Using AI to increase customer loyalty

When you think about Artificial Intelligence (AI), you might think of talking robots or the movie Her. 

In reality, most applications of AI are more commonplace and the use of them has exploded in every industry, including MarTech. At Simon, we’ve seen AI extremely useful in customer experience management, particularly when it comes to the personalization of content and customer data analytics.

To activate first-party real-time data and streamline marketing processes, marketing teams need to consider how to use AI within their martech stack.

AI, when combined with a cloud data warehouse and customer data platform (CDP), for example, can help marketers optimize their campaigns, predict customer behavior, and drive improved customer engagement.

Essentially, CDPs are a key piece in enabling AI to fully take advantage of all of your customer data from Snowflake and make each customer feel like your content is tailored just for them, increasing the chances of engagement and conversion.

How AI can improve customer loyalty through a CDP

Enhancing personalization in customer marketing

How does AI come into play when using a CDP? Well, AI analyzes customer data to personalize recommendations, offers, and content in real time.

Let’s look at an example. Whenever you are grocery shopping, you’re likely following a shopping list to make your shopping experience efficient and successful. 

With AI, you could create the ideal shopping experience for your customers with personalized recommendations and offers so that your content leads them one step closer to a fulfilled shopping list.

In addition, by taking advantage of the unified customer view (also known as a customer 360) your CDP provides, you can enable AI to tailor your customer experiences across all touchpoints in perfect coordination. 

Using predictive analytics in a CDP

What if you could predict each customer’s next purchase? With AI and a CDP, you could do exactly that. You can even predict when and how they are most likely to purchase. 

Armed with that data, you can create hyper-personalized content and real-time offers that will make both your customer and your bottom line happier.

When it comes to predictive analytics, it’s important to keep historical customer data in one spot, clean, and accurate (which is exactly why we recommend using a cloud data warehouse like Snowflake). 

Historical data is the foundation for accurate predictions. The more you have of it, the more accurate the predictions from AI become. With a CDP, your AI tool will have access to historical data from all of your marketing channels and data sources.

Automated customer marketing for your marketing team

As a marketer, you probably wear many hats and wish you had more time to do everything possible to make your marketing as successful as possible. 

While AI can’t freeze time, it can save you time by automating marketing tasks like executing email campaigns, social media interactions, and chatbots. This saves you time but also provides your customers with a more personalized and accessible experience with your brand.

The best part about using AI within your CDP is that while your AI tool handles the automation, your CDP provides it with an accurate log of past interactions (including conversations with chatbots!), ensuring consistent and personalized communication.

Automated customer segmentation

Analyzing customer data and creating effective customer segments is a time-consuming and arduous task. 

By using an AI tool that sits on top of a robust data infrastructure provided by your CDP, your customer data can undergo a more detailed and intensive segmentation process to create more opportunistic segments. 

With better segmentation, you can tailor content more effectively based on each segment’s behavior, demographics, preferred communication platforms, and data points.

For example, say you have a segment of customers who have purchased from your site in the last two days. You could send them a generic post-purchase message, or you could utilize an AI-tool and send them a personalized post-purchase message that offers a discount on the product they are most likely to purchase next. 

Customer journey optimization

Keeping track of when, where, and what communications were sent to your customers is a daunting task to begin with. Not to mention that analyzing customer journeys for each contact and coming up with a next step is nearly impossible. 

With a CDP, you can map the customer journey for all of your contacts, which can then provide AI with the historical data needed to identify opportunities to optimize customer touch points along their journey.

Example of AI-powered CDP use cases for driving customer loyalty

Let’s say that based on historical data, you have a customer that purchases tennis gear every two months. An AI-powered CDP solution could help nudge that customer closer to their next tennis gear purchase (or even gear for another sport!) by recommending the right products through the right channels.

Beyond personalized recommendations, AI-powered CDPs can also help with proactive outreach to at-risk customers with targeted offers and support.

Staying with our tennis-loving customer from above, perhaps they have stopped making purchases altogether for the last year. 

Instead of increasing the risk of churn with un-personalized content that your customer may not be interested in, an AI tool could automate targeted offers, special discounts, a “We Miss You!” email, and support to win back your customer.

Improving customer engagement with AI

Using AI within a CDP, marketers can automate loyalty programs that reward customer engagement and incentivize repeat purchases. For example, a loyalty program for a restaurant reservation system could offer a discount on their 10th reservation with automated messaging via email, text, and in-app notifications.

Additionally, AI-powered chatbots that provide 24/7 customer support and answer questions efficiently can help improve customer engagement and loyalty.

Have you ever had nights where the shipping status of your latest purchase has kept you up all night? You can probably guess that their offices are closed and no one is available to answer emails or calls. 

But, if the retailer has an AI-powered chatbot set up on their website, you could get the answers to all of your shipping-related questions 24/7.

Challenges and considerations for implementing AI in a CDP

In theory, AI might seem like a silver bullet to many workflow challenges, but there are many considerations you need to work through before implementing AI tools in your CDP.

Data quality and integration issues

Your AI tool is only as good as the data you feed it — a literal case of “you are what you eat.” To ensure the output from your AI is accurate and useful, the customer data you are importing should be clean, correct, and successfully integrated with your CDP. 

Remember: you also need a larger amount of historical data for your AI tool to start learning your customer base.

Bias and fairness concerns in AI algorithms

Even though AI algorithms are not human, they were created by humans. Unfortunately, this means AI, like humans, will always have a degree of bias. If you notice any sort of bias in the AI tool’s output, investigate its data sources and the algorithm to correct it.

The need for transparency and explainability in AI decisions

While AI is currently all the rage, the use of its output is not without accountability. As a marketer, you should be able to understand how the AI tool is making decisions and have the knowledge to be transparent about how it is being used. You, your company, and your customers will appreciate the transparency!

Balancing automation with human interaction

We’ve all had a negative experience with some form of automation — whether that be chatbots, automated phone calls, or other commonplace applications of AI automation. 

AI is far from perfect in its current state, which is exactly why there should be a balance between automation and human interaction to minimize customer dissatisfaction or errors.

Ethical considerations of using AI in customer experience

There are also ethical considerations when it comes to using AI in customer experience. 

As mentioned, there are risks like biased decisions that reinforce stereotypes or negative behaviors, as well as data privacy concerns. 

These considerations underscore the need for transparency, explainability, and consent when using customer data with AI.

Best practices for using AI in a CDP

When it comes to using AI in a CDP, having a strategy in place is key. Below are some best practices that can help ensure the successful and ethical use of AI in marketing:

• Define clear goals and objectives for AI-driven loyalty initiatives
• Invest in high-quality data and ensure its accuracy and completeness
• Choose AI algorithms that are transparent and explainable
• Monitor and evaluate the performance of AI models regularly
• Continuously learn and adapt AI strategies based on data insights
• Ensure ethical and responsible use of AI in all aspects of customer experience

Conclusion

When AI is implemented correctly and strategically with a CDP, marketers can drive customer loyalty through the roof by unlocking the potential of your customer data. 

You can use AI to predict future purchases or churns, create highly personalized and automated content that reaches your customers at the right time, and increase the efficiency of your day-to-day as a marketer. 

Data quality and transparency are essential to maintaining the ethical use of AI. Without the right data, the output from your AI will be flawed and biased. 

But with the right considerations and planning, AI can help you implement extensive and personalized customer loyalty programs. 

Make the most of your customer data by exploring and bringing AI solutions into your marketing strategies. 

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