With LTV Cohorts, you can take a deep dive into financial metrics to understand which segment of customers is the most profitable, where are repeat purchasers coming from, which marketing channels are the most valuable, and more.
In the first dropdown, you can select which conversion segment you want to analyze.
Then, you select your date range.
In the third dropdown, you can select which attribution model you want applied to the data. It is worth noting, that this will assign credit to respective marketing channels for the first conversion. The model is not being used for any additional conversions after the first conversion since it is built for understanding what marketing channels lead to new users.
In the last dropdown, you can filter which specific buckets you want to look at.
In the first dropdown, you can select your interval type between "Month" and "Weeks since conversion."
In the second dropdown, you can select either the specific month you want to "Analyze Until" or "Number of Weeks" since the conversion.
Based on previous selections, a third dropdown may appear where you can "Group by" a specific Tier.
With "Show Metrics," you can select and de-select which metrics you want to view for each of the marketing buckets.
Then, you can input the profit margin. Based on the marketing channels you've previously selected, you can customize your profit margin as you deem fit. For example, the profit margin may be different for Paid Social vs Paid Search.
Selecting the first checkbox will include "Paid Channels Only" in the data below.
The second checkbox will be to include "New to File" customers only.
Now, lets take a look at the actual Analysis and what the different rows mean for your business. (click image to enlarge)
The Repeat Purchase Analysis displays the different paid marketing buckets on the left. The data shown in the columns is between whichever date range is initially selected. Each marketing bucket has subsections, which include:
- Marketing Spend
The data being shown in the first column "First Purchase" shows the total numbers for the chosen date range. The data in the following columns (following months) is based on purchases made by customers who first purchased in the initial date range.
The date range is between November 1, 2018 and November 30, 2018. So, the data being shown in the first column labeled "First Purchase" is:
- The number of customers who converted in November
- The number of conversions in November
- The total revenue amount in November
- The total marketing spend in November
- The cost of goods and services in November
- The total profit generated in November
The second column, "Nov 2018," shows the totals of additional conversions after its first purchase. In the above table, we see that 49 of the 548 customers made an initial purchase in November and one or more additional purchases.
The third column, "Dec 2018," shows that 92 of the 548 customers from November 2018 made an additional purchase(s) in December 2018. The following conversions, revenue, COGS, and profit are results from those said purchases.
You'll notice that there is a number for Marketing Spend in the "First Purchase" column, but none of the following months. That is because the marketing spend is relevant until a new customer is acquired, AKA when the "First Purchase" is made. Every purchase thereafter is a repeat purchase from an existing customer.
Each subsection under each bucket expands even further, as you can see above.
Using the previous example, under "Paid Search," the "Customer" subsection breaks down Existing customers who made a purchase in November, and their subsequent purchases (Existing Repeat) in the following months.
The NTF (New to File) breakdown shows you customers who are new. These customers made their first purchase in November 2018, with "Paid Search" as one of the driving marketing channels. You can then look forward to see how many repeat purchases those new customers made over the next few months.
The data above offers you insights into which marketing buckets are the most valuable and which bring in new customers vs. repeat customers so you can best optimize your marketing spend.
The number of customers in the above examples vary based off the selected attribution methodology. An Even Weight model displays the number of unique customers that are touched by Direct Mail as first, middle, and last touchpoints (1,400). Whereas, a Last Touch model only shows a subsect of customers (761) — the customers who engaged with Direct Mail as a last touchpoint (right before converting).
Updated 3 months ago