One of the Categories for Business Model Analytics is Channels. It fits into the thesis around CLV (Customer Lifetime Value), whereby the best marketing Channels are identified to optimize CAC vs. LTV. Contextualizing Channel Analytics is a challenge because it isn’t always about the gross number or ‘ROI,’ but the optimal balance between the CAC and LTV.
‘Marketing’ is usually a discussion that happens tangentially to the business model; customer acquisition is usually tied to ‘growth,’ whereas ‘business model’ is more closely associated with ‘finance.’ In the last 6 – 8 months especially, we have seen public and private markets alike give ‘high-growth’ companies an 80 – 90% discount with no end in sight. Growth does not necessarily equate to a sustainable and profitable business model.
The ‘Startup Boom’ has helped create a new focus on ARPU (Average Revenue Per User) CAC (Customer Acquisition Cost) and CLV/LTV (Customer Lifetime Value/Lifetime Value). The diagram below shows a rough visual of how the cycle plays out.
Using simple, back-of-the-napkin math, we can see that:
- a) if the ARPU minus CAC is a negative number or flat, the business will be losing money – unless, the majority of those customers become Repeat Customers with a high LTV long term
- b) if the ARPU minus CAC is marginally positive or largely positive, the business can make a marginal profit (measured against other costs) and potentially be profitable – but if the majority of those non-contractual customers Churn (make only one purchase), the business is unlikely to be profitable on a mid to long-term level
- c) if the ARPU minus CAC is flat, marginally positive, or largely positive, the business can earn a sustainable (and potentially exponential) profit in the mid to long-term if it turns the majority of those customers into Repeat Customers, as measured by the LTV/CAC ratio (whereby a 3X or 300% is the threshold for a good ratio)
By default, a lower CAC is more likely to lead to a profitable and more sustainable business in the mid/long-term. There are always exceptions to these types of rules, as it depends on the many other independent variables, many of which are discussed in the Retention and Behavioural Analytics posts. As an example – when looking at referrals – there may be times when incentivizing customers to refer other customers requires a higher incentive than the average CAC, with the expectation that the extra financial outlay will be made back in the revenue via strong LTVs.
The company we have seen exemplify a lot of this type of logic is Farfetch due to their focus on using LTV as the core metric for success of their business model, along with their strategic focus on building a large Cohort of Repeat Customers.
In the diagrams from a recent investment blog below, we can see the data for ‘Demand Generation Expense’ (ie. Marketing Expense) on a per customer basis (CAC) for Farfetch was roughly $150 per customer between 2016 – 2020. The number went up > $300 in 2021 as the cost to advertise on Paid Acquisition Channels went up, which normally occurs as markets mature.
Even at $300 (let alone $150), the Order Frequency (per year) is high enough that Farfetch can make money given the AOV (Average Order Value) of ~$650 USD and consequential Take Rate of 30% on that order.
When calculating the LTV/CAC, we see how the above theory in relation to Channels plays out. It is apparent that even though Farfetch would be much more profitable with a $150 CAC, they can still be very profitable over the mid to long term on Repeat Customers with a roughly calculated LTV of 4.83.
The next step is to identify the individualized Channels that are used within the CAC to acquire customers. In the case of Farfetch, we see above that CAC doubled because marketing expenses on Paid Channels in the luxury eCommerce segment are inferred to have doubled. The question at that point is what are the other potential Channels that can be used and how can the Channel Analytics be structured as a whole.
Business Model Analytics – Channels
If we think about the goal of optimizing the business model and gaining more control over the levers that drive profitability, then we need to be able to quantify Customer Value. This has both a value in the present as a way to structure strategies and plan for different scenarios, but it also has a value going into the future if we can articulate that value to investors and stakeholders alike. Having a few core Analytics in each category to track success and quantify Customer Value can help shape a seemingly endless maze of data into a more structured process.
CAC is at the core of measuring Customer Value. While we know from Behavioural Analytics that for many businesses, the Pareto Principle applies and 20% of the customers usually drive 80% of the business (in aggregate), the question is how are those customers acquired?
Especially, since we know that – based on data from Retention Analytics – many of the most loyal and highest-grossing customers are referrals. Yet a referral could originate from a customer who came from either a Paid Channel or Organic Channel.
Channel Analytics – Paid vs. Organic
Paid Channels have grown exponentially in recent years on the back of more targeted digital advertising on platforms like Facebook, Google, Spotify, and others.
Organic Channels generally consist of activities related to SEO (Search Engine Optimization), Content Marketing, and other activities across various types of web and media platforms.
The major difference between Paid vs. Organic demand generation is money. Paid Channels require a large investment and see some sort of instantaneous, measurable result, which is why it is generally quantifiable as a CAC expense. Organic requires little money but a lot of time, as the goal is to capture the long-tail of customer demand over a mid to long-term time period. Neither is necessarily good or bad.
The major questions – from a Customer Value perspective – relate to the distribution and attribution of customers in each channel, specifically in relation to Repeat Customers.
For example, if the majority of customers who become Repeat Customers originate from Organic Channels, we need to know why. Furthermore, imagine that the majority of New Customers from a specific Paid Channel spend more on their initial order (AOV) but are more likely to Churn (only buy once) compared to other Channels.
Referrals can originate from Paid or Organic Channels, but generally require a monetary incentive in order to get Existing Customers to refer New Customers to a given brand. The CAC from referrals could even be higher than the average CAC for Paid Channels, for example, yet drive better LTV results. This is why CAC as a standalone is not enough information, as we saw above in the Farfetch example.
The combination of Analytics in relation to attribution from each Channel and distribution in relation to loyalty can help bring a lot more clarity around which type of customers originate from where and how to target more of the highest-value customers.
Business Model Impact – ARPU, CAC, LTV, Churn
Observing patterns over time of various business models has shown how important CAC is to the overall business model as a standalone metric. During the Go-Go Years of the last decade, we saw many companies raise 100s of $Millions for customer acquisition in hot markets and end up completely bankrupt.
Without a sustainable LTV component to a Customer Acquisition strategy, a lot of the efforts related to Growth will be wasted if not completely erratic. The problem is that most people think of these ARPU (Average Revenue Per User), CAC (Customer Acquisition Cost), LTV (Lifetime Value), Churn concepts in relation to contractual SAAS companies. We are talking about these concepts in relation to non-contractual businesses in relation to Theta CLV’s CBCV framework and theory of Customer Value as a quantifiable value.
Based on the diagram above and what we have discussed in this post, we know that optimizing CAC is important. Ideally, we want to lower CAC to increase margins on a per-customer basis, but not at the expense of losing high LTV customers. We need more granular data on New vs. Repeat Customers, where Referrals originate, and what marketing Channels are attributed to finding the highest-value customers for the business.
The market right now is stressed, especially for Growth companies in categories like retail, consumer discretionary and other similar consumer-facing sectors. Valuations are down and bankruptcies are looming for many brands who were high-flyers a mere 12 – 18 months ago. Applying these principles around Channel Analytics against the backdrop of a Customer Value framework can help optimize certain dimensions of the business model and reduce the need for continuous and large-scale investment into Paid Channels. Furthermore, other opportunities related to referrals and loyalty schemes could emerge.
As has happened in many cycles in history, empires rise and fall, and in the business world right now we are in the midst of another downward cycle. Identifying ad quantifying Customer Value, and then optimizing the business model around this, is the key to rising as many others fall.