As we know, a customer usually goes through a path/sequence of different channels/touchpoints before a purchase in e-commerce or conversion in other areas. In Google Analytics we can find some touchpoints more likely to assist to conversion than others that more likely to be last-click touchpoint. As most of the channels are paid for (in
The post Attribution model...
When conducting Cohort Analysis, one of the most important measures is Customer Retention Rate. I will share a few ideas for visualizing this parameter in this post. Last year I shared several charts for Customer Retention Rate visualization in this post. However, it is always helpful to analyze and visualize both relative (Customer Retention Rate) and
The post Cohort analysis:...
There are several posts connected with LifeCycle Grids on this blog. If you are not familiar with the concept I highly recommend you to start with Jim Novo’s book, his blog or, at least, from the first post about on my blog. We will study how to use LifeCycle Grids concept for measuring a health of... Read More »
Sales (purchasing or conversion) Funnel is a consumer-focused marketing model which illustrates the theoretical customer journey towards the purchase of a product or service. Classically, the Sales funnel includes at least four steps: Awareness – the customer becomes aware of the existence of a product or service, Interest – actively expressing an interest in a product... Read More »
Previously I shared the data visualization approach for descriptive analysis of progress of cohorts with the “layer-cake” chart (part I and part II). In this post, I want to share another interesting visualization that not only can be used for descriptive analysis as well but would be more helpful for analyzing a large number of cohorts.... Read More »
This is the third post about LifeCycle Grids. You can find the first post about the sense of LifeCycle Grids and A-Z process for creating and visualizing with R programming language here. Lastly, here is the second post about adding monetary metrics (customer lifetime value – CLV – and customer acquisition cost – CAC) to... Read More »
We studied a very powerful approach for customer segmentation in the previous post, which is based on the customer’s lifecycle. We used two metrics: frequency and recency. It is also possible and very helpful to add monetary value to our segmentation. If you have customer acquisition cost (CAC) and customer lifetime value (CLV), you can easily... Read More »
I want to share a very powerful approach for customer segmentation in this post. It is based on customer’s lifecycle, specifically on frequency and recency of purchases. The idea of using these metrics comes from the RFM analysis. Recency and frequency are very important behavior metrics. We are interested in frequent and recent purchases, because frequency... Read More »
This is the third part of the sequence of shopping carts in-depth analysis. We processed initial data in the required format, did the exploratory analysis and started the in-depth analysis in the first post. Finally, we used cluster analysis for creating customer segments in the second post. As I mentioned in the first post, the sequence can... Read More »
This is the second part of the in-depth sequence analysis. In the previous post, we processed data to the required format, plotted a Sankey diagram, and did some distribution, frequency, time lapse and entropy analysis with visualization. For dessert, clustering! Clustering is an exploratory data analysis method aimed at finding automatically homogeneous groups or clusters in... Read More »