Four Pillars

A customer is any person, team, department, or entity that purchases or uses goods or services from a business. Customers may be on the supply side—such as sellers, content creators, or external partners—or on the demand side, buying products or consuming content. Clearly identifying the customer is crucial for defining the product’s scope and ensuring successful delivery. Customers can be:

• External: Individuals or organizations who pay for products or help generate revenue.

• Internal: Teams or departments that use products to improve internal processes, such as customer service, audits, compliance, and / or legal reviews.

Product Managers should focus on understanding primary users—their needs, desires, pain points, and workflows. This approach helps eliminate friction, address real problems, develop measurable baseline , and objective and key results (OKR's), establish reporting metrics / key performance indicators (KPI's) , and avoid developing solutions that lack genuine demand.

Analyze data to understand how customers use your product, identify friction points, and uncover opportunities for improvement, including A/B testing. In AI and ML world, data quality directly impacts business outcomes. Transactional data, telemetry, logs, crowdsourced data such as reviews and feedback, and user interaction information provide valuable insights into the customer journey and user experience. Analyzing  structured and unstructured data—such as images, videos, speech, documents, and NoSQL helps in developing deeper operational insights. As a PM, data is our window into real-world product performance. As the adage goes - know your data to better understand your business and your customers. 

Successful products deliver real customer value and drive business growth. Product managers focus on understanding the business value chain, how the business operates, and key business models (B2B, B2C, B2B2C, marketplace, subscription, etc.) to identify true customer needs and problems. This knowledge fosters alignment across marketing, sales, finance, legal, operations, and technology. Understanding business models also clarifies practical constraints such as General Data Protection Regulation, California Consumer Privacy Act (CCPA)  data collection restrictions, and delivery channel limitations. For AI and ML products, strong business insight ensures solutions address real challenges rather than developing products based on speculative assumptions without a clear problem definition and measurable outcomes.