How AI Will Change the Basics of Marketing

AI is set to transform the foundational elements of marketing by enhancing precision, personalization, and efficiency.

To understand this impact, let’s explore how AI influences the core principles of marketing—understanding customer needs, segmentation, targeting, positioning, and the marketing mix (product, price, place, promotion)—while also considering the challenges it introduces.

The Basics of Marketing

Marketing revolves around satisfying customer needs through strategic planning and execution. Its core components include:

1. Customer Needs and Behavior: Identifying what customers want and how they make decisions.

2. Segmentation, Targeting, and Positioning (STP): Dividing the market into distinct groups, selecting which to serve, and crafting a unique value proposition for them.

Steps in market segmentation, targeting, and positioning, Kotler & Armstrong

3. Marketing Mix (4Ps): Designing the right product, setting an appropriate price, ensuring availability through distribution (place), and effectively promoting it.

AI reshapes these fundamentals by leveraging data and automation in ways traditional methods cannot match.

How AI Changes the Basics

AI is changing the basics of marketing, giving businesses a way to use technology for a competitive edge.

Enhanced Customer Understanding

AI's ability to process and analyze vast datasets is central to enhancing customer understanding. It leverages data from social media, purchase histories, and online behavior to reveal deep insights into customer preferences and trends, enabling marketers to anticipate needs and predict buying behavior with greater accuracy.

Data-Driven Insights: AI tools, such as machine learning algorithms, analyze large datasets to identify patterns and correlations. For instance, Amazon uses AI to analyze customer purchase history and browsing behavior, predicting what products customers might want next and recommending them accordingly. A report by McKinsey (2020) suggests that companies using AI for customer analytics see a 20% increase in customer satisfaction, highlighting the effectiveness of this approach.

Personalization at Scale: AI delivers highly tailored experiences, such as personalized product recommendations or emails, making marketing more relevant and engaging. This is achieved by analyzing individual customer data to create customized interactions. For example, Netflix employs AI in its content recommendation system, analyzing user viewing habits to suggest personalized content, which increases user engagement. A notable success story is Yves Rocher, a cosmetics brand, which increased its purchase rate by 11 times using AI-driven recommendations (AI in Marketing), demonstrating the power of personalization at scale.

Revolutionizing Segmentation, Targeting, and Positioning (STP)

AI revolutionizes the STP framework by enabling micro-segmentation, dynamic targeting, and nuanced positioning, each contributing to more effective and responsive marketing strategies.

Micro-Segmentation: AI goes beyond broad demographic categories (e.g., age or location) to identify micro-segments based on specific behaviors or interests. For example, it might target “coffee lovers who browse late at night” with precision. This is facilitated by machine learning algorithms that analyze customer data to create detailed segments. Companies like Segment use AI to help businesses with customer segmentation, allowing for more precise targeting and better engagement.

Dynamic Targeting: AI adjusts targeting in real-time as customers interact with brands, ensuring campaigns stay relevant and effective moment-to-moment. This is evident in programmatic advertising, where AI buys ad space in real-time based on consumer data, such as current online behavior and preferences. Platforms like Google Ads and Meta Ads use AI to sell, target, and place ads micro-second by micro-second across vast networks, enhancing campaign effectiveness.

Nuanced Positioning: By analyzing competitor moves and customer sentiment, AI refines how brands position themselves, helping them stand out in crowded markets with sharper, data-backed strategies. For instance, Sephora employs AI technology to refine customer engagement tactics, such as its virtual artist feature, which allows users to visualize products before purchase, enhancing its image as innovative and customer-centric. Amazon and Netflix also use AI to position themselves as customer-centric and data-driven, analyzing social media sentiment to understand brand perception compared to competitors.

Transforming the Marketing Mix (4Ps)

AI transforms the marketing mix by influencing product development, pricing strategies, distribution channels, and promotional efforts, each optimized for efficiency and impact.

Product: AI sifts through customer feedback and usage data to suggest new features or entirely new products, driving innovation that aligns with market demand. For example, Tesla uses AI in its design process to predict customer preferences and optimize product features, ensuring their electric vehicles meet evolving needs. AI tools, such as generative AI, enhance ideation and creativity, as seen with Boston design agency Loft using GPT-4 to suggest new product features based on customer preferences.

Price: AI enables dynamic pricing, adjusting costs instantly based on factors like demand, competitor pricing, or a customer’s willingness to pay. This is achieved through advanced algorithms and data analytics, allowing businesses to maximize revenue. For instance, Airbnb uses AI to suggest optimal pricing for hosts, helping them maximize earnings based on real-time market conditions and customer behavior. Companies like Amazon and hotels also use AI for dynamic pricing, adjusting rates based on demand and competitor pricing, enhancing profitability.

Place: AI optimizes distribution by predicting where products are needed most, streamlining inventory and logistics for maximum efficiency. This includes demand forecasting and inventory management, ensuring products are available when and where customers need them. Amazon leverages sophisticated AI algorithms for advanced inventory management, predicting demand and optimizing warehouse space utilization, allowing efficient management of its vast inventory. Walmart uses AI to predict demand and automate replenishment, ensuring optimal stock levels across stores, reducing stockouts and improving customer satisfaction.

Promotion: AI automates and personalizes promotional efforts, such as AI-generated ad copy, targeted social media campaigns, or chatbots handling customer inquiries, making outreach both scalable and impactful. For example, Nike created an AI-generated ad campaign called “Never done evolving” with Serena Williams, commemorating its 50th anniversary with an 8-minute video ad, enhancing engagement through emotional storytelling. Heinz used AI to generate imagery for its ketchup promotion, while chatbots, like those powered by IBM watsonx Assistant, provide 24/7 customer support, improving interaction efficiency.

Summary of AI Applications in Marketing

Aspect	AI Application	Company	Impact
Customer Understanding	Data analysis for insights	Amazon	20% increase in customer satisfaction
Personalization	Tailored recommendations	Yves Rocher	11x purchase rate increase
Micro-Segmentation	Behavior-based segments	Segment	Precise targeting and engagement
Dynamic Targeting	Real-time ad adjustments	Google Ads	Enhanced campaign relevance
Nuanced Positioning	Competitor and sentiment analysis	Sephora	Innovative brand image
Product Development	Feature suggestion based on feedback	Tesla	Aligned innovation with demand
Dynamic Pricing	Real-time price adjustments	Airbnb	Maximized revenue and profit
Distribution Optimization	Demand forecasting and inventory management	Amazon	Efficient logistics and stock levels
Promotional Efforts	AI-generated ads and chatbots	Nike	Scalable and impactful outreach

AI fundamentally changes those basics of marketing, enhancing every aspect from understanding customers to executing the marketing mix. By embracing AI, businesses can achieve greater efficiency, personalization, and effectiveness in their marketing strategies, ultimately leading to improved customer satisfaction and business growth.

However, challenges such as data privacy, ethical considerations in dynamic pricing, and the need for significant initial investment must be addressed. Viewpoints vary, with some advocating for AI's potential to democratize marketing insights, while others caution against over-reliance on automated systems, emphasizing the need for human creativity and ethical oversight.