AI News, What Marketing Professionals should Know about Data Science

What Marketing Professionals should Know about Data Science

As a marketer there’s a wealth of data that you have access to, containing information that waits to be harnessed.

In this post, by going through a simple use case of e-mail marketing optimization, we’ll see the overall workflow in applying data science for achieving this optimization and what a marketer should know about it.

The most typical scenario is to see how demographics might relate to the performance but again, considering the data that nowadays a company has access to, there are plenty of factors waiting for you to discover and fine tune.

This is where the data scientist comes into play, what she’s really good at is in getting these questions, formulate them into scientific terms and use a mathematical toolset together with her intuition to figure out if they can be answered and in which way.

Additionally, when you create campaigns do you add extra fields or information inside the titles that characterize the type of content that you are going to share?

This data will be the raw material on which you will build everything, from a simple dashboard to complex models that explain the behavior of customers, so take good care of it and try to keep in mind the following: There is information in the data that is explicitly there for you to read but that usually is just the tip of the iceberg.

Let’s consider that you want real-time notifications based on the outcome of online analysis that your data scientists have created, but your infrastructure is working with batches of data every 24h.

Based on a subset of the data that Mailchimp exposes we have the following: With the above we can do the following: So, we’d like to know how the above parameters affect the open rate of our email campaign and if we manage to do that with some certainty, then we can start adjusting them to optimize it.

2 might be done by: The rest of the steps are performed by the data scientist, but when a conclusion is reached on the analysis, a pipeline might be implemented that will automate all steps from 1 to 4 which will require again the involvement of engineers.

What will be reported back is of course heavily related to the questions asked if for example, the data scientist applied logistic regression to figure out how the above parameters affect the possibility of opening an e-mail campaign, she most probably will end up with something like this:

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