"If you can't measure it, you can't manage it."

As a Data Scientist, I deal with measurement constantly. Be it user contribution rates or rain gauges, I examine important business metrics and ellucidate the relationships between different sources of data.

However, I am unaware of any generalized effort to measure the most important metric of all: happiness. Emerging trends in positive psychology suggest that examining our own happiness can provide longterm benefits to mood and resilience in both normal and depressed individuals. With my project, Hapyer, I've created a quick and efficient way to both measure individual happiness and provide insights based on these measurements.

Data Collection

Happiness means something different to each individual and is accordingly hard to measure. Monitoring this kind of metric requires us to get personal. Hapyer collects this data by prompting its users once a day through email, asking "Hey! How happy are you?" Users are asked to respond with a numeric rating of their happiness on a scale from 1-10 and add optional text describing their current experience. By starting with an email interface, Hapyer is available to anyone who has access to the internet.


Even just by collecting and storing these responses, Hapyer stands as a minimalist journaling service that gives users a gentle nudge. But by using this data intelligently, we can do so much more.

Personal Insights

From an individual's data, Hapyer provides happiness insights, the first of which is visualization. As an individual collects data we provide back both a plot of the happiness ratings along with any text provided.


Users don't even have to log on - plots of their last ratings are sent along with the daily reminder.


Using this data we can spot trends in user happiness and report on insights, like words associated with your happiest days or seasonal and temporal effects of which we might otherwise be unaware.

Population Insights

It's important that a project like Hapyer is not social. Happiness is a subjective and individual experience; this is not the place for social media bias. But that doesn't mean we can't learn from the population as a whole. Happiness may be personal, but at the end of the day there's a lot of commonality in what makes us happy.

As more users start measuring their happiness through Hapyer, we will be able to derive powerful population level insights.

The future of Hapyer

It's been very rewarding to work on Hapyer during this challenge but one of my main takeaways is that I've only scratched the surface of this idea.

Population Data

By centralizing happiness data in one place, Hapyer can provide insights beyond what any one person's data could provide. Like the telephone, as more and more people use Hapyer the more powerful our reach becomes.

Input Variables

The response variable we are measuring here is Happiness. But what about the Input variables? By hooking up Hapyer to other sensors and measurements of our daily activities, Hapyer is well-posed to quantify other important questions, like exactly how much a longer commute degrades your daily experience or exactly how much better you feel after exercise.

It's been a pleasure working on Hapyer as part of the Happiness App Challenge. I believe Hapyer has the potential to make us smarter about our own happiness and as the project grows will continue to make us incrementally happier. Thank you for your consideration.

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