@andrewchen

The perils of ignoring brand value
The nature of internet marketing makes it easy to have a highly accountable, metrics-driven view – but companies that are highly metrics driven easily overlook hard-to-measure issues like brand and user experience. The reason is that when all product decision-making is run through metrics-driven reports, soft things like “Brand” show up as costs, but never as benefits.

This leads to systematic erosion in many “soft” but important factors, like customer experience, brand value, and “love.” And ultimately you need all of these things to create a massive, enduring consumer brand – it’s not enough to optimize funnels.

Let’s discuss why:

Two worlds: Direct marketing and brand marketing
In the advertising industry, there’s been a long, historic distinction between brands and direct response – and this distinction echoes its way into the online startup building world as well.

In the brand world, you have companies like Coca Cola, Apple, and others who pour millions of dollars into high-reach vehicles like TV which lack any real accountability. Thus the saying:

Half the money I spend on advertising is wasted; the trouble is I don’t know which half
– John Wanamaker, US department store merchant (1838 – 1922)

To many people, the brand advertising world is irrational and fashion-driven, because of the complex interactions between agencies, their partners, and the publishers that rely on them. Just watch Mad Men.

On the other hand, you have direct marketers who thrive on accountability. They buy into marketing channels like direct mail, coupons, infomercials, and most recently online remnant ads, because they can purchase cheaply and use sophisticated statistical techniques to optimize their media buys.

Startup engineers tend towards metrics-driven
So what side do startups tend to side on? It obviously depends, but because of the highly accountable and measurable nature of online, it’s much easier to become metrics focused. Similarly, startups are mostly poor Thus, expensive brand efforts are mostly out of reach. (Probably for the better!)

Also, with the possible exception of GoDaddy, I don’t know a single startup that made it or not based on their brand advertising strategy. The typical path is focused on products and technology, and large organic growth which builds large consumer audiences.

And obviously, readers of this blog will tend to be much more metrics driven compared to the average entrepreneur!

You optimize what you measure
The first issue that causes metrics-driven startups to ignore brand value has to do with the fact that it’s very hard to measure brand, and you tend to optimize what you can measure. As soon as you throw some numbers on a big report, there’s an inherent human desire to make the numbers go up!

This is why one of the fundamental tenants of metrics-driven startups is to build lots of highly accessible reports that everyone in the organization can look at. Even if it’s easy enough to pull something out via a SQL query, it’s another thing for everyone to be able to hit a URL and load it instantly, no matter who they are on the team.

Measuring brand value is hard!
But measuring brand value, or user experience, or community “feel” or other soft things like that is very hard. I think they’re hard because while it’s clearly important, at the same time:

• The quantitative effects accumulate over large periods of time
• These might be “source” variables that drive lots of behavior, but it’s hard to measure past surveys and explicit information collection
• Some of the most important datapoints may be qualitative, not quantitative
• Changing these soft things may require big efforts above and beyond small A/B-testable changes

The companies out in the marketplace that try to measure brand value mostly just use surveys to detect changes. Or, many companies simply resort to a pretty ineffectual number like “reach,” which refers to the number of people who saw the campaign. This can sort of work, but self-reporting also sucks, and the quantitative data you get out may not be as useful as the qualitative data.

In my previous online ad career, I was shocked to hear that the standard way to measure a brand advertising campaign online was to fork $50k over to Dynamic Logic, whose job was to run a dinky little survey and tell you if your campaign worked. $50k to run a survey!

Reports show the cost of branding, but not the benefits
As a result of brand advertising being hard to measure, you get two systematic, interrelated issues:

1. Product changes that result in brand value are overlooked, whereas the costs of delivering that value is not
2. Features that negatively impact brand value but show short-term quantitative value are accepted

Here are two examples – let’s say that you think your site’s interface looks like crap, and you want to improve it to make it higher class and more trustworthy. But your metrics czar says, let’s make a really small improvement and see if it affects anything before we revamp the whole site. That sounds reasonable, but then you find out that in fact, making a visually compelling site just doesn’t drive better metrics, and in fact, it’s expensive and maybe lowers certain metrics. What do you do? (This is case #1)

Another example is that you make it really hard to unsubscribe from your mailing list. Maybe you don’t have a link, or you have to login first, or whatever. Making this change clearly affects your ability to retain users, but you get a small percentage of complaints, but the overall quantitative metrics look good. Should you keep this hard-to-unsubscribe mailing list issue? (This is case #2)

Ultimately, it should be clear that both cases are not clear cut issues at all. I could find reasons to go either way, but when you’re trading off a qualitative metric versus a quantitative thing, the numbers-driven approach tends to win. But this may not be the right thing. Similarly, sometimes the numbers may justify the decision, and the brand costs are actually quite low.

How do you make these decisions then? I’ll just wave my hands and say, “Entrepreneurial judgement”

Who’s the brand advocate?
One of the big, important roles that you need on every team as a result is someone who can advocate for the soft things. Who’s your brand advocate? Or customer experience advocate? Having someone on your team who can make logical arguments to balance out the quantitative stuff is hugely key, otherwise you’ll inevitably go down a path of brand-eroding quantitatively driven decisions.

Similarly, if you find that you’re never making decisions that go against the numbers, then frankly, you’re probably doing something wrong. If the data drives all the decision-making, then a lot of “soft” data is getting ignored.

Want more?
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Here are some links I posted to my twitter account over the last week or two. You can follow me on Twitter if you like these! Many are work unrelated.
• GReader share: Flixel, a free Actionscript library for building complex games without Flash http://flixel.org/ #
• GReader share: Datablindness http://bit.ly/uYdFE #
• GReader share: What do American girls like? (crowdsourcing stereotypes) http://girls.yury.name/ #
• GReader share: Entrepreneurs on Twitter http://bit.ly/3TgJJ #
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• GReader share: Today, We Think Twitter Is Dead (for Now) http://bit.ly/E59ue #
• RT @garrytan: All models are wrong. Some models are useful. -George Box #
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• RT @dataspora: Also, "Never fall in love with a model." – G.P. Box (and several rock stars) #
• RT @cherrymochi @wesabe @palm: Awesome Billshrink post comparing costs of iPhone, Palm Pre, Android: http://bit.ly/1aaYgo #
• GReader share: Shocker: Study shows VCs plan to invest in fewer companies http://bit.ly/jiGoP #
• GReader share: Don’t Blame Venture Woes on the Economy; It’s the VCs’ Fault http://bit.ly/13glfn #
• GReader share: Revenue at Craigslist Is Said to Top $100 Million http://bit.ly/iFslh #
• RT @bryce @peHUB: @pkedrosky Don’t Blame Greedy VCs for Industry Bloat, Blame LPs: http://tinyurl.com/nklo55 #
• GReader share: Hey, Graduates, Check Out These CEOs' First Jobs (SLIDESHOW) http://bit.ly/VI2hm #
• GReader share: Twitter Hits The Ceiling http://bit.ly/12AlNW #
• GReader share: Kedrosky: Cut the VC Industry in Half http://bit.ly/b9M35 #
• GReader share: Trent Reznor Quits Web 2.0, Offers Cutting Advice To Haters http://bit.ly/1aDnp1 #
• RT @tworetzky: New Pew report on US use of online health content. Majority read UGC content but not on social networks! http://bit.ly/dhIrC #
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• GReader share: I want… who works at EDGE Magazine? http://bit.ly/RUZUi #
• GReader share: Jimmy Fallon on Microsoft Natal: 'Oh my God — this is awesome!' http://bit.ly/EIZSu #
• GReader share: Inside the Startup Office from Hell http://bit.ly/19dm4S #
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• rt @m2jr Regardless of one's politics, Letterman's behavior to Sarah Palin has been out of line. Salon nails it. http://bit.ly/17B8IM #
• WSJ on benefits and perils of data-driven education: http://online.wsj.com/article/SB124475338699707579.html #
• GReader share: Friday Fun: Tennis Serves and New Balls http://bit.ly/IYcbz #
• GReader share: MySpace Is In Far Worse Shape Than Its New Executives Thought http://bit.ly/jHdoF #
• GReader share: Electronic Arts No. 2 exec foresees shift coming toward digital game distribution http://bit.ly/Bmczc #
• GReader share: Facebook Nabs The Man Who Engineered Google AdSense For Many Years http://bit.ly/3bNdW #
• GReader share: Is John Donahoe Finally Turning eBay Around? http://bit.ly/LePS0 #
• On Will Wright’s Team, Would You Be a Solvent, or the Glue? http://post.ly/qEZ #
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• Overheard in Palo alto: new Stanford grad explaining full history of Apple in excruciating detail to his parents. Rite of passage? #
• Writing tomorrow morning's blog post about why you should make it easy for users to quit your product! Fun. #
• RT @ericnakagawa: Celebrity – We love to love them, love to hate them, love to create them, love to destroy them. #
• i'm tempted to change my safari search to bing.com, just to try it out… http://bit.ly/Je6Db #
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Don’t worry, I’m not a hippie
From the title of this blog post, you might think that I’m going to make a touchy-feely argument about why you should respect the right of your users to do all the terrible things that every entrepreneur fears:

• delete their accounts
• unsubscribe from email lists
• cancel their subscriptions
• uninstall their apps

… but you’d be wrong.

In fact, I’m going to argue that for every early product out in the market, making it really easy to quit is completely aligned with self-interested thinking. I’ll make the assumption that all the entrepreneurs reading this post are greedy, self-interested individuals, and target the appeal straight into your dark hearts

My central argument is that if you believe that every startup is an iterative learning process that converges towards product/market fit, then you need extremely high-fidelity signals to tell you if you’re going in the right direction. That means that along with trying to charge people money from early on, which is the highest form of “I love this!” you should give people valves to tell you “I hate this!” so that you can learn more faster.

Let’s drive into this further…

Product/market fit
There’s a notion of product/market fit that Marc Andreessen references in his blog, and he calls it the “only thing that matters” and says that every startup should do everything they can to get to this point. Let’s see what he writes:

The only thing that matters is getting to product/market fit.
Product/market fit means being in a good market with a product that can satisfy that market.

… and Marc continues:

Lots of startups fail before product/market fit ever happens.

My contention, in fact, is that they fail because they never get to product/market fit.

Carried a step further, I believe that the life of any startup can be divided into two parts: before product/market fit (call this “BPMF”) and after product/market fit(“APMF”).

When you are BPMF, focus obsessively on getting to product/market fit.

Do whatever is required to get to product/market fit. Including changing out people, rewriting your product, moving into a different market, telling customers no when you don’t want to, telling customers yes when you don’t want to, raising that fourth round of highly dilutive venture capital — whatever is required.

When you get right down to it, you can ignore almost everything else.

If you believe what he says, that gives you a pretty firm set of marching orders. And for early products on the market, getting to to this point in which your product is good enough and the market is compelling enough is a tough slog. So the question is, how do you navigate your way to product/market fit?

At the heart of every startup is a learning loop
For the idea that every startup is inherently a learning machine, we can turn to two of my favorite startup people, Steve Blank and Eric Ries. Eric has blogged in a lot of detail about how he believes that inside of every startup is an OODA loop that involves trying stuff out, learning, and trying more stuff again. And of course a lot of these ideas are built off of Steve Blank’s Customer Development framework that I’d encourage my readers to look into as well.

In this light, to combine the two ideas: Every startup is a series of iterative experiments that gets you from zero to product/market fit, and if you can do it before running out of money, then you might get rich

And the decision-making process in this approach is totally different. In most product strategy conversations I’ve been involved in, the most heated debates center around whether a particular product will work, and all the pros and cons of the situation. Contrast this to a learning-centric approach, which emphasizes whether or not experimenting with an idea will yield insights, and how much it’ll cost to learn these insights.

In other words, you’re much more likely to try things that will fail, if those failures teach you something important about the market.

Of course, all of the decisions that power these iterations rely data – and the better the data, the better your decisions will be, naturally. So where do you get the data to tell you if customers are happy or not about your product?

Explicit signals beat implicit signals almost every time
One of the key lessons I took away from my time from the behavioral targeting ad industry is that explicit data is much, much better than implicit data, when it comes to predicting user behavior.

That is, you’d prefer explicit “intent” data like:

• made a purchase
• used a student loan calculator
• searched for “palo alto bmw dealership”
• filled out a form

versus the less valuable implicit “interest” data like:

• have similar demographics to other people who buy
• visit the same publications as similar customers
• having a pattern of reading finance articles

So if you are looking to collect data to drive decisions, then the best kind comes from the explicit data of having users specifically take action, whether it’s positive or negative. Purchase intent data, as illustrated above, is positive – and quitting intent gives you the negative half. In fact, if you only look at the positive feedback, you might be ignoring 50% of your data.

As a result, you want lots of explicit data points in the axis of “I love it!” to “I hate it!” which includes people giving you money (maybe donations being the ultimate form of love) to allowing them to easily quit. Make it easy  for your users to quit, unsubscribe, or otherwise cancel – it gives you the strong signal when you’re doing wrong! And make sure to track it and include it in all of your quantitative experiments as well.

Better data = better learnings = Better product
So to summarize my key arguments here:

• Give users lots of explicit ways to show appreciation and hatred
• These datapoints will help you iterate your product
• Better product iterations will let you reach product/market fit faster
• Reaching product/market fit will lead to more money faster

You can only learn so much from reacting to positive data, and trapping your users in unwanted subscriptions won’t get you to product/market fit any faster.

And finally, don’t do it because it’s annoying
‘nuf said.

Want more?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.