We will combine Cohort Analysis and LifeCycle Grids approach for obtaining more detailed analysis This is the third post about LifeCycle Grids. You can find the first post about the...continue reading.
We will study how we can analyze and visualize other valuable metrics like customer acquisition cost and customer lifetime value using LifeCycle Grids approachWe studied a very powerful approach for...continue reading.
We will study a very powerful approach for customer segmentation that is based on customer’s lifecycle, specifically on frequency and recency of purchasesI want to share a very powerful approach...continue reading.
A few days ago, I collected 30 minutes of tweets all around the world. I used the twitteR and streamR packages for this. The nice thing about those tweets is...continue reading.
I enjoy using ggplot for visualization in ipython notebook environment. It is more flexible that other plotting modules in python. Moreover, it is fairly similar to ggplot in R, and...continue reading.
We will cover how to find patterns of events. We will find events that occur systematically together and in the same order, relationships with customers’ characteristics and association rules between...continue reading.
Consider the following example: there is a three-stage truck maintenance pipeline. Initially, when a Truck comes to the maintenance service, it is added to the first stage and its status...continue reading.
This is the second part of the in-depth sequence analysis and we will use clusterization for simplifying a large number of distinct sequences in a few types of trajectoriesThis is...continue reading.
Although the sankey diagram from the previous post provided us with a very descriptive tool, we can consider it a rather exploratory analisys.Although the sankey diagram from the previous post...continue reading.
We will study how to apply a Sankey diagram visualization when doing a sequential analysis of shopping carts that can bring you a helpful knowledge of patterns of customer’s behaviorWe...continue reading.
We will review a very interesting type of visualization – the Multi-layer Pie Chart – and use it for one of the marketing analytics tasks – the shopping carts analysis This...continue reading.
Many GPS devices and apps have the capability to track your current position via GPS. If you go walking, running, cycling, flying or driving, you can take a look at...continue reading.
This is something I did a while ago using the Berlin Affective Word List (BAWL).The BAWL contains ratings for 2902 German words (2107 nouns, 504 verbs, 291 adjectives). Ratings were...continue reading.
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 postWhen we spend more...continue reading.
We will continue to exploit a great idea of ‘layer-cake’ graph for Cohort analysis Continue to exploit a great idea of ‘layer-cake’ graph. If you liked the approach I shared in the previous topic,...continue reading.
We will study a pretty helpful visualization for Cohort Analysis that is one of the most powerful and demanded techniques available to marketers for assessing long-term trends in customer retention and...continue reading.
ggtern 1.0.3.1 has introduced a new series of geometries to represent known errors in data, they are relatively easy to use and are along the lines of the geom_errorbar(…) and...continue reading.
As if the R world needed another example of Twitter visualizations, right? Well, here we go anyway.At the beginning of 2013, Pablo Barberá released the first version of his R...continue reading.
Histograms and bar charts are almost always a part of data analysis presentation. If it is made with R ggplot package functions geom_histogram() or geom_bar() then bar chart may look like...continue reading.
When building visualizations with ggplot2 in R I decided to create specialized functions that encapsulate plotting logic for some of my creations. In this case instead of commonly used aes function...continue reading.
GIS / R / R News / Shiny / Visualization
Interactive visualization of geospatial data with R Shiny
April 17, 2021
