Referral Marketing Growth Concepts

At its core, a referral program can be broken down into a math equation that captures how effective we are at prompting users, the performance of shares, and the value created through the advocate and friend. Our strategy hinges on affecting all of these elements by order of priority.

Referrals can be broken down into equations that capture Quality, Performance, and Value.

Quality

To capture quality, we need to understand how successful we are when prompting a user, how many unique friends will click their referral link, and how many of those will convert to being customers (or whichever success metric we care about):

Q_R

Quality of referrals, a multiple that establishes the value of prompting a user to refer

f_IA

Fraction of users that are invite-active (ie: activation rate, the % of users that refer when prompted)

n_C/S

Average number of unique clicks per share by an advocate

f_Conv

The conversion rate of a referred friend to customer

Quality Example

In this example, Q_R comes out to 0.00225, meaning that for every user in our user base, we’ve gained 0.00225 new users through our referral program. But, this doesn’t mean much yet as we haven’t established any context - we’re just looking across the entire user population. We need to take it a step further.

Performance

To properly gauge performance, we need to add context around the population of users that we prompt. We are being verbose instead of using Q_R as the quality may differ based on the population.

P_R

Performance of referrals for a given cohort

P_U

Population of users

Performance Example

For this example, let’s assume we are gauging the performance of referrals for our most recent 100,000 new users that have signed up in the last 30 days. As you can see, Q_R is vastly better for this cohort!

According to this example, we’ve gained 5% more users simply by prompting our most recent users to refer friends to the program. In other words, the presence of a referral prompt in the first 30 days of a new user’s journey adds 5% to the performance of the entirety of new customer acquisition efforts.

Value

To capture our value and understand the financial impact of referrals, we need to go one step further and capture not just the LTV of each new referred friend, but also the change in LTV of the advocate when they successfully refer a friend.

This is critical because it reflects the nature of the incentives used in a referral program. If we rely on monetary incentives that save customers (both advocates and friends) money, we could reduce the value of customers that are active in the program. If we use incentives that drive further usage, we can increase the value of referral active users. Finally, we must capture the real cost of incentives for both the advocate and friend.

V_R

Value of referrals for a given cohort

n_ΔAdv.LTV

Average change in LTV for advocates

n_Fr.LTV

Average LTV of a referred friend

n_Incent.

Average Customer Acquisition Cost in terms of incentives given

Value Example

Here we will assume a $100 LTV for a new customer, and a $20 change in LTV for an advocate when they successfully refer a friend. We assume that both sides of the referral have a real incentive cost of $10, for a total of $20.

In this example, the referral program is creating on average $500,000 in real value after the cost of incentives for every 100,000 new users acquired in the last 30 days.