Here’s an excerpt from AllThingsD:

LinkedIn has acquired Connected, a small contact management startups that unifies and dynamically updates users’ connections on email, social networks, calendars and phones, according to sources close to the company.

Connected is similar to Xobni/Smartr, but it’s more of a dashboard than a plug-in, and it costs $9.99 per month. The company had raised a seed round of $500,000 led by Trinity Ventures in June. The service has been called “bloody awesome” by Tim O’Reilly.

Ada and Sachin posted some additional info via blog post with the annoucement. They’ll soon be moving down to Mountain View as part of the purchase so I’ll get to see them more often!

For everyone who didn’t make it, I wanted to share all the slides:

LaunchBit
Ad network for email

LaunchRock
Viral product landing pages

FromUs
Social gifting for friends/family

DailyGobbleCRM for restaurants

Manpacks
Subscriptions for men’s clothing/products

TinfoilSecurityWeb security made simple

Console.fm
Easy button for awesome music 

WillCall
Mobile app for last-minute tickets 

Storytree
Capture and share stories for families 

Welcu:
Manage unique, high-end events 

CardinalBlue
Social photo collage app for mobile

Volta
Lightweight CRM for businesses to manage outbound calls 

CultureKitchen
Authentic ethnic cooking classes

OVIA
Video-based screening for hiring

Loku
Local guides for travelers

Coderbuddy
Software development on tap

CraftCoffeeSubscription coffee

AppGrooves
A/B testing platform for developers

Skipola
White-label mobile food ordering tools

VidCasterVideo site creator

Kibin
Proofreading marketplace

ToutApp
Email template automation

BugHerd
Visual issue tracking

Snapette
Fashion mobile app

It’s hard to keep things simple, especially when adding so many new features
In my recent post on the virtues of marketing simple products, a couple readers wrote in to write a really interesting questions – here’s a particularly interesting one by Mark Hull:

How do you ensure that by simplifying your product too much, you are not losing a competitive edge by a lack of additional features/functions?

Every product team struggles with this question- it seems like naturally adding more featureset adds more power to the product, yet at the same time adds complexity that makes it hard for new users to even get started. This is a common problem in the initial version of a product, because most of the time the first version doesn’t work, and the most obvious way to solve the problem is to just keep adding features until it starts to click. Yet does this ever work?

Don’t compete on features. If your core concept isn’t working, rework the description of the product rather than adding new stuff.

Make sure you’re creating a product that competes because it’s taking a fundamentally different position in the market. If the market is full of complex, enterprise tools, then make a simpler product aimed at individuals. If the market is made up of fancy, high-end wines, then create one that’s cheaper, younger, and more casual. If the market is full of long-form text blogging tools, then make one that makes it easy to communicate in 140 character bursts. If computers are techy and cheap, then make one that’s human and more premium. These ideas are not about features, these are fundamentally different positions in the market.

BMW is the Ultimate Driving Machine
My favorite example of differentiated market positioning in a very crowded market is BMW’s “Ultimate Driving Machine” slogan. It’s not just a marketing message, you know it’s true when you sit inside a BMW and turn on the engine. Among other things, you’ll notice that:

• The center console is aimed towards you, the driver
• The window controls are next to your stick so it’s easier for your right hand*
• … and obviously the remarkable driving experience

Furthermore, when you go to the dealership, the entire experience keeps reinforcing the “Ultimate Driving Machine” message. The point is, the positioning is about the driving experience and the engineering to back that up.

In a price and features comparison, it’s unlikely that BMW would ever come on top- it’s expensive, and very little of the money goes into the interior and niceties that you’d expect out of a Mercedes. Yet people end up buying BMWs not for the features, but because it’s a fundamentally different car than a Mercedes (or at least it feels that way).

I’ve always felt that Apple goes this way too, where their products are more expensive and often do a lot less than competitive devices, yet win because they have a more cohesive design intention across their whole UX. Again, the idea here is more about competing via a differentiated positioning rather than based on a feature checklist.

You’ll never win on features against a market leader
The other important part to remember is that for the most part, if there’s a winning product X on the market, you’re unlikely to win by creating the entire featureset of X+1 by adding more features. Here’s why:

• First off, that’s crazy because you have to build a fully featured product right away, and that might already take years to match a market leader
• Secondly, as described in the Innovator’s Dilemma, if you’re mostly copying the market leader and then adding features, those features are likely to be sustaining innovations that is likely on the incumbents roadmap already- by the time you’re done, they’ll either have it or just copy you

Instead, the idea is to have a simpler product that attacks the low-end of the market leader’s product by taking a completely different market positioning. That way, you don’t have to build a fully featured product and you can take a completely different design intention, which leads to a disruptive innovation.

Ramifications for startups building initial versions of a product
I think there are three key ramifications for teams building the first version of a product.

The first is: Don’t compete on features. Find an interesting way to position yourself differently – not better, just differently – than your competitors and build a small featureset that addresses that use case well. Then once you get a toehold in the market, you can figure out what to do there. This doesn’t mean that new features are inherently bad, of course- they are fine, as long as they support the differentiation that you’re promising.

The second thing is: If your product initially doesn’t find a fit in the market (as is common), don’t react by adding additional new features to “fix” the problem. That rarely works. Instead, rethink how you’re describing the product and how you deliver differentiated value in the first 30 seconds. Rework the core of the experience and build a roadmap of new features that reflects the differentiated positioning. Avoid add-ons.

The third is: Make sure your product reflects the market positioning- this isn’t just marketing you know! If your product is called the Ultimate Driving Machine, don’t just slap that onto your ads and call it a day. Instead, bring that positioning into the core of your product so that it’s immediately obvious to anyone using it- it’s only in that way your product will be fundamentally differentiated from the start.

* UPDATE: An astute reader, Greg Eoyang, pointed out that the modern generation BMWs (E90s) are different now- I have an E46 that’s a few years old, so I was basing my observation on that. He writes:

First of all, a most modern BMWs do not have the window controls near the stick, that’s like 2 generations old, they are on the windows just like Honda’s these days.  BMW doesn’t even tell you about a lot of the features that have been standard for a long time – such as speed variable volume on the radios – Wide Open Throttle switch (back in the non-CPU days, it cut off the air conditioner when you floored it) – They have improved the concept of a car which is more than the features.

Thanks for the additions Greg!

Question: What UX considerations were built into Google+?
The most interesting design choice I’ve seen for G+ has been deploying it across all the Google properties within a navbar, and via the notifications – I’m talking about this thing right here:

(btw, looking at it now, I notice it’s the same coloring scheme as Quora, hilarious)

Building G+ on top of pre-existing, high-retention products
Obviously this is a smart decision because it lets them build on top of their own high-retention, pre-existing products: Google Search and Gmail, in particular. Contrast this to an approach where they would have started up G+ as its own independent property, which Google users could choose to adopt or not- but then that looks like Orkut.

Anyway, as a result of adding this new global navbar across all the Google properties, they have to deal with a very small amount of real estate to create some pretty rich interactions. Thus, it was very interesting to then see them building a mobile-like interface for interacting with comments, follows, etc., inline, without leaving whatever experience you’re already in:

And if you click on any of these, you see a quick sliding motion that lets you interact with the different notifications inline, without going anywhere:

Contrasting with Facebook
In comparison, the Facebook notifications dropdown is almost more like a inbox of “pointers” to the actual content. As a result, while you can see what’s new, you can’t actually do anything about it without leaving where you are. I found this a nice interaction on G+’s part given that they are building on top of things like email or search where you may not want to leave yet.

Someone should obviously do a much longer design discussion of the G+ main site, but I personally found the new navbar and notifications system pretty interesting so I thought I’d write a bit about it.

Simple products aren’t only better designed, they’re easier to market too.
Marketing and product UX are seen as conflicting with one another, but there are, in fact, many opportunities for the two to work together. Some of the best tools for increasing metrics are the same ones that are used to create effective interaction designs. These techniques include things like adding “soft” onboarding experiences, stripping out unnecessary features, having clear visual hierarchy and calls to action, and many more tactics. Ultimately, these tactics serve to create simple product experiences that are both desirable and well-optimized.

Let’s explore the different reasons why simplicity is a virtue for both designers and marketing quants.

Highly optimized flows make it easy to understand “what do I do next?”
Every product lives and dies based on how well new users are able to sign up and get oriented with the product’s core value. High signup and onboarding rates depend on a large % of users completing each step.

As a result, it’s important for each page to be as simple and directed as possible, so it’s constantly obvious what to do next. If each page gives the user too many options, thus distracting from the primary goal of the funnel, then the %s will decrease. As a result, some of the best landing pages and funnels fundamentally depend on extremely simple, stripped down designs. Here, removing things like navigation chrome, extraneous links, etc is not only simpler, but also better performing from a metrics standpoint.

More data and faster learning cycles
A metrics-informed team depends on deploying A/B tests and evaluating the results as the core of their product iteration process. Early on however, you often don’t have enough users to quickly evaluate tests at a statistically significant level. This data is then further diluted when you have a complex featureset, since only a small % of users interact with each option. However, if you have a simple product, where almost 100% of the users go through the same signup, invite, and sharing flows, then you’ll be able to collect data sooner and thus make decisions faster too.

This is a huge advantage because when you can run A/B tests in 3 days instead of 9 days, for instance, you can learn 3x faster and find product breakthroughs sooner. Think about this like compounding interest in the bank- finding 10% improvements faster leads to exponentially better performance.

Simple products are easier to optimize and pivot
Ultimately, it’s the optimized flow through your product that wins – you don’t get any credit for complexity. One optimized funnel beats any number of unoptimized funnels, because you only get credit for average conversion rate across all the funnels. Thus, more funnels means that on a practical level, it’s harder to keep them all optimized. It’s easier and better to push users through a small number of signup flows that you can keep well-designed and well-optimized, so that the overall quality stays high.

This is especially true if you decide to make some product changes in a classic “pivot,” or otherwise test significant new additions in a signup funnel like adding Facebook sign-on. If you have a simple product with a small number of onboarding flows, then it’s easy to experiment to see if it’ll work, collect data quickly, and then add it to 100% of new users’ experiences. Contrast this to a complex product where shifting the design takes a lot of time because you have to update so many different places in the product.

Keeps the focus on top of funnel rather than low-impact add-ons

When a product isn’t working, often the knee-jerk response is to “fully bake” the product by adding more features. However, I’ve found that when examining the data of new startups, the problem most often lies on the first couple pages of a product- often an unattractive value proposition, or clunky signup flow that kills the new user experience. Adding metrics to simple products often makes it clear exactly what’s going on, and most of the time, it’s a fundamental issue that needs to be fixed on the first page.

In this way, simple products with the “right” value prop will end up with better signup rates- this lets you put your attention on top-of-funnel issues rather than low-impact feature add-ons that won’t 10x the destiny of your product.

Short funnels result in more conversions
One of the most powerful things you can do to a key product flow is to shorten it*. Generally, because you lose a % of users at each step, reducing the amount of work to get started is a highly effective tool- rather than presenting a complicated homepage and asking for tons of information upfront from a user, perhaps you just let them signup with Facebook- that might reduce the number of steps, leading to a simpler product and better metrics too.

Ultimately, this all aligns with the highly opinionated design ethos that prioritizes what users most often want to do, rather than presenting many options equally. As is discussed in the Palm story in the book “Designing Interactions” the features of a product are used in a Power Law distribution- a small number of features are used constantly and the rest are long tail. As a result, you want to make the most commonly used features convenient while putting the unused features available but hidden.

(*in some outliers, lengthening signup flows with the right steps can help too)

Increasing the prominence of high-value actions by removing low-value actions
One of the most common (bad) design patterns I see among metrics-oriented products is continually layering more and more prominent calls to action for sharing or other viral mechanics. This got especially bad in early Facebook apps. The problem is that the user’s attention is easily diluted, and each new feature competes with the last- as a result, after a few iterations of this, it’s pretty easy to end up with a frankenstein of a product that’s cluttered and messy.

Instead, a compelling tool is to remove features in order to make what remains more prominent. Instead of making the high-value actions bolded and highlighted in yellow, simply remove the actions that are no longer necessary. This leads to both a simpler product experience as well as raised prominence for whatever actions you want to emphasize.

Conclusion- let’s make design and metrics work together
Ultimately, the key to the tools above are that they increase the effectiveness of the UI while simultaneously increasing the metrics. This can happen because highly optimized products are dead simple to use- they have landing pages that communicate a compelling value, soft onboarding flows, clear calls to action, and simple mechanics that drive a lot of value. The same things that make it a highly marketable product are the same things that make it well-designed, and a great thing for which every product should strive.

To use these tools effectively, those who are metrics-informed must also become design-informed. While it’s obvious that you can increase the prominence of something by making it blink and highlighted in red, there are many more tasteful tools that lead to less visual clutter and provide an even greater metrics benefit. Even Dave McClure!

In a classic A/B test, you’re metrics-driven and want to pick whatever test variant ends up with the higher numbers. This is a useful tool, but is only applicable to scenarios like signup flows where the conversion is obvious. This post will describe some different tactics that are metrics-informed and end up as an aid to your product design process, rather than driving it.

The tactics I’ll describe are for:

• Updating your product without negatively impacting numbers
• Streamlining your product by measuring and removing unused features
• Designing for the right level of prominence

Updating your product without negatively impacting numbers
Product teams are constantly pushing small updates to their products in response to customers and what’s happening to the market. When an update affects a key part of the product, particularly to the main signup flow or core viral loop, it’s often important to ensure that it doesn’t hurt the numbers.

For example, let’s say you’re building a new social site and you have a Facebook-integrated “friend finder” option that you want to add. If you build this and test it, you’ll likely find that since it’s unoptimized, it’ll have worse initial numbers. A classic A/B test will often eliminate the new design because it performs worse. But instead of killing it prematurely, you can use an A/B test to iteratively “bake” the new design with a small % of users until it’s ready to replace the old one.

If you know that it’s important to have this type of Facebook integration in your product design, what you do is leave it in, but only expose 10% of your users to it. Then keep making small updates to the design, working on the copy, call to action, and other aspects, until the new design performs as well as the original.

In this way, you can update your product without impacting the numbers negatively. And unlike a classic A/B test where you aim to just pick a winner, instead you are using it to incrementally benchmark a new design until it’s ready to replace the existing one. For this, you are design-led because you know you want to execute this product in a particular way, but you use the A/B test as a safety net to make sure you don’t push out something that’s not ready.

Streamlining your product by measuring feature usage
There’s an important design principle that says, “Do less, but better.” I’ll elaborate on my POV of this philosophy more in a future post, nevertheless many product teams struggle to remove features, or even to quantify unused features.

For example, you might have a legacy feature that suggests people to follow on your social site, which you’d like to replace with a Facebook-based “friend finder” screen instead. Sometimes it can be difficult to get rid of navigation on something like this because it’s not clear how many people are really using it and how that affects their behavior overall, especially new users

A nifty way of using A/B tests to handle this is to run an A/B test to remove the feature, and get the following information back:

• How many people actually get exposed to this feature? (Based on what % of people get added into the experiment versus your active users during the test’s time period)
• What metrics are affected by people who have this feature removed? (As long as the metrics are neutral to positive, then you can remove it safely)
• If some metrics are bad, can you counteract it by adding something else to the new design?

Similar to the process of updating your product, the important notion here is that you have a particular action you want to take on a design level (simplify the UX) and you use the A/B test as a tool to aid that design goal. In this case, rather than going with whatever has better metrics, instead the goal is to go with the better design as long as it’s neutral or better on the numbers.

Designing for the right level of prominence
As you model out the key metrics for your product, there’s often important assumptions that need to be made on things like what % of your users invite their friends, or how many friends they invite, etc. Oftentimes, entire product strategies hinge on making sure that certain kinds of metrics get hit- it could mean the difference between being a viral eyeballs business versus one based on lifetime value and ad spend.

From a product standpoint, this manifests itself as trying to figure out how prominent to make things like “Invite friends” or “Import your addressbook” or “Subscribe to the Pro version.” To build a great UX, you often want to make something as low-prominence as possible while still making sure it’s easy and accessible for users.

A/B testing can help a lot here since you can test multiple versions of prominence and see where it takes you. If you want to prove that a model is even possible (for example, in the very best case could we get 20% of our users to invite their friends?) then you can make a popup that asks for friend invites constantly and see if you are even close. The point here isn’t that you would ever actually close the experiment with the obnoxious popup, but rather, it helps you do a sensitivity analysis of what might even be possible, to see are realistic values within your model.

You can use this technique hand-in-hand with the other ones listed above so that you eventually take a high-prominence version of it and iterate until it’s acceptable to show to 100% of the users.

Final thoughts
The thing that all of these ideas share is that you are using A/B testing as a tool to aid in a broader and stronger design POV rather than slavishly following whatever has the better metrics outcome. As others have discussed before, it’s the difference between data-informed versus data-driven. Many features you’ll want to do in your product have lots of qualitative value, even if the short-term quantitative benefits are difficult to measure or not there at all- using these advanced tactics lets you continue to push out dramatic new designs but without hurting the metrics your business depends on.

This video is a much more comprehensive and detailed version of what I often talk to non-Valley entrepreneurs and startups about. It’s part of the “grooming” process that startups out here get to become fundable and to focus on the right things to get there. People spend a surprising amount of time on things that will contribute little or no value to getting them to a seed round, and this talk is the best I’ve seen in terms of presenting the issues in its entirety.

Naval broke down the 5 main qualities of an “exceptional startup,” in the following order:

1. Traction
2. Team
3. Product
4. Social Proof
5. Pitch/Presentation

And while all these qualities are important, Naval explained, the most important thing is to understand that:

“Investors are trying to find the exceptional outcomes, so they are looking for something exceptional about the company. Instead of trying to do everything well (traction, team, product, social proof, pitch, etc), do one thing exceptional. As a startup you have to be exceptional in at least one regard.”

(via Founder Institute)

Anyway, please watch it all the way through and enjoy!

7th Founder Showcase – Naval Ravikant Keynote from Founder Showcase on Vimeo.

You can have a cool idea for a new product, but how do you know if anyone cares about it? And for a consumer product, how do you know that tens of millions of people will care about it?

One of the key points that I argue in my 2011 blogging roadmap is that tapping into a large market with pre-existing demand makes things easier. More discussion here. This means going after markets where users are already familiar with with your product category, the different options, and you build a product that is better against some competitive axis.

This post is about one way to figure out if anyone cares about your product.

Innovating on product execution vs. Creating a new product category
Some of the Valley’s best companies, like Google, Facebook, and Apple’s mobile products, entered their respective markets late in the game and effectively competed with differentiated products to win. They competed by executing great products in pre-existing categories rather than creating brand new categories. At first I resisted this point of view, since it’s more fun to paint on a blank canvas and do something that is completely new and innovative. Yet over time, I’ve come to believe that “blank canvas” ideas may be superficially innovative, they are much riskier and “first mover advantage” is wildly overrated, especially when there’s ample room to innovate in existing product categories. And for ideas in spaces with lots of competition, if you are able to get to scale and differentiate along some key dimensions, ultimately your traction lets you do all sorts of fun innovative stuff later on.

It’s easier to reinvent something to invent it.

The reference example of this is Apple, which has created amazing and differentiated products in huge pre-existing categories like computers, laptops, MP3 players, phones, etc., and only occasionally go for new product categories (like the Newton and iPad). When Apple picks an existing category, they can take something that’s OK but fragmented, and take it to an entirely new level on design- and they can do this without the risk that the market is zero.

So these days, as I meet new startups, I like to think about the new/risky stuff in their product as part of a careful and coherent strategy to tap into pre-existing markets, rather than trying to create new categories. One of the key tools that you can use here is the Google Keyword Tool.

Introducing the Google Keyword Tool
The GKT was created for advertisers looking to buy Adwords- in fact, some of the best market research tools on the internet are designed for ad buying, so that advertisers can actually understand how much inventory is available to buy. I’ll do similar writeups for Google’s Ad Planner tool, Quantcast, the Facebook ad buying UI, and others when I have time.

The GKT lets you do something very simple - Plug in some keywords, and it’ll tell you:

• How many searches are happening on those keywords
• Related keywords to what you put in

Here’s a screenshot:

You can use this for a lot of different scenarios, but my favorite use cases are to validate how mainstream a product category is, make sure you are using customer-centric wording to describe your product, and to identify nearby product positioning options.

Here’s an example of how I might use the tool to research a movies site:

1. Go to Google Keyword Tool
2. Plug in “movies” and sort by searches
3. Notice that some words, like “film” or “theater” are related, and add them to the search
4. Repeat, to collect a large collection of related keywords
5. Start scrolling through the results (again, sorted by searches)

Based on a GKT search like this, you find all sorts of interesting things, which let you both validate pre-existing demand and make sure you’re speaking the same language as your customers.

Validating pre-existing demand
First off, the # of searches is a pretty interesting. If you plug in keywords related to your business and find very low numbers, it might mean you’re using wording that mainstream users don’t understand or don’t care about. This happens commonly when a phrase is used to describe the product to other entrepreneurs and to investors, and includes abstract/strategic notions of what the product encompasses, but not what end-users are actually doing.

One practice I might suggest would be:

Steer your product towards a category with millions of pre-existing consumer searches – this shows mainstream understanding and demand for your product category

In fact, this is an easy way to define a new versus existing market that only applies to consumer internet products:

If people are searching for products in your category then you are in an existing market.

I personally find this a very nice and clear-cut way to figure out where you are is in the spectrum of new versus existing markets, and how much consumer behavior risk a product takes.

Are you using the words your customers use?
One of the best uses of the GKT is for finding the right words to describe your product. Oftentimes people like to use “X for Y” descriptions, which are convenient, and the Google Keyword Tool can help you refine that thinking.

If you plug in your high-concept pitch and you get millions of searches back, then you’re in good shape. For example, it turns out tens of millions of people are looking for “free movies,” so if you can do that legally, you’re all set. But sometimes you plug in a term and it falls flat. For example, imagine a startup that self-describes as “a marketplace for whatever” – in this case, “marketplace” is the X and the “whatever” is the Y. If you look up “marketplace” in GKT, what you’ll find that is that there are very few searches for that keyword, and there may be better options for the product positioning. Why does “marketplace” get so few searches?

My theory is that a “marketplace” is an abstract, businessy description for a startup’s strategy, whereas consumers likely only care about 1) selling stuff, or 2) buying stuff, and they are only in one mode at a time. As a result, I’d argue that as a marketplace startup might want to consider one of the following strategies for positioning their product:

• Targeting primarily one audience (buyers usually?) and have some secondary UI/flows to bring in sellers
• Picking a clearer attribute for what’s being listed, like “free” “collectible” or “upscale”
• Using colloquial terms “buy and sell” vs more abstract terms like “marketplace” or “exchange”

This product position then informs how you describe the product through all of your marketing channels. You’re probably better off buying ads or having site invites that say, “sell your useless junk online” rather than describing it as a marketplace.

Don’t build what your customers are asking for, says Henry Ford
An important reminder for this type of exercise, which is so dependent on what customers are searching for, is that what you build and how you describe it are two very different things.

There’s a famous Henry Ford quote:

If I asked my customers what they want, they simply would have said a faster horse.

This is absolutely true, and of course we’re all fortunate that Henry Ford ended up building a car. Yet at the very beginning, remember that they ended up calling cars “horseless carriages” so that the product could be anchored against something consumers already understood. Horseless carriage hints at many things – how it’s used and why it’s valuable, most importantly, and also the primary axis of differentiation. It’s horseless but still gets you from X to Y!

So maybe an addendum to the quote would be:

Build a car, not a faster horse, yet start by describing as a faster/better horse until people understand what cars are. Afterwards, build on that term.

Research potential segments of a market
Once you have a big juicy market to go after, the other interesting question is how you’re going to segment it. Basically, what is your product going to do that’s better/different than what’s already out there? Steve Blank has written a bunch of interesting content on this, including this post and this slide deck.

The Google Keyword Tool can also help with this – when you do a GKT search for a big “X” like “movies” you find all sorts of interesting modifiers to that phrase. Just glancing at the results, you see potential subcategories focused on:

• free movies
• imdb
• movie times
• movie quotes
• movie trailers

Beyond these, you’re starting to get to very few searches per month. Looking at this, if a startup had a really compelling product for one of the above scenarios, I’d certainly be really excited about them.

(One of the reasons I’m optimistic about the new YC startup Hellofax.com is knowing how many searches are online for easy and free email-to-fax services. Just search for “email” and you’ll see what I mean).

When you search for these keywords on Google.com, sometimes there’s already great products that cater specifically towards this group. For example, there are a ton of cheap airfare ticket sites. But sometimes, you find millions of users searching for something that doesn’t exist- that’s pretty awesome, and a great opportunity if you can execute a really compelling product there.

Google Keyword Tool product research checklist
If you already have a product in mind, here’s a quick list of things to think about:

• What’s your “high-concept pitch” for your product? (Often an “X for Y”)
• Are you using terminology that millions of consumers actually understand and know how to search for? Or do only other smart hackers or social media douchebags know what you’re talking about?
• Along what dimensions does your product compete with substitutes? (hopefully only 1 or 2 axes)
• Are there millions of consumers who care about that competitive axis?

And again, to repeat the observation re: the Henry Ford quote, have a vision for your product, and execute against that. Invent and build cars, but market it as a horseless carriage so that people know what they’re buying and why.

Is this too conservative?
Short answer: Yes, if followed too strictly.

Remember that the Google Keyword Tool test can validate an idea, but I’d be hard pressed to use it to invalidate an idea. Sometimes people will use things, and be passionate about it, but not search for it. Or maybe the product is mostly on mobile or Facebook and the searches are happening there, not Google. Either way, it’s a conservative tool, that’s to be sure.

I think there’s a spectrum of risk on introducing new products- the safest thing to do is to execute the hell out of a product design for a huge pre-existing product category and a competitive axis that people care about. Ideally you could validate that this market was already pre-existing and the competitive axis was an important one.

Yet most startups aren’t started this way, and instead take some risk in either picking a new category, or a market segmentation that’s driven by intuition. Ultimately I think it’s still important to try this out, when your entrepreneurial intuition says it’s the right way to go- but remember that you might have to go through a step of cleaning up the marketing and messaging around your product to slot it into something consumers understand.

Credit for inspiring this post
I have to give credit to Sean Ellis, who inspired me with a coffee conversation a few years ago to think about using search data to identify new versus existing markets. He was describing his consulting stints at Xobni versus LogMeIn as “demand creation” versus “demand harvesting” and using Google SEM as a test of which mode you’re in. Hopefully he will blog about that comparison with more nudging sometime

There’s been some excellent Groupon analysis
Since the S-1 has come out, there’s been some incredible analysis done – two of my favorites are Rakesh Agrawal’s Quora answer on “What are some notable aspects of the Groupon S-1?” and also Yipit’s analysis on the deterioration of fundamentals in Groupon’s oldest markets. I would highly encourage everyone doing anything in daily deals or the (misnamed) “social commerce” space to check those out. Additionally, please comment if there are some other great blog posts that I’m missing.

“There’s no tech! Anyone can do it!”
One of funny things that you used to hear about Groupon is how easy it is to start a clone, and how any startup could do it. A lot of people, especially developers, also say the same thing about product like Twitter, which are easy to code v1.0s for. That’s often a critique of consumer internet companies because what they do seems deceptively simple- there’s often no tech and no “barriers to entry” that a lot of the more B2B/enterprise investors like to see.

After all, let’s look at something like Groupon:

• Technology: Trivial, it’s just a mailing list and a landing page
• Market: Trivial to enter, because it’s huge and fragmented
• Sales: Trivial, you need 1 sales guy/gal initially who can sell some local deals

Seems like there ought to be tons of successful local daily deal sites right? And yet Groupon and Livingsocial control the vast majority of the market, and I have no idea who the #3 is? In fact, the most interesting competition ends up being other huge companies with big established userbases, like Yelp, Google, Facebook, Amazon, etc.

Email subscriber costs
The real reason is that there was a temporary arbitrage in buying tons of demographically targeted ad inventory that no longer exists. The Yipit blog post referenced earlier has this handy diagram:

That’s a huge increase from 2010 to 2011.

So if you think about it, this is one of the key bottlenecks to getting a Groupon clone actually started- if you want to build a list of 100,000 users, that’s actually going to cost you $3M right off the bat.

The backend is scary too
Furthermore, to even be able to monetize to break even, you start to need to contort the backend of your business to get there. This means that you have to be comfortable with things like:

• Extended time periods before your LTV catches up to your CAC (for example, 12 month breakeven on your LTV)
• Large # of deals per week at high margin
• To support the # of quality deals, a high-quality sales team

If you need 5 deals per week, every week, for 12 months to break even, then you’ll need a great sales team for that. Then you’ll need someone to optimize your ad spend, a bunch of customer support people, and all of a sudden it doesn’t look so easy.

Getting to scale, let’s say to 1M or 10M email subs, costs gobs of money that very few people in the world would be able to raise in venture capital. That’s why I imagine the most successful Groupon “clones” start in other geography where the arbitrage still looks like <$5 and not $30, or where it’s a high-end niche with some built-in distribution to get the first 10k-100k on board.

The same is true for viral products too
I wrote this post originally about Groupon, but it’s important to note that the same is true for viral marketing channels as well. As with other marketing vehicles, users get “inoculated” over time to the same approaches. Getting an “invite” was a big deal in 2003, so addressbook importers were super effective. Banner ads used to get 10% clickthrough rates, and now they’re 0.1%. Over time, marketing channels naturally become saturated and that creates a built-in defense against new entrants in the market.

Thus, even if something looks easy to build, you better do it quick otherwise you may never be able to catch up. A corollary to this is that if you discover a new marketing channel or some new viral mechanics, you’ll have a huge advantage early on since your response rates will be great.

Send me any other interesting analyses of their S-1 or others!
As all of these S-1s are coming out, I’ll try to stay on top of any interesting analyses, but feel free to email any that I might be missing. Just shoot me a note or comment on this post.

UPDATE: A post drilling down into how Groupon defines “customer” and ratios in their oldest markets (via Dru Wynings). Also, Yipit did a great followup post called “Reports of Groupon’s Death are Greatly Exaggerated“.

When Does Paid Acquisition Work for SaaS Startups?
by Ada Chen Rekhi

Introduction
After recently moving on from adventures building a consumer gaming portal at Mochi Media (acquired last year for $80 MM), I’m now working on a new startup called Connected, which provides contact management without the work. I decided to blog some of my thoughts based on my experience thus far with deciding on the right user acquisition channels to focus on.

When does ad buying work for SaaS businesses?
It’s a convenient belief that after you decide to build your software as a service (SaaS), Google AdWords and other networks will enable you to outsource all of your marketing efforts and focus less about user acquisition. This is not always true. Here’s a “napkin math” model to quantitatively decide whether or not ad buying is right for your startup based on reality, not guesswork.

A model for user acquisition

Paid user acquisition works for you when the following proves true

• LTV > CAC

The lifetime value (LTV) of your users should exceed the cost of acquisition (CAC) to get them in the door. As a reminder

• LTV = Expected Life x Average Revenue Per User (ARPU) x Gross Margin

In addition, for SaaS, you care quite a bit about costs and conversion rate for your funnel to trial, and from trial to paid. In specific, these look like

• CPC – cost per click to get traffic
• % trial conversion rate – users who convert to a trial of your product
• % paid conversion rate – users who convert to paid account

To estimate your cost of acquisition, you can base it off of estimates for your trial and paid conversion rates.

• CAC = CPC / (% trial x % paid)

An example of cost of acquisition
Let’s pick an example and work backwards. Let’s say you have a

• $20/monthly subscription
• 5% paid conversion rate – from trial to paid
• 10% trial conversion rate – from visits to trial

Then let’s pick a two different points for cost per click

• $0.50 CPC
• $2.50 CPC

In order to get a user at these CPC points

• CAC = CPC / (% trial x % paid)
• CAC = $0.50 / (10% x 5%) = $100
• CAC = $2.50 / (10% x 5%) = $500

In this example, it costs anywhere from $100 to $500 to get a single paying user at $20 per month. If you were trying to acquire 100 users ($2000/month), at $0.50 CPC that’s $10k ad spend, and at $2.50 it’s $50k. Drew Houston from Dropbox brought up very similar issues from his Dropbox Startup Lessons Learned presentation, where their initial search marketing test had a whopping $233-388 cost per acquisition for a $99 product!

Compare this against lifetime value
Compare this against the lifetime value of your user, or the total amount of profit you expect to receive over the user’s use of your product. This value should factor in the churn that you’re seeing from users canceling their subscription over time as well as what the payback period and working capital which you expect. Even though you might expect a user to be retained over a period of years, most startups don’t have the capital necessary to tie up their money for that long.

Let’s go back to the example above. We have the two users who cost

• $100
• $500

Assuming zero churn and zero operating costs on their $20/month subscription, you would recoup your cost on these user over a fixed period of time

• $100 / $20 = 5 months
• $500 / $20 = 25 months

In the case of second user, it would take over two years to recoup the initial $500 you spent to acquire them. You can offset this issue of working capital by setting the value at the amount of revenue you receive over a fixed period of time, or by being more aggressive with pushing them to prepay for longer periods of subscription cost upfront.

For example, what if you could get these users to pre-purchase their $20/mon subscription for $149/year? You’d be able to recoup the first user’s cost instantaneously, and get back a significant percentage of the second user’s acquisition cost.

Making the model work
The path to achieving profitability looks like making the model of having your cost of acquisition beneath your lifetime value work. You can quickly get a back of the envelope idea of whether paid acquisition is for you based on the examples and model above.

Doing this will help you determine whether or not you can profitably use ad buying as a source for getting users. You can also fine-tune your model to incorporate even more granularity such as

• virality
• traffic source
• retention
• working capital
• churn
• etc.

Trying paid acquisition on for size

Now that we have the framework down, the question is whether or not paid acquisition works for you.

If this works for you, then congratulations- you are on the path to scalable riches! If it doesn’t work, then you should think about how far off it is. Getting ad arbitrage to work out profitably is extremely sensitive to changes in the steps of your conversion funnel, as well as the source of the traffic. So if you’re not many factors off, it may make sense to spend a few months refining your funnel and trying to optimize the channel the traffic is coming from. Here’s a few things to consider-

Does the math work?
Once you launch your product and get a sense of what the conversion rates are in each step along the funnel and the churn rate, it may be that the math doesn’t work out. If you’re not too far off, then it may be worth spending time trying to make the metrics work out through landing page optimization, increasing conversion along the steps of your funnel and trying to optimize your traffic sources. However, if you’re several factors off (this is common in highly competitive markets) paid acquisition may not make sense as a strategy for you.

Is your product in an existing market or a new market?
Intent-based paid acquisition channels like search advertising work best in an environment where users are aware of the problem and actively searching for solutions which your product meets. You can look up potential search terms and volumes through Google AdWords Traffic Estimator, including estimated average cost-per-click and monthly search volumes. If not, you can also experiment with targeting sites that reach the demographics of your users.

How much working capital do you have?
While theoretically you might be willing to pay up to the full LTV of the user, you may want to limit the amount you’re willing to pay based on a fixed time period, for example the expected value from the user over 6 months. This may be because at some point you run into working capital issues paying for users who may take years to break even.

Regarding monetization, I’ll note that…

• in general, consumer products mostly suck at monetizing
• any business model built on 1% subscriptions of 0.1% ads need millions of users
• costs are ridiculously low for new startups, and N millions of users is not expensive
• ignore this for products like marketplaces where monetization is part of the value prop

An ad-based example
Here’s some quick math- let’s say that you are a typical seed-stage team of 4 trying to get your startup off the ground, and your burn is approximately $40k/month. If you monetize at $0.25 CPM, which is a pretty typical ad rate for every thousand ad impressions, then that means you need a whopping 160M ad impressions per month to break even[1]. Even if you get 2X or 10X that ad rate, you’re still in the millions of users to get there. Scary right?

A subscription-based example
Similarly, if your site has a freemium business model, you’ll find that something like 1% of users subscribe for a pretty nicely tuned freemium configuration. So if you have 1% of registered users paying you $5/month, that means your average user is worth $0.05. Given this, you’d need 800k registered users, and if only 10% of your users register, you’ll need millions of users to get there.

Ultimately, the key is new user growth
Given the difficulty of monetization for consumer products, ultimately the best way to get to breakeven isn’t to try to optimize the 1% subscription rate to 2%, but rather to pick a huge market, create a killer product, and try to acquire millions of users. Because this is the biggest risk, you want to focus on growth first and foremost.

Here’s a different analogy that Steve Blank uses to get at this- let’s say that you wanted to create a cancer-curing drug. You don’t need to crunch the business model for that- if you had it, it’s valuable. You don’t need to price test or do customer development. All the risk is in the science, so you just focus on the science.

Similarly, I’d argue that in consumer internet, the real risk is that you can’t get millions of users actively engaged in your product, and that risk is ultimately driven by growth and long-term user retention. Thus focus on that first, then figure out the monetization once you’re at scale.

Stuff is so cheap these days
Note also that running a site with millions of users is cheap. The cost of hiring developers/designers will vastly overshadow the cost of maintaining the infrastructure- all you need are a few dedicated servers or just use Amazon Web Services- unlike the 90s, you don’t need a huge datacenter to get started. Because these costs are pretty low, you can just focus on making sure your designers and developers are productive and you’re getting to product/market fit.

Ignore this advice for products where revenue is part of the value prop
Of course for products where you are helping people make money as the central value, you need to do this sooner rather than later, so that you can make the entire network happen. So if you’re building a marketplace, collect money early, even if you don’t take very much profit. Same with Groupon-esque startups.

[1] CPM to revenue calculation
$0.25 CPM = $0.25 for every 1000 impressions
$0.25 / 1000 = $0.00025 per ad
$40k burn / $0.00025 per ad = 160M ad impressions per month

The original definition
In Marc Andreessen’s original post on the topic, he writes:

Product/market fit means being in a good market with a product that can satisfy that market.

You can always feel when product/market fit isn’t happening. The customers aren’t quite getting value out of the product, word of mouth isn’t spreading, usage isn’t growing that fast, press reviews are kind of “blah”, the sales cycle takes too long, and lots of deals never close.

And you can always feel product/market fit when it’s happening. The customers are buying the product just as fast as you can make it — or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You’re hiring sales and customer support staff as fast as you can. Reporters are calling because they’ve heard about your hot new thing and they want to talk to you about it. You start getting entrepreneur of the year awards from Harvard Business School. Investment bankers are staking out your house. You could eat free for a year at Buck’s.

His partner, Ben Horowitz, follows it up with a bunch of other observations about the fact the event isn’t a “big bang” kind of event – instead, there’s lots of gray area as your product starts working for the market: I’d encourage everyone to read his subsequent post here.

So the short answer is, there’s no easy test.

Now given that caveat, I’m going look at this through the lens of consumer internet to add some additional thoughts.

What is a market anyway? And how do you validate it’s real?
How do you even define a market for consumer internet? Ultimately, I concluded that the most useful definition of “market” is 100% consumer-centric. Here’s an attempt at a simple definition, focused on consumer internet:

A market consists of all the consumers who can search for and compare products for a use case they already have in mind.

This definition is very focused on the notion of pre-existing demand for products in your market, and is scoped narrowly to avoid confusion.

The most concrete test of pre-existing demand is using the Google Keyword Tool, which tells you how many people are searching on Google for a particular keyword. To try this out, you’d execute the following steps:

1. What keyword do people search to get to your site?
2. Put those keywords into Google Keyword Tool
3. How many people are searching for this keyword?

If the answer to #3 is large (millions or more), then you have a large market. This test is very concrete, and also very finicky. By design, terms like “vacation package” score high on this test, whereas “travel experiences” do not, even though an educated entrepreneur or investor might abstractly group them together. Similarly, by design, a person who’s building a “social network for musicians” might be inclined to list the # of musicians in the US as part of their market sizing, but under this test, you’d quickly see that there’s not too many people are specifically looking for that. Also interestingly enough, you’d never say there was a “Photoshop market” but a quick search will show that in fact almost 40 million searches per month on “photoshop,” and it might be a great strategy to position yourself relative to that keyword.

Validating that you are part of a pre-existing market comes with all sorts of benefits, which I’ll address in later posts. But for now, the most important benefit is that you know the # of potential customers is large.

(In general, I’ve been constantly confused about how to even define a market in consumer internet, given that there’s so much similar featureset between otherwise very different products. For example, early on, people talked about “social” as if it were a type of site, whereas now it’s seen as an aspect for all new products coming to the web. Similarly, people sometimes talk about “Facebook apps” as if it’s a market when, again, it’ll probably just end up an aspect of every new online service.)

What’s a great market?
What are other attributes that make a market attractive? For consumer internet, a great market is commonly defined by:

• a large number of potential users
• high growth in # of potential users
• ease of user acquisition

Not competition, in my opinion, because for consumer internet there is often literally billions of potential users, and you’re mostly competing against obscurity. So even if there’s a ton of competition, if it’s easy to acquire consumers to your product, that’s great! Then get a good enough product, and you’re ready to go.

Not monetization, in my opinion,  because making money is pretty straightforward. You can throw on some ads and get $0.1-$1 CPMs, or you can charge subscription rates and get 1% to convert, or you can do the virtual goods thing. The biggest risk in all of these monetization models is really about whether or not you can get millions of users or not.

Picking a great market leads to better products
Leading with a great market helps you execute your product design in a simpler and cleaner way. The reason is that once you’ve picked a big market, you can take the time to figure out some user-centric attributes upon which to compete. This leads to a strong intention for your product design, which drives a clean and cohesive UX. In a market of all black Model Ts, you can sell otherwise identical cars of different color and that’ll work. Picking the right attribute is it’s own topic though!

The important part here is that you can usually pick some key things in which your product is different, but then default the rest of the product decisions. This means that your product’s design can be more cohesive because you’re trying to do less, but better.

Once you’ve executed your product, then there are various ways to validate that it’s “good enough” and your product fits the market:

• When user testing, do people group your product in with the “right” competitive products?
• Do they understand the differentiation of your product versus your competitors?
• Will some segment of users in the overall market switch to your product?
• Are some users who’ve “rejected” the products in the market willing to try your product?
• How do your underlying metrics (DAU/MAU, +1 week retention, etc.) compare to your competitors?

All of the above are signals towards product/market fit. Thee above tests are interesting in that they fundamentally anchored on pre-existing competitive products in the category. In a new market, you don’t have the luxury of comparing yourself to other things.

In future posts, I’ll try to give some more concrete metrics based on my research for what are good numbers in each of these cases, but for now, the important idea is just that in a large existing market you have more datapoints to at least say, “my product is at least as good as the other guy’s.”

New markets are a danger to good product design
In fact, one of the scariest things to me about new markets is that doing great product design for them is extremely hard. It’s so unconstrained that it’s hard to do anything other than add features, see what sticks, and iterate. This is fun except that keeping a cohesive product experience is quite hard, and removing features is usually harder than adding them. So at the end, you incur tons of product design debt that never gets paid off. (It’s not a surprise to me that Apple has a history of simplifying already successful product categories, rather than inventing brand new ones from scratch)

Conclusion
To summarize my main points in this essay, I’ve come to some simplifying definitions on how to validate product/market fit in consumer internet. For market, if you constrain the definition to people who know how to search for products in your category, you can develop a pretty concrete test evaluating pre-existing demand. And by leading with a market, you can develop a central design intention that leads to better product design. This in turn can then be validated by comparing your product metrics to competitor numbers, as well as user tests that focus on grouping and differentiation.

This leaves lots of unanswered questions, but hopefully is a start to my new blogging roadmap! More to come soon.

One important question that comes up all the time is, what makes a product easy to market?  I had a fun chat about the topic with Eric Florenzano and Eric Maguire who worked together at Mochi Media with my sister Ada. They also recently did YCombinator.

After the chat, eflo wrote up a helpful summary of some of the ideas we covered. I wanted to quickly share them with some comments:

1. Come up with one resounding use case–one thesis for how people should use the product.  Preferably this fits in with something that users already do and already understand.

I’m going to try to start blogging again!
It’s been a long time since I was in a good blogging rhythm, and I’m going to try to start doing it again In preparation for this, I put together an outline of an output-driven set of milestones around product, that takes you from zero to a P/M fit product thats ready to scale on marketing/tech/etc.

As far as I can tell, this is all standard fare for companies in Silicon Valley. My desire to write these posts is ultimately about documenting what’s working for people and spreading the knowledge beyond Palo Alto, CA  All of these topics are ultimately derived by both my own projects as well as my advisory roles at venture-backed startups. (Some of these are listed here)

If you like the outline and want to stay up to date, just subscribe and follow me on Twitter.

Without further ado, here’s the outline- I hope to write at least a post or two per week:

Blogging roadmap goals

• “output-driven” roadmap for going from zero to product/market fit
• for small hackerish teams building consumer internet products
• the intention is to create a scalable startup that is going after a huge market, and generate huge returns for venture capital investors
• goal is to get to P/M fit in shortest time possible, defer everything else
  • defers monetization
  • defers marketing
  • defers scaling
  • (this is all by design)
• P/M fit takes a non-deterministic amount of time to get there, insanely hard, you’ll probably fail anyway
• the problem is 90% contextual, make up your own rules as you go

Concept prototype

Picking a product and market

• build for yourself (start with intuition)
• have a long-term vision
• base it off something that’s already big and already working
  • big makes it easy to test and collect feedback
  • already working means you have a good sense for minimum product
  • also, there’s pre-existing distribution channels as well
• figure out the options for competitive differentiation – this is the core design intention
  • talk to a lot of users, do a lot of research, compare a lot of products in the space
• dimensions for competitive differentiation
  • competitive dimensions
  • vertical audience
  • design intention
  • cheaper/niche
  • targeting rejectors
• validating that there’s LOTS of pre-existing “pull” for the market
  • search keywords
  • app leaderboards
• ideal goal: simple product with fundamentally different core design intention for large pre-existing market
  • bonus points for baked-in distribution, monetization, etc. but don’t let this lead the idea!!!
  • usually one killer feature (not a bunch of features)
• prototype: Landing page
  • what’s a good landing page experiment?
  • headlines, copywriting, hero shot, etc.
  • unique URLs
• anti-patterns:
  • “someone’s already done this” (desire for originality)
  • monetization/strategy-driven product ideas
  • technology in search of a market
  • “Wall Street” markets
  • lumping yourself into an aspirational market
  • comprehensive featureset done poorly

Paper/Wireframe prototype

Designing the initial product

• go for the minimum desirable product
  • might work
  • the central design intention drives the product design
  • supports only the core use case, as minimum as possible
  • core UX should be 2-3 pages
  • limited functionality, done well. “Less but better”
  • Should build bare bone prototype in less than 2 weeks (really!)
  • flow-based product design
  • user quotes, then fill in with UI
• low-fidelity prototyping tools
  • easier and cheaper to make changes
  • fix defects earlier (Toyota lean manufacturing model)
  • engineers always want to prototype in code, but then sunk-cost fallacy
  • get feedback from people and iterate
• prototype: Core user flows, mocked up and ready to build
• anti-patterns:
  • “database-up” design
  • feature creep and low product self-esteem (v1 should look like a feature!)
  • comprehensive featureset all of it done poorly
  • lots of pet features that don’t fit into the core design intention

Code prototype

Coding the initial product

• build the prototype as fast as possible
• fill in any blanks left out of the prototype
• use the product yourself, iterate on it while keeping with the core design intention
• focus on key flows and prioritize over ancillary ones
• don’t worry about corner cases
• get it ready to be used by other people
• prototype: Live product, usable by other people
• anti-patterns:
  • taking too long
  • losing focus of the central design intention
  • not adjusting based on intuition and usage
  • overarchitecting, trying to make it scalable or modular or future-proofing in general

Friends and family alpha testing

• private beta goals
  • clean up core experience
  • make product usable over multiple visits
  • validate the core design intention
  • not scalable
• recruiting friends and family
  • focus on retention
  • are users coming back?
• recruiting random people
  • Find people from the existing market, rejectors, and outside the market
  • Learn from extreme users
  • Craigslist
  • Usertesting
• user testing
  • do they get it?
  • how would you describe this to a friend?
  • usability – remove the friction
  • would they switch? (for existing market users)
  • Net promotor score
• interpreting user feedback and learning to say “no”
  • which users fall into the target market? Hear them out
  • which users don’t? It’s OK (and maybe even good!) to have them reject
  • try not to add new features unless absolutely necessary
  • what features can you remove that aren’t part of the core?
• prototype: Simple product, polished by real use
• anti-patterns
  • Delusion- it’s not working but you think it is
  • Melancholy from user testing
  • Adding features without interpreting
  • Adding features that violate core design intention
  • Listening to out-of-market users
• is it working?
  • people understand the product
  • some subset of your users like it and use it
  • you like it

Random people beta testing

• traffic testing goals
  • start polishing your onboarding flow
  • develop options for distribution
  • build some basic stats infrastructure
  • not meant to be scalable
• User acquisition tactics
  • ads
  • PR + launch page + slow stream
  • partnerships
  • power through it
• Collecting feedback
  • surveys
  • help and problems
  • recruit users to talk to
• prototype: Spreadsheet for signup flow, more polished signup flow
• is it working?
  • signups are happening
  • people are going through the core flow
  • retention/recurring usage from target users
  • product still works for you, and your friends/family

User flow optimization

• model your usage and figure out your core drivers
  • this is completely product specific
  • two examples- daily deal versus a chat site
  • whats your “metric of love?”
• prototype your funnel – explore!
  • flow chart
  • excel
  • SQL
  • formalize/finalize with dashboards
• identify major bottlenecks for why the product’s not working
  • start at the beginning of the flow
  • fix bottlenecks with A/B tests
• is it working?
  • how do the metrics compare to the usage model?
  • 10% signup
  • +1 day retention and +1 week retention
  • DAU/MAU
• anti-patterns:
  • trying to fix problems in core UX when signup is the problem
  • over-architecting stats infrastructure
  • trying to use a generic analytics product to answer situational questions

Ready to scale?

• Hopefully the major checkboxes are checked – at this point you’d have:
  • Huge market
  • Differentiated product
  • Product makes sense to normal people
  • Product is working for IRL people
  • Product is working for non-IRL people
  • Well-understood and optimized user flows
  • Ready to scale up
• Non-scaleable marketing, tech, and otherwise- that’s fine
• Now scale everything else

Crisis, terror, and melancholy

• Is it good enough?
• Nobody likes my product!
• My product is a mess!
• It’s taking too long!
• Investors hate my product!
• I’m iterating in circles!
• When to work on a completely new idea?
• Iterations are getting diminishing returns and people still don’t love the product

Final note: Thanks to my friends who helped review and add to this: Vinnie at Yipit, Alex at Penzu, Rob Fitzpatrick, Kevin at Hyperink, Jamie/Justin at Mocospace, Ada/Sachin at Connected, Noah at Appsumo, Jason at Kima, and the other folks who helped

In general, I think all of the various MVP/customer-development oriented startups out there are struggling with how to incorporate design into their product process. And at the same time, more designery teams are trying to figure out how to get more agile. It’s hard. As someone smarter than me has observed, the vast majority of the MVP-oriented companies end up with pretty uninspired, incoherent products- and they don’t seem to get any better over time. So I think it’s a great challenge for the whole community to get more informed about design and figure out how to really make it work.

Great Google color-testing followup
In particular, in the first few slides there’s a really funny followup to Doug Bowman’s complaints about Google testing shades of blue. These slides claim that in fact, the color choice really did matter, and quite a bit so, and quotes Bing search guy saying that the decision was actually worth $80M. I suppose in retrospect it’s not surprising, because the bluer something is, the more it looks like a link- so given the visual signal, it is meaningful for users over billions of searches.

Metrics-informed versus metrics-driven
All that said, I do have to say that I much prefer the term “metrics-INFORMED design” rather than “metrics-driven.” You should really be driven based on your vision of the product and where you want it to go, not the metrics that you use to validate or learn about your vision. (I first read the distinction of being data-informed over data-driven in a talk by some Facebook product folks, and have much preferred it ever since – this topic probably deserves an entire post by itself).

Finally, the slides
Anyway, the Joshua’s slides are excellent and I’d encourage you to flip through them. The official place to read the details around this presentation is here, on his site. His Twitter is here.