Creating value versus optimizing revenue


Revenue stems from value, not the other way around
One of the big thematic issues that has been referenced numerous times by myself, Eric Ries, Mike Speiser, and others is the limitations of quantitative testing in building a business. In particular, several objections have been mentioned:

  • Over-optimizing leads to local maxima, particularly in product design
  • Focusing too much on pageviews/uniques ignores actual product/market fit
  • Relying on quantitative models leads to anti-innovative behavior
  • etc.

For all the data geeks reading my blog – my opinion on all of this is, these are absolutely all true, and are all very important and relevant conversations that every data-driven startup needs to be having. Are you having them?

All of these have gotten me focused on one of the core questions of any business: What value are you actually creating?

Distribution-led approaches can lead to local maxima on value creation
Many new companies in this age of quantitative virality easily fall into hitting local maxima on value creation, all for very good reasons. By focusing on viral invites, addressbook scraping, A/B testing, and other techniques, you end up getting a big inflow of traffic and your question becomes, “What is the best product I can make to keep all these users around?”

There become three major temptations:

  1. First, there’s a huge desire to build as efficiently as possible. That is, build in just enough to satisfy the user, but don’t overpolish
  2. Similarly, there’s a big temptation to build for the lowest common denominator, because you’re trying to appeal to a huge audience. As a result, a lot of designs err towards persistent low-brow internet “recipes” – like quizzes, polls, forums, and other mechanics
  3. In addition, your product veers towards a portfolio of experiments, rather than one cohesive experience. After all, you’re still trying stuff out, and it’s a lot easier to add a new feature or use a crazy headline to get people to your site, rather than really going through the difficult synthesis process that’s at the heart of every design discussion

As a result of all of this, it’s very easy to build a shitty product that generates small to medium value, but doesn’t do something amazing.

I won’t go too much into the solution of how to solve this, but I think the key thing to think about is that the quantitative lean philosophy doesn’t allow you to skip the difficult process of coming up with a hugely value-creating product. You still have to do it, but you have a framework in which to think about the process.

Maximizing the source rather than your share
Another issue in all of this is that the focus for quantitatively driven companies ends up being on outputs rather than inputs. For example, it’s easy to start to optimize traffic as an entity in itself, rather than thinking about the fact that traffic comes out of product/market fit. Or similarly, you can optimize revenue, but I think it’s misguided to do it without considering the fact that you have to be creating value for whoever is paying you.

Thus, one can argue that “value creation” is the ultimate source for all of these secondary variables like revenue, traffic, etc. And you can make the decision to focus on extracting as much as possible from the secondary variables, but you become fundamentally limited by the primary value creation process within your product.

Another way to think of this is that ultimately, every product creates a bunch of “value” (however you want to define it) and then you end up taking some % of that value back as revenue. Abstractly, this is true regardless of whether your product is ad-based, freemium, or otherwise. If you think about things this way, the following two approaches are fundamentally different strategies:

  1. Create a massive amount of value, and capture a small amount
  2. Create a moderate amount of value, and totally dominate the economics

(and obviously this is a spectrum as well)

I would put companies like Wikipedia, Craigslist, Open Source, and others as extreme examples of #1. And unfortunately, I think a lot of short-lived apps on Facebook are really more or less examples of #2.

I think this is why, for people who question the value of internet companies like Facebook and Twitter, the natural thing to ask is, are these companies generating real value? If they are, I think the process of turning that into cash is much easier than the process of creating the huge value in the first place!

The biggest value drivers are qualitative
So the question then becomes, how do you systematically create value? I think that this is a very hard question, and one that it may be difficult to use quantitative tools to define, because the biggest value drivers are often qualitative. They are things like:

  • What’s does your product do?
  • Who’s your customer?
  • Why do people give you money?
  • etc.

Now, a lot of these you can turn from qualitative to quantitative. After all, after you build your product, you can generate hypotheses around how people ought to use it and make it better in the most common flows, by optimizing the page flows. Similarly, you can figure out how much money you should charge for something.

Yet simultaneously, the process of figuring out the core product requires the entrepreneur to have an opinion, perhaps one that is difficult to test or takes many years to test. And whether you do this quantitatively is its own thing – after all, companies like IDEO have a very evidence-driven design process, but is one that uses qualitative evidence gathering to generate the product prototypes.

Looking at landing page optimization as a value creator
I think that this entire perspective about maximizing value creation rather than optimizing outputs leads to a lot of interesting, subtle changes in how you approach things. Let’s take landing page optimization as an example of this.

Typically, the entire discussion around landing page optimization is just one about conversion rates, and all the different possible candidates to get to a conversion. Instead of this perspective, you might ask: What value does an optimized landing page generate in the first place? Ultimately, I think this optimization makes it so that people can grok what they’re signing up for better. It helps them scan the page better for relevant pieces of information. And it could make them less confused about the page they’re on.

Compare that line of thought with, “hey, let’s make a lot of random headlines and see what people react to” as two different ways to approach the same problem, with one prioritizing value creation and the other prioritizing the output (conversion rates in this case).

Looking at viral loops as a value creator
Same for viral loops, and the process of getting people to invite their friends to a site. If you are sincerely value-oriented, then the entire question is:

  • why do people WANT to invite their friends to the site?
  • how does having your friends on the site make the product a better experience?
  • what conveniences can you build in to make people expose their friends to the process?
  • etc.

Contrast this to a perspective that an outcome like the viral factor is all you care about optimizing, and however you can get that number >1.0, then the better off you are. I think that this numbers-centric model absolutely can lead to viral websites and apps, but also sucks at actually creating a huge base of value that you can recoup later.

Conclusion
My point with all of the above is simple: No matter what your product is, the only way to make money long-term is to make a lot of people happy, and then getting some % of the value you created back, in return. The right strategy to build a long-term sustainable business is to build long-term sustainable value. No amount of viral tricks or optimization will allow you to escape that truth!

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Published by

Andrew Chen

Andrew Chen is a general partner at Andreessen Horowitz, investing in startups within consumer and bottoms up SaaS. Previously, he led Rider Growth at Uber, focusing on acquisition, new user experience, churn, and notifications/email. For the past decade, he’s written about metrics, monetization, and growth. He is an advisor/investor for tech startups including AngelList, Barkbox, Boba Guys, Dropbox, Front, Gusto, Product Hunt, Tinder, Workato and others. He holds a B.S. in Applied Mathematics from the University of Washington

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