Customer Analytics and Cohort analysis
Retail Dashboard Series (3/3)
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This article is part of a series about Retail Dashboard. (Part 1: Sales Overview Dashboard), (Part 2: Product detail and Store Dashboard), and (Part 3: Customer dashboard and Cohort Analysis)
The same data set of Customer Analytics. (Part 1: Customer Profiling with Descriptive Analytics with SQL), (Part 2: Customer Segmentation with Clustering), (Part 3: Market Basket Analysis), and (Part 4: Product Recommendation)
Note:
- Data set from Dunnhumby_Carbo-Loading
Customer Analytics
- I think we start from “% Active customer” by using Gauge chart.
- Use Waterfall chart to show the amount of customer type (“New”, “Resurrected”, “Repeat”, “Churn”, and “Inactive”.
- Base on RFM model, separate customers into 5 groups (use percentile at 0-20, 21-40, 41–60, 61–80, and 81–100). Then, use Stacked bar chart to show the percentage of total lifetime spending based on R-score, F-score, and M-score.
- From Life time Total Spend by R-score graph, the customers who have high R-score (low days since last visit) have the biggest portion for spending.
- Use Scatter plot to show RFM information
- Overview of Customer Analytics dashboard
- After customer segmentation based on RFM model, it can show revenue contribution. We should take care top percentage more than others.
Cohort Analysis
- Example only 14 months.
1. Customer Count (Count the customers coming, based on their acquisition month)
- For example, out of the 28686 customers who purchased in Apr 2020 for their first time, 6918 purchased again one month later.
2. Percentage of new customers coming back
- For Apr 2020, 24.12% = 6918/28686
3. Average spend of customers
- The cohort is a very useful tool to show customer behavior month on month basis. I learn cohort coding from https://finance-bi.com/blog/power-bi-cohort-analysis/
Note:
- Thank you skooldio for an excellent course (Hand-on PowerBI).
- Thank you K.Luca for cohort coding that I learn from https://finance-bi.com/blog/power-bi-cohort-analysis/
Please feel free to contact me, I am willing to share and exchange on topics related to Data Science and Supply Chain.
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