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Overview Content

Introduction- What "Shaping" Revenue Streams Means / Requires
What is the Goal of "Shaping" Revenue Streams?
Which assumptions do I use when shaping revenue streams?
Why do we use "Optimize Metric Value" Assumptions in most cases?

Specific Revenue Strategies and the Assumptions for Each Metric
Where do I add assumptions to E-Commerce Revenue?
Where do I add assumptions to Marketplace Revenue?
Where do I add assumptions to Monthly or Annual Per Unit Subscription Revenue?
Where do I add assumptions to Monthly / Annual / Quarterly Subscription Revenue?
Where do I add assumptions to Services Revenue?
Where do I add assumptions to Transactional Revenue?

Summary / Recap
Summary of "Shaping Revenue Streams:

What does it mean to "shape" Revenue Streams?

Interestingly enough, "shaping" revenue actually consists of making changes in two sections of the model- customer acquisition (bringing in new customers) and revenue streams (managing existing customers and what / how they pay for products / services). This video is dedicated to assumptions in the latter category. Keep in mind though that even after we make changes in revenue streams, it will be necessary to "shape" customer acquisition since your revenue forecast is tied not only to price and purchase behaviors but also the number of new customers / sales you have coming into the model.

You want to make sure you've put in accurate price data.
- For example, if your subscription is $50 each month or your average transaction size on your e-commerce website is $200 you want to be sure these are the amounts put into the model.
You also want to change your initial purchase assumptions changing over time
- The goal is to make metrics that bring in more revenue (number of purchases per customer, average price paid, etc.) increase over time and metrics that limit revenue over time (churn rate)

The Goal of "Shaping Revenue Streams"

Which "assumption" types should you be using and when?

Optimize Metric Value- This will be the assumption you add in just about every situation in order to set long-term targets / expectations for changes in behavior.

Why We Use "Optimize Metric Value" Assumptions in Most Cases

E-Commerce Revenue

Average Order Value- How much does the average bucket of goods sell for on your website?
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the average order value to be by some target date.
Monthly Orders Per Customer-
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the number of orders to be by some target date.
Returning Customer Rate- By default, this is the percentage of customers purchasing last month who are expected to purchase again this month.
- This is also commonly set up to represent some % of all customers who have ever purchased that are expected to repurchase again in any given month by making a slight adjustment to the formula.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the returning customer rate to be by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.

Marketplace Revenue

Take Rate- What percentage of a transaction on your platform makes it to you as usable revenue?
- Assumption Type to Add: One Time Change Assumptions -Usually changes in the take rate for a marketplace don't occur over time they're narratively deliberate changes made at specific dates.
Average Transaction Size- The average value of a purchase between buyer and seller on your platform.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the average transaction size to be by some target date.
Average Monthly Transactions Per Customer-
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the number of orders to be by some target date.
Returning Customer Rate- By default, this is the percentage of customers purchasing last month who are expected to purchase again this month.
- This is also commonly set up to represent some % of all customers who have ever purchased that are expected to repurchase again in any given month by making a slight adjustment to the formula.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the returning customer rate to be by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.

Monthly Per Unit Subscription Revenue

Price Per Unit- Amount charged per person / per seat.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the average price per unit to be by some target date.
Average Units Per Subscription- Average Number of occupied / billable seats or people per subscription holder
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the number of units per subscription to be by some target date.
Churn Rate- Also called your rate of attrition, the percentage of customers you expect to leave at every interval of payment.
- Because this metric effectively limits or decreases revenue (when the value is higher) you'll want to show this metric decreasing over time.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the returning customer rate to be by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.

Subscription Revenue

Price- Amount charged per person.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the price to be by some target date.
Churn Rate- Also called your rate of attrition, the percentage of customers you expect to leave at every interval of payment.
- Because this metric effectively limits or decreases revenue (when the value is higher) you'll want to show this metric decreasing over time.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the returning customer rate to be by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.

Services Revenue

Average Hourly Rate- How much do you charge per hour for services provide on average?
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the average hourly rate to be by some target date.
Average Monthly Hours Billed Per Customer- Average number of hours that services are required on a typical job for each customer (within a month).
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the number of average monthly hours billed per customer by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.

Transactional Revenue

Take Rate- What percentage of a transaction on your platform makes it to you as usable revenue?
- Assumption Type to Add: One Time Change Assumptions -Usually changes in the take rate for a marketplace don't occur over time they're narratively deliberate changes made at specific dates.
Average Transaction Size- The average value of a purchase between buyer and seller on your platform.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the average transaction size to be by some target date.
Average Monthly Transactions Per Customer-
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the number of orders to be by some target date.
Returning Customer Rate- By default, this is the percentage of customers purchasing last month who are expected to purchase again this month.
- This is also commonly set up to represent some % of all customers who have ever purchased that are expected to repurchase again in any given month by making a slight adjustment to the formula.
- Assumption Type to Add: Optimize Metric Value -Set a target for what you expect the returning customer rate to be by some target date.
% of Total New Customers- What % of customers are choosing this specific product offering out of 100%?
- (Optional) Assumption Type to Add: Optimize Metric Value- Set a target if you expect the % of customers to shift because of new tier offerings or expectations that customers will gravitate to certain tiers.