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Looking to build an analytics system? This post covers storing time-based metrics vs user-centric metrics.
In just a few short years, data-driven teams went from not enough data to drowning in a sea of metrics. Every action, reaction, and result can now be tracked, processed, and analyzed. However, a key question we’ve received is which metrics are important and which analytics architecture and data store is best suited for a particular analytics requirement.
For the last few years, there has been an explosion of API-powered businesses. There are revenue-generating APIs, developer platforms, partner marketplaces, and even internal APIs powering single-page apps.
With this explosion, there has also been a large increase in API tooling to help these companies go to market with their API platforms as quickly as possible and out-innovate any competition. Much of this increase in tooling mirrors what we saw in the mobile era. However, with this explosion, there is now an increase in the number of tools and solutions to build and grow APIs and platforms.
Purchasing a new enterprise analytics solution can be a great experience if you’ve never purchased software before, yet it can be a daunting task. There can be a variety of analytics vendors with overlapping features for a use case yet each has its strengths and weaknesses. As an alternative to purchasing ready-made SaaS, you can also build your own in-house API analytics infrastructure on top of open-source software like Spark, Druid, and Elasticsearch. This article digs into when it makes sense to build vs buy ready-made analytics solution and provide a point-based framework for evaluating API analytics solutions and perform the proper diligence.
The first decision a company should make is whether they want to build the infrastructure or purchase a ready-made solution. There are benefits and risks to both. In general, purchasing shortens the delivery of a well-polished analytics solution with lower cost in time and money compared to homegrown, but a homegrown gives you greater control over what is tracked and presented.
There are few metrics more critical than retention for a platform business. If you’re acquiring customers for $25, but they stop using your API after a month, then you have a leaky boat. Don’t spend more money on developer acquisition until retention is fixed. This requires accurate measurement of API retention.
If you came from a web or mobile product background, you may already be familiar with mobile retention to measure how many acquired users keep using a mobile app. Growing a B2B platform requires tracking similar KPIs to measure the success of your acquisition and product strategies. This article will dig into the best practices for tracking and increasing API retention.
You may also like: Analyzing API Call Performance From Different Global Locations Based on cURL Metrics
We’ve all probably experienced it. We have this great project, we have this great interface, and suddenly UX (User Experience) comes in and points out all these flaws and stops things, making us scramble to catch up.
If you’ve had this happen, you know that the goal of a great interface, put out on time, isn’t met. So how can we change this?