Charles | E-commerce Analyst @CasmirCodesData

@iksly2 😂😂😂

@Data_girrl Happy Early Birthday 🎂

@callmi_koko Congratulations 🎉

@_dorcasmicheal Drilled? No be thrilled again? 😂😭

@stellaanalyzes Congratulations 🎉

@DabereNnamani Good morning Annie

I love the idea of learning new things, new language, new words etc….

@_VictorUgwu Nice one 🫡

Final look and report x.com/casmircodesdat…

Charles | E-commerce Analyst @CasmirCodesData

3 days ago

Designed a wireframe using figma and Claude for an E-commerce Customer Behavior dataset using the RFM segment ( Recency, Frequency and Monetary Value)

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@GrinAge_001 Thank you 🙏

Designed a wireframe using figma and Claude for an E-commerce Customer Behavior dataset using the RFM segment ( Recency, Frequency and Monetary Value)

This is the final look and report x.com/casmircodesdat…

Charles | E-commerce Analyst @CasmirCodesData

2 days ago

I found a group of customers spending $777 per order… and almost missed them completely. Buried in 4,338 e-commerce customers was a segment that barely shows up, barely orders, and barely gets noticed but when they do buy, they outspend everyone. Meanwhile, 93% of this

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Big Spenders (At Risk) — low recency, low frequency, smallest segment by customer count (374) but the highest average order value of any segment ($777.67). Quiet, easy to overlook, but valuable per order. Converting even a portion of this group into Champions could meaningfully boost revenue. At Risk -used to buy frequently, but hasn’t purchased recently. This is the clearest churn-warning pattern in the data high past engagement, recent silence. Hibernating — low across all three RFM measures. The second-largest segment by customer count (951) but the lowest average order value ($203.93) and among the lowest total revenue ($0.2M) a large group that’s both inactive and was never especially high-value. Limitations The dataset doesn’t include variables like pricing changes, marketing campaigns, or customer service interactions so while it’s strong at showing patterns (who, how much, where), it can’t explain the underlying causes behind churn or spending behavior.

I found a group of customers spending $777 per order… and almost missed them completely. Buried in 4,338 e-commerce customers was a segment that barely shows up, barely orders, and barely gets noticed but when they do buy, they outspend everyone. Meanwhile, 93% of this business’s $8.9M in revenue came from customers who simply came back. Turns out the loudest customers (Champions) aren’t always the richest ones. Sometimes the real money is hiding in silence. E-Commerce Customer Behavior & RFM Analysis (2010–2011) Tools used: @Microsoft365 @powerbidays I picked up a dataset on e-commerce customer behavior and used RFM segmentation to break customers into groups: •R (Recency) — how recently a customer last purchased •F (Frequency) — how often they’ve ordered •M (Monetary) — how much revenue they’ve generated Note: This is a descriptive analysis, not a diagnostic one it shows what happened and where, not why. The dataset doesn’t include the variables (pricing, marketing, customer service) that would be needed to explain causes behind churn. Questions this dashboard answers 1.How many customers do we have, and how many are active vs. churned? 2.What’s the average order value and total revenue? 3.How much revenue comes from repeat customers vs. one-time buyers? 4.How does average order value differ between active and churned customers? 5.Which countries drive the most revenue, and how does churn vary by country? 6.Who are our most valuable customer segments, by revenue, order frequency, and average order value? Overview We have 4,338 total customers for the period Dec 2010–Dec 2011. 33.4% have churned (defined as no purchase within 90 days of their last order), and 66.6% are still active. Average (weighted) order value across the business was $480.87, generating $8.91M in total revenue. Repeat customers Repeat customers generated 93.08% of total revenue ($8.30M), compared to just 6.92% ($0.62M) from one-time buyers. Retention, not acquisition, is where most of the value in this business sits. Active vs. churned spending Active customers have a higher average order value ($497.11) than churned customers ($385.15) churned customers were, on average, lower-value spenders even before they stopped buying. Country breakdown The dataset spans 37 countries; the table shows the top 10 by revenue. The United Kingdom leads by both revenue and total customers, and because of its sheer size also has the highest raw number of churned customers, though its churn rate (33.3%) is in line with the overall average, not unusually high. Countries with fewer than 20 customers are flagged, since churn rate and AOV for very small groups can swing heavily on just one or two customers. Customer segments Using RFM scoring, the 4,338 customers split into 7 segments: Champions —bought recently and often. Highest customer count (1,035), highest total revenue ($5.8M), and the most orders overall. Their average order value ($525.11) is still above the overall average, though not the highest Big Spenders and Potential Loyalists edge them out on a per-order basis. Loyal Customers — reasonably recent, reasonably frequent, a step below Champions in intensity. Still a solid, dependable group (592 customers, $1.1M revenue), though their average order value ($418.65) is below the overall average. Potential Loyalist — bought recently, but not often yet. This is a genuine growth opportunity: they can be nurtured toward becoming Loyal Customers or Champions, and they already carry the second-highest average order value ($534.61) in the whole customer base. New/Occasional — moderate recency, low frequency. Lower spend, lower revenue, lower frequency across the board ($370.97 AOV, $0.2M revenue). This group needs consistent follow-up to avoid losing them before a habit forms. Check the comment for the rest of the report👇🏾👇🏾

@AdetunjiAbassi @kudehinbu_jr Congrats skii 👏

@vheeorji22 Kaggle and generating from AI, but dataset from AI is not reliable most times.

@iksly2 Well done 😂

@Rita_tyna Congratulations 🎊

@Data_girrl Congratulations 🎊

@DabereNnamani 😂😂