Pre-fill referral messages to make them more effective

Referral messages that say the sender already bought the product increase conversions by 11%. Messages that include an incentive for further referrals lead to 53% more referrals.

This study is just a start in this area. If pre-designing messages for customers is easy and has such a strong impact, what other variations could be tried? Could something similar also work for offline or larger purchases (e.g. buying a Tesla)?

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Design your referral messages to drive additional sales or referrals

Impacted metrics: Referrals | Website/app conversions | Customer satisfaction
Channels: Word-of-mouth | Ecommerce sales


Pre-fill your online “Tell a friend” messages with either:

Option A) Information that sender bought it (e.g. “Just bought this, check it out”), to boost conversion rates by 11%.

Option B) Information about a referral incentive (e.g. “Buy this, then invite friends and you’ll get it for free”), to encourage 53% more referrals.

Don’t try both at the same time. It probably won’t work.


  • Option A’s average effect (11%) is even higher for receivers that are less experienced, less popular products, and just launched products. This option also increases the customer satisfaction of the receiver. It’s usually the most profitable option when you are selling something (low conversion rates, e.g. Ecommerce).

  • Option B’s effect (53%) is the most powerful option when you’re giving something away for free (high conversion rates, e.g. free products, content, and apps).

  • On average, if you try both options at the same time, you lose both effects.

  • You can use both at the same time only when the sender and receiver know each other well (they’ve referred products to each other before), with sales increasing 37% and 53% more referrals.

(Click on the image to easily zoom in)

Why it works

  • Option A happens because of:

    • Social learning: if their friend bought it, they are more comfortable buying it themselves.

    • Social utility: if their friend bought it, they may get to enjoy it together (especially true if the product is social in nature). This is what increases customer satisfaction.

  • Option B is less socially beneficial, with a financial incentive being the main driver instead.

  • Using both options at the same time cancels the sales boost because the recipient questions the sender’s motives, and cancels the further referral increase because they are worried others will think the same of them, damaging their image.

  • The researchers built a model to calculate the overall profits of using either option A or option B. According to the model, total profits are usually higher if you choose option A when selling something, and option B (which includes the cost of the referral incentive) when giving something away.


  • Based on trackable messages sent from the website. We don’t know what happened when users shared a product outside this channel.

  • Focused on 1-to-1 messages, although senders may send them to multiple recipients (usually via email). We don’t know how it applies to 1-to-many (e.g. social posts or other communications).

  • The experiment was on clients of one daily-deal company. These deals are often social in nature and may attract a specific type of customer.

Companies using this

  • Some companies that experiment heavily with referrals (e.g. Airbnb, Monzo, Transferwise) may have already discovered similar results on their own.

  • However, most companies seem to not be taking advantage of this opportunity.

Study type

Market experiment at a leading online daily-deal platform (brand kept confidential), United States


Sun, T., Viswanathan, S., & Zheleva, E. (August 2020). Creating social contagion through firm-mediated message design: Evidence from a randomized field experiment. Management Science.

[Link to paper]


Marshall School of Business, University of Southern California; Smith School of Business, University of Maryland; and University of Illinois.

Remember: Because of the groundbreaking nature of this paper, it 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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