into the system so they can test things out and engage with the product. Maybe they use it over the course of several days in a row, because once their data is in there, they start to find it more and more useful. Finally, if it’s useful enough, the user may convert to an enterprise, paid plan and start generating revenue.
What does your funnel look like? Get everybody on your team to put all their sticky notes onto the wall in the order in which they would happen to the user you’re modeling (see Figure 1.6). The answers to the different questions will probably cluster. You may be missing a conversion step or even a revenue step for any particular user. Your education step might be quite small, if you have a very simple-to-understand product. Those are all normal variations.
Once you’ve got all your sticky notes in order, label them. If the answers to question number 3 are showing up in the fifth spot, then engagement happens fifth. There’s no right or wrong order here.
FIGURE 1.6 Put the sticky notes in the order they would occur for someone using your product.
Why Did You Do That Exercise?
What was the purpose of all that? Once you’ve established your User Lifecycle Funnel and made sure that your team understands it, you have a very powerful tool. Remember this picture shown in Figure 1.7?
Now that you know what needs to happen at each step of the funnel, you can start gathering real user metrics to find the friction points. The friction point is anything that is keeping users from moving from one step to the next. In the example, only 5% of people who become aware of the product are taking the next step. That might mean a lot of things. But specifically, it may mean that you need to look at the marketing and messaging for this product, because it doesn’t seem very compelling, or it may be that the idea itself simply isn’t that interesting to people. It means that you’re paying to show an ad or a message to 1,000 people and only 50 of them are responding to it with any interest at all. Why? Is the ad badly targeted? Is it badly worded? I have no idea. But whoever is in charge of this product should probably figure that out.
FIGURE 1.7 Add your product metrics to spot friction points.
If you’re wondering how to gather the metrics for your funnel, there’s more information about that in Chapter 11.
The other reason that you should establish your User Lifecycle Funnel is because it will allow you to create your User Lifecycle Algorithm, which we’re going to do in the next exercise.
The User Lifecycle Math
Now that you’ve got the funnel for your product created, let’s talk about math. Did I mention there would be math in this book? Don’t worry. There isn’t a lot of it. But the math I did put in is really helpful in understanding your business need.
As you just saw, people fall out of the User Lifecycle Funnel at every step. It’s almost never the case that people who enter a funnel (sieve) come out the other end, and it costs money to pour people into the funnel.
Depending on how much it costs to get someone into the funnel, you’re going to have to come up with a system that gets enough people through the funnel to pay for that acquisition cost (plus a bunch of other costs that we’re not going to address here, because they’re outside the scope of this book).
For example, if it costs $1 to get somebody into a funnel, and you predict that the average lifetime value (LTV) of a retained customer is $20, that means that for every 20 people who enter the funnel, at least 1 has to make it all the way through to the end just to break even on acquisition. That’s this picture shown in Figure 1.8.
Now, your job is to get the dollar amount at the end of the funnel to be equal to or higher than the dollar amount at the beginning of the funnel. To do this, you’re either going to have to lower the price going in (the cost of acquisition) or plug up some of the holes in your funnel in order to get more people through. The next few chapters deal with how to plug the holes, but the first step requires understanding where your biggest gains can be made.
You need to understand the math of your product funnel. At the beginning of the funnel, you put the expected cost of acquisition, and at the end, you put the actual LTV of an average retained user multiplied by the percentage of a person left at the end of your funnel.
When the dollar amount at the bottom is bigger than the dollar amount at the top, you’re earning more than you’re spending to acquire users.
Savvy readers and economists have been fuming for awhile now, since I’ve only talked about the cost of acquisition, and not about all the other associated costs, like the salaries and free lunches and massages your team keeps demanding.
FIGURE 1.8 New users aren’t free.
That’s true. Whether you include those costs at the top of the funnel depends on the stage of your product. More mature companies with better tracking should have breakdowns of the costs associated with producing products. Companies making physical products need to include cost of goods, shipping, and manufacturing costs (or at least the predicted costs at scale) in their algorithms along with lots of other specific amounts. Venture funded companies focused on growth likely won’t look much beyond acquisition costs, at least at first.
PRO TIP
Eventually, you’ll obviously have to include all your costs at the top of the funnel if you want to become profitable, but you’d be surprised at how few companies even manage to make more per user than they spend to bring them in. I’m focusing on the very first step of this journey, but if you have more specific breakdowns of your costs, by all means include them.
I know, some of this stuff is hard. It’s hard because there is a tremendous amount of information that you need to gather, often from many different sources. You need to understand average revenue per user and how long people tend to remain users, in order to calculate LTV. You also need to understand the costs of different channels for acquisition. And, if you don’t have any revenue yet, you need to understand how to estimate those numbers in the future.
It’s supposed to be hard. Understanding the fundamentals of how your product attracts users and makes money (or is projected to do those things) is complicated. But it’s critical to your ability to function, because it allows you to pick better metrics and make better decisions.
Mostly, though, this is important because it tells us what to work on and when to stop. Remember how the final step of the process is to learn and iterate? I get a lot of people who run various experiments—which we’ll get to later in the book—and they often ask how they’ll know when the experiment is valid. How do they know it’s time to stop iterating on something and start working on something else?
It’s
