You're a product engineer looking for affordable analytics tools. What's the best way to get started?

3 Compare features and pricing

While open source and free analytics tools can provide you with a lot of value and flexibility, they may not always meet your specific needs or expectations. For example, some of them may have limitations on the number of events, users, or integrations you can track, or they may not offer advanced features such as predictive analytics, user feedback, or custom dashboards. If you need more functionality or scalability from your analytics tools, you may want to consider some of the paid options that are available in the market. However, before you commit to any of them, you should compare their features and pricing, and see if they offer any free trials, discounts, or plans that suit your budget and requirements.

4 Test and evaluate

Once you have selected a few analytics tools that match your goals and metrics, you need to test and evaluate them on your product. This means integrating them with your product code, setting up the events and properties you want to track, and checking the data quality and accuracy. You also need to assess how easy or difficult it is to use the tools, how fast or slow they are to load and process data, how reliable and secure they are, and how well they support your analysis and reporting needs. You can use various criteria and methods to test and evaluate your analytics tools, such as user feedback, usability testing, data validation, performance testing, and cost-benefit analysis.

5 Optimize and iterate

The final step to get started with affordable analytics tools is to optimize and iterate your analytics strategy and implementation. This means reviewing your goals and metrics regularly, and adjusting them as your product evolves and your user needs change. It also means optimizing your data collection and analysis processes, and ensuring that you follow the best practices and standards for data quality, privacy, and security. Moreover, it means iterating your product features and user experience based on the insights and recommendations you get from your analytics tools, and measuring the impact of your changes on your product performance and user satisfaction.