A brief introduction to causal inference using Bayesian structural time series models in R with the CausalImpact package

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If you work in marketing, sometimes what you do can seem like a bit of a lottery. That’s perhaps not how you like to report it upstairs, but how confident are you that your campaign really led to that increase in revenue?

In this article, we’ll take a brief look at a technique that might help you put some numbers behind your confidence in those statements: Bayesian structural time series analysis.

If your company has a great deal — perhaps 25% off in January — you may well have some budget for a marketing campaign to promote that. …


Are graduates equipped with the expertise an increasingly data-driven field requires?

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A recent article in Marketing Week discussed whether universities were properly equipping their graduates for a career in marketing. With marketing producing ever larger amounts of data, combined with a desire to have more certainty when assigning attribution, it certainly seems as though having a range of data analysis and statistical skills would be helpful to any marketer — something that the industry is aware of — but are those skills a priority and a focus for the universities?

How marketing can fill its data skills gap by rethinking its recruitment strategy

I wondered, in these data-driven marketing times, how…


It might not help the message, but it looks good on Twitter

My first article in Towards Data Science was the result of a little exercise I set myself to keep the little grey cells ticking over. This is something of a similar exercise, albeit a bit more relevant to a project I’ve been working on. As I spend my time working in a marketing department, I have to get used to wearing [at least] two hats.

Often, these hats are mutually exclusive. In this case, the disagreement is in the form of another piece of animated data visualisation. …


Using your skills to improve the world and the lives of its people

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There’s no denying that data science is one of the major trends of the moment, from artificial intelligence research, disease diagnosis and autonomous vehicles, to fraud detection, digital marketing, human resources management and beyond.

Many of you reading this may well be data scientists and you’ll know all that already. Some of you might not be, but have heard a lot about it, and some of you might not be, but have a professional interest in how it can be applied in your sector.

Many organisations have already embraced data science and can support large teams working on everything from…


With the CASE Europe Development Services Conference recently concluded in Sheffield, and as we near the first six months post-GDPR, it seems an appropriate time to talk about something that continues to generate press: personal data.

In this brief post, we’ll take a quick look at the current state of play post-GDPR and ask how this is affecting fundraising.


Why setting yourself some random challenges every so often can help your life as a part-time data scientist — an example using Scottish Rugby title winners and animated bar plots…

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Data science isn’t like riding a bike. Not for me, anyway. If I don’t keep using it, I lose it. I need to keep the little grey cells active before they forget the important R functions or syntax they spent time learning.

I find this particularly hard as my R work is spread so widely. I might build a quick ML model every three or four weeks, basic data wrangling, EDA, data visualisation and some regressions a few times a week, some unsupervised learning every fortnight and maybe a sentiment analysis or geo-mapping project every couple of months. …


Techniques for when the data isn’t a straight line…

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If I were to recommend one technique for marketers who want to understand their data better, it would be linear regression. It’s quick, it’s relatively easy to get to grips with and, as I hope I got across in a previous article, the code to perform a regression analysis in R is very straightforward.

In this article, we’ll expand on part one and look at a real-world dataset from a pay-per-click campaign to explore what we can do when our dataset starts to curve away from a straight line.

For further adventures where marketing meets data science, follow Chris on…


Planning my goals for #HeroConf

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It has now been about six and a half years since I had my career change from academic science. The majority of my working days since have been spent as a marketer, first in content and SEO, then into PPC, attribution modelling and analytics across a range of channels. However, this article isn’t about my background. It’s about how, after several years in the industry, I’m about to attend my first marketing conference. And, if I’m honest with myself, at the point when I registered, I wasn’t exactly sure what I was hoping to get out of it.

As a…


How to use regression to analyse everything from PPC to print to web analytics to radio and more using R

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Regression analysis is a powerful tool for marketers. Regression sounds really important, doesn’t it? It sounds like proper ninja mathematics stuff, particularly when you add various qualifiers in front of it: linear regression, multiple regression, polynomial regression. “What are you working on Katie?”, “Oh, just analysing how our ROAS changes with PPC spend using non-linear regression.”

Pretty dang sweet.

While it sounds really ninja, it’s a concept that’s very easy to get started with. Completely understanding the assumptions that underpin the models, interpreting models and drawing conclusions, tweaking and improving models (and more besides) can take time, but that doesn’t…

Chris Bow

Former immunologist turned data scientist and marketer. Proponent of applying scientific thinking to non-scientific problems. Consultant for Cairney & Company.

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