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 ...

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 ...

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 ...

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 ...

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 – ...

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 ...

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 ...

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 ...

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 ...

Although the sankey diagram from the previous post provided us with a very descriptive tool, we can consider it a rather exploratory analisys. As I mentioned, sequence mining can give us the opportunity to recommend this or that product based on previous purchases, but we should find the right moment ...

We studied how we can visualize the structure of a shopping cart in the previous post. Although you can find a great deal of materials on how to analyze combinations of products in the shopping cart (e.g. via association rules), there is a lack of sources on how to ...

In this post, we will review a very interesting type of visualization – the multi-layer pie chart – and use it for one marketing analytics task – the shopping cart analysis. We will go from the initial data preprocessing to the shopping cart analysis visualization. I will share the R code in that ...

When we spend more money for attracting new customers then they bring us by the first but, usually, by the next purchases, we appeal to customer’s life-time value (CLV). We expect that customers will spend with us for years and it means we expect to earn some profit finally. ...

I want to consider an approach of forecasting I really like and frequently use. It allows to include the promo campaigns (or another activities and other variables as well) effect into the prediction of total amount. I will use a fictitious example and data in this post, but it works ...

Continue to exploit great idea of ‘layer-cake’ graph. If you like approach I shared in previous topic, perhaps you have one or two questions we should answer. Recall “Total revenue by Cohort” chart : As total revenue depends on number of customers we attracted and on amount of money each of ...

Cohort analysis is one of the most powerful and demanded technique available to marketers for assessing long-term trends in customer retention and calculating life-time value. If you studied custora’s university, you could be interested by amazing “layer-cake graph” they propose for Cohort analysis. Custora says: “The distinctive “layer-cake graph” ...

Continue to dig tweets. After we reviewed how to count positive, negative and neutral tweets in previous post, I discovered another great idea. Suppose positive or negative is not enough and we want to understand the rate of positivity or negativity. For example, “good” in tweet has 4 points rating, but “...

Recently I designed a relatively simple code in R to analyze the content of Twitter posts by using the categories identified as positive, negative and neutral. The idea of processing tweets is based on a presentation http://www.slideshare.net/ajayohri/twitter-analysis-by-kaify-rais. The algorithm evaluates tweets based on the number ...