AI in Marketing: Revolution or Evolution?

The AI application is not new! Ever since 2017, we could see how 星巴克 apply AI in their services. However, with the development of AI recently, we can see that marketing within businesses is transformed by advancements in AI. This revolution is not a shift in tools and technologies but a complete reimagining of how marketing departments operate, strategize, and engage with their audiences.

Let's see how AI is used to shape Marketing?

1. How AI is used to shape Marketing?

1.1. AI-driven personalization and customer engagement

A study by Epsilon found that personalised emails had a 29% higher open rate and 41% higher click-through rate than non-personalized emails. So, we can use AI to deeply understand customers' insight and increase customer engagement. This way allows for the creation of highly personalized marketing messages to customers by AI.

Here are some AI tools you can use to deliver personalized responses: Customerly, Voicegenie,..

1.2. Predictive analytic.

According to a study by McKinsey, companies that use predictive analytics are twice as likely to be in the top quartile of financial performance within their industry. So, marketers can use AI to analyze customer data to forecast future trends, identify patterns and optimise campaigns, identify new opportunities.

Here are some AI tools that marketers can use: AI-driven predictive analytics is the Google search engine, SAS Viya, Google Cloud AutoML, IBM Watson Analytics,..

1.3. Voice search optimisation

Prior to the #GenAI boom, optimising content for voice search is becoming popular as more people use voice assistants such as Siri, Alexa, and Google Assistant. Marketers can leverage AI to ensure when people use voice commands to ask questions, marketer's content can provide the answers they're looking for.

Here are some AI tools that marketers can use: Elevenlabs, Resemble, Vbee, FPT for Viet AI...

2. The challenges faced by AI marketing

Although AI marketing brings many benefits for marketers, it also has its own challenges. Here are some challenges that marketers need to prevent:

2.1. Accuracy

AI algorithms completely rely on data to make predictions and decisions. Therefore, if the data provided is biased or incomplete, that can cause the results to be inaccurate, which in turn would make the marketing strategies flawed.

2.2. Humanize AI-generated contents

The challenges of marketers is how to make the content generated by AI more human. If content is robotic, the content can not touch the customers' insight. Marketers need to add personal experience, adjusting the language, training an AI system to mimic style which can be more human.

3. Real-World case studies in AI Marketing

3.1. Starbuck's voice-powered barista services.

Starbucks Barista was a voice ordering feature that 星巴克 tested. The feature allowed customers to place orders for drinks and food through the Starbucks mobile app using their voice.

Let's explore how it worked:

1. Open the Starbucks app: Customers open the Starbucks app on their phone.
2. Select the Barista feature: In the app, customers select the "My 星巴克 Barista" feature or a similar icon to activate the voice ordering feature.
3. Place an order by voice: Customers spoke their order on the phone, for example, "I'd like a grande latte with oat milk and a croissant."
4. Confirm the order: The app used voice recognition technology to convert the customer's speech to text and display the order for the customer to confirm.
5. Pay and pick up: Customers confirmed the order and paid through the app. Then, they could go to their chosen 星巴克 location to pick up their order.

3.2. Amazon: Personalized recommendations.

Amazon has launched the 亚马逊 recommendation system to provide personalized product suggestions to its users. Its primary goal is to enhance the customer shopping experience and drive sales by suggesting items that customers are likely to be interested in.

Let's explore how it works?

1. Data collection: 亚马逊 collects vast amounts of data from users, including their browsing history, purchase history, ratings, reviews, wishlists, and demographic information.
2. Filtering: This technique analyzes user behavior patterns and identifies similarities between users who have purchased or interacted with similar items. It then recommends products that similar users have purchased or shown interest in.
3. Hybrid approaches: 亚马逊 often combines multiple techniques to create a more robust and accurate recommendation system. This can involve using collaborative filtering to identify potential items of interest and then applying content-based filtering to further refine the recommendations.
4. Machine learning: 亚马逊 employs machine learning algorithms to continuously learn and improve the recommendation engine. The system adapts to user behavior and feedback, refining its recommendations over time.
5. Contextual factors: 亚马逊 considers contextual factors like the user's location, time of day, device type, and current browsing session to deliver more relevant recommendations. For example, it might recommend weather-appropriate clothing based on the user's location.
6. Personalization: 亚马逊 tailors recommendations to individual users based on their unique preferences and interests. This is achieved by analyzing their past behavior, interactions, and demographic data.

4. What do marketers need to do now to thrive with AI ?

4.1. Experience of AI Tools & Platforms

With the application of AI in marketing, marketers need to have an understanding of the foundation and principles of how AI works, practice and explore different AI tools and use for everyday tasks. Knowing how to use these tools can help marketers perform better.

4.2. Upskill and Reskill:

As AI is developing, marketers need to continuously learn and update their skills to keep pace with the latest developments. For marketers, this might involve learning about new digital marketing channels, mastering data analytics tools, or understanding the nuances of AI-powered marketing platforms.

As an AI enabled #Edtech platform with the mission to empower individuals and organizations to upskill and reskill to be ready for the future of work, we Edtronaut provide AI in Marketing 2.0 which is part of the AI training which can help marketers learn how to apply AI across Marketing value chain.

What are your thoughts? How have you been using AI in your daily tasks? Any observations or challenges you have? Please share with us your thoughts in the comments below.

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