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15 Apr

CTO of the family

“There is a CTO in every household, one who is aged between 10 and 18.”
— Kunal Shah

Young kids in households, often in their teens or younger, act as the “CTOs of their house,” driving technology adoption and assisting family members with various tech-related tasks. They help set up devices, troubleshoot issues, and help their family navigate online platforms.

These tech-savvy youngsters are knowledgeable about the latest trends, try out new apps and shopping sites, and even research and select products online, leaving only the payment step for others in the family. In other words, these CTOs often become authorities in the family when it comes to dishing out advice on any tech-related purchase.

But this phenomenon isn't just limited to CTOs in families and technology.

India being a society low in trust, most of us have a go-to advisor in our mind when it comes to taking recommendations and solving problems in any domain. There's a friend for fashion advice, a friend for financial advice, a friend for discussing marital issues with, a friend who is great with recommending dishes and restaurants, and a friend who is “a guy who knows a guy.”

These chosen set of individuals in every Dunbar network are the authorities in their respective domains. We go to them when we need “expert” help.

Naturally, customer advocacy plays a vital role in the success of Indian startups.

Businesses commonly offer incentives to motivate their customers to refer others to their products or services. And by offering such incentives, these businesses leverage the power of customer referrals to increase their customer base and revenues.

For instance, the ride-hailing platform Ola provides referral credits to both referring and referred customers. Similarly, Paytm and many other startups in its category reward customers with cashback when they refer their friends or family members. Same for food delivery startups like Swiggy and Zomato.

But the issue at play here is that most good intentions of a customer remain just that — good intentions. And just good intentions don’t lead to conversions.

So, the challenge while designing referrals boils down to figuring out two things:

  1. The factors that hinder customers from following through on their referral intentions and taking measures to overcome those barriers
  2. Segregating the acquired customers and identifying the “CTOs of the family”

Conventionally, businesses measure the value of a customer by estimating their Customer Lifetime Value (CLV).

Customer Lifetime Value (CLV or CLTV) is a metric that represents the total net profit a company can expect to earn from a customer over the entire duration of their relationship.

It helps businesses understand the long-term value of their customers and informs decisions on customer acquisition, retention, and marketing spend. Because it may just so happen that a customer who makes small yet repeated purchases for years ends up bringing more revenue in the long run than the one who splurges on a discount once and doesn’t come back. In the case of the latter, the cost spent on acquiring them may far exceed the profit generated on their one-time purchase, making it an unprofitable proposition.

CLV is typically measured by estimating the average customer lifespan, purchase frequency, and gross margin per customer and subtracting the costs of marketing to them.

But now, you might also realize that a big part of a customer's lifetime value is also their referral value! So, to get a correct CLV estimate, you also need to account for their referral value (CRV).

You need to estimate the average number of successful referrals they will make after being incentivized through a marketing campaign. You also need to determine how many of those referrals would have become customers anyway and survey them to find out.

Yes, this does sound like a lot of work than simply targeting a new set of customers via paid ads. But it is important in a country like India, where a lot of buying and usage behaviour is predicated on what the “CTO of the family” or the domestic authority buys and uses.

A Harvard research paper on how effective Word-of-Mouth referrals are, explains:

“To accurately estimate a customer's overall value, a company should ideally consider the customer's potential to attract new profitable customers. However, in reality, most companies only rely on the customer's willingness to refer others as a measure of their referral power.”

So, in case you do conduct such an exercise, the paper suggests two types of referrals will emerge:

  1. Type-one referral is the same as the lifetime value of the referred customer. Essentially, a person may refer a new customer who is similar in consumption patterns to the one who refers.
  2. Type-two referral is the present value of the savings in acquisition costs. An existing customer's referral can be a liability if the cost of acquiring them exceeds alternative methods.

Basically, while CLV might be a highly valued and discussed metric from a business perspective, CRV could potentially offer a better read on how valuable a customer really is.

And even though one might assume that a high CLV would be related to more CRV, the paper highlights how misleading that can be —

“If customer lifetime value and customer referral value were simply and positively correlated, the difference between them would not be particularly interesting from a managerial perspective. Any action that would increase lifetime value would immediately translate into higher referral value. But when we looked into the specific referral behaviour of customers with different CLV levels, we found that a high CLV is not a good predictor of CRV and so is a very questionable proxy for a customer’s total value.”

Here’s why.

Let’s say that Raj refers two customers for the company in each quarter, who go on to acquire two more customers each in the subsequent period.  Over two typical periods, Raj can be credited for onboarding eight new customers.

This makes CRV an interesting metric to think about. Because at first, it does appear like an elaborate pyramid scheme, and referral programmes do have a high likelihood of going wrong, as we have written about previously.

But there could be more attention paid to how a good referral programme can be designed.

We can think about it better by mapping this questionable relationship between CLV and CRV, called The Doing-Saying Gap.

On a 2x2 matrix, The Doing-Saying Gap is useful to segregate customers based on the following labels:

The Doing-Saying Gap demystifies traditionally held notions about brand loyalists and low-frequency users because it shows that it is completely possible that high-frequency users are not the strongest advocates and that the best referrers have low CLV themselves.

In line with this framing, here are some things to keep in mind while designing for growth.

Affluents become Champions when you encourage them to refer more new customers while maintaining their highly valuable purchasing behaviour.

Advocates turn into Champions by increasing their lifetime value without compromising their high referral value.

Misers turn into any of the other three by offering incentives for them to both buy more products (increasing CLV) and referring new customers (increasing CRV).

Of course, a major caveat to applying this metric is that CRV may not make sense for a B2B business.

Customers in many B2B markets, for example, don’t make referrals because they compete with one another and wouldn’t want to do their rivals a good turn.

Nor do customers make referrals if they don’t feel much attachment to the product, which is the case with many categories in fast-moving consumer goods markets. (In these instances, it’s also difficult to track individual customers’ behaviour anyway.)

Also, marketers should never make the mistake of assuming that customers who recommend one product in their company’s portfolio will necessarily tout another.

But the central point here is that you might think that the most active members of your community would provide the best referrals, and that assumption may be completely false.

In case you do experiments or have experimented with a referral programme in the past, I would love to know what you learned.

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