Benchmarking is a useful way to monetize existing data, leading to new revenue streams and improved product fidelity.
Case studies demonstrate different applications of benchmarking like offering scouting services for esports, providing real estate market data, and offering eCommerce performance insights.
Implementing benchmarking as a data monetization strategy starts with understanding the value of the aggregate data you can provide to customers.
When designing a data delivery layer, focus on maximizing the value customers can realize from your product by integrating data into their workflows effectively.
Understand your customers' needs and workflows to choose the best data delivery options like user interfaces and machine interfaces, which can be combined for a cohesive solution.
Data delivery options range from BI dashboards for insight viewing to APIs for seamless integration and data marketplaces for extending the reach of data products.
Understanding technology pricing models and unit costs in embedded analytics can help predict ROI and optimize expenses. Choose tools with fair pricing like MotherDuck for cost-effectiveness.
Adding value to customers and business in embedded analytics is achievable with tools like MotherDuck for speed, Cube for data curation, and React for front-end performance and flexibility.
Choosing a developer-friendly stack like MDCuRe (MotherDuck, Cube, React) enhances productivity by enabling efficient team collaboration, tool integration, and continuous delivery workflows.
Using a semantic layer in data monetization aids in building effective pricing strategies by translating complex data into actionable insights for all types of users.
Adding a semantic layer to data increases its value by saving customers time and money, as it provides pre-packaged insights and answers to common questions.
Semantic layers offer easily managed pricing tiers, allowing companies to provide different levels of data access and insights based on subscription types.
Embedded analytics bring data to where customers are, sparking curiosity and increasing engagement by providing data in easily interpretable ways.
Themes of modern embedded analytics include leveraging headless BI tools with semantic layers for defining business logic, and ensuring data governance for reliable data access.
Building embedded analytics solutions not only drives product engagement by integrating data analysis seamlessly, but also opens avenues for data monetization and fosters internal data-driven cultures within businesses.