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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.

[kkstarratings]

Why companies should not focus exclusively on customer acquisition costs

In this article, I share my opinion on the very one-sided focus on Customer Acquisition Cost (CAC). In my conversations with clients who I help to accelerate new customer acquisition, I have become aware of a very one-sided focus on CAC.

What are customer acquisition costs (CAC)?

Marketers use CAC to calculate the cost of acquiring new customers. Especially nowadays, when we can calculate the cost of new customer acquisition fairly accurately using website analytics tools, CAC is used as the main KPI for optimization.

There are numerous KPIs that marketers can optimize. Optimizing the CAC is already one of the better KPIs for sustainably increasing the company's success. With some new customers, I even see that the focus is placed on optimizing the cost-per-click (CPC) or the click-through rate (CTR), especially with Google AdWords. Basically, it can be said that optimization on KPIs such as CPC and CTR is undoubtedly one of the worse strategies.

Why is optimization for CPC and CTR not effective?

Many marketers optimize on KPIs such as CPC and CTR, especially if no conversion tracking or no conversion has been achieved. However, these factors have no impact on the success of a campaign, neither with Google AdWords nor with Facebook. KPIs such as CPC and CTR are only useful 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, optimizing for CPC and CTR is too short-sighted.

Facebook was even able to find out in the analysis of a study that people who click on an ad more often do not buy more often. Furthermore, Facebook found that optimizing for people who click on ads frequently leads to a 5.5 times higher cost-per-mille (CPM) than targeting people who are less likely to click on ads. This means that optimizing for CTR and CPC does not lead to lower CAC, but can even lead to higher CAC (CPM on average 5.5 times higher).

Why is the optimization on CAC not optimal?

Optimizing campaigns for CAC is undoubtedly the first step in the right direction. However, sooner or later the focus should be placed on optimizing customer lifetime value (CLV). CLV reflects the total value of a customer over their life cycle. (You can find out how to calculate the CLV in the last section of this article).

Facebook, Google and Amazon are all marketplaces on which advertising space is auctioned. Each of the three advertising platforms mentioned determines the display of the ad 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 ad. If we assume rising advertising costs, this means that those advertisers who place the highest bids in the short or long term will receive the advertising space. As a result, advertisers with the highest revenue per customer will win the advertising space and ultimately the customer.

Advertisers now have the choice of increasing either the revenue per purchase or/and the CLV. The revenue 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, optimizing for revenue per purchase has the disadvantage that the initial purchase (product margin - advertising costs) must be profitable. By optimizing for CLV, on the other hand, customers can also be acquired for whom the initial purchase is not yet profitable, but who become profitable over the course of the life cycle. Ultimately, this means that more customers can be acquired and growth is faster than for companies that only optimize for CAC.

Another disadvantage of optimizing for CAC is that there is no optimization for total profit, but for CAC. This means that the objective is to reduce CAC, although an increase in CAC leads to more sales and a higher overall profit.

Note: I can well imagine that this is quite theoretical, which is why I will illustrate my train of thought with an example:

I am referring to a product that is listed on Amazon and advertised via 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 sales price of €100 and a margin of 25 % (€25). It currently requires an average of 10 clicks on an ad with a CPC of €0.50, so €5 is spent per sale.

Is this a good or a bad result?

It is a result with a good starting position. At the moment, purchases via the ads are profitable (€25 - €5). However, increasing the maximum CPC could also increase the number of sales. This would ultimately also lead to higher AcoS. The CPC could theoretically be increased to up to €2.49 (assuming the conversion rate remains the same) and the profitability of the  campaigns would be guaranteed. By increasing the maximum CPC, more conversions can now be generated so that the overall profit is ultimately higher than for campaigns with a lower CPC.

It is important to note that the Total profit does not equal CLV is. By focusing on the overall profit, the aim is to increase  of CPC/CPA to sell the maximum number of products while maintaining profitability on the first customer transaction. Optimizing overall profit is another step in the right direction to accelerate sales and customer growth.

How do you calculate the CLV? (eCommerce)

In order to calculate the CLV, we need a few more 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 obtain this figure, we simply need to divide the customer's total turnover by the total number of orders.

AOV = total sales / total number of orders

Purchase Frequency (PF)

The PF (purchase frequency) represents the average number of orders per customer. Using the same time frame as when calculating the average order value, we need to divide the total number of orders by the total number of individual customers. The result is the purchase 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 over a period of time. To calculate the customer value, we simply need to multiply the average order value by the purchase frequency.

CV = AOV * PF

We have now calculated the CV for our customers. I recommend calculating the CV for different customer segments so that the different customer groups can be compared with each other and the most attractive customer group can be determined.

Since we have already calculated the customer value, we only need to multiply the customer value by the average customer lifetime to calculate the CLV.

The Customer Average Lifespan (CAL) is the length of time of the relationship with a customer before they become inactive and no longer make purchases. Particularly for new retailers without meaningful customer data, the  customer lifetime can be estimated. It is common to assume a customer lifetime of 1 to 3 years. Of course, these values depend on the product sold, as fast-moving consumer goods (FMCG) are ordered more frequently than a mattress, for example.

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 to carry out optimization based on these KPIs. If possible, KPIs such as CLV or total profit should be optimized. In order to optimize marketing measures for total profit, calculations must first be made for margin, conversion rate and CPC. If optimization is to be carried out on a CLV basis, the existing customer base must be analysed using values such as AOV, PV, CV and CAL. By precisely analyzing these values, marketing expenditure can be allocated much more effectively.

[kkstarratings]
Tobias Dziuba

My name is Tobias and I am the founder and managing director of the Amazon agency Adsmasters GmbH based in Düsseldorf.

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