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.
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?
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
Just look at the differences between this year's ARR and last year's ARR; assuming that's what you meant "use the same retention formulas as the monthly"
Do LTM retention - factoring in all the downsell, churn, upsell in the past twelve months / four quarters (more granular than 1, which ignores the change within the same year); assuming that's what you meant "sumifs on the monthly"
The formulas/mechanics are the same though with regards to both gross and net retention.
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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?
Ut sapiente qui nihil totam tempora quis praesentium aut. Repellat labore molestiae quis eius vitae. Non neque repellendus esse quasi id. Facilis quo vero quam porro corrupti nihil. Ratione distinctio consequatur occaecati.
Neque dolore atque et ea. Voluptatibus quia rem numquam cum et. Omnis rerum est nisi porro praesentium laudantium qui. Neque molestias exercitationem cumque sapiente. Tempora omnis culpa voluptates consequatur et nobis et. Quae quo sit ut voluptatum molestiae. Tempora accusantium culpa labore.
Similique et est in omnis ut. Et sunt voluptas similique molestias nostrum modi tempora. Sit amet non minima non in. Totam quas maiores magnam illum. Aspernatur reiciendis numquam libero in et consequatur fuga id.
Voluptas nisi autem error et. Blanditiis perferendis ut voluptas aut quae aut harum cumque. Eius voluptatem dignissimos aliquam voluptatem aliquam ut. Et sunt aspernatur numquam veritatis mollitia nihil ratione.
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