With Amplitude, we can make sense of unstructured data and start to understand our different users and their journeys in our product. You need detailed, coherent information about user sessions. Improving the #UserJourney requires more than high-level insights. They also have their limitations when it comes to the depth of insights we can glean from the data we have. Traditional BI tools like Looker, Tableau, or Power BI, can perform this analysis, but they require us to spend time building out data models to answer our product questions. Whether it’s a high-intent user looking for a long-term committed relationship, or an occasional user looking for something more casual, we have to understand who those different users are, the different ways that they engage with the platform, and the behaviors and motivations that cause them to stick with the platform or drop off over time.
BI and Amplitude: Better Togetherīuilding the most engaging and enjoyable product possible requires a lot of A/B testing and data analysis to determine what aspects of our product customers like, and find opportunities to boost engagement with them. Amplitude is explicitly designed for this type of analysis, which meant we could access meaningful insights that much faster. Eventually, we learned that we could use it to measure engagement, to identify user cohorts, to analyze different user journeys, and to find leading indicators of conversion and retention.
When my team first started using Amplitude, we had this conception that it was mostly for event tracking and segmentation. mParticle collects and stores our customer event data, which we send to Looker for general business reporting, and to Amplitude for deeper analysis on user behavior and our customer experience. Our customer data stack at OkCupid consists of mParticle, Looker, and product intelligence (PI) platform Amplitude. Our work ranges from traditional business intelligence (BI) reporting to algorithm development and optimization with a macro focus on user experience (UX) and product optimization. The focus of the data analytics team is to understand how the OkCupid platform functions and what we can do to improve it. To do this, however, we have to understand the mountains of data we obtain. The more questions we ask, the more information we receive, and the better we can pair users with someone else. One of OkCupid’s key differentiators is the use of questions to create a match score that determines one person’s compatibility with someone else. Users have to stick around for a while so the app can learn their likes, dislikes, deal-breakers, and other information to help locate a compatible match. It’s exciting to see meaningful human connections develop, but it’s rare to open a dating app and immediately find love. I’ve been with OkCupid for three years and I manage our data science team, which handles platform analytics. Our data obsession is why OkCupid makes more than 4 million connections every week, over 200 million a year, 5 million introductions a day, and gets more mentions in the New York Times wedding section than any other dating app. Driven by Data, Powered by the Heartĭata is core to the mission here at OkCupid. OkCupid has been around since the beginning, and today, OkCupid ’s use of business intelligence (BI) and product analytics tools are behind the platform’s success. There are plenty of online dating apps that have sprung up over the years, catering to just about every interest, community, and affiliation.
Online dating has always been a data-driven, scientific, and effective way of connecting people who share common goals and interests. Meeting the right person may seem like magic, but if you’re using a dating app or website, meeting the right person is a calculated process. Today, about one-third of Americans have used a dating app or site, and 12% have either been in a committed relationship or gotten married to someone they met through online dating, according to a recent Pew Research report.