How to use AI in Marketing to Move Customers and Prospects

Discover how the AI revolution is changing the buyer’s cycle

Artificial intelligence (AI) in marketing lies in the hotbeds of data analysis, machine learning, and deep learning. Marketing and artificial intelligence are gaining more and more attention because of their potential for strong, actionable insights. Data-driven technology has enabled businesses to develop more efficient, effective, and even more powerful products and services. And marketing is no exception; it is one of many areas where AI is increasingly being used. It’s no secret that the big data revolution has been driving changes across business processes for the past decade or so. Lakes of customer data enable unconventional and powerful new methods of marketing. 

Today, let’s discuss some high value-add ways to use AI in marketing. Some will be easier to implement than others, but they all have the potential to greatly impact your business processes and how you interact with customers. With an open mind, let us get your imagination working and dive into a non-exhaustive list on how to implement AI in marketing.

AI Tools in Marketing

Grouping Customers

Unsupervised learning can be used to group prospects or customers for targeted marketing campaigns. For those unfamiliar, unsupervised learning is a type of machine learning where the goal is to learn about the structure of the data, rather than using it to make predictions. 

Depending on the data you collect on your customers or prospects, you can use clustering techniques to find out important groups and subgroups within your customer or potential customer base, and then use the learned characteristics from these groups for targeted marketing. Market research has been done well before the advent of machine learning. With the power of AI, similar research can now be done without even explicitly talking to consumers. 

However, if you already group your customers into three buckets – loyal, occasional, and one-time for example – you can use supervised learning and automated machine learning platforms like the Ople.ai Platform to predict which category a prospect will fall into. You can then quickly use the appropriate targeted marketing/sales tactics to convert them faster.

Propensity to Buy

You can help your revenue forecasts or find out more about how consumers traverse your product offerings by applying a Propensity to Buy model to your webpage or customer base. The Propensity to Buy models take in the data you have about how a potential customer interacts with your company or website, and output how likely they are to buy from you.

Using this method of AI in marketing reveals the beaten paths customers take when purchasing your product. These results may uncover certain parts of your business that you may want to improve, such as recommendation systems on your e-commerce site. If you have customer data such as age, lifetime with the company, interests, location, etc., and whether they made a purchase, you can use the Ople.ai Platform to build a predictive model within minutes and discover the features that reflect a potential customer’s propensity to buy. By moving to the simulation tab, you can alter different features in your model and see how they affect your buyer’s propensity to buy.

Email Campaigns

Let’s imagine you are a large retailer, and you interact with customers using email campaigns. You amass a collection of emails to send out for different scenarios, such as buyer re-engagement emails for customers who have been inactive for 6 months, or incentive emails for customers who have left an item in their cart for over 24 hours. Now, you would like to know when to best send these emails out to improve customer engagement and prevent customers from unsubscribing from you.

If you are stuck with a similar problem and are interested in learning more about this specific application of AI in marketing, check out our case study about how a large online retailer used Ople.ai to enable their data team to quickly solve this problem.

Writing Content

You may be amazed at how well a computer can write. Using modern deep learning methods – namely transformers – and huge datasets, machine learning can almost beat the Turing test when writing an article. The Turing test is simple: if somebody is interacting with an artificial system and cannot distinguish it from a real person, then the system is said to pass the Turing test and display intelligence. Modern AI writers can fool most people most of the time into thinking they are real authors.

An article written by one of these AI will seem like it was written by a real person and can be about a real topic or something that has never occurred. If you have a lot of marketing content to get out about some topics, you may want to consider using AI to do the heavy lifting for you.

Chatbots

Chatbots are a great tool for interacting with customers on your website, regardless of the volume of visitors that your site experiences. If, for example, you’re a doctor and someone would like to find your personal work availability to book as a new patient, they could interact with a chatbot on your website or on your Facebook Page to book their first appointment. If you use Slack for work or personal reasons, you’re likely familiar with the chatbots there and may have even built your own chatbot for other Slack users. 

The usage of chatbots is rising steadily across many industries, including banks, hotel chains, retailers, and e-commerce companies to name a few. Most chatbots live on messaging apps such as Facebook and Slack, but some third party companies also offer chatbot services for your business site.

AI can boost your marketing productivity

Using advanced machine learning algorithms, AI can quickly build solutions for complex tasks that marketers often spend a lot of time on. AI solutions can make business processes smarter, effectively eliminate waste, significantly increase conversions, and promote real-time decision-making. AI can not only be used to uncover hidden insights but can also teach and learn to integrate previously uncovered insights into new campaigns, thereby optimizing reach by targeting only the most relevant users. We have only touched on a few use-cases for AI in marketing here, and expect to see many more in the future.

Sources
Artificial Intelligence (AI) for Marketing – https://www.smartinsights.com/tag/artificial-intelligence-ai-for-marketing/
Emarsys – https://emarsys.com/learn/blog/artificial-intelligence-marketing-solutions/ 
Digital Marketing Institute – How AI is changing Digital Marketing – https://digitalmarketinginstitute.com/en-us/blog/how-ai-is-changing-digital-marketing