¿Cómo utilizas la inteligencia artificial y el aprendizaje automático para mejorar tu programa de fidelización?

To date, most of what has been promoted as AI in Loyalty is closer to machine learning. That's not a bad thing as applying more computing power to run data scans, test offers and improve algorithms will add to the effectiveness of a loyalty program. But, the promise of AI in Customer Loyalty is yet to come. We saw an announcement from Koginitiv this week which we covered in The Wise Marketer. And, Loyalty Juggernaut has AI embedded throughout its platform. There will be more but if you want to gather context of the future, have a look at these two organizations. Since we are consumers first and business people second, it is hard to resist identifying AI with generative AI. The future of AI in Customer loyalty will yield so much more.

The benefits are endless. AI & ML can significantly enhance both customer segmentation and personalization. Although it was (kind of) an automated process in the past, making an accurate decision after looking at the data was time consuming. However, AI and ML allow for more precise and dynamic grouping based on behaviors and preferences, which leads to highly tailored offers, rewards, and experiences while saving time and hassle. The process of optimizing program design and performance by identifying the most effective combinations to boost NPS, retention, and CLV is now fully automated, along with providing accurate data analysis and reports, which leads to increased efficiency.

The way we use AI and ML at my current company, Laasie, is within the rewards network, campaign design and segmentation strategies. AI dynamically optimizes campaign designs and messaging to maximize conversion and customer satisfaction, as well as ensures customers are presented the most relevant, personalized rewards based on their interests that results in maximum conversion rate. While there are many more applications for AI and ML within a loyalty strategy, I recommend the above as a solid starting point.

From a client perspective I'd want to know who my premium customers are and how they interact with my brand. There are various clustering techniques that can be used to solve these questions. Clustering helps in analysing customer data to identify valuable customer segments. You want to look into demographic, transactional, and behavioural data to be able to segment and understand each segment. This then helps you tailor your program strategies to target the right customers with the right offers, which means a more personalised experience, which results in customers having a stronger connection with your brand.

Three strong use cases for AI and ML in loyalty programs to boost sales, service, security and satisfaction that I've seen work are: Reward / campaign decisioning - considering this customer's history and value to my business, determine the NBA (next best action) I should serve this customer to trigger a desired action? Fraud detection - flag any customer account that has unusual activity. Unusual activity to be defined at a data activity level Customer service - considering this customer's history and value to my business, should I offer them a human 1:1 service or direct them to a chatbot? Prioritise them in my queue or offer them a special service to reduce possible churn?

In my experience AI boosts the way we could create personalised customer experiences by analyzing data to understand individual preferences, behaviors, and purchase patterns. So, AI-powered personalisation makes the customer centricity and Omnichannel comms a reality, enhancing customer engagement and satisfaction by delivering relevant messages at the right time through channels like email, websites, and mobile apps.

To avoid sounding generic, I'm sharing applications of AI and ML from my own experience with our Rowad loyalty program. Using advanced clustering and classification, we segmented customers based on lifetime value and purchasing behavior, which is crucial for our soon-to-launch Rowad Tier system. This segmentation will allow us to personalize our home page, offers, and rewards based on each customer's tier. We employ A/B testing (in and off-platform) to refine program features, optimizing for customer satisfaction; it helps us identify exactly what kind of rewards are the customer looking for. We’re also planning to launch an AI-powered chatbot to enhance communication and provide instant, relevant responses within the program.

AI can play a vital role in fraud detection in loyalty programs. By meticulously analyzing extensive data sets, AI swiftly pinpoints anomalies and discerns patterns suggestive of fraudulent activities. Employing advanced behavioral analysis and real-time monitoring, AI significantly bolsters security measures, ensuring the protection of both businesses and customers.

AI & ML can be leveraged in the following ways: ● Build disengagement models to identify & target customers who are likely to disengage. ● Create customized baskets for customers to increase customer lifetime value. ● Send automated communication to dormant users in order to activate their account.

Very useful tips here on AI/ML usage into customer engagement (loyalty, Personalisation, CX). My suggestion here, in addition to the above, would be to fully leverage vendor capabilities- most leading vendors would have market leading functionality for AI/ML already pre-packaged and tested for efficacy. It will require careful strategic planning and execution as there are quite a few difficulties in this area- but well worth the effort to realise it as the benefits can be differentiating in terms of the common metrics of customer engagement eg NPS, retention, stickiness etc.

The first step to an AI/ML effort is to make sure you have clean data. Clean data is data that is well-structured, properly tagged and regularly updated. Some people launch software and then add data analytics at the end. This won't work with an AI effort. It is garbage in, garbage out. So before investing in new functionality make sure a technical resource has confirmed that data required is clean. For instance, say you want to update your loyalty program to use AI/ML to test out perks based on geography. The idea is that it learns over time what offers work in what areas of the country. Great. But what if your customer's location data is stored on 3 different systems and often is out of sync? Not great. The results will be garbage.

So far, AI is in the marketing collateral language of many providers, but few have the substance to back up the claim. I do know of three providers that have legit incorporation of AI into their platforms and most are using it to expedite personalization efforts. One area I would like to see impacted is data protection and fraud. Loyalty data is valued by fraudsters and most LMS platforms have fraud prevention algorithms. These could be enhanced by adding in AI to drive more rapid recognition of fraudulent activity.

You can use the relevant data to understand your customer's preferences, their patterns of consumption and provide them with personalized and customized offers based on their own choices. That would help the organization in creating a connection with the customer and the customer will feel valued!!

Be mindful of how you or your loyalty technology provider are adding AI into your strategy. The past 18 months has seen everyone “adding ChatGPT” into their products, as a way to leverage AI, but one could argue that they might not have been the best use cases to deploy.

Other aspect that can be leveraged using AI in loyalty porgrams is program optimisation by doing some of the following: 1. Fraud detection: Analyze transaction data to identify unusual patterns and potential fraudulent activity, protecting the program's integrity. 2. Sentiment analysis: Analyze customer feedback through social media and reviews to understand customer satisfaction and identify areas for improvement within the program. 3. Program performance tracking: Use machine learning to track key program metrics (engagement, redemption rates) and optimize program elements based on data-driven insights.