How AI Can Enhance Customer Loyalty by Replacing These 5 Old Strategies

During the initial excitement around AI, many brands rushed to integrate it into their marketing strategies, trying to utilize its boundless potential. But a lot of brands overdid it—trying to apply AI everywhere without a clear focus. The truth is, leveraging AI effectively requires balance: it’s about choosing the right tactics that genuinely add value, not just jumping on every trend.

Today’s consumers expect transparency, control, and real value in exchange for their data. Yet many brands continue to rely on outdated AI strategies that miss the mark. Are you making these mistakes?

1.   Customer Loyalty, AI & Overpersonalization

According to our recent Customer Loyalty Predictions 2025 report, 65% of global customers want to receive personalized offers, with this sentiment rising to 77% in the United Arab Emirates. Does this mean more personalization is always better?

Absolutely not!

There’s a fine line between hyper-personalization and overpersonalization.

Overpersonalization happens when artificial intelligence uses too much customer data to customize experiences to the point where it feels excessive or invasive. For example, bombarding customers with recommendations that are based on obscure data, such as a product they bought months ago, or tailoring messages that feel unnatural or even creepy.

The Pitfalls of Overpersonalization:

• • Loss of Customer Trust: When personalization feels too "in-your-face," it can make customers uneasy, quickly leading to erosion of trust.
  • Irrelevant Recommendations: Overpersonalization often leads to irrelevant or over-targeted recommendations, which can frustrate users rather than delight them.
  • Stale or Misleading Data: AI models that over-rely on outdated or incomplete data may make inaccurate assumptions about customers' needs, further diminishing the value of personalized experiences.

How AI Loyalty Programs Prioritize Personalization and Consent

Modern AI loyalty programs, like those created with the Comarch Loyalty Marketing Platform, balance personalization with user consent and control over how their data is used. Offering personalization that is contextually relevant and based on current customer preferences is fundamental to keeping the experience enjoyable and beneficial.

2.   Outdated Price Targeting Models in Dynamic Pricing

Not too long ago, dynamic pricing was a controversial marketing strategy.

Today, it has become a popular tool for businesses to adjust prices based on market demand, customer behavior, and competitor pricing. In fact, almost 70% of global customers feel positive or very positive towards it. However, many companies still rely on outdated price-targeting models that fail to capture the full potential of modern AI and data-driven personalization.

Outdated Price Targeting Models vs. New, Improved AI Approaches

1. Location-Based Pricing

• • Old Model: Prices set based on geographic location.
  • New Approach: Contextual Pricing – Uses real-time data (weather, events, competitor prices) for more relevant, personalized pricing.

2. Historical Purchase Data

• • Old Model: Prices based on customer purchase history.
  • New Approach: Behavioral Segmentation – Prices adjusted based on current browsing behavior and intent, not just past habits.

3. Demographic-Based Pricing

• • Old Model: Prices based on demographics (age, income).
  • New Approach: Willingness-to-Pay Models – AI analyzes broader data (social media, feedback) for more accurate pricing predictions.

4. Time-of-Day Pricing

• • Old Model: Prices change based on the time of day or week.
  • New Approach: Dynamic Demand Pricing – Real-time supply-demand analysis, factoring in competitor pricing and market conditions.

5. Standard Discounting

• • Old Model: Blanket discounts for all customers.
  • New Approach: Personalized Discounting – AI tailors offers based on individual shopping habits and preferences.

AI can analyze traffic, weather and demand patterns to offer real-time, location-based pricing, maximizing profitability while rewarding customers with better deals during low-demand periods.

Leila Poleszczuk, Loyalty Marketing Consultant at Comarch

3.   Excessive Data Collection in AI Customer Loyalty

AI-powered loyalty programs rely on data, but collecting excessive amounts without a clear purpose can backfire. Customers are increasingly aware of how their personal information is used, and if they feel brands are gathering too much without offering meaningful value in return, trust erodes.

43% of global consumers are willing to share their data and purchase history in exchange for personalized offers, rewards, or better benefits. However, this willingness comes with expectations—customers want to know why their data is collected and how it will be used.

Valuable Customer Data: Collect What Matters to Enhance Trust

Instead of hoarding unnecessary data, businesses should focus on collecting only what’s relevant to improve customer experience. A well-structured, transparent data policy should clearly explain what information is gathered, why it’s needed, and how AI is used to enhance personalization.

Prioritize transparency and limit data collection to what truly enhances the loyalty experience. This way, your brand can foster trust, increase customer engagement, and ensure its AI-driven program remains effective without crossing into intrusive territory.

4.   Confusing Data Policies

A confusing, jargon-heavy personal data policy does more harm than good. When companies bury their privacy policies in “Legalese” or make them difficult to find on their website or loyalty program app, they create unnecessary friction for customers. Worse, it signals that the brand might have something to hide.

In the past, consumers paid less attention to how their personal data was used, but with AI-driven data collection and growing tech-savviness, transparency has become essential. We found out that 26% of consumers are willing to share their data only if they know exactly how it will be used. If companies fail to communicate this clearly, they risk losing trust and engagement.

Enhance Customer Loyalty with Transparency

Modern, customer-first brands take a different approach. They ensure that data policies are easy to find, written in plain language, and explicitly state how AI is used in data processing and personalization. Some companies even offer interactive tools or summaries that break down key points in a user-friendly way.

In 2025, many programs will increasingly focus on leveraging AI and the data they've collected over the years. A transparent data privacy policy will become a key differentiator, as program operators must be extremely clear about how they use data—not only for personalization but also within AI-driven processes.

Maria Wróblewska, AI Solutions Coordinator at Comarch

5.   Missed Data Sharing Expectations & Customer Satisfaction

Consumers are willing to share their data for additional rewards—but what if the rewards aren’t worth it? According to our global study, 7% of consumers are skeptical about loyalty program benefits, believing the rewards don’t justify sharing their data. If customers feel they’re giving up valuable information without receiving meaningful perks in return, engagement drops, and trust weakens.

Better Personalized Recommendations & Rewards

Instead of providing generic, one-size-fits-all benefits, businesses should focus on selectable or personalized rewards. AI-powered customer loyalty programs can determine the most relevant benefits, ensuring that each member receives offers tailored to their unique interests and needs, fostering greater loyalty.

Be transparent about how data sharing leads to better experiences to ensure that loyalty rewards feel like a fair exchange.

Programs are migrating from a traditional general reward basket approach to a customized list of rewards to be redeemed, much more oriented to individual’s desire, using AI not only to predict a next best offer, but also to reward customer with the right products and services.

Luiz Felipe Paveloski Caper, Business Director at Comarch LATAM

Avoid AI Customer Loyalty Pitfalls: Use Smarter Strategies for Stronger Engagement

AI has revolutionized customer loyalty, but outdated strategies can do more harm than good. Overpersonalization, flawed dynamic pricing models, excessive data collection, unclear data policies, and underwhelming rewards all risk alienating existing customers rather than strengthening their connection to a brand.

To avoid these pitfalls, focus on transparency, relevance, and value. AI should enhance the customer experience—not overwhelm, confuse, or exploit it. This means striking the right balance in your strategies.

Interested in more insights into how AI is shaping the future of customer loyalty? Our Customer Loyalty Predictions 2025 report explores key trends in personalization, dynamic pricing, and data privacy. Download the full report now to stay ahead of evolving customer expectations.

Download the report 

* All statistics used in the article come from the Customer Loyalty Predictions 2025 report.