what are the best practices for upsells and downse...
# ask-stanley-ai
c
what are the best practices for upsells and downsells using suite billing and revenue recognition?
c
AI generated answer (using internal Slack threads):
****** Disclaimer: This response was generated by an artificial intelligence assistant based on previous conversations from multiple Slack channels, and may contain inaccuracies or mistakes. ****** Based on the conversations, here are some best practices for upsells and downsells using SuiteBilling and revenue recognition: 1. Automation: SuiteBilling allows you to set up a lot of billing automations, including handling advanced revenue recognition with the ARM module. This can automate aspects such as upselling and downselling. 2. Customization: If it's about "normalizing" invoicing according to Up/Down selling, creating a custom billing schedule on the fly can work perfectly. This allows you to adjust the billing schedule based on the upsells and downsells. 3. Tracking: For tracking net recurring revenue, you can create a SuiteAnalytics workbook that annualizes the MRR to ARR. You can also create an ARR rollforward where you track New, expansion (upsell), downsell, churn, and adjustments. 4. Third-Party Solutions: There are third-party solutions similar to SuiteBilling, such as ZoneBilling, that can handle upsells, downsells, and usage charges. These solutions can be useful if you have a large organization with multiple subsidiaries and a significant amount of recurring revenue. Remember, the choice between SuiteBilling, third-party solutions, or a custom solution will depend on your specific needs and the complexity of your upselling and downselling processes.
Slack thread 1: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.837434649
Slack thread 2: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.828197062
Slack thread 3: here (available only if the original thread was posted in the last 90 days)
Similarity score: 0.827173
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