How can I use artificial intelligence (AI) for marketing?

First published on October 15, 2021

Last updated at August 15, 2022


9 minute read

Thomas Chung



Artificial intelligence (AI) is transforming the landscape of 21st century marketing. Long gone are the days of throwing spaghetti on the wall and shooting in the dark to acquire new customers and to regain their business. With the amount of data growing exponentially on a daily basis, AI can help businesses scale their marketing efforts and leverage the data for actionable insights leading to greater ROI.


  • What is marketing?

  • Components of AI used in marketing

  • AI used in marketing today

  • Challenges

  • Conclusion

What is marketing?

Look up the term “marketing” and you’ll find something that mentions actions or activities involving a business or company, promoting or selling products or services. Is that something that you or your company does? Of course it is!

The real question is how do you achieve those goals or maybe a better question is, how well? Marketing has evolved tremendously throughout history from traditional efforts such as newspapers and magazines to radio and billboards to digital channels such as TV and well, basically anything with a screen. We’re now marketing to consumers who were born swiping, where anything longer than 30 seconds is too long, and streaming is the only way to go. I may be exaggerating but consumer behavior has changed significantly within the past few decades, if not just a few years.

It feels like that sometimes (Source: Giphy)

I guess it’s time to throw in the towel, or is it? Though technology and innovation may be a factor in the drastic change in consumer behavior, it can also be a powerful tool in understanding and building strong affinity with those same consumers.

Yes we do! (Source: Giphy)

Components of AI used in marketing

AI-powered solutions

AI can provide marketers with a centralized platform to manage the vast amount of data they collect on a regular basis. Such platforms can provide valuable insights on target audiences to help you make data-driven decisions to best connect with current and prospect customers.

Big data & analytics

As the world heads more towards a digital realm, an influx of big data brings about great opportunities for marketers to analyze and understand their efforts across their digital platforms. Though having a lot of data is a good thing, the struggle nowadays is often figuring out which datasets to use for meaningful insights.

Machine learning (ML)

Machine learning is a branch of AI that involves the use of data and algorithms to imitate the way humans learn and gradually improve accuracy through experiences. ML algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

AI used in marketing today


It’s not uncommon to be directed to a chatbot on a website nowadays. It’s likely you may have had a recent interaction, maybe even on Facebook Messenger. This is a common example of AI used to improve customer service. Chatbots are often used to curtail repetitive questions from customers and provide an instant response 24/7. Customer data is often used to personalize content marketing and essentially become a proactive advertising tool.

Working hard 24/7 (Source: Giphy)

Content generation

Imagine a tool that can automatically generate emails and reply to your customers, create social media post messages, or even blogs! Through the power of AI, it is now as easy as selecting some parameters and clicking a button to generate content or copy.

Meet Jarvis (Source:

Examples of AI content generation: 




Dynamic pricing

Have you ever tried looking for an Uber at peak commute hours? Surge pricing is an example of dynamic pricing, a pricing strategy where a product’s price is determined by supply and demand. Apps and websites may use cookies, history, searches, and other activities to predict and provide real-time pricing. This can cause a fluctuation in pricing at any given moment. Other examples include air flights, hotels, and utility companies.

I guess it’s time to swim… (Source: Tenor)

Examples of dynamic pricing: 





Personalized experiences

Don’t you just love it when the places that you shop at know exactly what you like and want? Maybe a bit creepy, but wow, does it provide a great experience (not to mention an easier way for you to spend your money). AI technology can help with personalization to increase sales, engagement, and customer retention.

How do they always know!? (Source: Quickmeme)

Examples of AI-powered personalized experiences: 




Sales forecasting

AI helps marketers better understand their customers and anticipate their actions based on their contracts and purchase history. AI can then help predict future purchases and assist with product recommendations to promote, driving up sales. This can also help with cost savings with inventory management.

If only it was that easy (Source: Giphy)

Examples AI-based sales forecasting tools: 



Aviso Insights

Speech recognition

Unless you’ve been living under a rock, you’re probably familiar with smart home technology with speech recognition such as Alexa, Google Assistant, or Siri. These AIs recognize verbal communication and convert them into text to execute specific commands. Speech recognition is used in many hands-free applications including navigation systems, Google maps, and Shazam. Though it may not have an active role in marketing at the moment, it has a lot of potential to influence spending using the convenience of voice command to automate purchases such as Amazon reorders.

The one time Alexa decides not to listen… (Source: Giphy)

Examples of speech recognition systems: 


Google Assistant



Proving its value

Though return on investment (ROI) is something that is easily quantifiable, proving how AI has improved customer experience and its contribution towards conversions is not as easy. This can create some challenges around demonstrating the value of AI investments to business stakeholders. Depending on the route you take to implement AI within your business (check out our previous Q&A:

Should I build or buy AI?

), it can also take significant time, money, and effort, making it even more difficult to get buy-in.


AI may still be relatively new to some businesses and can feel a bit daunting to those who have never implemented or deployed within their marketing team. This mind-frame has never led to any great innovations or breakthroughs in history. Don’t become a statistic; dare to be great!


There is an ongoing concern over how organizations use their data — ever heard of Facebook? Marketers and AI tools alike must ensure the ethical use and compliance of consumer data and understand the heavy penalties and damages that can ensue if compromised or used inappropriately.

I think he agrees (Source: Giphy)

Data quality and expertise

AI tools cannot automatically know what actions to take and will require some human intervention and expertise to figure out specific goals and metrics, under specific parameters and context. On top of that, it’ll require some data quality assurance through data cleanup in order to provide consumers accurate and optimal predictions and results.

Mr. Data Clean (Source: Giphy)


Modern day marketing would not be what it is today without the integration of AI technology. It is likely that you have already interacted with a form of AI marketing whether you realized it or not. If you have not yet incorporated AI-power marketing tools into your marketing strategy, it’s not too late, but don’t wait too long. In an age of rapidly emerging new technologies, AI will be the competitive edge we all will need to better understand, communicate, and connect with our customers.

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