WHY COMPANIES SHOULD NOT FOCUS EXCLUSIVELY ON CUSTOMER ACQUISITION COSTS
In this article I describe my opinion on the very one-sided focus on Customer Acquisition Cost (CAC). In my conversations with customers whom I help to accelerate the acquisition of new customers, I become aware of a very one-sided focus on CAC.
WHAT IS THE CUSTOMER ACQUISITION COST (CAC)?
Marketers use the CAC to calculate the cost of winning new customers. Especially in today's world, where we can calculate the cost of acquiring new customers fairly accurately using website analysis tools, the CAC is used as the main KPI for optimization.
There are numerous KPIs that marketers can optimize. The optimization of the CAC is already one of the better KPIs to sustainably increase the company's success. With some new customers I even experience that especially with Google AdWords the focus is on optimizing the Cost-per-Click (CPC) or the Click-Through-Rate (CTR). Basically, optimization on KPIs such as CPC and CTR is undoubtedly one of the worse strategies.
WHY IS THE OPTIMIZATION ON CPC AND CTR NOT EFFECTIVE?
Many marketers optimize for KPIs such as CPC and CTR, especially when conversion tracking or conversion has not been achieved. These factors have no impact on the success of a campaign, neither on Google AdWords nor on Facebook. KPIs such as CPC and CTR are only effective if the goal is to increase website visitors. However, since most companies do not aim to increase the number of website visitors, but to generate sales and leads, the optimization on CPC and CTR is thought too short.
Facebook even found out when evaluating a study that people who click on an ad more often don't buy more often. Facebook has also found that optimization on people who frequently click on ads results in a 5.5 times higher cost per mille (CPM) than targeting people who are less likely to click on ads. This means that optimization on CTR and CPC cannot lead to lower CAC but even to higher CAC (CPM on average 5.5 times higher).
WHY IS THE OPTIMIZATION ON CAC NOT OPTIMAL?
Optimizing campaigns on CAC is without a doubt the first step in the right direction. However, sooner or later the focus should be on optimizing the Customer Lifetime Value (CLV). The CLV reflects the entire value of a customer over its life cycle. (In the last section of the article you will learn how to calculate the CLV.
Facebook, Google and Amazon are all marketplaces where the advertising space is auctioned. Each of the three advertising platforms mentioned determines the playout of the advertisement on the basis of a "quality factor". The quality factor is determined on all platforms based on the maximum CPC/CPM or CPA and the relevance of the advertisement. If we assume rising advertising costs, this means that those advertisers who will sooner or later place the highest bid will receive the advertising space. As a result, advertisers with the highest revenue per customer win the advertising space and ultimately also the customer.
Now advertisers have the choice to either increase the return per purchase and/or CLV. The return per purchase can be achieved either by increasing the shopping basket or by improving the margin per product.
The increase in the shopping basket can be achieved through UpSales and the improvement in margins through price increases or reductions in purchase prices.
However, the disadvantage of optimizing the return per purchase is that the initial purchase (product margin - advertising costs) must be profitable. By optimizing on the CLV, however, customers can also be won for whom the initial purchase is not yet profitable, but which become profitable in the course of the life cycle. Ultimately, this means that more customers can be won and growth is faster than for companies that optimize exclusively for CAC.
Another disadvantage of optimizing to CAC is that there is no optimization on the total profit, but on the CAC. As a result, the objective is to reduce the CAC, although an increase in the CAC leads to more sales and a higher overall profit.
NOTE: I CAN WELL IMAGINE THAT THIS IS QUITE THEORETICAL, SO I WILL ILLUSTRATE MY THOUGHTS WITH AN EXAMPLE:
I am referring to a product that is listed on Amazon and advertised through sponsored product campaigns.
Selling price of the product: 100 €
Product margin: 25 €
Average CPC: 0,50 €
Average conversion rate: 10%.
AcoS: 5 € = 5 %
The advertised product has a retail price of €100 and a margin of 25% (€25). At the moment, it takes an average of 10 clicks on an ad with a CPC of €0.50 to spend €5 per sale.
IS THIS A GOOD OR BAD RESULT?
It is a result with a good starting position. At the moment the purchases through the advertisements are profitable (25 € - 5 €). However, an increase in maximum CPC could also increase the number of sales. This would ultimately lead to higher AcoS. The CPC could theoretically be increased (with a constant conversion rate) to up to € 2.49 and the profitability of the campaigns would be guaranteed. By increasing the maximum CPC, more conversions can now be generated, resulting in a higher total return than campaigns with a lower CPC.
It is important to note that the total profit is not equal to CLV. In focusing on total profit, an attempt is made to sell the maximum number of products by increasing CPC/CPA while maintaining profitability on the first customer transaction. The optimization of the overall profit is a further step in the right direction to accelerate sales and customer growth.
HOW TO CALCULATE THE CLV? (ECOMMERCE)
So that we can calculate the CLV, we still need some key figures: Average Order Value (AOV), Purchase Frequency (PF) and Customer Value (CV).
AVERAGE ORDER VALUE (AOV)
The AOV (average order value) represents the average amount of money a customer spends on each order. To get this ratio, we simply need to divide the customer's total sales by the total number of his orders.
AOV = total turnover / total number of orders
PURCHASE FREQUENCY (PF)
The PF (purchasing frequency) represents the average number of orders per customer. We must divide the total number of orders by the total number of individual customers within the same time frame as when calculating the average order value. The result is the buying frequency.
PV = total number of orders / total number of customers
CUSTOMER VALUE (CV)
The CV (customer value) represents the average monetary value that each customer brings in during a period. To calculate the customer value, we only need to multiply the average order value by the purchase frequency.
CV = AOV * PF
Now we have calculated the CV for our customers. I recommend calculating the CV for different customer segments so that the different customer groups can be compared and the most attractive customer group can be determined.
Since we have already calculated the customer value, we only have to multiply the customer value by the average customer lifetime to calculate the CLV.
The Customer Average Lifespan (CAL) is the time span of the relationship with a customer before the customer becomes inactive and no longer makes purchases. Especially for new dealers without meaningful customer data, the customer service life must be estimated. It is customary to assume a customer service life of 1 to 3 years. Of course, these values depend on the product sold, since Fast Moving Consumer Goods (FMCG) are ordered more frequently than, for example, a mattress.
Here you have to decide individually for your company which customer lifetime is appropriate.
CLV = CV * CAL
Conclusion CAC VS: CLV:
Long-term business success depends on finding the right customers for your company. The first step is to determine the right KPIs for online marketing reporting and use these KPIs as a basis for optimization. If possible, KPIs such as CLV or the total profit should be optimized. In order to optimize marketing measures on the total profit, first of all calculations must be made to the margin, conversion rate and CPC. If the optimization is to be carried out on a CLV basis, the existing customer base must be analyzed using values such as AOV, PV, CV and CAL. By precisely analyzing these values, marketing expenditures can be allocated much more effectively.
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