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Data Analytics Mistakes to Avoid | Data Driven Marketing

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Today we’re going to break down the 3 major data analytics mistakes that lead to misleading results in your data driven marketing, this can ultimately affect the growth of a business! Data is becoming more and more available, so data driven marketing is becoming easier and more essential! But without good data analytics, your conclusions can become misleading! Even with good quality data, we find people make frequent data mistakes in their rapid experimentation process! These are due to both inexperience with data analysis and a pressure to report significant findings to the rest of the business. How to Prepare Data for Machine Learning and A.I. video 👉https://youtu.be/TK-2189UcKk Link to data fallacies article 👉https://www.geckoboard.com/assets/data-fallacies-to-avoid.pdf The 3 Major Data Analytics Mistakes to Avoid in Data Driven Marketing: 1. Data Dredging Data dredging is repeatedly testing the new hypothesis against the same sample data until you finally find some significant results. This is common in cases where we test lots of different variations of a website or product, often with ab testing copy, calls to actions etc. When following the conventional level of significance, which is 5%, the risk is that 5 random variations could be significant. This is called the false positive risk or error of type 1. 2. False Causality The second data analytics mistake to avoid is false causality. Remember, Correlation is not equal to causation! This is why we run ab testing and multivariate testing. We always want to compare the performance of new ideas with the business as usual control group. At Growth Tribe, our growth process encompasses both machine learning steps to predict customer behavior in the pirate funnel but also the causal inference is given by experimentation. This is basically the data science definition of growth hacking! Machine learning can help us find the correlations but you should also test if there is a causal effect between out idea and the growth metric! 3. Overfitting in Machine Learning The last data analytics mistake is overfitting in machine learning. More complex algorithms can become overly tailored to the data. It can lose its generalization power to predict future cases. Different to statistical analysis, in machine learning, we have to keep a smaller portion of the sample to test how the model will perform when looking at new cases. If the accuracy is lower in the test set than compared to the training set then the model is probably suffering from overfitting. To prevent overfitting in machine learning we can apply regularization techniques. ------------------------------------------------------- Amsterdam bound? Want to make AI your secret weapon? Join our A.I. for Marketing and growth Course! A 2-day course in Amsterdam. No previous skills or coding required! https://hubs.ly/H0dkN4W0 OR Check out our 2-day intensive, no-bullshit, skills and knowledge Growth Hacking Crash Course: https://hubs.ly/H0dkN4W0 OR our 6-Week Growth Hacking Evening Course: https://hubs.ly/H0dkN4W0 OR Our In-House Training Programs: https://hubs.ly/H0dkN4W0 OR The world’s only Growth & A.I. Traineeship https://hubs.ly/H0dkN4W0 Make sure to check out our website to learn more about us and for more goodies: https://hubs.ly/H0dkN4W0 London Bound? Join our 2-day intensive, no-bullshit, skills and knowledge Growth Marketing Course: https://hubs.ly/H0dkN4W0 ALSO! Connect with Growth Tribe on social media and stay tuned for nuggets of wisdom, updates and more: Facebook: https://www.facebook.com/GrowthTribeIO/ LinkedIn: https://www.linkedin.com/school/growt... Twitter: https://twitter.com/GrowthTribe/ Instagram: https://www.instagram.com/growthtribe/ Video URL: https://youtu.be/cmTdTtQR3G0
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Text Comments (19)
Growth Tribe (2 months ago)
Check out the video on how to prepare data for machine learning 👉 https://www.youtube.com/watch?v=TK-2189UcKk
Christoph Schachner (1 month ago)
Great video. Curious to know how you use Machine Learning and AB Testing. Neither Google Optimize nor VWO have them so what tool do you use for that?
Bernardo F N (1 month ago)
For machine learning experimentation and deployment we use Dataiku. Also, for experimentation, only you can use Orange Data Mining. Both have free versions. You can do mean comparison tests in both, and they also support Python scripts.
Thomas Henson (1 month ago)
Great video! Data dredging happens all too often when constrained with data sets. This is the main reason I believe if you want to win in innovation (analytics/machine learning) YOU have to keep collecting data. Those who hold the most data WIN.
Conti Music (1 month ago)
Quite nice, i think you would like my music for your videos as well, perhaps you would be intersted to use my music for your next video? Good work!
Giulia Forghieri (2 months ago)
It's really difficult to find good and relevant videos about Data. Thanks for your content!
Lasse Grosen (2 months ago)
Thanks - very helpful content 👌
Ferdinand Koelewijn (2 months ago)
Cool vid!
Regan Kirk (2 months ago)
Great video again guys.
Paolo Campagnoli (2 months ago)
Super useful!
Adriana Petre (2 months ago)
Great value as always!! Thanks Job:)
Judit Szaffenauer (2 months ago)
cool video! looking forward to the next one ;-)
Luke Johnson (2 months ago)
Awesome video Job! And great animations!
Max van der kolk (2 months ago)
Great insights in such a compact video!
Roxane Caudron (2 months ago)
Such a clever video, thanks!
jucikad (2 months ago)
nice one! very useful that you explain these ☺️☺️
Jean Bonnenfant (2 months ago)
Nice one!!!!!!
Bernardo F N (2 months ago)
Correlation != Causation.
Camiel Roex (2 months ago)
Amazing content!

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