be the year of the AI agent, with traffic to retail sites from gen AI-powered chatbots up by a multiple of 13 YoY in 2024, and major players like Salesforce and SAP launching or working towards launch of an AI agent offering. The marketing technology thought leader interviewed several key marketing executives in organisations such as Cisco, Crunchbase, SOCi, and Talkdesk, to canvas experiences, learnings, and future plans for agentic AI innovation in the field of marketing. Jay Patel, SVP and GM of Webex Customer Experience Solutions at Cisco believes that 2025 will see AI becoming intrinsic to customer interactions, driving efficiency, personalisation, and satisfaction to new levels, and enabling the creation of personalised customer AI agents by brands (MarTech, 2025). Meg Gautum, Chief Product Officer of Crunchbase, predicted that 2025 will be the year that AI must prove its value in business through a return on investment, and companies will become more targeted in their AI efforts, ceasing efforts that fail to generate results and strengthening commitment to and investment in solutions that solve specific, high-value business problems. Monica Ho, CMO of SOCi, emphasised the value of AI agents in bringing a reliable, consistent, personalised experience of a brand to customers that is easily scalable and builds trust. Pedro Andrade, VP of AI at Talkdesk, observed that the holistic view of the customer journey provided by agentic AI, not otherwise available through the often disconnected systems involved in customer relationship management, facilitates harnessing of that knowledge to generate insights for more effective and innovative marketing strategies.
Summary
The marketing information system has evolved from the most rudimentary contact management tools like the Rolodex through to today’s conversational AI interfaces that continually learn about and adapt to customers’ needs, preferences, and behaviours, and use that data to generate insights that can be applied by marketing teams in campaign strategy, planning, and implementation. Characteristic of the development of the marketing information system is that, since the marketing orientation of the 1960s and Kotler’s timeless work, it seems to have been led not by problem-solving technologists, visionaries, or inventors, but by the consumer and their changing circumstances and expectations, with marketing teams closely following and anticipating these expectations in order to create the best solutions. As Kotler’s early framework made clear, the most effective marketing systems are those that allow brands to see, understand, and respond to their customers, and in a world where cultural touchstones like the Super Bowl ad still shape perception while AI agents increasingly influence who owns the customer relationship, the brands that best integrate insight, resonance, and reach will be those that continue to win hearts, minds, and market share.
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