Revenue, Personified

Revenue seems like the one part of the AARRR metrics that shouldn’t need explaining.  More money = better, right?  It hardly takes a rocket scientist to know that.

But as you’ve probably figured out by now, my personal bias is towards starting with metrics where you can immediately translate them into a natural-language statement (“this means X customer type is more likely to pay us”).

So instead of talking about customer lifetime value or average revenue per user, I’m going to talk about revenue in terms of two simple questions you should be asking to start with:

  • What percentage of Activated customers have given us money, even once?
  • What types of customers were more likely to give us money?

These are both fairly short-term questions, but what I like about them is that they can immediately narrow where you should focus on improving next.

(Everyone ought to be improving their customer lifetime value, but that’s about as vague as your New Year’s Resolution to lose 10 pounds.  And about as likely to succeed, unless you break it down.)

So let’s look at some examples.

What percentage of Activated customers have given us money, even once?

Let’s look at fake-Amazon.com:

You’d have defined Activated customers as the people who were had reached the potential to get value — in this case, they found a product that was interesting enough for them to view in detail. This is your potential pool from which to extract Revenues.

How many of those people give you money?

In this example, 2.2% of your potential pool is giving you money.   So now the obvious question is, how can I get more of this potential pool of pre-qualified, full-of-intent, people to give us money?

I’d expect that it’s quite common for someone to view a product and not proceed with purchase — you could be just browsing, or perhaps you were just curious about that electronic toenail polisher.  But here I’d hone in on the big dropoff between “Added to Cart” and “Completed Purchase” — and try to figure out why via customer interviews, user testing, or a KISSinsights survey.

If I could win back just 5% of those “Added to Cart” people, that’s more than 150 additional purchases.  Assuming a $20 average sale, that’s more than $3,000 additional revenue per day, and the solution is probably just fixing something that you should’ve fixed anyways.

For a second example, let’s look at fake-Netflix.com:

This would probably signal to me that my focus belongs on getting more Acquired users, versus trying to improve upon either of these steps.

What types of customers were more likely to give us money?

Most of us are targeting more than one type of customer.  I strongly suggest collecting that information — either via the registration form, or some required post-signup configuration — and using it to confirm where your dollars are coming from.

In this example, suppose you were selling a website service, and asked your customers what type of site they maintained:

The overall percentage of upgraders — 2.8% is interesting — but a lot less interesting than the fact that people running e-commerce sites are dramatically more likely to pay you than other types of customers.

This tells me that, rather than trying to create a “generic” marketing homepage that appeals to all customers, you should try tailoring your marketing site to specifically appeal to e-commerce customers.

Once you’ve experimented and improved some of the more obvious areas, you’ll feel a lot more comfortable tackling the more complex “lifecycle” metrics (and these can only help those, as well).

8 thoughts on “Revenue, Personified

  1. > If I could win back just 5% of those “Added to Cart” people, that’s more than 150 additional purchases

    Hmm, excuse me, but I’m looking at a figure that has the number 1099 over the text “Added to Cart”. 5% of that is 55.

    Thanks for the article.

  2. Superb post, thank you for sharing – great insight, well defined, applicable examples and colorful graphs too!

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