I recently was picked to lead a longitudinal LTV model for our analytics dept. The final deliverable will be for external stakeholders, so essentially how the users on our platform (can't specify the company) are providing life time value to our external partners.
We'll be building this model from the ground up. We have nothing in place for this currently, just a sea of data (assume very generic assets, e.g. users, sign ups, user interaction with platform, etc.)
So... where do I even start? I've just been reading random docs on google for the time being. Any specific resources that are good? Are there different LTV methodologies? What's the "best" one (please take that with a grain of salt)?
I know this is an extremely broad topic so any answers even loosely related to LTV will hold significant value. Thanks all
I haven't tried anything yet. Just reading up on a few resources.
First thing you want to do is lay out the reasoning for having LTV. What's it's gonna be used for and by whom. I'll give some examples, but your industry and your business will have to have it tailored to them.
Next, you have series of meetings with all the stakeholders so that they would agree on a good definition for LTV under a tight guidance of someone who understands the data, or at least what dimensions have to influence it and what format it has to be in.
An example would be: you have an app that offers seven products. The first two products are freebies. Another requires an email to get. The fourth product is just one buck per month, the fifth costs a hundred, but one-time payment, the sixths is 20$/month and the final product is an enterprise/b2b level solution.
An arbitrary model would be to have something like:
No products (guests) => LTV = 0 Product 1 => LTV + 1 Product 2 => LTV + 1 Product 3 => LTV + 3 Product 4 => LTV + 10/month of subscription Product 5 => LTV + 1000 Product 6 => LTV + 200/month of subscription Product 7 => LTV + 10k/month of subscription
Then the LTV stakeholders, - mainly business owners and PMs refine the model depending on what kinds of analysis they need conducted typically. That basically depends on what and how they report to their executives or the board.
This is if you want to go with a simple integer as an LTV. Most commonly used for weighting users. Going with integer is a very comfortable starting point since it allows for easy mathematical aggregations. Just to make your user-based analysis more robust. Say, you found out that 2% of your users encounter certain issue that blocks them from navigating somewhere or finishing a process. How should it be prioritized? Should it just be ignored? Should it be addressed immediately?
Well that depends on who those users are. If they're just free users or even just guests and the error is not blocking them from product onboarding, then it's worth to get the ticket to the backlog, but realistically it won't get released any time soon if ever.
However, if those users are enterprise customers, then the issue not only has to be hotfixed. It has to be hotfixed immediately. Probably paying overtime to the devs, qa and devops to work till late today.
Generally, LTV should be a user-level dimension. There are implementations of it as a session-level, but it's way more difficult.
From the technical standpoint, LTV is most commonly implemented on the tracking stage, so commonly in a TMS, say, GTM by a tracking specialist.
Another way it's implemented is in or after ETL, by the data engineers or data scientists.