Data Analysis?

I was inspired by this analysis done by the folks of Looker, which looks at the amount of screentime different characters get in the Game of Thrones series. The dataset they used comes from data world and an imdb list. I’m grateful to the people that have collected and shared this data. The dataset includes 191 named characters, the amount of screentime they have (in minutes), and the number of episodes they appear in. [Read More]

People decide to leave or stop paying for Buffer every day. It’s unfortunate, but it happens for one reason or another. We collect a lot of data from these users in the form of surveys. We thought that it might be beneficial to analyze the text of these survey comments to see if we can identify common themes that we could address. Data collection We’ll use data collected from four separate surveys that represents different types of churn: [Read More]

I’ve wanted to build a content recommendation engine for a long time. Buffer is sitting on a mountain of data, which some argue is the most valuable commodity on Earth. We used to provide content suggestions to users through manual curation, but we ended up retiring that feature/service. In this analysis we’ll build a simple recommendation engine with the recommenderlab R package, using collaborative filtering techniques. To keep it simple, we’ll only look at updates shared by Buffer team members. [Read More]

I haven’t asked myself this question before, and I’ve never tried to optimize my Instagram posts for maximum likeage. I have made a couple of observations though: Instagram posts that are shared earlier in the day, while my friends and colleagues in Europe are still awake, seem to get more likes more quickly. Posts that include people and faces seem to get more likes. Images of New York (which happen to include the #nyc hashtag) tend to get lots of likes. [Read More]

Introduction Over the past few weeks, simplifying the free plan has become a theme and key component of realizing our product vision. This Paper has a lot of good context and discussion around what that involves. There are four major components to this change: Reducing the profile limit to 2 Reducing the number of updates people can send to 5 per day per profile Removing the ‘Share Now’ option Removing custom scheduling The goal of this analysis is to estimate how many active users would be affected by each component. [Read More]

Introduction A significant part of the Kodiak product cycle and Buffer’s product vision going forward is simplifying the experience for users on a free plan. One of the components we are considering simplifying is the Queue Limit. As of June 2017, the Queue Limit refers to the number of updates users on a free plan can have in the Queue of a single profile at any single point in time. This can understandable cause some confusion with users, and can also be exploited (e. [Read More]

Introduction As part of the next product cycle, we will try to create a simpler, more streamlined experience for Buffer users on a free plan. Part of that simplification process could include the limiting or removal of the option to ‘Share Now’, when drafting updates. Sharing an update immediately doesn’t add a post to the queue, which could possibly allow for the sending of many updates in a single time period. [Read More]

Introduction The goal of this analysis is to better understand how users use the Free plan, in order to better inform our decisions around changing its limits. In this analysis specifically, we’ll look at the number of updates that active users share on a daily basis. Data collection Let’s start by getting the Free users that have scheduled at least one update in the past 28 days. We’ll use the following SQL query to grab them. [Read More]

Introduction There is a big focus on simplifying the product for Buffer users on free plans in 2017 in order to create a more intuitive experience and encourage more upgrades. The purpose of this analysis is to get a better understanding of how users currently interact with the free plan, in order to better inform decisions around future plan limits. Specifically, we would love to learn about how many profiles users connect and actually schedule updates for. [Read More]

I don’t know.