Optimize Marketing Expenses: ROAS, CAC, Contribution Margin

From the last LTV post, we explained how to analyze CLV(Customer Lifetime Value) and how to estimate CAC(Customer Acquisition Cost) based on cohort. MakeDashy provides filters to analyze CLV and retention in various ways. For example, you can check cohort by quarter, by channel, by particular purchased item, and by segment. Depending on these various conditions, CAC optimization, Contribution Margin calculation, and estimate of upselling promotion are possible based on ROAS(Return on Ad Spend).

The reason we are tracking ROAS(Return on Ad Spend) is to optimize CAC. If we divide marketing expenses for a particular period by number of new customers by the same period, we can calculate CAC. We can find this from the LTV cohort of customers who came through performance marketing. If you divide the LTV cohort by this CAC again, you can see how much the marketing expense spent per person is being recovered over time.

For example, let’s say Table 1 is company A’s monthly marketing expense and Table 2 is Monthly cohort LTV.

Dividing Marketing Expense in Table 1 by Monthly new customers in Table 2, we can calculate CAC shown in Table 3. We can also calculate ROAS by cohort once we divide LTV by calculated CAC. From the Table 3 above, we can see that marketing expenses can be recovered in the third month, but if you simply track ROAS in the first month without looking at LTV as a cohort, the recovery rate is about 50% which indicates you need to drastically reduce advertising costs.

As you can see above, ROAS gives you an idea of how much CAC is recovered in the first month, and how long you will have to wait in order to recoup the full CAC amount. What if our service’s first month ROAS is 50% and full recoup period is 3 months, how can we shorten this period?

The best way is to break down and control more conditions that form this amount. There are various conditions such as region, segment and channel that divide cohort and estimate marketing expense. Out of many conditions, marketing teams use channels the most to subdivide the amount as it is easy to divide the expense in accounting and immediately implement the strategy.

Likewise, analyzing ROAS by channel helps businesses to find out which channel’s marketing expense is overly spent and which channel is performing well. However, decreasing expense on particular channels involves detailed targeting. Just reducing expenses won’t make changes in ROAS as the number of converted customers reduces at the same time. That’s why we have to check under what conditions the customers are being targeted in channels with high expense. If you don’t know detailed target conditions, the most effective way is to have a survey. However, there are many practical difficulties. In such cases, it is common to analyze our existing customers in more detail and find out their characteristics. MakeDashy provides a segmentation analysis technique for this purpose.

Like Table 2 above, if you insert the contribution margin (selling price - manufacturing cost) into the cohort data, you can find the contribution margin per product sale. Also, as shown in the image below, by using the various filters provided by MakeDashy, you can find out the time spent for customers who bought a particular item to buy the next item, and the increasing value.

One of MakeDashy's clients operates a subscription-based service. Recently, this client analyzed how long it takes customers to buy a certain product and its added value after they have received the sample of the same product. The samples were sent for upselling purposes. Previously, because the sample was added in the middle of the subscription, sales of other products were mixed, so it was not possible to accurately know the value driven only from giving the sample. However, in MakeDashy, we set the cost of the sample as the CAC concept and the contribution profit as the LTV concept to calculate ROAS, and found that the payback period is very long.

Unlike coupons that are spent on purchase, the samples were given regardless of customers purchase. This was the expense we had to optimize. Many clients were concerned about low upselling conversion rate after they distributed samples. With the help of MakeDashy, our clients are optimizing upselling promotion efficiency as MakeDashy’s segmentation result tells which customers’ upselling conversion rate would be high.

Information that we can find out from cohort is truly countless. Refining the data and visualizing the logic is the hard part. As the importance of cohort analysis rose recently, many companies are trying to come up with their own dashboard. However, making a practical cohort takes at least a year. If you don’t have your own team right now, you have to add the time needed for hiring. MakeDashy can solve these problems right away. All of the above analysis is possible by uploading only the data you have so far.