Top Applications & Use Cases for AI in Marketing

Beating your competitors with a long-term strategy is how most of today’s top companies got to where they are today. This post will discuss the top applications of AI in marketing and include several interesting topics that we expect to dominate marketing within a few years. 

Additionally, this blog is dedicated to understanding how artificial intelligence (AI) can improve your current processes while laying the groundwork for a smarter tomorrow. 

Send Emails to Nudge Your Customers Along

The first application we’ll discuss is predictive email sending. This application has been used commercially in consumer email marketing products for more than three years. 

Email software tracks when emails are opened, how many times they were opened, and if any of the links within it were clicked. Some inboxes will let you turn off this feature to let you choose if you want to send that information. 

With this application, you can know when the best time to send your email to a specific customer. If a customer has viewed several of your last email offers and has an item sitting in their cart, nudging them about that product might increase their likelihood to follow through on the purchase. 

Cluster Your Customers and Give Them the Best Offer 

Personalization in marketing tactics has grown in popularity and has seen great results. Not all customers are the same, but there are trends in types of customers. Customer segmentation uses clustering algorithms to group customers together based on factors like location, user profiles, purchase behavior, and demographics.

If we know a group of customers really like cat-related products, we can be sure not to send them dog-related products. Taking it a step further, you can offer them discounts for products that they will respond well to. 

Generic emails with unpersonalized discounts don’t get the same engagement from your target audience that personalized marketing does. You can even tailor your specific discounts so that you maximize profits while still converting to a sale. 

For example, if a customer segment is just as likely to purchase if you provide them a 10 percent discount as they would a 20 percent discount, then you should offer them the 10 percent to maximize your profits.

Dynamically Update Prices to Increase Profit Margins

You don’t have to sell a product at the same price in every location. For example, you want to book a trip to Seattle. You go to a travel website and the cost is $700 round-trip. You then do the same search in a private browser or from a different IP address and the cost is now $400. 

If you use the same sites or if the site has clustered you into a group, they are going to change the price based on the supply and demand and what they believe you will pay for that ticket. 

Price optimization models integrate with your point of sale systems to change the prices dynamically to increase the end ROI. At certain times, this can mean that the algorithm optimizes the cheapest possible ticket price to simply fill seats to cover the fuel cost. Models can be built to address multiple aspects of your business concern, whether it’s maximizing profits or finding ways to break even on sunk opportunities. 

Determine Campaign Effectiveness

Marketing campaigns do not always have a measurable ROI. In the past, there was no proven method for determining effectiveness. Now, marketing campaigns can include trackable URLs, tags, and codes that can be followed across a customer’s journey. 

Perhaps you were testing multiple marketing funnels and you wanted to determine which was the most effective. AI models can determine which funnel will be the best one to use to achieve a certain result based on a variety of factors.

For example, let’s assume you want to maximize the conversion rate within a given budget. The AI model can determine which funnel is the most statistically significant to give you that result based on how customers have responded recently to digital marketing tactics.

AI models can monitor the spending on the campaign and compare it to the revenue produced. It can even integrate with your sales forecast to help to determine which funnel to shut off first if results are not realized.

Cross-Selling Recommendation Engines 

What if you could compare the profile of a new customer to that of an existing one to see what their customer journey could look like? You would be able to determine the new customer’s purchasing affinity based on similar customers and recommended those products to the new customer. This is all possible through a recommendation engine.

Cross-selling recommendation engines look at trends in customer profiles to determine similarities, such as purchasing patterns. It learns these trends from information on account transactions as well as attributes. 

With a recommendation engine, you can flag a new customer profile as a candidate for a particular sales funnel to recommend certain products. It can be as explicit as a sales representative calling that customer, or as passive as sending them useful information and tidbits. 

Conclusion

Before jumping into building an AI model, it’s important to prioritize items in your business strategy plan. Identifying areas where your organization needs improvement are the best opportunities to implement AI. 

Small steps now could lead to big gains later. Starting simple can help set a clear vision for your company on your AI journey. 

If you need direction on where to start with your AI strategy, please reach out to our team for more advice, and be sure to download our most requested free AI resource, How to Implement a Successful AI Strategy for Your Company.

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