This post is the ninth in a series discussing a machine learning use case for a mobile app provider. The link to the full case study can be found in the first post of this series: https://www.inetsoft.com/blog/machine-learning-concepts-defining-churn-predictive-metrics/
We end this series of informational posts with a question:
When do I need a data scientist?
To get started and to experiment with machine learning, in marketing or any business function, you do not need a data scientist. You just need to be as skilled as an Excel power user, someone who knows the data and is comfortable doing analytics.
When you define the business goal, the conceptual knowledge you possess so far on machine learning from reading our series of articles should allow you to decide which class of machine learning algorithms to look at. With a proper software tool, experimenting with a small dataset is becoming more and more practical. As mentioned previous post, you can even just start with your PC.
And as mentioned before, beyond the type, you don’t need to understand much what the algorithm is doing. With just trial and error you can see which of the models gives the best accuracy.
When you’ve identified the best model and its fit is over 70%, then it is worth planning on putting it into production. And that is the right time to engage with a data scientist and your IT support staff to fine tune things and take the necessary steps to productionalize the model and the subsequent business actions it calls for.
Also, if you can’t manage to reach a worthwhile level of accuracy, that is another time to ask a data scientist for help, and learn what possible tricks they might have up their sleeves to augment the data or seek other models.
If you’re ready to embark on a machine learning project, and you’d like even more handholding, we are raising our hand. Our software makes it as easy as possible to run machine learning models, and an InetSoft data scientist will be assigned to help!
