AI in Marketing: How to Use It to Enhance Your Marketing Efforts

Key takeaways

Artificial intelligence (AI) makes it easier for marketers to automate tasks, gain insight into customers, and offer engaging personalized experiences.

• Marketing teams are using AI to personalize campaigns, scale content, and qualify leads, among other use cases.  

• There are numerous benefits to using AI in marketing, such as reducing manual tasks and increasing conversion rates, but there are challenges to navigate as well.

• It's important to follow best practices when integrating this emerging technology into new or ongoing marketing efforts.

Learn more about how marketers are using AI and seven best practices to follow. Afterward, if you're interested in strengthening your AI marketing skills, consider enrolling in the IBM Generative AI for Digital Marketing Specialization.

How are marketers using AI?

Artificial intelligence (AI) is transforming digital marketing, advancing goals such as speed, scale, and specificity. There are several different ways for marketing teams to use AI in campaigns and daily workflows, including scaling email personalization, enhancing SEO content, and automating qualified leads. Here's how marketers are using AI to drive results:

1. Personalization

In the age of digital marketing, it's important to personalize campaigns, but AI algorithms make it possible to create hyper-personalized experiences, such as customized product recommendations or individualized emails. Machine learning models predict customer behavior patterns, enabling marketers to deliver the right message to the right person at the optimal moment.

2. Content creation and optimization

Marketers can use AI-powered tools to generate blog posts, social media posts, ad copy, and even video. These same tools can also optimize SEO marketing efforts, enhancing content for LLMs while maintaining readability and engagement for humans. AI can also assist with A/B testing headlines, meta descriptions, and calls-to-action.

3. Predictive analytics

AI processes customer data to forecast purchasing behavior, identify churn risks, and predict lifetime value. These insights enable proactive campaign adjustments and more strategic budget allocation across marketing channels.

4. Automated lead qualification

More brands are relying on AI to create intelligent chatbot experiences for customers. These tools can qualify leads and nurture prospects through the sales funnel. AI systems can also identify high-intent customers and automatically route them to sales teams for immediate follow-up.

5. Ad placements

AI algorithms can optimize ad placements in real-time, adjusting bids based on audience behavior, time of day, and conversion probability. This automation improves ROI while reducing manual campaign management overhead.

6. Dynamic pricing and competitive analysis

AI monitors competitor pricing, market demand, and customer sensitivity to automatically adjust pricing strategies. This ensures optimal profit margins while maintaining competitiveness.

Real-world examples of AI in marketing: 

Let's zoom in to more specific examples so you can see how real brands are working with AI to enhance their digital marketing.

• AI-powered dynamic pricing: When algorithms change prices in real-time based on demand, competitor pricing, and other factors, such as with Uber’s ride-sharing app or airline ticketing

• Personalized recommendations: When algorithms personalize product or content recommendations based on user behavior, such as with Spotify or Netflix’s media streaming services

• Customer support chatbots: When AI bots powered by natural language processing interact with customers to answer questions and meet their needs 

What AI tools are marketers using?

There are many different AI marketing tools to consider. The ones you use will depend on your marketing goals. Here is a short list of tools beyond ChatGPT to get you started: 

• Sprout Social: Enables sentiment analysis, reputation management, social media scheduling, automated workflows, and customer service chatbots.

• DALL-E: Creates customizable, brand-specific images. 

• Keyword Insights: Enables content research, writing, and search engine optimization. 

• Zapier: Enables automation of tasks and custom chatbots. 

• Optimove: Enables data-powered multichannel marketing and personalization. 

• Microsoft Copilot: This chatbot works with the Microsoft suite of products to amplify your marketing efforts.

Benefits of using AI in marketing

Let’s look at some of the other ways AI can benefit marketing teams. 

• Reduced manual tasks: Automates repetitive processes like email scheduling, social media posting, and data analysis.

• Faster decision-making: Instantly processes data to make real-time optimizations and recommendations.

• Hyper-personalization: Delivers tailored content, product recommendations, and messaging to individual customers.

• Improved customer service: Provides instant responses and support through intelligent chatbots and virtual assistants.

• Multi-channel consistency: Maintains unified customer experiences across all touchpoints and platforms.

• Advanced analytics: Uncovers hidden patterns and trends in customer behavior that humans might miss, and forecasts future customer actions, market trends, and campaign performance.

• Real-time optimization: Continuously adjusts campaigns based on live performance data.

• Higher conversion rates at reduced costs: Optimizes targeting and messaging to reach the most qualified prospects, while improving targeting precision to attract more valuable customers.

• Scalability: Handles growing data volumes and customer bases without proportional resource increases.

• Innovation opportunities: AI may help enable new marketing approaches that weren't previously possible.

Challenges of using AI in marketing 

Along with the benefits, AI in marketing presents some challenges, particularly around data, that marketing teams should keep in mind as this technology evolves.

• Transparency: How should content marketers disclose when content, such as an industry report or blog article, has been generated (partly or totally) by AI? What regulations are in place to determine how businesses should inform customers about data usage? 

• Intellectual property: AI in content marketing poses challenges when AI systems use the intellectual property of a third party to generate new content without attribution. There may be disputes about ownership: Is it the user, the prompt engineer, the creators of the original data the system is trained on, or the AI system itself?

• Legal and ethical concerns: With AI systems often handling vast amounts of data, it’s important to consider how customers’ personal data is stored and used to avoid security breaches. In addition, marketers should be mindful of liability in instances of AI marketing errors. 

• Data quality concerns: Some AI systems are trained on historical data, which might present biases or inaccuracies in outputs. 

Best practices of implementing AI in marketing

Since AI is an emerging technology, there are several best practices to follow as you begin to integrate it into your marketing efforts. Follow the guidance below to get started.

1. Start with clear goals.

As with any strong marketing strategy or campaign, make sure you have a clear understanding of what you're using AI to improve and that it aligns with your organization's larger business goals.

• Define specific objectives: Identify which marketing challenges AI should solve, such as personalization or customer retention.

• Set measurable KPIs: Establish the marketing analytics you'll track.

• Begin with pilot projects: Start small with one or two use cases before scaling across your entire marketing operation.

• Align with business goals: Ensure AI initiatives support broader company objectives and revenue targets.

2. Prepare your data foundation.

If you plan on using AI to improve customer segmentation and personalization, it's important to ensure the data you're working with is clean, follows protocols and privacy compliance, and integrated into your new AI workflows.

• Audit existing data quality: Clean, organize, and standardize customer data.

• Establish data governance: Create protocols for data collection, storage, and privacy compliance.

• Integrate data sources: Connect CRM, website analytics, social media, and other platforms for comprehensive customer profiles.

• Implement tracking systems: Ensure proper attribution and conversion tracking before AI optimization begins.

3. Choose the right tools.

There are numerous AI marketing tools to choose from—some that can apply to a range of marketing needs and others that fit more specific use cases. Make sure to identify the right tools for the marketing goals you initially established.

• Assess your technical capabilities: Determine whether to build in-house, buy existing solutions, or partner with AI vendors.

• Start with user-friendly platforms: Choose tools with intuitive interfaces that don't require extensive technical expertise.

• Prioritize integration capabilities: Select AI tools that seamlessly connect with your existing marketing technology stack.

• Consider scalability: Choose solutions that can grow with your business and handle increasing data volumes.

4. Build and strengthen your team's capabilities.

Any new tools require human oversight. Make sure your team is trained to work with any AI tools you begin using.

• Invest in training: Educate your marketing team on AI fundamentals and tool-specific best practices.

• Foster collaboration: Create cross-functional teams between marketing, IT, and data science departments.

• Maintain human oversight: Keep experienced marketers involved in strategy and creative decisions.

5. Implement gradually and test continuously.

It's important to monitor AI efforts compared to more manual workflows, identify what's working, amplify what is, and adjust what's not.

• Run tests: Use A/B tests to compare AI-driven campaigns against traditional approaches to measure effectiveness.

• Monitor performance: Regularly review AI recommendations and outcomes to ensure they align with brand values.

• Iterate and optimize: Continuously refine algorithms and strategies based on performance data and customer feedback.

• Document what you learn: Create playbooks and processes for successful AI implementation across teams.

6. Address privacy considerations.

Because AI tools will have access to customer data, it's important to be transparent about how you're using that information and stay compliant.

• Prioritize transparency: Clearly communicate to customers how AI is used in marketing communications.

• Respect customer preferences: Honor opt-out requests and privacy settings in AI-driven personalization.

• Avoid bias: Regularly audit AI systems to ensure fair and inclusive marketing practices.

• Stay compliant: Keep up with evolving regulations around AI use and data privacy in marketing.

7. Plan for the long-term.

Using AI in marketing is a long-term strategy that requires evaluating ROI, staying current with marketing trends and AI technologies, and creating feedback loops.

• Budget: Account for tool subscriptions, training, and potential technical support.

• Create feedback loops: Establish processes to capture customer responses and continuously improve AI performance.

• Stay current: Regularly evaluate new AI capabilities and tools that could enhance your marketing efforts.

• Measure ROI: Track both hard metrics (revenue, conversions) and soft benefits (efficiency, customer satisfaction).

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