Posts

Recently, I’ve been involved in experiment design and measurement - specifically AB Testing. This experience has encouraged me to learn more about experimentation because of unique challenges faced with conversion optimisation. This post, goes hand in hand with lead score experimentation. In brief, the unique challenges posed were: Dealing with largely a non-technical audience Low sample sizes Costly human interventions as a variant. Because of this, I chose Bayesian AB Testing as a measurement framework. In brief, without bashing Frequentist methods, some benefits of Bayesian AB testing are: ...

In TV shows that focus on home design, designers often place seemingly a large amount of importance on the entry of the house. Why? Well, it’s the place where you first show your home and create an impression. Sales teams are often the entrance way into their company. Creating an excellent sales experience will lead to higher conversions and generate loyal customers. Because companies are always trying to become more efficient, efficiency within sales can be increased by the use of a key tool – lead scoring. In this post, we’ll cover: ...

Gross Margin is perhaps of the most followed metrics when evaluating software companies. Investors and companies themselves are absolutely obsessed with it In this article, we break down Gross Margin to figure out what drives it, and whether companies are actually rewarded for having a good gross margin. In my last article, I explored the relationship of Sales and Marketing expenditure to examine whether there is an ideal ratio of spend. Today, we’ll use this knowledge and apply it to Gross Margin. ...

Sales and Marketing (S&M) and Research and Development (R&D) often compete when it comes to company budgeting. For budget managers and shareholders alike it’s interesting and beneficial to compare companies spend in this area to get insight into strategy and design budgets. A good start would be to look at how some of the largest US listed Software companies split their spending between S&M and R&D. This plot below illustrates that there is a large range of spending choices, right from Square through to Splunk. ...

There are a lot of articles out there explaining how to wrap Flask around your machine learning models to serve them as a RESTful API. This article assumes that you already have wrapped your model in a Flask REST API, and focuses more on getting it production ready using Docker. Motivation Why do we need to further work on our Flask API to make it deployable? Flask’s built-in server is not suitable for production Docker allows for smoother deployments, more reliability, and better developer-production parity than attempting to run Flask on a standard Virtual Machine. In my mind, those are the two biggest motivations on why further work is needed. ...