Bringing referral into your marketing campaigns

Bringing referral into your marketing campaigns

Steve Savioz
March 2, 2020 · 3 min

When your customers receive a bonus or a gift for referring new customers, beyond the new customers they bring directly, it creates a snowball effect that works very much in your campaigns’ favour. Make the most of it.

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Why does taking referral into account change how a marketing campaign is optimised for profit?

As we saw in the previous chapter (how to calibrate marketing campaigns to maximise profit), a campaign optimised to maximise profit is one that seeks, at every point of acquisition, to sit at the top of the profit curve.

The profit maximisation function being defined, over a given period, as:

Y = (number of customers acquired × LTV) – cost of acquisition

Since the number of customers acquired and the acquisition costs are values that come out of the maximisation, the only constant to set is the LTV (lifetime value). We have since also set out how to estimate it customer by customer rather than on average: Lifetime value (LTV) targeted per customer.

The first article explains how to calculate LTV. But LTV is the value of a customer, and it does not take in the positive indirect effects of that customer. For that we need a new value: LTV Extended (LTVext).

The first element to bring into the LTVext calculation is referral — a customer earns a bonus by bringing in another, who may also receive a bonus.

Why should the referral effect be counted?

If a marketing budget brings in a customer, and that customer brings in another, then without the initial marketing campaign neither of those two customers would have signed up. Counting only the first customer as coming from the campaign undervalues what that campaign achieved.

How to calculate LTVext, step by step

Data (example):

LTV of a customer from channel X	600
LTV of a referred customer (*1)	700
Financial bonus given to the referrer	30
Financial bonus given to the referred	20

Over a given period:

Number of new direct customers (D) acquired on channel X	5
Number of referred customers (R) coming from the D customers	2

The LTVext analysis

Projected revenue from the D customers (based on LTV) 5 customers × 600 LTV	3,000

Projected revenue from the R customers 2 customers × 700 LTV	1,400
Cost of acquiring the R customers (*2) 2 referred bonuses + 2 referrer bonuses	100

Projected referral revenue, net of referral costs 1,400 – 100	1,300
Total projected revenue, net of referral costs 3,000 + 1,300	4,300

LTVext for a customer from channel X: (4,300 / 5) = 860

Conclusion

We see, then, that a new customer coming from the campaign is worth more once referral is counted — 600 becomes 860 in our example. That shifts the marketing balance considerably, since it lets us either recalibrate the bids, or consider marketing channels whose direct acquisition cost is above 600 but below 860, and so raise volumes considerably.

Taking referral into account when optimising campaigns has a considerable influence on marketing investment policy. The final impact on growth and profit is a genuine game changer.

(1) The LTV of a referred customer is often higher than that of a customer coming directly from a campaign, because the service was recommended to them and they are more loyal.

(2) In a consistent data model, the bonuses given to the referrer and the referred must be counted as a cost of acquiring the referred customer, and not as a fall in revenue.