Rigor, visibility, and transparency. That is Eppo. Eppo puts everyone on the same page. Everyone is using the same picture and the same numbers to understand experiment results. It is no longer a game of telephone where a data scientist analyses an experiment, writes something up, shares it with the product owner, and then the product owner crafts their narrative and shares it with executives. Now, executives have very simple, easy access to look at the result of an experiment, which all use the same standardized definitions for metrics.
Our team is very happy with the way we're running experiments with Eppo across front-end, back-end and Machine Learning use cases. Our business stakeholders now utilize the experiment results without questioning it, and our data team can self-serve using Eppo’s intuitive interface. I'm confident that Eppo is going to be the leader in the experimentation and product analytics space.
Switching to Eppo has led to a massive improvement in the quality of our experimentation analysis. With Eppo, our Product team is confident that their tests are bug-free and they are making decisions based on true metric impact, not noise. Product Managers are spending 50% less time making dashboards and debugging issues, which leaves more time to develop the features our users want.
Before learning about Eppo, we weren't aware of how much analyst time we could save. Now that all of our growth experimentation runs through Eppo, we're gaining at least half an FTE worth of product analyst time. This is time that our analysts can be spending uncovering insights rather than checking on experiments. Integrating Eppo into our systems and workflow was straightforward — we went from discussions to value in less than a month.
Effortlessly deep dive into modeling performance on subsets of users by configuring segments you configure offline, giving you critical insights for further improvements even for black-box models.
We automate the statistical inference using state-of-the-art techniques, freeing up time for you to keep iterating on the prediction performance of your models.
Machine learning teams continuously iterate on a single surface, e.g. the search model. Setting up a new experiment should not take more than 5 minutes, nor require involvement from other engineering teams.