One of the Categories for Business Model Analytics is Retention. It fits into the thesis around LTV/CLV (Customer Lifetime Value), whereby New Customers who become Repeat Customers will drive the majority of sales into the future. Quantifying the effects of that loyalty is the challenge, which is why we dig into the Analytics around Retention.
Turning New Customers into Repeat Customers is one of the biggest keys to a successful and sustainable business model for non-contractual businesses. Looking at the aggregate Cohort(s) of Repeat Customers typically opens the door to insights about how the group can drive the business model.
As a proxy, we can look at the Farfetch business model and see that a lot of their success was driven by Repeat Cusotmers as repeat purchasing is the key to driving CLV/LTV (Lifetime Value).
Referral-based customers is generally the largest subset of Repeat Customers; analyzing data from those customers can yield insights about how Repeat Customers impact the business model in aggregate. The research shows that referral-based customers have higher retention rates, lower churn, and the highest CLV/LTV.
Lessening the need to continuously bring in New Customers to drive margin and profitability is the key to keeping CAC (Customer Acquisition Costs) down, and margins (on a customer basis) up.
Simply put, we can infer based on all the aggregate data on Retention – which we will dig into below – that building a strong referral funnel is one of the majors keys to creating a large Cohort of Repeat Buyers. A quantifiable level of loyalty can manifest itself it many different ways, depending on the business, which is why Analytics are helpful.
Business Model Analytics – Retention
Most businesses look at their business model through the lens of accounting and the typical revenue/cost/margin/net income items. Part of the revolution in Accounting driven by CBCV (Customer Based Corporate Valuation) is to understand the inherent value – in a quantifiable way – of the customer base.
To try and break down what can be a fairly complex topic into more simple terms, we created the above infographic with three Categories – Behavioural Analytics, Retention Analytics, Channel Analytics. What we know generally, based on the Pareto principle, is that the Top 20% of the customers in non-contractual businesses will often drive 80% of the profits. Naturally, this statement has a lot of nuance to it – which is covered in the Behavioural Analytics post – but as a precept it can help to reshape how we think about ways to unlock Customer Value, particularly as it pertains to identifying key patterns related to Retention Analytics.
If the Top 20% of customers have higher-than-average AOVs (Average Order Values), make multiple repeat purchases, and demonstrate multi-X LTVs, then the goal should be to target more of those customers. Through various channels – social media, in-person conversations, referral programs, etc. – they can be leveraged to bring more customers to the business. The bulk of customers that they bring in will likely be Referrals.
In contrast to Paid Channels, where someone will hear about a business through some form of advertising, Referrals have staying power. They are less likely to Churn (18%), have higher Retention Rates (37%), and more profitability over the long-term (16%). The key is to bring in customers that can be retained, maintain repeat-purchasing patterns over the mid to long-term, and who are likely to spread the message via word-of-mouth or referral-based marketing.
Retention Analytics – Trust, Frequency, and Loyalty
One of the keys to Retention is trust, and how a customer comes to a certain brand initially. If people trust the person who brings them to a brand, they are much more likely to a) make a purchase b) make a bigger purchase than someone who comes from a paid ad c) become a Repeat Customer.
Let’s say, for example, a business is able to break down their Cohorts of customers (Year over Year), as Farfetch did above and see the % of Repeat Buyers. Imagine the profile was the same as Farfetch’s in 2017 – 55% ‘Existing Customers’ and 45% ‘New Customers.’ We know that the goal is always to convert more New Customers into Repeat Customers; this simple strategy is at the core of the CLV/LTV methodology. Simply put, to spend money to acquire a customer for a one-off purchase is not scalable if the vast majority of customers only purchase once.
Within the Cohort of Existing Customers, we could break down what % are Referral customers, what % use the Loyalty Program (if one exists) and what % make up the bulk of Repeat Purchases.
Here is the kicker – many infamous Silicon Valley and other high-growth companies (Airbnb being one example) realized that they could incentivize referrals in their most loyal customer Cohort to drive scale. The calculation for the amount per referral depends on the CAC calculation, but generally the loyalty derived through the Referral Cohort is enough to offer Existing Customers a strong financial incentive to refer more.
The specific channels, methods, and terms used is for another conversation. Suffice to say that loyalty leads to scale and profitability in the vast majority of cases.
Business Model Impact – ARPU, CAC, LTV, Churn
If you look through the business models of various successful non-contractual companies – on this blog or otherwise – there are many different patterns that can be observed.
First and foremost, Growth-At-All-Costs has been the trend for the last 5+ years, with companies doing everything they can to achieve growth. Many investors reflected this via Revenue Multiples and drove company valuations up to stratospheric levels. Midway through 2022, many of those same companies who grew at all costs have seen their market caps shaved down by 80% or more since fall 2021.
The primary mechanism these companies used was a relentless Customer Acquisition strategy via Paid Ads. Word-of-mouth, Referrals, and Loyalty were a lot less important than being seen as a Unicorn in the media.
But based on the simple diagram above, we can see how a high CAC (Customer Acquisition Cost) against average ARPU (Average Revenue Per User) would be a disaster in terms of LTV (Lifetime Value) outlook.
On the other hand, if we can imagine that the % of Repeat Customers from a Yearly Cohort continues to increase YoY (Year Over Year), then the LTV will continue to trend upwards over time. This means that customers who leave the business after one purchase (Churn) would be expected to trend down.
In aggregate, the Customer Value associated with a business capable of driving a continuously-increasing rate of customer Retention is massive. The actual nominal value of acquiring customers (discussed in Channel Analytics) is measured relative to the projected future revenues over time in order to calculate the LTV. The goal is to ensure customers don’t just buy once and leave; we want customers who spend more frequently, make repeat purchases over the mid to long term, and who refer other customers to the business.