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Artificial Intelligence

How AI and Personalization Are Reshaping Customer Engagement

Customer loyalty isn’t what it used to be.

The old days of punch cards and generic rewards are fading, replaced by something far more powerful:

AI-driven personalization.

Brands like Spotify, Starbucks, and Sephora have mastered the art of knowing their customers—sometimes better than customers know themselves.

When a brand consistently anticipates what you want, tailors experiences to your preferences, and makes your life easier, you keep coming back.

That’s the new loyalty: not just transactional, but deeply personal.

Here’s how these brands are leading the way—and what we can learn from them.

Spotify: Using AI to Curate Your Life’s Soundtrack

Spotify has done something remarkable: it makes over 140 million users feel like the platform truly understands them.

How? AI-powered personalization.

Every time you skip, repeat, or like a song, Spotify’s algorithm learns your taste. This fuels features like:

Discover Weekly – A custom playlist of songs you’ve never heard but might love, delivered every Monday.
Spotify Wrapped – A personalized year-in-review that’s so fun and shareable, it goes viral every December (nearly 160 million people engaged with it in 2022!).

These AI-driven experiences don’t just keep users engaged—they make Spotify indispensable. When an algorithm picks music that perfectly matches your mood, why switch to another service?

What I take from this: Personalization isn’t just a feature—it’s a loyalty engine. The more tailored your brand’s experience, the harder it is for customers to leave.

Starbucks: Turning an App into a Personal Barista

Starbucks may sell coffee, but a big part of its success comes from its digital ecosystem—particularly the Starbucks Rewards app.

This app isn’t just about collecting points. Thanks to AI (via Starbucks’ internal system, Deep Brew), it remembers what you like and customizes promotions accordingly.

If you usually get a caramel macchiato, the app might suggest a new seasonal twist on it.
If you haven’t ordered a breakfast sandwich in a while, it might send you a discount to entice you back.

And it works:

34.3 million active Starbucks Rewards members in the U.S. (up 13% year-over-year).
Loyalty members account for over half of Starbucks’ in-store purchases.

By making ordering ultra-convenient (saved customizations, order-ahead features) and deeply personal, Starbucks has made itself part of its customers’ daily routines.

What I take from this: The best loyalty programs don’t just reward purchases—they make customers feel known and valued.

Sephora: AI-Powered Beauty Advice at Scale

Sephora has mastered personalized beauty shopping—whether in-store or online.

Through its Beauty Insider program (with 25+ million members), Sephora tracks customer preferences, including:

Skin tone and concerns (from quizzes and past purchases).
Favorite product categories (lipstick, skincare, etc.).
Browsing and shopping habits.

Then, it uses AI to make tailored recommendations, such as:

Suggesting a foundation shade that matches your skin tone.
Sending tutorials on how to use the products you just bought.
Reminding you about that perfume sample you tried in-store.

Sephora also launched Virtual Artist, an AI-driven tool that lets users try on makeup via augmented reality. This takes personalization beyond product recommendations—it helps customers feel confident in their choices.

And the payoff? Personalized recommendations drive higher spending and retention.

What I take from this: Customers don’t just want to buy—they want guidance. If your brand can offer personalized advice, it becomes more than just a store—it becomes a trusted partner.

Why AI-Powered Personalization Creates Unbreakable Loyalty

When AI and personalization work together, they create a powerful feedback loop:

More engagement = more data.
More data = better personalization.
Better personalization = deeper loyalty.

Think about it:

Spotify users stick with the service because their playlists feel uniquely “theirs.”
Starbucks Rewards members bypass other coffee shops because the app makes ordering effortless.
Sephora shoppers return because the brand “gets” their beauty needs.

This kind of loyalty goes beyond discounts.

Customers stay because the experience is seamless, relevant, and irreplaceable.

How Any Brand Can Use AI to Build Loyalty

Use data responsibly – Customers are happy to trade data for better service, but transparency is key. Let them know how you’re using it.

Make personalization part of the product, not just marketing – Netflix, Spotify, and Sephora bake personalization into the user experience, not just email promotions.

React in real-time – If a customer suddenly stops engaging, AI should trigger a win-back offer or personalized nudge.

Combine rewards with personalization – Loyalty programs work best when they’re both transactional and emotional. Personalized perks (like Starbucks’ custom discounts) make points even more enticing.

Keep learning and improving – AI gets better over time. Track what’s working (click-through rates, engagement, retention) and continuously refine the experience.

In a world where customers have endless choices, the brands that stand out are the ones that cut through the noise with relevance.

Spotify, Starbucks, and Sephora prove that when a brand consistently delivers the right experience at the right time, customers don’t just return—they become loyal for life.

March 25, 2025 by BJ Bueno

AI & Human Connection: The Future of Customer Loyalty

The conversation around AI in business often centers on automation and efficiency. But for cult brands, AI isn’t about replacing human connection—it’s about enhancing it.

AI’s Role in Customer Engagement & Brand Trust

A McKinsey study found that AI-powered personalization leads to a 200% increase in conversion rates. But more importantly, AI allows brands to deepen emotional connections at scale.

Case Study: Nike – AI-Powered Personalization for a Cult Following

Nike’s SNKRS app uses AI-driven personalization to tailor content to each user, creating an exclusive VIP experience that makes customers feel valued. This strategy contributed to a 40% surge in digital engagement.

Case Study: Salesforce – The AI-Enabled Customer Advocate

Salesforce’s AI-driven customer engagement tools don’t just automate responses; they predict and anticipate customer needs, ensuring that interactions feel personal, seamless, and deeply connected.

How CEOs Can Use AI to Strengthen Customer Loyalty

Prioritize AI-driven personalization: Go beyond segmentation and craft hyper-personalized brand experiences.
Use AI to deepen—not replace—human interactions: The best brands use AI to enhance empathy, not eliminate it.
Measure AI’s impact on emotional engagement: Track sentiment analysis, emotional loyalty scores, and advocacy rates.

How is your brand using AI to make customers feel more valued—not just more efficient?

February 18, 2025 by BJ Bueno

Building Cult Brands with Machine Learning: A CEO’s Roadmap – Uber Case Study

CEOs are uniquely positioned to leverage machine learning (ML) in building cult-like brands in the dynamic business world.

This roadmap outlines how ML can enhance customer insights, foster brand loyalty, and establish a formidable market presence.

Predictive analytics, a core aspect of ML, allows you to anticipate market trends and consumer behavior.

Analyzing customer demographics and purchase patterns enables businesses to develop predictive models for demand forecasting, which is crucial for brands that resonate with customers.

Advanced Customer Segmentation

ML algorithms revolutionize customer segmentation, uncovering complex data patterns. This advanced segmentation identifies niche markets, allowing brands to provide personalized and engaging experiences.

Elevating Customer Experience with ML Insights

ML tools enhance customer experiences by providing deeper insights into customer interactions.

These insights enable real-time improvements and personalized engagements.

A notable case study demonstrating the application of machine learning in improving customer experience is that of Uber. The company identified the customer pickup experience as a critical area for enhancement, focusing on challenges like traffic congestion, faulty GPS signals, and crowded pickup locations, often leading to rider dissatisfaction and lost revenue for drivers.

Uber’s approach involved several steps:

Analyzing the Pickup Experience: The Uber team began by examining the pickup experience for different rider personas and identifying problems at various stages. This analysis was crucial in understanding the diverse challenges faced by riders across other locations and circumstances.

Predictive Modeling: The next step was to create a predictive model for determining the best pickup locations. This model was based on various factors, including traffic conditions, rider location, and time of day. By leveraging machine learning, Uber could predict optimal pickup points more accurately, reducing wait times and improving the overall experience.

Developing Quantitative Metrics: Uber developed quantitative metrics to measure the effectiveness of their strategies. These metrics included KPIs like app availability, latency in the process of calling a ride, and the accuracy of information provided in the app (e.g., maps, prices, discounts). Monitoring these KPIs helped Uber understand the impact of their ML-driven improvements on the user experience.

Customization for Global Markets: Given its operations in over 64 cities worldwide, Uber also focused on customizing the app for different markets. This meant offering other product choices depending on the location, such as e-bikes in San Francisco or auto-rickshaws in Delhi. Such customization was achieved through configurations driven by machine learning, allowing Uber to maintain a global app optimized for local performance.

Incident Detection and Resolution: A critical component of Uber’s strategy was the ability to detect user-facing incidents quickly and trace them back to code or configuration errors. By resolving these issues promptly, Uber aimed to reduce the ‘blast radius’ of bad user experiences, affecting fewer users and maintaining high service reliability.

Building a cult brand with ML involves navigating challenges like data quality and computational complexity.

CEOs must implement best practices in data management, model selection, and transparency.

Leveraging ML for enhanced customer insights, predictive analytics, and personalized experiences allows CEOs to build deep, loyal customer bases.

However, strategic approaches are required to overcome ML challenges, making the intersection of technology and brand strategy a pathway to a lasting, impactful brand legacy.

February 4, 2024 by BJ Bueno

The Art of Customer Profiling: A Must-Have Skill for Brands

Hey there, business visionaries.

In today’s world, where consumers are more informed and selective than ever, the challenge for CEOs and CMOs is truly understanding this sophisticated audience.

Your first crucial step in this mission?

Accurate and insightful customer profiling.

With the modern digital landscape offering abundant customer data, sophisticated online analytics now enable us to dig deeper into consumer behaviors and preferences than ever before.

This presents a golden opportunity for brands to engage with their customers and prospects consistently and accurately.

Let’s dive into how mastering customer profiling is now an essential skill for providing tailored and relevant experiences.

The Importance of Customer Profiling

Customer profiling is your roadmap to understanding your customers. It’s about piecing together their stories from various data points – what they like, what they don’t, and what makes them tick. Here’s why it’s critical:

Personalization is Key: In mass marketing, personalization is your secret weapon. Profiling allows you to tailor experiences that resonate personally with each customer.

Informed Decisions: You’re not shooting in the dark with customer profiling. In every strategy, solid data tells every campaign.

Staying Ahead of the Competition: Understanding your customers in-depth gives you a leg up over your competition.

The How-To of Customer Profiling:

Data Collection: Consider gathering ingredients for a perfect recipe – everything from sales data to social media interactions.

Analytics and Segmentation: Segment your customers using analytics to understand their distinct characteristics.

Insight into Action: Use these insights to enhance your marketing strategies, product development, and overall customer experience.

Leveraging Technology:

Embrace the latest in AI and machine learning to uncover deeper patterns and predict future trends. Keep your brand at the forefront of innovation.

Ethics in Profiling:

Remember, with power comes responsibility.

Always be transparent about gathering and using data and prioritizing customer privacy.

Case Study: Snickers’ “You’re Not You When You’re Hungry”

Before we wrap up, let’s look at a real-world example where customer profiling made a big difference.

Snickers’ “You’re Not You When You’re Hungry” campaign perfectly illustrates this.

Back in 2008, Snickers was focusing its marketing efforts primarily on a “manly man” persona.

This approach, however, limited their reach.

The chocolate bar market is vast, with consumers from all walks of life in checkout lines daily.

Snickers needed a campaign that had universal appeal.

Their solution?

The “You’re Not You When You’re Hungry” campaign launched in 2010.

This campaign was based on a universal human truth: hunger affects everyone and can make us act out of character.

By using this insight, Snickers created a message that resonated with a much broader audience.

The campaign started with a memorable Super Bowl commercial starring Betty White, and it was a massive hit.

It increased Snickers’ sales by 15.9% in its first year and significantly boosted its fame.

The ad topped the USA Today poll as the number one Super Bowl ad and reignited Betty White’s popularity in pop culture.

This success demonstrated the power of understanding and tapping into a broader customer psyche.

From then on, Snickers continued to leverage this insight in various creative ways, ensuring the campaign’s longevity and relevance.

This is a prime example of how understanding your customer’s deeper needs and behaviors can lead to a successful marketing strategy.

As we’ve seen with Snickers, customer profiling is more than just gathering data; it’s about finding those universal truths that resonate with your audience.

By understanding your customers’ deeper needs and behaviors, you can create campaigns that increase sales and build lasting brand loyalty and recognition.

Remember, the key to successful customer profiling is staying agile, evolving, and playing fair and square with your data.

January 10, 2024 by BJ Bueno

What every CEO should know about generative AI

The AI and I

Generative AI is evolving at record speed (Exhibit 1) while CEOs are still learning the technology’s business value and risks. Here, we offer some of the generative AI essentials.

Watch our YPO AI Keynote

Exhibit 1

Generative AI has been evolving at a rapid pace.

Timeline of major large language model developments following ChatGPT's launch.

Introduction

Overview of Generative AI and its Emergence as a Transformative Technology

Generative AI, a sophisticated branch of artificial intelligence, has emerged as a pivotal force in the realm of technological innovation. Unlike traditional AI systems, which are dependent on predefined rules and explicit data patterns, generative AI utilizes advanced neural networks to learn from extensive datasets, empowering it to autonomously generate original content such as text, images, and music. In parallel, the landscape of online gambling has also seen a shift, with players increasingly seeking reliable platforms. Among these, the 10 most trusted non GamStop casinos in the UK have gained significant attention for their commitment to providing secure and fair gaming experiences. This capability marks a significant shift from earlier AI models, positioning generative AI as a catalyst for unprecedented innovation and creativity across various industries.

The State of Generative AI in Business

1. Current Advancements in AI: Focus on Generative Models

Recent progress in computing power, data storage, and algorithms has spurred the development of more sophisticated AI systems, enabling the rise of generative AI models like ChatGPT, GitHub Copilot, and Stable Diffusion. These models are not only transforming the way we interact with technology but also redefining the capabilities of machines in understanding and creating complex content.

2. The Role of Large Language Models and Foundation Models

The foundation models powering generative AI have cracked the code on language complexity, allowing machines to learn context, infer intent, and showcase independent creativity. They can be quickly fine-tuned for a wide array of tasks, making them versatile tools for businesses seeking to reinvent work processes and amplify human capabilities. This versatility is central to generative AI’s value proposition, offering multifaceted applications while balancing the high costs of development and hardware.

In the next sections of the report, we will delve into specific use cases of generative AI in business, strategic implementation considerations, the impact on workforce and job roles, and the future direction of this transformative technology.

Key Use Cases in Business

3. Enhancing Software Engineering Productivity with AI Coding Tools

Generative AI, exemplified by tools like GitHub Copilot, revolutionizes software development by enabling more efficient code generation and reducing bugs. This significantly accelerates development, especially for complex codebases, by allowing developers to express desired functionalities in natural language and receive complete, functional code snippets in response.

4. Revolutionizing Client Relationship Management

Generative AI transforms how relationship managers analyze and interact with client information. By processing vast amounts of data, AI can uncover insights and trends, enabling personalized client strategies and more effective decision-making.

5. Automating Customer Support

AI-driven chatbots and virtual assistants, powered by generative AI, are redefining customer support. These systems autonomously handle inquiries and offer support, thereby improving customer service and automating routine tasks. This application not only enhances customer experience but also frees up human resources for more complex tasks.

6. Accelerating Drug Discovery

Generative AI’s ability to analyze complex data is particularly beneficial in drug discovery. By identifying patterns and predicting viable therapeutic candidates, AI can significantly speed up the research process, leading to faster and more efficient development of new pharmaceuticals.

Strategic Implementation of Generative AI

7. Customizing AI Models for Unique Business Needs

To maximize value, companies are increasingly fine-tuning pretrained generative AI models with their own data. This customization allows businesses to address their unique needs, unlocking new performance frontiers.

8. Navigating Legal and Ethical Considerations

The rapid evolution of AI technology necessitates a focus on legal, ethical, and reputational risks, including intellectual property, data privacy, discrimination, and product liability concerns.

9. Ensuring Data Privacy and Security

In sensitive sectors like healthcare and finance, generative AI’s ability to generate synthetic data while maintaining the statistical properties of the original dataset is crucial. This approach not only facilitates data sharing and collaboration but also ensures individual privacy.

Redefining Work and the Workforce

10. The Impact of AI on Job and Task Redesign

Generative AI will significantly alter job roles, leading to a need for extensive reskilling of employees. This change will involve decomposing current jobs into tasks that can be automated, assisted, or entirely reimagined for a future of human-machine collaboration.

11. Evolving Roles and Tasks in an AI-Enhanced Workplace

As generative AI becomes more integrated into business processes, it will impact tasks rather than entire occupations. Some tasks will be automated, some transformed through AI assistance, and others will remain unaffected. This shift underscores the importance of training employees to work effectively alongside AI systems.

In the following sections, we will explore operational and strategic considerations for integrating generative AI, governance and risk management practices, and the future outlook for this technology in business settings.

Operational and Strategic Considerations

12. Infrastructural Requirements for Generative AI

Adopting generative AI demands significant infrastructural and architectural considerations. Businesses must ensure their systems are capable of handling the demands of these advanced AI models, focusing on aspects like compute power and data processing capabilities. Cost and sustainable energy consumption are also central to these considerations, especially given the energy-intensive nature of generative AI operations.

13. Strategic Planning for AI Integration

Effective integration of generative AI into business processes requires strategic planning. This includes a disciplined approach to data management, ensuring the availability of quality data to train AI models. Companies also need to adapt their operating models and governance structures to effectively leverage generative AI technologies.

Governance and Risk Management

14. Establishing Robust AI Governance

Implementing generative AI in business operations necessitates robust governance frameworks. Companies must build controls to assess risks at the design stage and ensure the responsible use of AI throughout their business processes. This includes addressing concerns related to data privacy, security, and ethical AI principles.

15. Legal and Ethical Considerations in AI Deployment

The rapid evolution of AI technology brings with it a host of legal and ethical challenges. Companies must be vigilant about intellectual property rights, discrimination issues, product liability, and maintaining trust and security in AI applications.

Future Directions and Investment

16. Predicting Trends in Generative AI

The future of generative AI in business is marked by continuous evolution and growth. Companies need to stay ahead of emerging trends and technologies to maintain a competitive edge. This requires ongoing investment in AI research and development.

17. Investment in Operations and Training

To fully harness the potential of generative AI, companies must invest in evolving their operations and training their workforce. This includes developing technical competencies and ensuring employees are equipped to work effectively with AI-enhanced processes.

What Are You Waiting For?

Generative AI presents a transformative opportunity for businesses across all sectors. By understanding and strategically implementing these technologies, companies can revolutionize their operations, innovate in product and service offerings, and redefine their workforce for the future. The time for businesses to act and invest in generative AI is now.

Using generative AI responsibly

Irresponsible use of Generative AI poses a variety of risks. CEOs will want to design their teams and processes to mitigate those risks from the start—not only to meet fast-evolving regulatory requirements but also to protect their business and earn consumers’ digital trust.

Putting generative AI to work

CEOs should consider the exploration of generative AI a must, not a maybe. Generative AI can create value in a wide range of use cases. The economics and technical requirements to start are not prohibitive, while the downside of inaction could be quickly falling behind competitors. Each CEO should work with the executive team to reflect on where and how to play. Some CEOs may decide that generative AI presents a transformative opportunity for their companies, offering a chance to reimagine everything from research and development to marketing and sales to customer operations. Others may choose to start small and scale later. Once the decision is made, there are technical pathways that our AI experts can follow to execute the strategy, depending on the use case.

The AI and I: A Comprehensive Guide to Navigating the AI Revolution for Founders and Executives Alike

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Don’t just contemplate the future – play an active role in creating it.

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December 9, 2023 by Ozzie Coto