Referrals are one of the most reliable and trusted factors in customer acquisition resulting in remarkably high profits. It is an effective way to acquire anonymous customers by the existing ones. When establishing a referral program, many questions arise for businesses like:

  • Whether they are using the right type of referral program
  • Do referral programs drive signups? 
  • Most importantly: Does referral have an impact on customer lifetime value (CLV) and is the impact statistically meaningful?

Does referral have an impact on customer lifetime value (CLV) and is the impact statistically meaningful?

Historically, restaurants, and chain owners, in particular, experience significant struggles with these questions due to limited knowledge of their customers.

In order to overcome these challenges, many restaurants look to cloud marketing and customer data platform solutions to effectively target different customer segments with offers, gaming, online ordering, gift cards, referrals, social media, surveys, feedback, reviews, and ratings. 

Zeroing in on Referrals

According to Nielsen[1] and Referral SaaSquatch[2], referral plays a significant role in the guest acquisition and says that every referring customers make an average of 2.68 invites. The studies show that referral programs have significant value, but businesses are still facing issues in defining an appropriate referral program.

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To understand what referral program would suit business one should know their priorities clear, as there are different kinds of programs used in industry such as: 

Direct: Wherein existing customer refers anonymous and both get rewards, 

Tangible: Where you offer a tangible incentive to your customer, 

Dual-sided: Where business ensures that both parties referrer and the referee gets benefits for the referral action performed. 

The dual-sided reward is a most industry-wide adopted program based on their situation and intentions. Dual-sided is further categorized based on a business’s goal as Type A and Type B: 

Type A (e.g. Uber)  A guest signs up using an invite code, and irrespective of his contribution to business both referral and the referee gets the benefit. For example, Uber offers both the referrer and referee $25 at signup. 

Type B (e.g. Amazon Prime / PayPal)  This is the case where a referral gets the benefit only when the referee adds value to the business. For example, Amazon Prime and PayPal are among the few businesses wherein referral will receive the benefit if the referred customer signs up and adds some revenue. 

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In our own research, we focused primarily on Type B, which is used most frequently by restaurants, and analyzed 80+ businesses and 30M+ users to evaluate the benefits of Type B referral programs within six months of business go-live.

Signups

Based on our study, we found the total signups using invite code accounts for nine percent of the overall signups. 

This percentage of signups varied from three percent to 55 percent based on offers gifted to the guest where the highest was for an American casual dining restaurant chain. 

A further drill-down analysis was conducted to check on various restaurant types, inferring that average referred signups account for nine percent, 12 percent and nine percent for fast casual, casual dining and quick serve, respectively. 

Referral Impact on Loyalty

The natural follow-up question is: do referrals have any impact on loyalty? There may be guests who visit the store because of referral benefits however doesn’t stay associated with the business for a longer duration. How businesses can measure the referred guests loyalty? To assess this comparison was done among guests who did signup using an invite code called “referred” versus other guests termed as “organic”.

First, we conducted a proportional test helps to validate the statistical significance between two numbers or percentage. We used this test to check the significance of referrals while converting an anonymous guest to a loyalty guest.

  • Below is the sample data set wherein one can see that for anonymous business B:
  • Signup is the count of guest who did signups in the specific duration
  • Loyalty is the count of guests who made a visit after signup 
  • Conversion is the percentage of guest who converted to loyalty (Loyalty/Signup)

Organic

Referred

Business 

Signup

Loyalty

Conversion

Signup 

Loyalty

Conversion

A

6198

2861

46%

224

109

49%

B

70262

51835

74%

3580

2716

76%

There are 2.7K loyalty guests coming out of 3.5K signups for referrals and 51K out of 70K signups for organic. This implies that conversion to loyalty guest from signups is 76 percent for referred and 74 percent for organic. Now, both referred and organic guests are two different groups with different sample sizes how one can say that 76 percent and 74 percent  conversion is significant? For this, we used a proportional test.

Using the proportional test on all businesses combined, we determined, with a five-percent level of significance:

Referred guest are 18 percent more likely to convert to loyalty than organic – a big win for Restaurant B’s referral signup program.

CLV of referred versus organic guest:

Given the finding that referred guests are 18 percent more likely to convert into loyal customers, the next big question is “Who are or will be your most valuable customers?” – otherwise known as “Customer Lifetime Value” (CLV). CLV is a forward-looking metric that estimates how much a customer is worth over the entire time. 

One-year forward-looking horizon is normally considered as an industrial convention. With an accurate CLV prediction, the business can carry out many marketing operations. This is no easy task, but we built sophisticated machine learning models that predict, with high confidence, what the CLV for any given customer will be over the course of a year.

To check the impact of referrals, we performed the analysis for businesses with different restaurant types and found the average CLV of a referred guest in casual dining is $9 higher than that of an organic signup.

To recap, based on our analysis, we found:

Type B referral programs, like those used by Amazon Prime and many of Punchh’s restaurant customers, are significant drivers of referral customers converting into brand loyalists.

Referred guest are 18 percent more likely to convert to loyalists than organic guests

The average CLV of a referred guest in casual dining is $9 higher than that of an organic signup.

All of this gives us strong evidence that intelligent execution of referral programs, particularly those in which the referee only benefits when their referral becomes a paying customer, drives strong conversion to new loyal customers. 

References:

[1] The Nielsen Company: Personal recommendations and consumer opinions posted online are the most trusted forms of advertising globally: https://www.nielsen.com/content/dam/corporate/us/en/newswire/uploads/2009/07/pr_global-study_07709.pdf

[2] Referral Squatch: Referral marketing statistics 2017: https://www.referralsaasquatch.com/infographic-state-of-referral-marketing-statistics/

[3] Wikipedia on survival analysis: https://en.wikipedia.org/wiki/Survival_analysis

[4] Wikipedia on censoring in statistics: https://en.wikipedia.org/wiki/Censoring_(statistics)

[5] British Journal of Cancer(2003) MJ Bradburn, TG Clark, SB Love, and DG Altman: Survival analysis Introduction to concepts and methods: https://www.nki.nl/media/837544/bradburn2003a.pdf

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