Puneet Yamparala: Chief Product Officer with 13+ years at Amazon and startups; cofounder of Bizeev Brands; e-commerce strategist.
In today’s digital economy, personalization has evolved from a “nice-to-have” feature to a major driver of competitive advantage. Business leaders across industries are recognizing that tailored experiences can deeply influence customer satisfaction, loyalty and revenue growth. Whether you’re in retail, entertainment or technology services, AI now enables the kind of large-scale, data-driven personalization that once seemed impossible to achieve manually.
Historically, businesses relied on broad market research and labor-intensive processes to guess what customers wanted. This was often slow, expensive and insufficiently targeted. Today, AI—particularly machine learning (ML)—allows companies to analyze massive datasets, identify behavioral patterns and predict individual preferences with remarkable accuracy. This shift isn’t just about efficiency; it’s about deeply understanding customers so you can serve them better.
Real-World Impact Of AI-Driven Personalization
A prime example comes from the streaming industry. A leading streaming provider I worked with built an AI-driven recommendation engine that uses a two-phase process to fine-tune suggestions for each viewer. First, a large language model (LLM) employs its broad, pretrained knowledge to generate a baseline list of content. Next, a more specialized step—commonly called retrieval-augmented generation (RAG)—injects real-time internal data such as plot summaries, cast details and user ratings to refine those recommendations.
Systems like this can boost click-through rates and raise average viewing time over the long term. For businesses, that can translate directly into higher user engagement, longer subscription retention and a healthier bottom line.
Practical Steps For Business Leaders: Harnessing AI For Personalization
While AI’s potential is immense, the real question is how to implement it in ways that deliver measurable returns. Based on my experience leading the personalization team of a large streaming platform, here are five practices I have found to be effective for business leaders looking to personalize their offerings through AI:
1. Develop a robust data strategy.
When creating your strategy, start by identifying the right data. I recommend focusing on information that truly reflects customer behaviors and preferences (e.g., purchase history, browsing patterns, demographic data). Next, invest in tools and processes that will validate and cleanse the data before you feed it into AI models.
Also, remember the importance of privacy and compliance. The key to collecting data while maintaining customer privacy is to effectively log all customer interactions on your website/product and then anonymize the data that can be tied back to specific people. Taking these steps, as well as implementing strong data governance and adhering to privacy regulations like GDPR or CCPA, can help ensure you not only stay compliant but also retain the trust of your customers.
2. Select the appropriate AI techniques.
Start simple: Begin with straightforward ML models (e.g., regression or classification) to tackle immediate challenges like churn prediction or basic recommendation systems. For new AI practitioners, my simple rule of thumb is that if you want to predict something, use ML; if you want to generate something, use AI.
For example, if you want to predict the probability of a customer purchasing a specific product, use ML. But if you want to generate a list of all products a customer might be interested in, you can use AI (generative AI specifically). As you collect more data and refine your approach, consider deeper neural networks, large language models or hybrid systems that combine multiple AI techniques for more nuanced personalization.
3. Build cross-functional teams.
Provide training and resources that will help your non-technical employees understand AI’s capabilities, limitations and ethical considerations. Also, to ensure that what you’re building aligns with real business needs, have your AI engineers work closely with product managers, marketers and data analysts. For example, while building a solution for generating movie/TV recommendations for customers, our AI engineers were planning to build a large-scale ML model requiring several months of effort. But based on discussions with a product manager and a data scientist, the AI engineers were able to switch tracks and use an LLM agent to achieve the same goals with just three weeks of work.
4. Test, measure and iterate.
Perform regular A/B testing and controlled experiments to compare AI-driven features to baseline methods. Monitor engagement metrics, conversion rates or other KPIs, and use feedback loops to refine your algorithms and data inputs. This can help ensure that your personalization strategies remain relevant as customer preferences evolve. When I was heading the streaming service personalization team, we created a human-annotated “golden dataset,” which would be used for periodically running evaluations on the AI models to ensure that they were not drifting from their original performance baselines.
5. Align AI with your company’s strategic goals.
For this step, start by defining what your company’s success metrics look like. Clearly link your AI initiatives to top-level business objectives, such as increasing market share, boosting cross-selling opportunities or improving customer lifetime value.
The AI landscape is rapidly changing, so remember to stay agile. Regularly review your strategy to incorporate emerging technologies, such as new LLM breakthroughs, that can enhance your personalization efforts.
Looking Ahead
AI-driven personalization has already transformed industries like entertainment, retail and online education, but the journey is far from over. With larger and more sophisticated language models on the horizon, I believe more and more businesses will be able to deliver hyper-contextualized experiences that respond to a customer’s real-time context and even predict future desires. For leaders, this represents both a challenge—staying abreast of fast-evolving technology—and an enormous opportunity to differentiate their products and services.
Personalized experiences are no longer reserved for tech giants; they are accessible to organizations of all sizes thanks to the power of AI. By building a solid data foundation, choosing the right AI approaches and continuously iterating on insights, you can create offerings that resonate deeply with individual consumers, fostering not only higher sales and engagement but also a more meaningful and lasting connection with your customer base.
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