And how to track them - CAC

Cohort analysis shows you many things, which are helpful to track the health of your business over time by allowing you to compare the behavior and metrics of different customer groups.

This report can be so impactful because it removes all the noise we normally deal with

And focuses on only one metric, showing how different user groups behave over time.

In this article series, we are going to cover cohort analysis for three key metrics:

Cac
Ltv
Retention

Our team is currently free Analytics help to solve any analytics challenges like data tracking that may be hindering your growth: Schedule your free slot here.

Customer acquisition cost

Customer Acquisition Cost (CAC) is the total overall cost to acquire a customer.

And should essentially include the cost of content production, Ads, and other marketing actions taken to generate new customers.

CAC used to determine whether or not your marketing budget is being used effectively and efficiently.

It is a measure of the success or failure of your combined marketing efforts,

Where success is when the cost to acquire a customer is less than their lifetime value to your business.

But if CAC is higher than LTV, then this is clear evidence for a need to rethink your marketing strategy.

Optimizing for CAC with Cohort analysis

Cohort analysis for CAC provides you with insights into how different customer groups associated with a given cost perform. Insights you can use to answer questions like:

Which channel, Ad, or post generates the lowest CAC?
Which country or Geo-location is associated with the lowest CAC?
Which customer demographic is associated with the highest CAC?
Which product categories generate the lowest CAC?

There are mainly two ways you can reduce CAC with cohort analysis:

Defining customers with the lowest CAC and acquiring more of them
Copying behavior patterns of customers associated with low CAC and making sure all the other customer follow the same patterns

Here’s how you do it.

How to analyze CAC with cohort analysis.

Before you begin any analysis, make sure you have accurate data.

This is very important because you don’t want marketing decisions like resource allocation to be made based on wrong data.

There’re a number of reasons why your CAC could be wrong including improper data tracking or attribution discrepancies between platforms - Read more on this here.

So if you are not 100% sure of the accuracy of your marketing data, get an expert to sort this out before you make wrong decisions or,

Schedule a free analysis audit with our team.

Cohort analytics platforms exist to automate the tedious parts of cohort analysis which is mainly a 3 steps process.

1. Determine what question you want to answer

The point of an analysis is to produce actionable information you can use to improve your business.

To ensure that happens, it is important that you are answering the right questions. Answering the wrong questions can make your analysis essentially useless.

When it comes to CAC, the question usually is; “How do I reduce it?”

Analysis paralysis is caused by a lack of focus, so before you do any form of analysis, make sure you have a question in mind.

2. Define the specific cohorts that are relevant

Each person in a cohort must share a related yet distinguishable aspect that separates them from the other cohorts.

With our focus on CAC, the common aspect between all users is their respective cost of acquisition.

And this can be segmented further to examine differences between other traits such as acquisition channels, pages visited, or number of purchases.

These traits will help you create more user segments across a number of dimensions.

Some of the most popular are:

Marketing Channels - This is the best way to find channels with low or high CAC by segmenting users based on the channels used. Possible channels include affiliates, referrals, organic, social, direct, or any other meaningful acquisition source for your store.
Product Categories - Segmenting by product category allows you to see which products are associated with high or low CAC.
Demographics - Age, gender, and geographic areas can all reveal how much it costs for your brand to acquire various groups of customers.
RFM Analysis - A more comprehensive eCommerce behavior segmentation based on past purchases across recency, frequency, and monetary values. All traits that can be used to define behaviors of customers with high CAC and those with a lower CAC.

The more granular you get, the more insights you can generate.

Unfortunately, acquisition cost is not a metric that most analytics tools are able to track beyond the acquisition channel.

Tools like Google Analytics, Mixpanel, Amplitude, or Heap all focus on what users are doing on your website and not how they were acquired or the cost associated.

3. Perform the analysis.

Cohort analysis is about comparing user behavior of specific groups over time. In step 2, the segments created should help you understand the behavior of low CAC users over time, compare that with high CAC users over time so that,

When optimizing for CAC, you actually know the:

Channels, Ads, or posts that generate customers with low CAC.
Geo-locations or Age groups that are associated with the lowest CAC.
Pages customers with the lowest CAC visit.
Purchase frequency and LTV of low CAC customers.

Useful insights you can use to prioritize your marketing resources to channels, Ads, Pages, and locations that are already generating a low CAC.

Ideally, any tool that visualizes user data, can be used to perform this analysis including simpler tools like excel and others like Tableau, Looker, which have several more advanced options available for cohorts.

See how fast-growing eCommerce businesses like William Painter are using data analytics to grow even faster. Download here.

Conclusion

When optimizing for CAC, this analysis is focused on two things:

Finding customer cohorts with a low CAC and those with High CAC
Identifying behavior differences between the two groups.

The end goal is to reduce CAC by either attracting more low CAC customer groups and by making sure the behavioral gaps between the two segments are bridged.

We created two guides on the other metrics, Customer Lifetime Value, and Retention showing how you can measure and optimize them for improved performance

Check them out.:

See you next time!