What else can AI marketing do? What are the risks?

AI can not only handle complex data analysis and automation tasks, but also provide strong support in content creation, customer prediction, advertising, etc. However, the application of AI in marketing is also accompanied by risks such as data privacy, decision bias, and implementation costs. This article will explore 8 application scenarios of AI in marketing, including predicting customer purchase behavior, dynamic pricing, email marketing, advertising optimization, etc., and analyze its potential risks. At the same time, it will provide a collection of overseas AI marketing tools to help marketers better use AI to improve work efficiency and marketing effectiveness.

DeepSeek continues to be popular, and the most discussed topics among marketers are "Will he take my job if he is so good at writing?" and "How to do AI SEO?"…

The article “How to use AI in marketing?” that I published a few months ago needs an update. I happened to see this article “How to leverage AI in marketing: Strategies and Best Practice” from DEMANDBASE, written by Jonathan Costerllo.

The article specifically talks about how to use AI and what risks it has. I have selected and translated some of the content and made some simple interpretations for everyone to discuss.

However, with new technologies changing with each passing day, there is no standard answer to every question, and every attempt is meaningful.

I hope to bring you inspiration and thinking, happy reading!

When marketing meets AI, it becomes possible to analyze large amounts of data, predict customer purchasing behavior, and create personalized experiences on a large scale.

If we imagine it as your “always-on digital assistant”, it can do the following:

• 24*7* customer service
• Smart content optimization
• Advertising in digital marketing
• Customer behavior prediction analysis

These can improve efficiency and boost return on investment (ROI). AI excels in handling complex data work and allowing marketers to focus on creativity and strategy.

8 Ways to Use AI in Marketing

1. Predict potential customers’ likelihood of purchase

AI analyzes data about people interacting with you through various touchpoints (social media, official website, emails, ads, etc.) and uses historical data to identify potential customers who are most likely to purchase.

For example, when a prospect downloads your case study, visits your pricing page, or adds contact information, AI can automatically adjust their rating.

Hanni: In the past, marketers hoped that CRM and SCRM tools would have the ability to predict and analyze customers' next actions. Now, with the help of AI, this may be achieved more quickly.

2. Dynamic Pricing

AI tracks market trends, customer behavior, and competitor prices to automatically adjust pricing.

Hanni: I have reservations about this. Will AI take advantage of old customers without taking responsibility?

3. Email Marketing

Know exactly who to email, when to send it, and what content works best.

Hanni: In the past, automated emails were a bit blunt, so if AI can provide some humanistic care, it would be considered progress. However, how can AI solve the problem that domestic customers don’t read emails?

4. Ad targeting and optimization

AI can match ads with user interests and behaviors, test different versions on different audiences, predict the best time to display ads, and automatically adjust bids to get a better return on investment…

Hanni: AI is slightly better at data analysis and can indeed greatly improve efficiency.

5. Content creation and search engine optimization (SEO)

Research industry trends, competitor content, and search patterns to help you create content that ranks and converts well.

For example, if procurement managers frequently search for “supplier compliance requirements,” AI helps create targeted content for those issues. When a CTO researches “enterprise cloud migration,” AI suggests optimizing existing content to meet the technical specifications they are searching for. When existing content loses traction, AI suggests updates based on new industry requirements or pain points.

Hann: AI translation and text writing have already been realized. "How to optimize content" and "How to let AI recommend my company and my products to the questioner?" are topics that all marketers are exploring. How to do it specifically? It is worth thinking more deeply.

6. Customer feedback analysis

Use AI to interpret and categorize customer feedback and likes, comments, and reposts on social media posts. Help understand your customers’ true feelings about your products or services in real time.

Hanni: In the past, this was the job of manual customer service, but now AI processes each customer’s feedback to identify potential problems and suggest directions for improvement.

7. Expand A/B testing

AI adjusts test parameters based on multiple factors without delay, thereby optimizing results faster and improving user experience.

Hanni: Real-time adjustments and the ability to scale testing to a larger customer base.

8. Content localization

AI breaks down the language barriers in your overseas GTM work. It automatically translates and adapts communication content to different markets while maintaining consistency in technical accuracy and industry context. In addition to translation, AI can ensure that your content adheres to local business customs.

Hanni: True localization is not just about language adaptation, but also about cultural adaptation. AI can remind you and help you.

When talking about AI usage scenarios above, you must have felt that there are also risks, such as the following:

2. Risks of using AI for marketing

1. Data Privacy and Security

AI marketing relies heavily on customer data to create personalized campaigns and predict behavior. However, this reliance introduces significant privacy and security risks, especially when dealing with sensitive data such as personal information and purchase history.

Add to that the threats from cyberattacks, data breaches, and unauthorized access that can not only undermine customer trust but can also result in severe regulatory penalties.

This challenge is further complicated by privacy laws like GDPR and CCPA, which impose strict rules on how you collect, process, and store data, with violators facing hefty fines.

2. Data quality and decision bias

AI marketing tools are completely dependent on the quality of the data their machines are built on. Poor or incomplete data can lead to poor decisions and misguided marketing campaigns, wasting time and money.

Algorithmic bias in data can cause even bigger problems. For example, when historical marketing data favors certain groups, AI may prioritize them in targeting and recommendations, perhaps ignoring other groups, which in turn affects decision-making.

3. Lack of expertise and skills gap

Adopting AI marketing tools requires expertise that many teams lack. Most marketers lack training in data science, machine learning, and AI platforms, which makes it difficult to use these powerful tools effectively.

Additionally, AI technology changes rapidly, requiring constant learning and adaptation. Without proper training, it’s difficult for marketing teams to understand AI insights, make data-based choices, or adjust when technical issues arise. This knowledge gap causes expensive AI tools to sit idle or be misused.

4. High implementation costs

Using an AI marketing platform often requires a significant upfront investment. Companies need to pay for usage fees, system upgrades, and integration work, making it difficult for smaller businesses to get started. Ongoing maintenance and update costs can put a strain on marketing budgets, especially when the return on investment is unclear. Making mistakes during implementation can waste significant resources.

5. There is no substitute for human connection

While AI performs well at analyzing data and automating tasks, it struggles with emotional intelligence and creativity. Over-reliance on AI can make communications seem robotic and impersonal, potentially neglecting important context or cultural nuances, potentially damaging relationships rather than strengthening them.

This is especially critical in B2B relationships, where trust and understanding are vital. Complex business decisions require human insight and empathy that cannot be replicated by AI.

6. Integration with existing systems

Connecting AI tools with today’s marketing technology software presents significant challenges. Many businesses struggle with making AI work smoothly with their CRM, email platforms, and other existing tools.

When systems don’t communicate well, siloed pools of data form. This fragmentation prevents AI from accessing the customer information it needs to make accurate predictions and provide valuable insights.

Poor integration can lead to other problems as well. Implementation takes longer and costs more, such as the need for technical expertise to bridge the gaps between different systems.

Hanni: While making good use of AI, we must also consider the possible risks. If possible, we can use A/B testing when humans and AI are handling the same task, and whoever is more capable will be used.

Sometimes people take the lead and AI assists; sometimes AI takes the lead and people control quality.

Finally, the article mentioned:

3. Overseas AI Marketing Tools Collection

Jasper.ai: Creates various types of marketing content, including blogs, ads, and social media posts. Its AI ensures that the content is relevant to your audience and aligns with your marketing goals.

Copy.ai: Provides ready-made templates to quickly create marketing copy. You input key details and it generates professional content for different marketing channels.

SurferSEO: Research over 500 ranking factors from top-performing pages. It shows you how to optimize everything from keyword placement to content structure, helping you match what works in your industry.

Clearscope: Checks content quality and SEO performance as you write. It compares your work to top-ranking pages and suggests improvements to help you create better content.

Seventh Sense: studies when each person on your email list typically reads emails. It then sends messages at the optimal time for each recipient, increasing open and response rates.

Mailchimp: Leverages AI to help create better email content and understand your audience. It suggests improvements to your messages and helps segment your mailing list for more targeted campaigns.

Optimove: Combines customer data and AI to create targeted marketing campaigns. It observes how customers interact with your brand and helps send personalized messages to increase engagement.

Blueshift: Integrates customer information from different sources in real time. It predicts the customer’s likely next action and helps you reach them with relevant content at the right moment.

Drift: Uses AI to communicate with website visitors like your best sales reps. It helps identify qualified leads and starts sales conversations as soon as a lead shows interest.

The above tools are recommended by the original article. I have not tried them one by one. You can try them yourself. If you have better recommendations, please share them with me and other readers. Thank you in advance.

Well, you can also go and read the original text:

[https://www.demandbase.com/blog/how-to-leverage-ai-in-marketing-strategies-and-best-practices/]