Just like an old engine that is past its prime, some AI marketing strategies fail as the technology rushes forward at warp speed. What once seemed like cutting-edge solutions have now lost their edge. Let’s take a look at which AI trends have lagged behind and why they no longer deliver the results you need.
6 AI Marketing Trends You Need to Get Rid of
1. Basic Chatbots
SO: The first chatbots appeared in the late 20th century, with ELIZA debuting in 1966. These early bots relied on pre-programmed scripts to simulate a conversation, automate basic customer service tasks, and handle routine inquiries. Although effective for simple, repetitive tasks, they lacked the ability to adapt to more complex customer needs.
NOW: As expectations for personalization have increased, traditional chatbots have failed. Today’s consumers expect AI-based assistants powered by advanced technologies like natural language processing (NLP) and machine learning. Almost 90% of executives report faster resolution of complaints and more than 80% see improvements in call volume management, all thanks to AI.
Modern AI assistants, such as those using models like GPT, offer dynamic, personalized interactions and can handle much more complex queries. By leveraging customer data, these advanced bots provide tailored solutions while delivering a more human experience.
2. AI-powered social media monitoring (sentiment analysis)
SO: By the late 2010s, AI was widely used for basic social media listening, primarily focused on monitoring brand sentiment through keywords and simple text analysis. This allowed brands to broadly understand what consumers thought of them, but it lacked depth and nuance.
NOW: With the advent of more advanced AI models incorporating deeper contextual understanding and multimodal analysis (text, image and video), sentiment analysis is much more sophisticated. Today, Consumers expect brands to not only capture the feelings of text, but also capture the emotional nuances of media content. These richer insights allow brands to build customer loyalty by responding to changes in sentiment in real time and crafting marketing that resonates on a more personal and emotional level.
3. Predictive analysis based on historical data
SO: AI-driven predictive analytics based on historical data, such as past purchasing behaviors, have been widely used to predict future purchasing patterns. This trend has shaped personalized offers and recommendations.
NOW: Basic predictive analytics is no longer enough as customers expect businesses to adapt in real time. Innovative AI systems now combine predictive and real-time analytics, leveraging real-time behavioral data and evolving trends rather than just historical records. This way, marketers can ensure more precise personalization and faster adaptation to customer needs.
4. Simple predictive product recommendations
SO: Early AI-based product recommendation engines based heavily on purchase history and browsing behavior were considered cutting edge. These systems primarily focused on “frequently bought together” and “customers who bought this also bought” recommendations.
NOW: Basic recommendations are no longer enough. AI has gone beyond simple product suggestions to provide smarter, more contextual recommendations, such as predict lifestyle changes or understand the underlying intention behind a customer’s actions. Algorithms such as collaborative filtering, deep learning, and reinforced learning do not rely solely on past behavior; they analyze real-time data, user intent, and even external factors like seasonality or social trends.
In 2023, 56% of millennials worldwide have turned to generative AI tools, bypassing traditional search engines, to receive product or service recommendations that are not only personalized but also intuitive to their current context.
5. Voice Search Optimization (VSO)
SO: With the rise of voice assistants like Alexa and Google Home around 2018-2019, voice search optimization has quickly become a major AI-driven marketing trend. Brands have focused on voice search SEO to ensure their content is easily discoverable through voice queries. At the time, some expected voice search to transform the way customers search for products, with many choosing to use specific keywords instead of full questions or conversational phrases.
NOW: However, voice search optimization alone has stagnated as consumer adoption of voice search has not increased as quickly as expected. While more than a third (35%) of American adults express interest in voice shopping, but they have not yet fully adopted it. Instead, the focus has shifted to more interactive and task-oriented conversational AI experiences, such as voice commerce (v-commerce) and voice-based applications. These platforms allow users to perform tasks, such as making purchases or managing services, directly through voice commands.providing a more transparent and functional experience beyond simply searching for information using keywords.
6. AI for customer segmentation based on basic demographics
SO: Early AI models for customer segmentation relied heavily on traditional demographic factors such as age, location, and gender to target marketing messages. Marketers often used this basic information to personalize emails, creating static segments that provided limited personalization and engagement.
NOW: AI-based segmentation has advanced significantly, incorporating more complex psychographic and behavioral data. This shift allows dynamic customer segments to adapt in real time, making marketing efforts much more personalized and responsive.
In today’s omnichannel environment, AI-powered micro-segmentation allows brands to deliver personalized messages across different touchpoints beyond email. Marketers can deliver personalized content via SMS, push notifications, in-app messages, social media ads, and even personalized website experiences. By taking advantage hyper-personalizationbrands ensure customers receive relevant and timely communication on the platform they interact with most.
From Generic to Dynamic: Leveraging AI for Hyper-Personalized Marketing Success
As we’ve seen, many AI trends in marketing have improved significantly from basic techniques to sophisticated tools that can drive real results. Marketers who embrace these innovative solutions will be better positioned to stay ahead of technological changes and meet consumer expectations. Leveraging the power of AI and machine learning is crucial to thriving in the age of hyper-personalization.
To learn more about how to use AI in your marketing strategy, explore Comarch’s eBook, “How AI Personalization Drives Customer Loyalty.” In this resource, you learn why generic ads fail in loyalty programs, the challenges of personalization without AI, and how AI/ML models can be used for churn prediction, customer lifetime value prediction, and product recommendations. Learn how to test, learn, and adapt your AI-powered loyalty programs to create highly personalized experiences that resonate with every individual.
