How we publish user events for 100,000 customers
The FreeAgent application is currently used by more than 100,000 companies. When users send their invoices, explain their bank transactions and do any other action to take care of their business, these actions are recorded automatically. We record them in different systems and for different purposes. One of the systems where we record actions like these is an event system and these records are called events. Events contain information about… Continue reading
How to Measure Pointless Things?
Here at FreeAgent we, like so many other workplaces around the world, have been adjusting to a fully remote setup over the past few weeks. Whilst a significant number of our company’s employees are permanently home-based, only one of our seven Analytics & Data Science team members is usually based away from our Edinburgh office. It has felt strange. We decided fairly quickly that we needed to create new opportunities… Continue reading
On the road to real-time reporting with incremental data transfer
Reporting Data at FreeAgent We keep track of a range of customer behaviours in the FreeAgent application as a means to understand engagement and to formulate and measure our OKRs. Examples of this include support requests raised through our ticketing system, Zendesk, the scores and comments provided by our users during NPS surveys and event data detailing specific actions taken within the application. As a concrete example of the latter,… Continue reading
Deriving and verifying the uncertainty on conversion rate predictions
For the past few weeks I’ve been working on building a machine learning model that can estimate the probability that a customer will convert from the free trial to a paid subscriber. In practice, I combine the predictions from this model for cohorts of companies, which are defined by their acquisition channel and acquisition month, and so a method is required for calculating the conversion rate uncertainties for each cohort.… Continue reading
How to count what counts
At FreeAgent we’re building a new platform to allow our teams to explore their data and glean new insights from it. The platform is built using Looker on top of Amazon Redshift, and so far it’s been enthusiastically received by the teams that use it. However, the process of building up the platform and driving adoption hasn’t been entirely straightforward. There has been a recurring issue that we’ve have had… Continue reading
Micro-batching Event Data Into Amazon Redshift
Data is at the heart of our business. We use data to make business critical decisions on a daily basis. It is important that this data is not only accurate but also available when required. Traditionally reports would be generated at a set schedule which made it difficult to decide on next steps in a timely fashion. New technologies like Amazon Kinesis Data Streams enable us to generate these reports… Continue reading
Separating job applicants in multiple dimensions
The team I work in at FreeAgent is achieving great things - from rolling out a new Business Intelligence tool, to working on machine learning models to improve our product. With so many ideas but not enough time to action them, we recently advertised a number of roles to expand our team. FreeAgent is a superb place to work, and the roles are a real opportunity for someone to achieve… Continue reading
Accurately ascertaining attitudes: designing unbiased survey questions
It would be great if we knew exactly what our customers thought so that we could adapt our products and improve our performance. However, it isn’t possible to read the minds of our customers, so surveys are a popular alternative to understand their attitudes. Yet, if the answers recorded on surveys don’t truly reflect the attitudes of the customers, how can we really know how to improve? This blog post… Continue reading
Sourcing a suitable sample: understanding selection bias in survey data
During my time at FreeAgent, I have been analysing attitudinal customer survey data to predict their behaviour. Getting to the bottom of how exactly this data was collected has helped me to understand the data and has given me a few ideas about how the data could be collected in the future. This blog focuses on how we choose who is selected to take part in surveys: a process is… Continue reading
Dealing with dirty data: useful functions for data cleaning in R
In this blog post, I’ll explain how to use some simple R-based data cleaning solutions (mostly in the ‘tidyverse’ package1) to address the most common dataset errors with the help of my favourite analogy: the untidy kitchen! NB: There are a plethora of valuable data cleaning tools in other software and even within R there are many different tools available. While the approach that I describe here is not necessarily… Continue reading