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Based on the most helpful WSO content, a "customer cube" in the context of software banking could be interpreted as a multidimensional analysis of customer data, often used to understand customer behavior, segmentation, and profitability. While the term "customer cube" isn't explicitly mentioned in the WSO dataset, there are relevant insights on how customer data is analyzed in banking and SaaS contexts:
Transactional Sales Data Analysis: In restructuring or corporate banking, transactional sales data is often sliced and diced using tools like Tableau to create visualizations. This helps in identifying patterns such as long SKU tails, low-margin products, or inefficiencies in promotions and discounts. A "customer cube" could similarly involve breaking down customer data by dimensions like geography, product usage, and revenue contribution.
Customer Metrics in SaaS: For SaaS companies, key customer-related metrics include gross/net retention rates, customer acquisition cost (CAC), new customer growth rate, and annual recurring revenue (ARR). A "customer cube" might integrate these metrics to provide a comprehensive view of customer performance and profitability.
Customer Concentration Analysis: In investment banking, analysts often create slides showing the top customers and their percentage of total revenue. This type of analysis could be a component of a "customer cube," focusing on revenue concentration and dependency.
If you're looking to build a "customer cube," consider integrating these dimensions and metrics into a multidimensional model to analyze customer data effectively.
Sources: PE recruiting technical questions (software specific), Basic Overview of the Consumer Sector, Ask CompBanker, Thoughts on Restructuring Groups?, SaaS LBO
It’s just a big file with the names of the customers on the left and the monthly revenue on the right usually over a couple years
You can do a retention analysis off of it and customer concentration
How do you calculate retention using the file?
You can see the revenue per customer month over month so make a formula that looks for and calculates the upsell downsell new customers churned customers for the period then calc you gross and net retention
Thanks I understand you calculate monthly but for the quarterly or yearly summaries, would you use sumifs to roll up the monthlies or the same formulas?
Bump please help
Yes you can do that
Which is correct for annual and quarterly: use sumifs on the monthly OR use the same retention formulas as the monthly? I ask because retention would change depending on the approach
You can do both:
The formulas/mechanics are the same though with regards to both gross and net retention.
Sorry I'm probably slow but do you have a good example/resource you could share?
No - typically the way to think about it is you'll have 3 sections. One is the monthly ARR or MRR by customer for each month over the historicals and then the second section, you'll have tags for each YoY change of the ARR or MRR s.t. your tags are upsell, downsell, new or churn. Upsell formula is saying the ARR increased, downsell means it decreased, new is going from 0 to nonzero and churn is nonzero to zero. The third section is just going to be YoY change in ARR so current period ARR minus the ARR 12 months ago for each customer. Then you can create annual ARR rollfowards where the beginning of period (BoP) is just the total ARR for that period (ie. let's say we're looking at the 2024 retention so BoP would be the 2023 total ARR), then you can run some sumifs on the different types of changes (new, churn, downsell, upsell) on the YoY change column for Dec-2024. If done correctly, the 2023 ARR plus the different changes for 2024 will equal the total 2024 ARR. To calculate the gross retention, you do the 2023 ARR + downsell + churn divided by the 2023 ARR and for net retention you also include upsell in the numerator. Some sellside bankers try to only include churn in the gross retention since it's less punitive but that's not really the best way to look at it.
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