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Altruistic referrals are 86% more effective

Use an altruistic CTA for your referral program (“give $20 off to a friend”). It increases likelihood of referrals by 60% and total referrals by 86% compared to the most commonly used CTAs.

October 15, 2020

Topics: Referrals
For: B2C, B2B Friendly
Research date: May 2020
Universities: Fox School of Business and Management, Temple University; University of Minnesota and Marshall School of Business, University of Southern California.

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📝 Intro

Today’s research is closely linked to a previous tip (Tianshu Sun is an author of both studies): Pre-fill referral messages to make them more effective.

The findings of this research build on a core human behavior we often forget about:

We are much more altruistic - and less selfish - than we think.

We love helping others.

📈 Recommendation

When asking customers to refer you, use an altruistic “Invite a friend to give them $20 off” rather than a selfish “Invite a friend to get $20 off”.

Try to ask for a referral right after the customer has purchased your product (or consumed it, depending on what you sell).

If you can segment your audience, target your referral campaign to loyal and/or highly satisfied customers (e.g. that made repeat purchases).

🎓 Findings

  • The researchers tested the same offer (both the sender and recipient will always get 70% off) and compared different subject lines and CTAs when asking for a referral.

  • They find that an altruistic description of the referral (Subject: “Give your friend a 70% discount!” CTA: “your friend will get 70% off, you will also get 70% off”) is by far the most effective of all options.

  • The altruistic description increases by 60% the probability of a customer making at least one referral and the total number of referrals by 86% compared to the control option.

  • The effect is significantly higher on customers that have made at least 2 purchases, and an additional 73% higher for those that report high NPS scores.

  • In general, referral campaigns become less effective the more time passes after the last purchase.

  • Combining this with previous research, the best way to maximize growth is to target loyal and/or satisfied customers with the altruistic referral program, while targeting less loyal and satisfied customers with a promotion to repurchase or stop them from churning.

🧠 Why it works

  • Word-of-mouth (a referral) is by nature something we do altruistically because we think the other person would enjoy it. When we add a selfish financial incentive, the two incentives contrast each other and damage each other. As the authors say “it’s akin to mixing oil and water”.

  • Instead, describing a referral as something altruistic reduces the senders’ guilt from getting a financial reward.

  • It also encourages better targeting by senders, which then leads to higher overall conversion rates.

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✋ Limitations

  • The researchers conducted the experiment mainly on a hobby collage website (Collage.com). That might make it riskier to generalize to other industries (e.g. Uber) or higher-priced products (e.g. Home, Pets, Education).

  • The study did not test different offers referral offers, such as only giving the benefit to the receiving friend. It only tested variations in describing the same offer.

⚡ Steps to implement

  • Update (or create) your referral CTA copy to emphasize the benefit it will give the receiver (“your friend will get X% off, you will also receive X% off”).

  • Test how well offering the discount/bonus only to the receiver works, which could help you reduce costs.

🔍 Study type

Online survey and market experiment (on 100,000 Collage.com users)

📖 Research

Jung, J., Bapna, R., Golden, J. M., & Sun, T. Words Matter! Toward a Prosocial Call-to-Action for Online Referral: Evidence from Two Field Experiments. Information Systems Research (May 2020).

🏫 Affiliations

Fox School of Business and Management, Temple University; University of Minnesota; Collage.com; and Marshall School of Business, University of Southern California.

Remember: This research could be disproven in the future (although this is rare). It also may not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.

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