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.
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Video URL: https://youtu.be/cmTdTtQR3G0