Understanding Extended Customer Revenue - how ECR measures the full value of advocates, including segments, performance tracking, predicted data, and subscription considerations.
Extended Customer Revenue (ECR) is the revenue a customer spends (their Individual Customer Revenue, or ICR) plus the revenue generated by their referred friends.
ECR = Individual Customer Revenue + revenue from referred friends
Brands that only focus on their highest spenders could be missing out on their most valuable customers: their brand advocates. ECR allows you to track the additional revenue brought by customers who refer their friends, revealing previously unrecognised value.
Advocates generate more total value than high spenders alone
The acquisition filter determines the time period for calculating individual spend and referred friend spend, starting from the acquisition date of the original customer. You can select 30, 90, or 180 day periods.The longer the time period, the more time there is for the original customer to make additional purchases and refer more friends, and for those friends to purchase.
The acquisition filter with 30, 90, and 180 day period options.
The active/dormant/lapsed time periods are unique to your business, based on your customers’ purchase cycles. You can see the exact values in the “How we’ve defined Active, Dormant and Lapsed” table at the bottom of the page.For a customer to fall into active, dormant, or lapsed, they must make either a purchase or a referral within the specified time period.The periods are calculated using the cumulative sum of subsequent orders in relation to time since the previous order, as a proxy for the average probability of repeat purchases. The Dormant and Lapsed states are found at the intersection of the probability curves with specific statistical thresholds.
Predicted ECR requires the Optimise, Advanced, or Ultimate package.
Predicted ECR is a machine learning model designed to forecast how advocacy is likely to evolve over the next 12 months within your existing customer base.You can use Predicted ECR to see:
Historical patterns are used to predict customer lapsing, purchase, and referral behaviour in the next 12 months. This gives personalised propensities for each customer, enabling calculation of how the make-up of your base might change. The prediction takes into account recency, frequency, and value (RFV) for both orders and referrals.
If your business model is subscription-based (or partly so), additional data is needed to get full value from ECR, Network Insights, and Earned Growth. The existing tag integration only captures the initial purchase or signup, meaning subsequent subscription revenue is not visible.
Provide subscription orders not captured through tags (e.g. renewals).
Then set up a regular SFTP transfer of those orders on a daily or weekly basis. Check your Integration Instructions for SFTP setup guides, or reach out via the help form.
