Data Science - The Marketing Technologist | The marketing technology blog

AI meets graphic design

Over the last decade, the transition from TV towards digital has caused the advertising market to shift from offline to online as well. Retailers are required to follow this trend along with the consumer in order to catch – and keep – their attention. This means designing extra content, like banners, to

Data Science

Using AWS Lambda and Slack to have fun while saving on EMR costs

We all have these times where we hack a piece of code together in 5 minutes. Usually, these pieces of code are not hidden gems, they tend to do simple stuff. Every once in a while though, you will find yourself writing a simple script which gives you a big

Data Science

Improving the consistency of your projects with virtual environments in Anaconda

Virtual environments in python are very useful for managing different projects and especially when multiple people have to work on them. It is also useful for using packages which are not entirely supported on every version of python. Personally I needed a virtual environment when using Tensorflow. Tensorflow was not

AI

The Genie in the Bottle: How to Tame AI?

While we're seeing some great progress in AI, there is increasing concern about the danger of it by people like Stephen Hawking. This 'possible existential threat to humanity' is the reason why people like Elon Musk and Sam Altman started OpenAI. The authority on the threat of AI is Nick

Analytics

7 reasons to use Snowplow besides Google Analytics 360

We recently got the question: if we start using Google Analytics 360 and Big Query, do we still need to use a second data logger like Snowplow Analytics? A great question. I discussed this question with our data team and it turns out using Snowplow (an open source data logger)

Data Science

Helping our new Data Scientists start in Python: A guide to learning by doing

The Data Science team at Greenhouse Group is steadily growing and continuously changing. This also implies new Data Scientists and interns starting regularly. Each new Data Scientist we hire is unique and has a different set of skills. What they all have in common though is a strong analytical background

Data Science

Calculation of confidence intervals for ratios

The science of campaign optimization was among the very first things I was introduced to when first coming to work in programmatic media at Bannerconnect, and it was with great interest that I learned the particular steps our campaign managers regularly take to ensure campaigns run in the most effective

Data Science

Send Slack notifications whenever a Pokémon spawns nearby using a Pokémon GO SlackBot

CURRENT STATUS (04/08/2016): Niantic seems to have made some big changes to the API last night, so currently all API scripts (including PoGoMap and bots) seem to be down. Feel free to share any fixes if you come across anything that could be of help. Meanwhile, I hope

Data Science

Upload your local Spark script to an AWS EMR cluster using a simple Python script

Apache Spark is definitely one of the hottest topics in the Data Science community at the moment. Last month when we visited PyData Amsterdam 2016 we witnessed a great example of Spark's immense popularity. The speakers at PyData talking about Spark had the largest crowds after all. Sometimes we see

Data Science

A recommendation system for blogs: Content-based similarity (part 2)

In this second post in a series of posts about a content recommendation system for The Marketing Technologist (TMT) website we are going to elaborate on the concept of content-based recommendation systems. In the first post we described the benefits of recommendation systems and we roughly divided them in two

Data Science

A recommendation system for blogs: Setting up the prerequisites (part 1)

The goal of data science is typically described as creating value from Big Data. However, data science should also meet a second goal, that is, avoiding an information overload. One particular type of projects that really meet these two goals are recommendation engines. Online stores such as Amazon but also

Data Science

Is there time for coffee? Your execution time is ticking in Python!

Last month I was working on a machine learning project. If you make use of grid search to find the optimum parameters, it is nice to know how much time an iterating process costs, so I do not waste my time. In this blog you’ll learn how to: Install

Data Science

Slashception with regexp_extract in Hive

As a Data Scientist I frequently need to work with regular expressions. Though the capabilities and power of regular expressions are enormous, I just cannot seem to like them a lot. That is because when they do not function as expected they can be a really time-consuming nightmare. In this

Data Science

The GAM approach to spend your money more efficiently!

In an earlier blogpost we described how Blue Mango Interactive optimizes the media spend of clients using S-curves. S-curves are used to find the S-shaped relationship of a particular media driver on a KPI such as sales. Moreover, when a S-curve is obtained, we can determine the optimal point that

Data Science

Optimizing media spends using S-response curves

A key focus of our Data Science team is to help our clients understand how their marketing spend affects their KPIs. In particular, we create models to understand the effect of individual marketing channels such as television or paid search ads on KPIs such as sales, visits and footfall. Knowing

Data Science

Data collection and strange values in CSV format

When you start a new innovation project your data is not always in a structured database. Most of the times you need to import a CSV file for a quick analysis. Thereafter you can manipulate your data. Finally you can start data modelling. In one of my last projects I

Data Science

Calculating ad stocks in a fast and readable way in Python

As Data Scientists in the world of advertising we often need to proof the direct and delayed effects of advertising. A phenomena what we see in the last years is that consumers use multiple screens when they watch television. This is called screen-stacking. After a television commercial they can visit