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Outdated AI Marketing Trends to Ditch in 2025

by Colling Media - October 29, 2024

Outdated AI Marketing Trends to Ditch in 2025

In the ever-evolving AI landscape, staying competitive means knowing when to let go of outdated AI marketing trends. Below, we’ll break down obsolete AI marketing practices, offering upgrades that help marketing directors future-proof strategies in 2025.

Moving Beyond Basic Chatbots

Outdated Practice: Standard chatbots have served well in handling repetitive inquiries, but they fall short in addressing complex questions, making them one of the primary outdated AI marketing trends.

Example: A retail chatbot answers basic questions, such as store hours or order tracking, but can’t help with detailed queries like product compatibility or returns.

Upgrade: Integrate AI-driven assistants using NLP to support nuanced customer interactions and learn from each engagement. This enhancement offers customer satisfaction and personalized responses, which are part of advanced AI marketing trends today.

Better Example: A banking assistant that suggests relevant financial products or offers advice based on a user’s unique transaction history, creating a much richer customer experience.

Evolving Beyond Basic Sentiment Analysis

Outdated Practice: Keyword-focused sentiment analysis captures sentiment but overlooks context, limiting insight and missing out on important user feedback.

Example: A clothing brand that tracks explicit complaints or praise misses nuanced customer dissatisfaction, like a disappointed photo captioned neutrally.

Upgrade: Use multimodal sentiment analysis to gain insights from text, images, and videos, offering a richer view of customer feedback and enhancing your future-proof marketing strategies.

Better Example: An AI-powered system that detects a rise in neutral Instagram posts showing damaged products, allowing the brand to address product quality proactively.

Moving Past Historical Predictive Analytics

Outdated Practice: Predictive analytics based solely on historical data often overlooks recent trends, making promotions feel out of touch and irrelevant.

Example: A food delivery service runs “rainy day discounts” based on last year’s weather patterns, only to launch it during an unexpected sunny spell, resulting in low engagement.

Upgrade: Enhance predictive models by combining historical and real-time analytics, incorporating external factors like trends and behavioral shifts.

Better Example: A clothing retailer promoting summer clothing as real-time data reflects warmer weather trends, resulting in timely engagement and increased conversions.

Moving Beyond Simplistic Product Recommendations

Outdated Practice: Generic recommendations lack context, failing to address each customer’s unique interests or needs, which weakens personalization.

Example: A bookstore app that suggests random bestsellers rather than titles that align with a user’s reading history.

Upgrade: Implement intent-driven recommendations by analyzing customer behavior, purchase history, and seasonal data. Personalized recommendations reflect advanced AI marketing strategies, keeping engagement high.

Better Example: A bookstore suggesting career development books to a customer recently purchasing business-focused content, demonstrating AI’s ability to match intent and improve conversion.

Emphasizing Task-Driven Voice Capabilities Over Voice SEO

Outdated Practice: Optimizing for voice SEO helps search visibility but lacks functionality. Without task-driven voice capabilities, it often leaves user needs unmet.

Example: A restaurant optimized to rank in voice searches for “best pasta near me” doesn’t allow customers to place orders through voice, cutting off potential conversions.

Upgrade: Implement task-based interactions, like ordering, making reservations, or booking appointments, which turn your voice presence into a practical service for customers.

Better Example: A restaurant app that allows customers to make reservations, place orders, and check wait times using voice commands, providing a convenient, user-centered experience.

Retiring Basic Demographic-Based Segmentation

Outdated Practice: Relying solely on demographic data, such as age and location, limits the effectiveness of personalization and often misses crucial behavioral insights.

Example: A skincare brand sending generic anti-aging promotions to every customer in their 30s, regardless of unique skin concerns.

Upgrade: Shift to hyper-personalized segmentation that uses psychographic and behavioral data. This trend in advanced AI marketing strategies enables campaigns that resonate deeply with target audiences.

Better Example: The skincare brand segments customers based on individual concerns, like dryness or sensitivity, sending each group personalized recommendations rather than broad-age promotions.


Retiring outdated AI marketing trends in favor of advanced, customer-centered approaches is essential for companies wanting to maintain relevance in an AI-driven world. By adopting these future-proof marketing strategies, brands can create deeper, more meaningful connections with customers, optimizing engagement and building lasting loyalty.


Based on article from Search Engine Land

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