In the grand scheme of metrics, measuring things, big data, small data and all the associated buzzwords, there does come a point where you need to be able to effectively display the results of all your hard work and discern whether or not the results are anywhere near what you hope to achieve.
There are many ways to do this, and while each way displays data, not every way is the right way to determine the effectiveness of your UX, marketing plan, sales strategy, etc.
One of the most popular methods is the sales funnel. Popularized as a term by companies like salesforce.com, SugarCRM, and ZohoCRM, the base method involves taking the entire group of prospects and measuring the drop out rate through your sales process to determine the final conversion rate for a specific group, either macro or micro, in terms of signups, sales, or whatever metric you are attempting to measure.
This is a rudimentary system when dealing with a mostly automated process, though you can cover the basics using this math. For instance, you have 1000 people who see your app (and you’ve confirmed this via some sort of measurement), and then 500 of them download the app, 250 of them open the app and use it once, and then 10 of them make a purchase in-app, and you collect X number of dollars after the app store takes its’ cut of the sales amount.
While this measures your base conversion rate and certainly indicates where you have issues (or might be leaking like a burst pipe at a fire hydrant), this type of measurement does not give you much more than a base indication of things and certainly doesn’t help you to decide what to change, when to change it and how to make it better (more profitable). If your situation requires simply that you get larger numbers of users, regardless of their profitability or action path, then by all means, set up an installation of Flurry, Mixpanel, Tapstream or one of their competitors. Each makes a more advanced funnel process than I have described here, and are reasonably priced between free and not so free to use their SDKs and extrapolate your data out of it.
However, a basic funnel approach does not give you drill down analytics that allow you to understand what customer is worth considerably more in LTV (life time value) and offer you the opportunity to engage your users in activities that result in better LTV across the board. The funnel approach also means that you’ll need to set up instances and variations and monitor them over time with some data extraction and formulas that you administer outside the funnel, with spreadsheets and so on.
In order to learn more about your target user and how to effectively engage them in an optimized way, you need to learn to segregate your users into groups that have relevance (date of install, type of install, referring source, or other differentiator) and monitor the users by group. Users can be members of multiple groups (installed, opened, used once, used multiple times, power user, whale, etc) and the users with the best LTV will be members of several groups.
Tunneling more deeply into your user base to understand why and how they behave the way they do when faced with your app is important when determining what path to choose to increase ROI per user, per campaign, per referrer source, etc. Knowing that a user from China who saw an ad displayed by X network on a weekend day is worth $5 versus a user from France who came in through editorial content on a Monday afternoon in June and is worth $12 is a powerful piece of knowledge when used the right way. Compare these two cases with a user from Ukraine who found the app through a web search on Black Friday and didn’t open the app after installing and it seems that your best targeting dollar for dollar is to spend more of your marketing budget on editorial placement in EU countries.
While this is an overly simplified case study of simple examples, the bottom line is that in order to effectively grow your user base into profitability, it’s necessary to do more in depth studies of user behaviour patterns to determine the value of a user (assuming you have users who are paying for something or being rewarded through gamification or who are eyeballs in your ad based income plan) than to just run them into a funnel and call it a day.