Using AI for personalization in customer marketing

Personalization should be the center of any customer marketing team's strategy. Using personalization within your customer marketing workflows can range from emails addressed to each customer’s first name to different product images being promoted on websites based on the visitor’s web activity.

Similarly, AI also comes in many forms, some of which are extremely useful when it comes to personalization in marketing. With advanced AI and ML becoming more accessible and embedded in marketing tools like customer data platforms (CDPs), every marketer has the opportunity to take advantage of AI to personalize customer experiences at scale.

Not having access to accurate, real-time customer data greatly impedes a marketer’s (and AI’s!) ability to build highly personalized customer experiences. But if your team uses a cloud data warehouse (CDW) like Snowflake and a CDP, the possibilities of AI- and data-driven personalization are endless. 

With a CDP built on top of a cloud data warehouse, you can create 360-degree views of your customer (also known as a Customer 360), which allows you to activate and execute personalized marketing campaigns.

Using AI for customer marketing personalization

Use AI to understanding customer intent and behavior

Similar to how we can pick up on patterns and context in social situations, AI can be used to uncover hidden patterns in customer behavior by analyzing customer data and predicting future behavior.

AI can also help identify customer preferences and purchase intent, which, when applied to an individual level via personalized content, can increase the effectiveness of your marketing campaigns.

Dynamic content personalization

Dynamic content personalization is the use of customer data to deliver personalized content in real time across various channels. For example, you use an AI product to help create a multi-channel marketing campaign promoting a holiday sale on your website. 

Using your customer data, the AI algorithm can segment your customers into groups based on things like purchase history and demographic data, making personalized marketing a breeze. Once your campaign is launched, customers will see marketing relevant to their interests as opposed to a generic canned message.

Let’s say Customer A is a cat owner with recent purchases, and Customer B is a dog owner with no recent purchases. Instead of sending both of them the same message, you could use AI to generate individual product recommendations and offers so that both Customer A and Customer B feel more of a connection with the content and copy.

By utilizing AI, Customer A might receive product recommendations for cats and a 10% discount, while Customer B might receive product recommendations for dogs with a heavier discount to increase the likelihood of a purchase.

Real-time customer engagement

Within the world of real-time customer engagement, AI can automate personalized interactions. 

Continuing with our example from above, let’s say Customer B (dog owner) visits your website after receiving a personalized email about dog products by clicking on one of their product recommendations: a blue dog leash. 

Because we have the data point that Customer B is specifically interested in the blue dog leash, their website experience will automatically be geared toward promoting dog products, and, in particular, the blue dog leash.

After Customer B has been browsing the website for a while, AI can calculate the most opportune moments to trigger personalized offers or promotions such as coupon codes for Customer B, all the while creating an entirely different cat-centered experience for Customer A, our cat owner.

Even after Customer A and Customer B leave your website, AI can ensure that they will continue to have consistent and personalized experiences across all touch points. 

The CDP: A powerful platform for AI-driven personalization

Customer 360s

A CDP helps collect and centralize customer data from various sources to create a single unified view of your customers through the use of identity graphs. Instead of logging into each of your marketing platforms (ESP, SMS, analytics, etc.,) and manually mapping customers to their activity and data, a CDP can automatically do this once it is connected to all of your disparate data sources.

Customer 360s empower marketers and AI to personalize experiences across the different channels and ensure that customer engagement in other channels informs the holistic experience for each customer. 

Data segmentation and insights

AI is only as good as the data it has access to. A CDP keeps all of your data in one place, so, instead of AI being limited to the set of data within just one of your marketing platforms, a CDP with integrated AI can give you results based on your entire marketing stack. 

One great application of AI within a CDP is segmentation. Instead of segmenting on one dimension (e.g., email engagement), AI can group your customers across multiple dimensions like demographics, email engagement, SMS engagement, and purchase history, resulting in more meaningful and specific segments.

Then, AI can take these segments and personalize communication across channels, predict future behavior, and create highly informed segments downstream based on how customers react to the communication.

Real-time data activation

CDPs make real-time data activation possible. The moment a data point is brought into the CDP, it can be used to inform the next steps for a customer’s journey. AI within your CDP can continuously learn and analyze customer behavior in real time, then help marketers optimize their campaigns based on its analysis. 

Activating data in real-time empowers brands to respond quickly to changing customer behavior or trends. 

For example, say a customer is browsing your website and places a product in their cart. For a new customer, this action could trigger a form to collect their email address in exchange for a discount. 

For returning customers, it could trigger a message that includes a discount code through the channel, like SMS, where they have the highest engagement rate.

Here are some real-world examples of AI-powered personalization in action:

• Personalizing movie recommendations based on viewing history and preferences
• Recommending related products to customers based on their past purchases
• Creating personalized playlists tailored to individual music tastes
• Offering personalized rewards and promotions based on customer spending habits

Benefits of using AI for personalization in customer marketing

By accessing and using AI and predictive analytics within your customer data platform, marketers can benefit from: 

• Increased customer engagement and loyalty
• Improved conversion rates and sales
• Enhanced brand perception and customer satisfaction
• More efficient marketing spend and resource allocation
• Greater competitive advantage in the market

Getting started with AI for personalized customer marketing

• Start with clearly defined goals and objectives.
• Ensure data quality, accuracy, and completeness.
• Monitor and measure the performance of your AI personalization efforts.
• Continuously adapt and improve your AI-driven personalization strategies.

Conclusion

Combining AI with your customer data unlocks transformative opportunities for personalized customer marketing, and by using a tool like a CDP, you can significantly optimize your marketing campaigns, streamline processes, save time and resources, and quickly adapt to customer trends to create the enhanced personalization customers crave.