Analytics at the stage of attraction: cohort analysis
Before considering LTV, let’s start with the cohort analysis.
A cohort is a segment of users, who made the same purposeful action at the same period of time, for instance, created accounts on your website (cohort by the registration date), or made the first purchase (cohort by the date of the first purchase).
Here is the analogy: the students who entered in 2015 is one cohort, the ones from 2016 form the second one.
Important: every cohort is a segment, but not every segment is a cohort.
The segments can be combined arbitrarily (by sex, gender, etc.), whilst for cohorts the simultaneous action is primarily important.
We need cohort analysis to understand the lifecycle of a client. The client comes to you, makes the first purchase, then possibly returns to buy a couple more things. Then they leave forever. Cohort analysis allows to understand how much time you have before the client will leave.
For example, there is a January cohort: customers bought your product for the first time in January (this is your cohort base — 100%), in February the purchase was made by only 40% of people from the January cohort, and in March — by 15% in relation to January. You can see how the cohort is gradually being blurred until the last client leaves it.
Hint: you can segment your customers in a cohort by the acquisition channels. It will help you to understand whether your marketing investments are coming back to the channel or not.
Now when you have a basic understanding of cohort analysis, let’s have a closer look at such metrics as LTV/LTD.
Read also: Cohort Analysis and User Retention
LTV и LTD: work with the actual data and forecasts
LTV (Lifetime Value) metrics is discussed by many. The point of it is to forecast the amount of money one client will bring you during their lifetime within your business.
Conceptually LTV can be explained by the formula:
LTV = ARPU * A Lifetime of a Customer,
where:
- ARPU is the average revenue per user,
- A Lifetime of a Customer is a client lifetime within your company/project.
*ARPU = The total amount of all customers’ purchases / The number of customers
For example, one client brings you $20 once a month and uses the services for 15 months.
LTV=$20*15 months=$300
The client brings you $300 during their lifetime. If the attraction costs you $100 per user (CAC — Customer Acquisition Cost), then you will be left with $200 — the financial leverage to attract new clients and provide the service to the existing ones. If your LTV is $300 and user attraction costs $350, then it’s an attraction with a loss. Such businesses are not viable in the long run.
Below is a standard formula for assessing the viability of a business:
LTV / СAC ≥ 3
Let’s clarify from the start that this calculation doesn’t work for all startups, and here is why:
I wouldn’t recommend calculating the LTV, if:
- There are no repeat purchases (or there is a large percentage of one-time buyers)
I evaluated several online stores selling electronics in Ukraine, the ones that are in the top 10 list. The number of customers who make only one purchase there is insane — it is more than 95%. There is no point in working with LTV in this case: only 5% out of 100% will make repeat purchases and, therefore, there is literally no basis for LTV forecast.
- The clients “live” on the website for less than 12 months — there is no need for making the prognosis.
Similar logics, if the Lifetime is less than 1 year — there is no point to make a forecast.
What else, other that LTV?
You can try to calculate the LTD (Lifetime value to Date).
LTD is not a predictive value (the current value + the future value), but rather a factual one (the current value). Here you won’t have to predict the future cash flow. It works the opposite way: you calculate how much one client brings you during a limited period of time until a certain date.
For example, I want to find out LTD for the past 12 months (you define the time period yourself). I take a cohort, count its income for this period, and then “ration” it for the number of customers. PowerPivot is a great tool for such calculations. It is a high-performance column database within Excel 2016.
For instance, we defined that the client brings the startup $250 during the 12 months period.
This clarifies certain limitations for a business:
- to try to recoup the customer during the period, shorter than their lifetime — up to 12 months (this is your payback timeframe),
- to spend on attraction less the ⅓ of the LTD (under $80 — this is your CAC).
Summing up the above, I would advise calculating LTV only for the businesses which have the subscription-based business model (like the SaaS). For e-commerce companies (including marketplaces) I would recommend calculating LTD.
Preview photo: doyata / depositphotos.com
Header photo: kentoh / depositphotos.com
