Apart from being a data scientist, I also spend a lot of time on my bike. It is therefore no surprise that I am a huge fan of all kinds of wearable devices. Lots of the times though, I get quite frustrated with the data processing and data visualization software that major providers of wearable devices offer. That’s why I have been trying to take things to my own hands. Recently I have started to play around with plotting my bike route from Python using Google Maps API. My novice’s guide to all this follows in the post.
Driving marketing budget sometimes seems to be a mysterious art where decisions are based on ideas of few enlightened people, who know what’s right. But you should not fool yourself, the times are changing and so is the way successful marketing is managed. The same as in other fields, experienced marketing managers use information hidden in data to help them. With the amount of data and methods available, it is however often tricky not to get lost and be able to distinguish the signal from the noise. Typical examples are the marketing attribution models – a tool that is widely used, but in my experience rarely maximizes the leveraged value of data.
Typically, in marketing attribution, marketers want to know, which part of the business KPI (typically site visits, sales, new customers, new revenues etc.) result from which marketing activity. Mainstream approach is to use attribution models that are often very simplistic – like single source attribution (last click, first click) or fractional attribution (where the contribution is distributed among multiple touch points given some simple rule). These methods provide marketers with the importance of each marketing channel or campaign in respect to their KPI. Based on this historical information the marketing managers make a decision about how to allocate the marketing budget. This approach however puts a great deal of pressure to tedious and demanding data detective work to make sure all client touch points are measured correctly. More importantly, there is no way of knowing that this work has been done correctly, which of course has significant impact on credibility of the attribution models.
Knowing these difficulties, we decided for an alternative approach. We thought: Why should we dig into the individual touch points? Shouldn’t we rather focus on marketing investments and model the ultimate business output? And that is exactly what we did. We took investments into individual marketing channels in time and used time series analysis to predict our client’s business goal (number of sales). On top of it, we also added seasonality, marketing investment of competitors and some other simple parameters.
“Even though we are using data to drive marketing decisions on a daily basis, most of the tools that we have used up until now focus on describing the past. Recently we decided to work together with aLook Analytics to change that. Thanks to their modelling approach to marketing investments we now have accurate information about the expected future developments as well.
Using the interactive Shiny application that is built in Keboola Connection, we want to make informed decisions on the fly, which will help us to reach our sales goals in the most cost efficient way.”
Daniel Gorol, BNP Paribas Personal Finance SA / Cetelem
Every student of statistics I know has at least once thought about making easy money by predicting the stock market or by predicting sports results. To be honest, I certainly was not an exception. Intimidated by uncapped randomness of the stock market, I always tended more to the second option – the sports betting. Nevertheless, it has not been until recently, that I asked the guys from the team and we decided to actually really try if we can make an easy living from predicting football results. [For impatient readers: it looks promising but it is not so easy]
In the beginning we knew absolutely nothing about how the betting works, where to find the data, neither what some standard prediction methods are. So let me walk you through what we have learned.
In the final post about practicing data science in SMBs, I will get into more details about how exactly a data science project looks like, what are the essential phases and last but not least how to maintain the deployed solutions.
In this part of the series I would like to explain what does “data science as a service” mean. What are specific examples of data science solutions? What should you know when you decide you want to give it a try? What will typically be the requirements on you and your internal team?
The post is especially aimed at applications of Data Science in e-shop environment.
The benefits of tailor-made solutions
The great thing about purchasing “data science as a service” is that the service can and should be tailor-made, designed to suit the exact needs of your business. This is important for two main reasons. Firstly, it gives you an edge over competition (who does not have it) and secondly, because you only pay for features that you will use.
Data science or its alter egos – predictive analytics, machine learning or AI – can, if implemented correctly, offer an impressive ROI. Moreover, together with new technologies and products being developed, the data-driven approach becomes more accessible every day. Even medium and small businesses can now leverage the value of data science. In our experience, the proposition is even more rewarding for them than for large corporations.
Why is that? What are some typical applications of data science for small companies?
Watch our Brighttalk webinar to hear more.
In the previous post, I explained how I view data science and its value added in the business context. Despite it clearly offers a huge potential for many companies, I think data science is not used successfully and widely enough as it should be. One important barrier is that it stays very abstract. And surprisingly, it is the small/medium businesses that can overcome the barriers in its implementation the easiest.
Why is Data Science so difficult to grasp?
It really does sound quite glamorous – being able to read and predict from data, having automated dashboards and apps…. All the fancy buzz-words that go together with data science such as big data, artificial intelligence or deep learning show that it is something that attracts attention. But as one nice quote put it, many of these words are like teenage sex – everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it…
So why it is so difficult to make Data Science happen when it so great?
Data science is far from being a new field. Yet, for quite a substantial portion of mostly smaller businesses, the term data science stays obscure and difficult to grasp. Many of them keep hearing about it, but they are not really sure if or how they should use it. In my practice, I have come to the conclusion that it is above all them, the SMBs, for whom data science represents a great opportunity.
This is the first of blog posts, in which I would like to share my thoughts on what is the value added of data science as well as how businesses can make use of it.
Question #1 – What the heck actually is Data Science
The term “data science” is considered just as a new buzzword by many people, all the more so that understanding what it means ideally requires at least some technical awareness. Not that it is necessary to understand data science to be able to make use of it, but unfortunately people tend not to like things they don’t understand.
When I need to explain what data science is, I use the very general saying that ‘data science is about extracting knowledge and insights from large amounts of data’. Sure, it can seem that this is nothing new – after all, analyzing data in Excel has been possible since 1985. But let’s not do the same mistake as many famous consultancy companies by believing that applying business intuition over Excel and PowerPoint visualizations is data-driven approach that can give anyone an edge over competition in the 21st century.
Data science is much more than that. It enables us not just to describe and form an impression, but to get verified conclusions based on data in various forms and locations and seamlessly present business-relevant results via lucid, easy-to-distribute, scalable data visualizations and automated products.
The extreme power of data science solutions then in my opinion lies especially in their ability to predict future outcomes – the famous “from descriptive to predictive” – and to get the results to the end-user on an automated basis.