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Loyalty360 Supplier Member Perspective: Tata Consultancy Services on Leveraging AI and Building Last



With the increasing practice of leveraging artificial intelligence (AI) across multiple disciplines, brand marketers seek to learn how to implement the new technology to elevate and personalize experiences for customers and loyalty program members. However, the opportunity to raise customer engagement and foster stronger emotional loyalty comes with challenges. Brands and loyalty teams must effectively integrate powerful technologies with existing and planned loyalty strategies to produce a truly meaningful customer experience while achieving organizational goals. 

Tata Consultancy Services (TCS) is an IT services, consulting, and business solutions organization that has worked alongside large businesses across the globe for 50 years. Serving industries such as banking, financial services, insurance, retail, hospitality, travel, and more, TCS’ products enable businesses to navigate crucial digital transformations with modular, scalable, and fully integrated industry-tailored licensed software products, including a next-gen loyalty management solution which delivers hyper-personalized experiences for customers across multiple channels.  

In this article, Padmashwini Raghunathan, Product Manager for TCS Customer Intelligence & Insights™ for retail, TCS Digital Software & Solutions, discusses how brands can leverage new technologies to shape customer loyalty programs, AI’s potential to deliver real-time analytics and insights, and addressing concerns about the overuse of AI. 


Influence of AI 

In the past few years, AI has enabled loyalty programs to become more customer-centric, which leads to better customer relationships and retention.  

While conventional loyalty programs contribute to transactional loyalty, emphasizing repeat purchases and discounts, these approaches center around price sensitivity and fail to cultivate enduring loyalty. Transactional loyalty is susceptible to replication, and customers driven only by price considerations tend to switch to a competitor offering a lower price at the earliest opportunity. AI’s ability to analyze vast amounts of data has allowed businesses to understand customer motivations and needs. With this wealth of information, brands can personalize offerings, predict customer preferences, understand customer values, and optimize rewards and experiences.  

This has helped brands graduate from transactional/rational programs to more aspirational, emotional, and purpose-driven loyalty programs. These strategies have successfully connected the brand’s purpose with the purpose of its loyalty program, guiding customers toward a less price-sensitive mindset and fostering genuine loyalty. 

Brands can leverage new technologies and capabilities to shape customer loyalty programs in several ways: 

  • Personalization through AI: Brands are using AI to analyze customer data, values, and behavior, allowing them to create personalized offers, rewards, and recommendations. This enhances customer engagement and builds stronger emotional connections. 

  • Predictive analytics: By employing predictive analytics, brands can anticipate customer preferences and behaviors. This enables them to offer timely and relevant incentives and experiences, improving the effectiveness of loyalty programs. 

  • Omnichannel integration: Brands are integrating loyalty programs across various channels, including online, mobile apps, and physical stores. Real-time customer data platforms (CDPs) deliver a single customer view based on multi-source data, enabling brands to deliver seamless interactions to increase customer satisfaction and encourage ongoing engagement. 

  • Gamification: Introducing gamified elements like challenges, levels, and aspiration rewards that tie back to the core brand values make loyalty programs more interactive and enjoyable. This increases participation and creates a sense of achievement for customers. 

  • Internet of Things (IoT) integration: Brands are using IoT devices to gather real-time data on customer usage patterns. This data helps tailor loyalty rewards and offerings based on actual product usage. 

  • Data privacy and security: With increasing concerns about data privacy, brands are adopting advanced security measures to protect customer information and ensure their trust in loyalty programs. 

Incorporating these technologies modernizes loyalty programs and strengthens the brand-customer relationship by providing relevant, engaging, and convenient experiences. With modern loyalty technologies, brands can acquire capabilities that complement their current tech landscape without replicating data (or capabilities) across multiple systems. 


How AI Can Help Improve Personalization 

AI-driven personalization improves customer loyalty efforts by tailoring experiences and offerings to individual preferences. For example: 

  • AI assists brands in discovering personas that customers exhibit at a particular moment of interaction in real time. This persona discovery can help brands instantly give contextual and relevant recommendations to propel the customer to the next step in the journey. 

  • AI-driven contextual recommendations in real time could be an important aspect in enticing customers to join the loyalty program by offering exactly what they need (and not a one-size-fits-all approach). 

  • With increased relevance and benefits in the loyalty program, customer trust grows, and customers are more open to sharing critical first-party data on their preferences. This acts as a lever in further personalizing the program and delivering better engagement. 

  • By having journey-based engagements, where each interaction with the customer across channels is treated as a moment of truth and next-best actions (rewards, offers, or recommendations) are provided based on the journey stage, an exit barrier is created for the customer. The emotional connection makes them less likely to leave the brand or the journey. Additionally, customers are less price-sensitive in this zone. 

AI can facilitate increased customer retention, higher conversion rates, and valuable insights from data analysis, all of which benefit brands. Customers enjoy relevant content, better user experiences, and more meaningful rewards, ultimately leading to greater satisfaction and long-term loyalty. 


Chatbots and Virtual Assistants Can Improve Customer Satisfaction 

Brands can leverage AI-powered chatbots and virtual assistants to address customer inquiries promptly and enhance overall satisfaction by providing instant responses, 24/7 availability, accurate information, and seamless escalation to human agents when needed. All these advantages speed up issue resolution, reduce wait times, and ensure consistent service, leading to improved customer experiences and higher customer satisfaction. 

Brands can leverage these technologies to enhance human interaction with the customer and not employ them as a replacement. While regular inquiries are handled faster and more efficiently by a chatbot, when a response from the customer suggests the need for human intervention, it should be available. A customer care person can then take over knowing the context of previous conversations fully to avoid customer fatigue. AI-based prompts can further personalize interactions with the customer. 


AI and Real-time Data Analysis 

Real-time analytics and insights are critical to offer instant gratification at the moment of truth. AI can help in the following ways when performing a quick analysis of data: 

  • AI identifies the customer engaging in the interaction from various channels or devices. While customers could use a multitude of devices and identifiers, an intelligent matching process is required to understand the exact “human” behind the interaction. This is available in CDPs.   

  • After resolution of identity, AI then efficiently discovers the customer’s preferences based on recent interactions (i.e., likes and dislikes that are not explicitly stated). AI can then map the customer to other like-minded customers and create a real-time audience (e.g., sustainable shopper, value-conscious buyer). 

  • AI can also help analyze the context of interaction and map it to real-world journeys to understand the customer’s current stage. 

  • Using this advanced analytics character profile, AI can deliver personalized rewards (monetary or aspirational) — such as gamification milestones, badges that can be redeemed for a dream product, offers, next-best-action recommendations to encourage joining a community of like-minded customers, and more. This will improve customer engagement with the loyalty program and the brand. 


Challenges and Solutions 

Brands leveraging AI and ML technologies have encountered a number of challenges, including: 

  • Data quality and availability: Insufficient or poor-quality data can hinder AI/ML effectiveness. Brands address this by improving data collection methods and implementing data cleansing processes. 

  • Algorithm bias: AI algorithms can inherit biases from training data, leading to unfair or inaccurate outcomes. Brands have countered this by using diverse and representative training data and implementing bias mitigation techniques. 

  • Integration complexity: Integrating AI/ML into existing systems can be complex. Brands have addressed this by seeking loyalty management, customer analytics, and real-time CDP software solutions built on an extensible, scalable, and configurable platform.  

Look for a solution that can be deployed on-premises or in the cloud based on your data security and compliance needs. An agile, modular, and micro-services architecture can:  

  • Complement and extend the value of a brand’s existing technology and data infrastructure investments. 

  • Enable brands to accelerate time-to-market by plugging into their existing environment. 

Some industries may be reluctant to embrace AI/ML due to:  

  • Complexity: Some industries find AI/ML difficult to understand and challenging to implement.  

  • Traditional mindset: Industries rooted in traditional practices may resist change, fearing disruption to established processes. 

  • Regulatory constraints: Industries with strict regulations might perceive AL/ML as a risk due to potential compliance issues. 


Concerns Around AI 

Within the brands TCS works alongside, some concerns about the overuse of AI have been expressed. The following is segmented by industry. 


  • Bias and fairness in banking: AI algorithms in banking could unintentionally introduce bias into lending decisions, potentially leading to discriminatory outcomes. 

  • Fraud detection: While AI is used to detect fraudulent activities, there’s a concern that over-reliance on AI might lead to missed or misclassified fraudulent transactions. 

  • Lack of human oversight: In critical financial decisions, the lack of human oversight and intervention in critical financial processes could lead to unexpected errors or undesirable outcomes. 

  • Data security and privacy: The increased use of AI for data analysis and decision-making raises concerns about data breaches and privacy violations. 


  • Biased decision-making: AI algorithms might unintentionally introduce biases in underwriting and claims processing, leading to unfair outcomes.  

  • Reduced human involvement: Relying too heavily on AI could reduce the personalized touch in customer interactions, affecting customer satisfaction and trust. 

  • Algorithm complexity: The complexity of AI algorithms could make it challenging to understand and explain decisions, creating difficulties in accountability and transparency.  

  • Regulatory compliance: The insurance industry is heavily regulated, and the overuse of AI could pose challenges in terms of compliance with industry standards and regulations.  


  • Loss of personalization: An overreliance on AI might erode personalized customer experiences that consumers value and expect, leading to a decrease in customer loyalty. 

  • Data privacy and security: The collection and use of customer data for AI-driven personalization, if not overseen and managed properly, can raise concerns about data breaches, privacy violations, and potential misuse. 

  • Algorithmic bias: AI algorithms might unintentionally introduce bias in product recommendations, pricing, or targeting, leading to discriminatory outcomes.  

  • Lack of human interactions: Excessive automation might result in a lack of human touch and empathy, which is important in building trust and increasing customer loyalty.  

To address these concerns, it is critical to engage with vendors and partners committed to responsible AI, a focus on transparency and fairness, and continuous monitoring of AI systems.  

Look for a partner that offers the following in a software solution: 

  • The data preprocessing techniques are used to identify and mitigate biases present in the training data, such as reweighting samples to remove sensitive attributes. 

  • Regular assessments and audits of AI models for biases using tools designed to identify discrimination patterns in their decisions.  

  • Transparency and “explainability,” allowing brands to understand how decisions are reached.  

  • Gives out-of-the-box industry-specific use cases that can help you realize value immediately without the need for further training (avoiding cold start). 

Partnering with a software provider that implements these strategies can help mitigate potential risks and ensure that AI serves the best interests of both the brand and its customers.  


Looking Forward 

As customers become increasingly technologically savvy, their expectations will continue to evolve, influencing how loyalty programs need to adapt. 

Hyper-personalization will be a must-have, not a nice-to-have. Customers will require evermore personalized experiences, tailored rewards and experiences, offers, and recommendations based on their values, beliefs, behaviors, and needs. They will demand a seamless experience when switching between all channels and platforms as new channels are added.  

Expectations for immediate interactions and responses will rise, requiring loyalty programs to offer instant rewards. Gamified challenges and interactive features will be essential as more customers want to engage and be entertained within loyalty programs.  

A growing number of customers seek programs that offer value beyond transactions. They expect brands to demonstrate ethical practices and offer community-focused initiatives that benefit the earth. Corporate social responsibility (CSR) initiatives must be communicated to customers. Data privacy and transparency concerns will continue to grow, and customers will demand robust security systems in loyalty programs. 

Loyalty programs must continually innovate and leverage technology to meet these evolving expectations. By embracing new technologies and delivering unique, meaningful customer experiences in real time, brands can build lasting customer loyalty in an increasingly tech-savvy landscape.  


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