The recent advances in artificial intelligence (AI) are transforming how companies approach marketing. AI marketing tools can reduce the workload of marketing teams, improve decision-making, and boost the customer experience. But developing an integrated AI marketing strategy is essential. Despite its promise, AI can be complex and daunting—especially for those unfamiliar with its technology. Here we’ll explore how AI tools operate, potential use cases, and how marketers can get their organizations on board with AI.
How are AI marketing tools built?
Although artificial intelligence (AI) is not new, recent advances in machine learning have created additional opportunities for marketers. Before we explore how to use them, here’s a brief overview of how these tools work. AI uses algorithms to replicate human thinking and behavior. AI differs from traditional computer programming because it can learn from new data and improve over time.
Machine learning
Machine learning predicts outcomes based on data. Scientists prepare large quantities of data and use algorithms to “train” the AI. The tool can then identify new patterns and generate insights as the system receives additional data. Machine learning relies on two types of learning:
- Supervised learning: Humans provide an input and an output. The machine learns the correct response and can replicate this across different scenarios.
- Unsupervised learning: Humans provide the inputs. The machine draws conclusions without human interference.
An example of machine learning is a streaming service—like Netflix or Hulu. These algorithms generate personalized recommendations by analyzing your watch history and similar users’ preferences.
Deep learning
Deep learning is an area of machine learning. It uses a multi-layered set of algorithms called an artificial neural network—modeled after the human brain. Unlike machine learning, deep learning can assess images, videos, text messages, audio, and other unstructured information to make decisions.
Natural language processing
Natural language processing (NLP) uses machine learning and deep learning to analyze and understand human language. This allows tools—like ChatGPT or Grammarly—to process inputs, understand context, and respond as humans would. NLP relies on two methods:
- Syntactic analysis: The computer examines the grammatical syntax of a sentence to determine its meaning.
- Semantic analysis: The computer identifies a word’s context by examining the words around it.
How marketers can use AI
Today, AI marketing tools help organizations speed up decision-making, streamline workflows, and drive customer engagement. Here are nine use cases for AI marketing.
1. Identify consumer needs and preferences
With its predictive analytics, AI tools provide insights into consumer behavior and identify future trends. This data helps companies forecast inventory and drive a more personalized customer experience.
According to a study by LivePerson, consumers are 69% more likely to complete a purchase if brands apply their purchasing history. This number increases to 73% for younger consumers (i.e., 18-24 years old). AI tools can make hyper-personalized product recommendations by analyzing consumer behavior and attributes.
2. SEO
Search engine optimization (SEO) is constantly evolving. Today, numerous AI tools are available to keep up with SEO best practices and execute your SEO strategy, through steps such as the following:
- Conduct personalized keyword research
- Recommend topics
- Create SEO-optimized content briefs
- Provide assessments of headlines, meta descriptions, title tags, and content
- Review backlinks
- Analyze your website
- Track the visibility and performance of keywords over time
- Complete competitor analysis
3. Content generation
AI tools also streamline the content generation process. AI content tools make creating outlines, headings, blog posts, and web copy easier for brands. And AI art generators help brands produce images that complement their content.
4. Social media listening
AI tools can also offer insights based on conversations and interactions across your social media accounts. First, companies use these tools to identify industry trends and patterns. As a result, companies better meet customer needs and remain competitive.
AI tools also analyze comments and direct messages to understand consumer sentiment. And with video and image recognition capabilities, businesses understand how customers use their products.
5. Ad targeting
Ad targeting uses demographics, attributes, browsing behavior, and purchasing history to allow advertisers to reach the right consumers with relevant ads.
With AI tools, companies can analyze user behavior, identify trends, and segment customers faster. These platforms also scan websites and social media accounts to locate ad placement opportunities. Finally, marketers use AI to understand ad performance and adjust ads in real time.
6. Lead generation
Once you’ve captured a potential customer’s attention, AI tools simplify lead generation. These platforms analyze customer data to qualify leads and identify individuals with the most potential to convert. With these insights, marketers prioritize and personalize their outreach efforts.
7. Customer service
AI also has the potential to transform the customer service experience—starting with chatbots. Today, 85% of brands say AI is “extremely” or “very important” to their customer engagement strategies. But only 43% of customers find chatbots simple to use.
Leveraging NLP, AI-powered chatbots comprehend natural human language instead of expecting customers to limit their inquiries to a few words. These chatbots can ask follow-up questions, understand nuance, and react to customer sentiment.
At the same time, AI also helps customer service agents provide a better experience. These tools “coach” associates on interacting with customers. They also direct associates to drive upselling and cross-selling.
8. Predict churn
Additionally, marketers use AI tools to improve customer retention. These tools identify customers at risk of churning, segment customers, and track customer attitudes. This data allows marketers to determine which customers to target and customize messaging.
9. Task automation
Finally, AI tools increase efficiency by automating routine tasks, such as:
- Sending automated email or SMS marketing campaigns—including following up with qualified leads or at-risk customers
- Scheduling social media posts
- Generating custom reports and dashboards
By delegating routine tasks to AI tools, marketers can focus on strategy, skills development, and nurturing customer relationships.
How to design an integrated AI marketing strategy
When implementing an AI marketing strategy, companies should ensure that AI tools solve problems without creating more work. Here are seven steps to developing an integrated AI marketing strategy.
- Start small: Take a “crawl, walk, run” approach to AI marketing. Consider tools that are non-consumer facing and automate processes.
- Define objectives: Examine your marketing organization’s long-term goals and set objectives for implementing AI.
- Conduct a marketing audit: Evaluate your current marketing practices and identify areas for improvement.
- Identify use cases: Determine where AI can improve efficiency and enhance your marketing strategy.
- Evaluate AI tools: When exploring AI platforms, consider how easy it will be to integrate them into your current workflow. Are these platforms compatible with third-party applications?
- Develop an implementation plan: Identify stakeholders, create a training plan, determine resource requirements, and establish a timeline.
- Monitor and measure: Define and track key metrics to ensure tools deliver results.
The disadvantages of AI in digital marketing
While AI marketing tools can offer several benefits, companies must use them ethically to enhance their businesses. Here are some disadvantages of AI marketing to consider.
AI is not a replacement
AI tools can streamline workflows and make companies more efficient. However, they are not a replacement for marketing functions. Companies should be wary of becoming overly dependent on AI tools. While AI tools can analyze large data sets, companies must ask the right questions. At the same time, AI can lack the human touch necessary to build customer relationships. If misused, companies risk making poor decisions and angering customers.
Dependence on technology
AI platforms can also be vulnerable to errors or technical difficulties. If there is an issue with a tool, it can affect marketing operations and the customer experience. Companies should establish procedures to monitor technical performance.
Bias
AI is not immune to bias. These algorithms rely on vast amounts of data. AI tools may exhibit bias if the data set or data collection method is not diverse. At the same time, user-generated data may reflect societal biases resulting in discriminatory conclusions. Companies must maintain data integrity and monitor outputs to avoid offensive insights or messaging.
Privacy and security
Over the last few years, new legislation and restrictions on third-party browser cookies have raised consumer awareness of data privacy issues. The rise of AI has added to the scrutiny. In 2022, 17 states introduced legislation or resolutions addressing the commercial use of AI. AI is an area of interest for customers, with 69% of consumers supporting using AI to improve the customer experience. Transparency is essential to maintain consumer trust. Businesses should disclose how they collect data and how they use it. At the same time, companies must monitor changing legislation to ensure compliance.
Getting your organization to embrace AI marketing
Let’s face it—AI marketing tools are still new. Best practices and use cases are constantly evolving. And implementation can be costly and time-consuming. As a result, getting buy-in from executives and colleagues can be challenging.
- Identify key stakeholders: AI marketing apps won’t just affect the marketing team. Consider how these tools will impact other departments.
- Inform colleagues: Build trust by sharing your strategy early and inviting questions and concerns.
- Consider timing: AI implementation should not burden your team or create competing priorities. Avoid implementation during busy seasons or transitional periods.
- Avoid jargon: Introducing new technology can be intimidating. Avoid technical terms and acronyms. Make the tool accessible using simple language.
- Ask for feedback: Encourage stakeholders to participate in the process by asking for feedback at every stage.
- Share metrics: Increase transparency by regularly sharing data about performance.
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