End-to-end segmentation solution

1. Implement end-to-end segmentation solution

Fantastic work! You are now fully equipped to build end-to-end customer segmentation projects and that's what we're going to do now.

2. Key steps of the segmentation project

Now, let's recap the key steps of the segmentation project. First, you gather the data. You will use an updated dataset that has recency, frequency and monetary values from the previous lessons, and an additional variable to make this more interesting. Then, you will pre-process the data to ensure k-means clustering works as expected. Next, you will use the techniques from the previous lessons to identify the number of clusters. Finally, you will run the k-means clustering, and then use various analysis and visualization techniques to interpret the results.

3. Updated RFM data

Now, we have created an updated version of our RFM dataset. The additional variable called tenure has been added. This value is calculated as the time since the first transaction, as opposed to recency which is the time since the customer's last transaction. Ultimately, it defines how long the customer has been with the company since their first transaction. And here's the data - as you can see it has the RFM values as previously, and the new tenure feature as the last column.

4. Goals for this project

Now that you've seen the data, you can start working on the final segmentation project. The main goals for running this end-to-end segmentation solution are: First, remember the key pre-processing rules and steps. Second, apply data exploration techniques and identify the number of segments to be used. Then, you will practice running several k-means iterations, and interpret the results by applying quantitative and visual techniques.

5. Let's dig in!

Great job, now let's start working on this segmentation project, good luck!

Create Your Free Account

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.