How to use AI in marketing

Companies can use AI in marketing to analyze data, create content, manage campaign delivery and optimize results. AI can spot patterns in data, suggest ideas and help teams decide what to do next within existing workflows. But marketers still need to set the goals and review the results.

Key Takeaways ⓘ

• AI in marketing supports analysis, content creation, campaign delivery and op­ti­miza­tion, while marketers still set the goals and review the results.
• Typical use cases include data analysis, content creation, SEO and GEO, paid ads, social media, email marketing, CRM and customer service.
• AI can support every stage of the customer journey, from awareness to retention.
• Small and midsize busi­ness­es can complete repet­i­tive tasks faster and make better use of the content they already have.
• Risks include un­re­li­able data, output that sounds con­vinc­ing but is incorrect, and questions around data pro­tec­tion and trans­paren­cy.
• It makes sense to start with one clearly defined use case and measure the results against a reliable baseline.

What does AI in marketing mean?

AI in marketing means using ar­ti­fi­cial in­tel­li­gence to support marketing tasks. Some AI systems learn from data, while others generate content. An­a­lyt­i­cal models, for example, can predict how likely someone is to make a purchase or group customers into different segments. Gen­er­a­tive AI systems can produce text and images or suggest ideas. Other systems use current signals to choose a channel, time or ad variation.

AI does not replace a marketing strategy or reliable customer data. It works with the in­for­ma­tion, goals and limits a company gives it. AI is most useful where marketing teams spend a lot of time making the same kinds of decisions, working with large amounts of data or creating similar content time and time again.

Where can companies use AI in marketing?

Ar­ti­fi­cial in­tel­li­gence can be used in many different areas of marketing. It’s often used to handle recurring tasks more ef­fi­cient­ly or to analyze large data sets.

Analyzing data and con­duct­ing market research

AI can help teams work through large and varied sets of in­for­ma­tion more quickly and turn them into useful insights. AI-supported data analysis and AI-powered market research combine tra­di­tion­al analysis methods with automated pattern recog­ni­tion and data-driven forecasts.

Typical examples include:

• Grouping customers into different segments based on their behavior
• Detecting unusual changes in traffic or sales figures
• Fore­cast­ing future demand based on his­tor­i­cal data
• Iden­ti­fy­ing common themes in open-ended survey responses
• Iden­ti­fy­ing sentiment and common issues in customer feedback

Creating marketing content

When creating content, AI mainly acts as an assistant for editorial and creative work. It can speed up in­di­vid­ual steps in content marketing, but experts still need to review the output, editors still need to approve it and any necessary rights still need to be cleared.

Typical examples include:

• De­vel­op­ing several headline vari­a­tions for an article
• Turning an existing blog post into a newslet­ter draft
• Turning product in­for­ma­tion into a rough draft for a landing page
• Sug­gest­ing images for an upcoming campaign
• Con­dens­ing longer pieces of content into social media posts

Improving SEO and GEO

AI can also help with research, planning and op­ti­miza­tion for organic vis­i­bil­i­ty. AI for SEO focuses on tra­di­tion­al search engines, while Gen­er­a­tive Engine Op­ti­miza­tion also looks at how content can be struc­tured so AI-powered answer systems can un­der­stand and cite it.

Typical examples include:

• Grouping search queries that have the same intent
• Finding topics that existing pages do not cover
• Sug­gest­ing titles and meta de­scrip­tions
• Finding subtopics that are missing from longer pieces of content
• Or­ga­niz­ing key points around the questions they answer

Managing paid ad­ver­tis­ing campaigns

When used in paid campaigns, AI con­tin­u­ous­ly analyzes signals from ad delivery and user behavior. In pro­gram­mat­ic ad­ver­tis­ing and other automated ad formats, it can make certain decisions faster once the campaign goals have been set. The marketing team still sets the budget and campaign goals and checks the quality.

Typical examples include:

• Adjusting bids based on how likely users are to convert
• Au­to­mat­i­cal­ly testing different ad creatives to see which performs better
• Pri­or­i­tiz­ing audiences based on recent in­ter­ac­tions
• Adjusting how ad budgets are dis­trib­uted across campaigns
• Spotting poorly per­form­ing com­bi­na­tions of ads and audiences early on

Managing social media

AI can support social media marketing by helping teams plan their posts, organize their work and analyze how people respond. This can be es­pe­cial­ly helpful when a team manages several platforms, as AI can adapt a post to suit different formats and audiences.

Typical examples include:

• Adapting a post to suit different platform formats
• Creating a content calendar based on topics the team already covers
• Iden­ti­fy­ing questions that come up re­peat­ed­ly in comments
• Sorting reactions to a campaign as positive, neutral or negative
• Finding suitable times to post based on past en­gage­ment data

Email marketing

AI can help tailor both the content of an email and when it is sent to different groups of re­cip­i­ents. This works best when the company has up-to-date data that it is allowed to use. When using AI in email marketing, teams should also test changes carefully and set clear quality standards.

Typical examples include:

• Trying different subject lines in an A/B test
• Grouping contacts based on links they have clicked in the past
• Choosing send times based on when re­cip­i­ents have opened previous emails
• Choosing which newslet­ter content to feature based on each recipient’s known interests
• Iden­ti­fy­ing inactive re­cip­i­ents for a re­ac­ti­va­tion campaign

Per­son­al­iza­tion and CRM

AI can use the customer in­for­ma­tion a company already has to make its com­mu­ni­ca­tion more relevant. Instead of showing everyone the same content, companies can tailor what they show or send based on a customer’s interests and previous in­ter­ac­tions. CRM systems can provide much of the in­for­ma­tion needed for this. Companies should only use data they are allowed to use and make sure they can explain how their per­son­al­iza­tion process works.

Typical examples include:

• Pri­or­i­tiz­ing contacts who are more likely to make another purchase
• Adapting website content to a customer’s known interests
• Sug­gest­ing the next offer based on previous in­ter­ac­tions
• Scoring leads based on signs that they may be ready to buy
• Iden­ti­fy­ing groups of customers who may be more likely to churn

Chatbots and customer service

AI can help marketing and customer service teams work more closely together. For example, an AI chatbot can answer routine questions, while employees step in for more complex or sensitive issues. Questions that come up re­peat­ed­ly can also show where existing in­for­ma­tion or marketing content could be improved.

Typical examples include:

• Answering questions about shipping and delivery around the clock
• Sorting customer inquiries by topic and urgency
• Sug­gest­ing relevant help pages based on a customer’s problem
• Col­lect­ing contact details before passing an inquiry on to the sales team
• Spotting common mis­un­der­stand­ings that could be addressed in an FAQ section

How can AI support different stages of the customer journey?

In the awareness phase of the customer journey, a marketing team can group search trends and customer questions, use them to decide which topics to focus on and test several ad creatives. This lets the campaign learn faster without the team having to create every variation manually.

During the con­sid­er­a­tion phase, AI can look at how someone has in­ter­act­ed with a website, newslet­ter or other touch­points and use this to decide what in­for­ma­tion might be of interest to them. This can make com­mu­ni­ca­tion more relevant, but the scoring should be checked regularly for bias and problems with the un­der­ly­ing data.

In the con­ver­sion phase, AI can choose between different versions of a landing page or email based on the goals of the campaign. Then, when someone is making a purchase, chatbots can answer common questions and pass more complex issues on to sales or customer support.

After a purchase, AI can spot customer questions or problems that come up re­peat­ed­ly and suggest content that could help them. For retention campaigns, companies can also look for signs that a customer might leave and offer support or advice at the right time. An om­nichan­nel marketing strategy can help make sure the messages customers receive across different channels are con­sis­tent.

What kinds of AI marketing tools are there?

AI marketing tools mainly differ in the jobs they are designed to do. When choosing one, companies shouldn’t only look at its features, but also at what data the tool uses, how it handles that data and whether it works with the systems they already have. AI marketing au­toma­tion brings several of these functions together so they can work across different channels. Marketing au­toma­tion is the broader term for au­tomat­ing recurring marketing tasks and workflows.

The AI Ac­qui­si­tion Manager brings several of these functions together. It can help create text and images, adapt posts for different channels and manage campaign planning through a central content calendar. Teams can still review and approve posts before they are published. For small busi­ness­es and teams with limited resources, having these tools in one place can save time and reduce the need to switch between different ap­pli­ca­tions, while still allowing teams to review what gets published.

What are the benefits of using AI in marketing for small and midsize busi­ness­es?

For small and midsize busi­ness­es, one of the main benefits of AI is that it can help teams complete repet­i­tive tasks faster and make better use of the content they already have. A small team can, for example, create versions of content for different channels, analyze campaign data and decide what to focus on next without needing a separate tool or spe­cial­ist team for every task.

AI can also make it easier to test different ideas and per­son­al­ize marketing on a scale that would be difficult to manage manually. But using AI does not au­to­mat­i­cal­ly lead to better results. It usually makes more sense to start with one clearly defined use case and set clear quality standards than to try lots of unrelated ex­per­i­ments.

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What lim­i­ta­tions and risks are there?

AI can be useful in marketing, but it still needs to be used carefully. If the data is in­com­plete, outdated or biased, the results may not be reliable. Gen­er­a­tive AI can also produce in­for­ma­tion that sounds con­vinc­ing but is incorrect, so content still needs to be checked before it is published. Clear brand guide­lines are important too. Without them, the tone, ter­mi­nol­o­gy or messages used across different channels can become in­con­sis­tent. And when a process is fully automated, mistakes can spread quickly if nobody catches them.

Companies also need to be careful when personal data is involved. They should be clear about why they are using the data, only collect what they need, decide how long to keep it and know which service providers can access it. Trans­paren­cy matters as well. If customers are in­ter­act­ing directly with AI or AI has been used to create or alter content, companies should consider whether people need to be told about it.

Copyright, trade­marks and rights relating to a person’s identity also need to be con­sid­ered. Companies should therefore check sources, review content before it is published, control who has access to AI tools and make it clear who is re­spon­si­ble for the final result.

How can companies start using AI in marketing?

1. Start with one clear goal or problem: Choose a specific task where AI could help, such as analyzing comments on a campaign or drafting social media posts.
2. Check the data and possible risks: Look at where the data comes from, whether it can be used, who has access to it and whether poor-quality data could cause problems.
3. Decide how you will test it: Record how things work before using AI, choose a metric to measure success, set a test period and decide when a person needs to review or approve the results.
4. Compare the results: Don’t just look at whether AI saves time. Also check the quality of the work, con­ver­sions, com­plaints and how much needs to be corrected af­ter­wards.
5. Expand what works: If the test is suc­cess­ful, document the rules, add the AI-supported process to the team’s regular workflow and review it from time to time.

How can success with AI in marketing be measured?

How you measure success depends on what you are using AI for. If AI is helping with content creation, for example, it makes more sense to look at how long the work takes, how much editing is needed and how often drafts are approved than simply counting how much content is produced. For campaigns or customer retention, useful metrics can include:

• Click-through rate
• Con­ver­sion rate
• Cost per qualified lead or the revenue generated
• Repeat purchase rate
• Customer churn
• The amount of work handled by customer service

AI-supported versions should be compared with a reliable baseline so you can see whether they actually perform better. In A/B tests, it is also important to change one clearly defined factor at a time. Companies should also include the cost of the AI tool itself, as well as im­ple­men­ta­tion, training and quality control. Otherwise, time saved in one part of the process may simply be lost again through extra cor­rec­tions or rework.

FAQs about AI in marketing