“It may be necessary to stand on the outside of one is to see things clearly.”
― Peter Høeg, Tales of the Night
I believe that the best way to learn about marketing data science is to work through examples. In this blog post, I would like to talk about
Customer segmentation is the process of dividing customers into groups based on common…
“To be, or not to be, that is the question…”
- William Shakespeare, Hamlet
In real-world situations, data scientists often start an analysis with a simple and easy to implement model such as linear or logistic regression. There are various advantages of this approach such as getting a sense of the data with a minimum cost and giving food for thoughts on how to solve a business problem.
In this blog post, I decided to start from the opposite side by applying a multilayer perceptron model (neural network) to predict customer churn. …
“It is the time you have wasted for your rose that makes your rose so important.”
― Antoine de Saint-Exupéry, The Little Prince
As a data scientist working in marketing, I find it quite challenging to combine a few fields like marketing, machine learning and statistics, and produce insights, which make sense. In this blog post, I want to show an application of machine learning in marketing, particularly, in defining and predicting Customer Lifetime Value (CLTV). …
In this part of the blog post, I would like to share with you how we can draw inspiration from patterns that have been noticed and explored by others to make logical reasoning. I believe that it is useful to gain a good mental library of various examples of reasoning to have a map to lead us to well-worn trails that take us where we need to go. I think this approach can be applied to analytics as well to optimize our decision-making process. But this is probably a topic for another blog post.
We will explore the following groups…
“What has been affirmed without proof can also be denied without proof.”
I believe that every data scientist is aware of the necessity to have “soft-skills” and critical thinking, which have the same importance as an ability to write clean code and apply machine learning algorithms. That is why I decided to learn some approaches and frameworks from the social sciences to improve the quality of analyses I do. Particularly, I wanted to get to know more about arguments and patterns of reasoning. In the first part of the blog post, I would like to share with you…
CoNVO framework to structure and systematize our thoughts
“The only true wisdom is in knowing that you know nothing.“ — Socrates
Have you ever thought about how to pose the right questions while working on a data science project? I got this question after joining my current company, where I closely work with about 25 data scientists. I have noticed that some really great data scientists, apart from having strong “hard-skills”, are also good at asking questions. These great data scientists tend to attend to the problem of why and so what before diving into how. …
Unleash new opportunities with data
Bringing new customers to a service is a common business problem, which can be solved by analyzing data of existing customers and the general population.
In this blog post, I would like to talk about how a company can bring new customers through customer segmentation and analyzing general population using supervised and unsupervised machine learning.
1. Explaining types of customer segmentation
2. Getting to know the data
3. Data cleaning
4. Data preprocessing
5. Applying k-means clustering to find segments within existing customers
6. Calculating Euclidean distances to find similar people in the general population
Some of my friends aspired by “the sexiest job in the 21st century” ask me how to enter the field and how to learn Data Science within less than one year as I did. I often start my answer with a few suggestions and following questions, which I want to share with all of you.
My name is Aigerim. I fell in love with Data Science while I was attending a presentation of an AI and ML circle at The University of Tokyo, where one of the post-docs was showing how to use Random Forest Classifier to solve a problem…
In this post, I would like to interpret a Random Forest classifier using SHAP values and along with that to answer the following questions:
1. What kind of characteristics have customers who placed a deposit?
2. What kind of characteristics have customers who did NOT place a deposit?
3. Based on the available data, what can be done next time to increase CVR?
I used a public dataset with the results of Portugal bank marketing campaigns. Conducted campaigns were based mostly on direct phone calls, offering bank client to place a term deposit. …