Blog posts transformed into a book format. Although you can find all the content from this book in Andrew’s blog, I highly recommend you buy this book, as you will find a lot of content about viraliy, startups, and marketing all in one place. Quite shallow in depth for a book, but it’s still good enough for you to read it.
Author: Andrew Chen.
Date Finished: 12/06/2014
Here’s a link to the Amazon page.
Viral Marketing Is Not a Marketing Strategy
Many times, viral marketing is seen as a “marketing strategy” that is interchangeable with other methods of acquiring users. That is, you go through three steps:
- Develop your product
- Think through a plan on how to make people use it
- Declare viral marketing is one of N approaches (along with SEO, SEM, PR, etc.)
Or perhaps you already have an existing product, and you have gotten interested in using a Facebook widget or something like that to make it “viral.”
Successful viral products don’t have viral marketing bolted on once the product has been developed. It’s not a marketing strategy. Instead, it’s designed into the product from the very beginning as part of the fundamental architecture of the experience.
Viral marketing is not a product feature
No single feature determines the virality of the product – instead, it’s part of a viral loop that connects a disparate set of functions into a cohesive motivation for the user to tell their friends. If the fundamental product doesn’t drive a viral motivation from its users, then it’s very hard to force it.
Viral marketing is a fundamental product design discipline
So what happens when you try to start a new viral product from scratch? Ultimately, you ask the reverse question of what most folks do.
Instead of: “We have product X, how do we virally spread it?” … we ask: “We have viral loop X, what’s the right product to put into it?”
Once you have that question in mind, it becomes a lot easier to start brainstorming compelling experiences that might be inherently viral.
The skillset for effective viral marketing
Because of the above issues, “viral marketing” is not really something that ought to be in the domain of soft-skill folks like PR, advertising, and marketing people. Nor is it in the world of hardcore technical folks that can architect systems but not consumer interactions.
Instead, it’s something that needs to bridge both soft and hard skills. You need an interesting combination of skills, including:
- Understanding the motivations behind user behaviors
- Understanding and exploiting the technical loopholes to create viral loops.
I think that the fundamental compartmentalization of these two skill sets is what ultimately drives huge companies being worse at viral products than startups.
What’s Your Viral Loop? Understanding the Engine of Adoption
To define the viral loop, you can think of it as the steps a user goes through between entering the site to inviting the next set of new users.
Simple enough? Well, because this core loop is repeated so many times over generations and generations of users, getting it right is incredibly important.
Building your own viral loop
- What’s your viral media? The first (and last) choice you have to make is where people are going to receive an entryway into your viral loop. That might be email, Facebook newsfeed, or blogs. The main factors to evaluate here are how difficult it is to integrate your entryway into their surface, and the response rate. The first factor, integration, is obviously important because a difficult integration means that perhaps fewer people will see your messages, or your messages will be filtered out altogether. The second factor, response rate, depends on how in-your-face your messages are (think Facebook invites versus email spam), and how competitive the medium is.
- What’s your funnel design? The next choice to make is the design of your viral “funnel.” First off, you want it to be short and as accessible as possible, since each page is a barrier you’re asking your users to leap over. Assume up to 80 to 90 percent attrition if you are asking them to register for a username/password, for example.
- What’s the viral hook in your product? Another important choice is product, of course. At the end of the day, a bad product can adversely affect your viral experience, because a poor slideshow (or a widget that no one wants) will lead to very few embeds. So picking something that is either a deep personal expression (music, avatars, slideshows, celebrity posters, etc) or a communication mechanism (voice messages, text, etc) are all great for getting people to WANT to put the apps on their homepages.
- What are the onramps to your viral loops? Once you’re done with a very tight viral loop, then it’s time to create the on ramps. In this case, you are looking at places like your website homepage, paid advertising, traditional marketing campaigns, SEO, etc, to create places where users can discover your viral loop and begin the process
5 Crucial Stages in Designing Your Viral Loop
I’ve come to believe that creating viral loops is akin to building a software project – at best, it still comes down to a great team, a strong understanding of the tools available, and relentless iteration. There’s no recipe at the heart of it which guarantees a viral process every time, the same way that you can’t guarantee that any software project will result in market success.
There are no silver bullets in viral marketing
In fact, the core of virality ensures that there will never be a dominant “recipe.” If everyone knows how to build a viral loop around social network invites, then everyone will do it, resulting in consumers will become desensitized, which finally leads to lower response rates. Thus this causes the viral loop to unwind, which leads to long-term disaster.
The only way to combat this is to build a viral loop around the core of your product – something that no one will seek to duplicate, unless they are a direct competitor. These viral loops are incredibly effective because they are lasting and sustainable.
Strategize: Stage 1
The first stage of a viral loop is developing the core strategy around the loop. This requires the viral loop designer to think through, step-by-step, how a user will come to find their product and how they will ultimately pass it along to their friends.
Even if you decide to use an existing recipe, here are some higher-level strategy questions that should be answered before proceeding:
- How does this viral loop fit into your core product?
- What is the fundamental value proposition you are presenting to your users?
- If your loop is successful, will users transition to your core product or will they bounce when reaching the switchover point?
Implement: Stage 2
The next stage is the rapid development of the core viral loop. This part should hopefully take days or weeks, not months. It will also certainly be wrong. The best advice I can give here is to follow agile development models and to build the smallest number of features and pages to create the initial flow of pages.
The other implementation advice I’ll give is to treat the viral loop code as an iterative, prototyping process. So copy and paste all you need, keep it in a separate codebase, and make it easy to refactor. You’ll need to do a lot of messy stuff like changing the order of pages or page elements later, and once you develop your own recipe, it’s easy to rewrite it in the “right way.”
Launch: Stage 3
The next step is to beg, borrow, or steal traffic. The easiest way is often to pay for it, $50 per day or so, just so you have a trickle of traffic coming in.
Optimize: Stage 4
As you get a flow of incoming traffic, this allows you to deeply optimize the experience. This will involve building out some basic infrastructure to do A/B testing, or using Google Web Optimizer, and otherwise. The key thing here, of course, is to measure whether or not the $50 per day you’re spending results in traffic above and beyond what you’re paying for – the more the better, and eventually you’ll cross the threshold where traffic scales infinitely.
In this stage, there are a lot of common fixes that you’ll want to consider:
- Shortening the flow of pages (can you shrink a five page funnel down to two?)
- Rearranging UI elements to emphasize next steps
- Testing different value propositions for going through the flow
- Increasing the number of people invited
This optimization step can take a very long time (months is not uncommon) as you zero in on the dozens of small and large changes needed to create a viral loop.
After months of work, two outcomes can result:
- You don’t reach your goal, and you’re stuck on traffic.
- You reach your goal, and your traffic is going bananas!
If you don’t reach your goal, then it’s time to stop your optimization process. Often the changes that result are just too small to drive substantial increases in metrics. Instead, you’ll have to rework your entire value proposition, which means to either go back to Stage 2 or possibly Stage 1. This means you’ll want to stop A/B testing and start building out a deeper featureset.
Refine: Stage 5
If your optimization step was successful, your work is probably not done. The final step is polishing your viral loop.
This includes figuring out issues like:
- Making your loop as user-aligned as possible
- Building a pleasant user experience and removing unnecessary flows or page elements
- Refactoring the code to move it from prototype to production
- Integrating it into your core product in a way that makes sense
Is Your Site Really Viral? Viral Branding Versus Viral Action
Broadly speaking, you have two categories:
- Viral branding
- Viral action
Most people, when they talk about viral marketing, are in fact talking about viral branding. That’s the philosophy of: do something REALLY cool and people will tell all their friends.
Another variation of this is the types of things people do on their blogs, where they try to write something genuine or interesting, or attention-whoring or controversial, and people pass it around to all their friends. This is really great and has its place.
So if you find yourself reading books about “breaking through the noise” and “identifying influencers” and other soft-skill marketing strategies, then you are reading about the viral branding industry. Another good way to benchmark this is how many of the examples are based on offline word-of-mouth examples versus interactive media.
There’s another segment of viral marketing that is really about direct response marketing. That is, the entire focus of the PRODUCT (not marketing, but deep down into the product) is getting more people to use it. That means you are ultimately focused on one issue only:
Do something that’s REALLY easy to spread to other people
In this case, you are focused more on the mechanism of viral transmission than you are the content of what you are transmitting. For many products, this means you are making it highly efficient to take over their communications media to spread your message.
Furthermore, you end up focusing more on metrics than in the branding case. You end up measuring and optimizing things like:
- Sources of traffic
- Landing page views
- Percent of users that register
- Percent of users that send out invites
- Number of invites sent out, per user on average
- Percent of invites delivered successfully
- Percent of invites read by users
- Number of virally added users, per user on average
And of course, you’d want to A/B test the hell out of each step of the way.
The viral equation?
Obviously, one cannot live without the other. It makes me think that there’s a very short checklist of things you need to do in order to make your site viral:
- Make it EFFICIENT to spread your site
- Give people an INCENTIVE to send it to their friends
- Have a GREAT product that keeps people around spreading it through time
I think these are the three things you need to create an enduring viral site… just don’t get too sucked into the branding/soft-side of it without addressing the stuff around your product.
Social Network Marketing: Getting from Zero to Critical Mass
I’ve heard several pitches in which an entrepreneur outlines a marketing plan for their business which is lots of hard work, but eventually they reach a “critical mass” point where all of a sudden magic kicks in, and smooth sailing is ahead. What these discussions often leave out is, what exactly is a critical mass point anyway? How do you know where it is, and how do you know if you’ve hit one?
What is the chain reaction that happens for a web property? Let’s look at it from two separate contexts – user acquisition and retention.
One way to interpret this is that initially, your site has difficulties with user acquisition, until you hit some scale points in terms of total user base. Then all of a sudden, your site goes “viral” and you start getting lots of users coming in.
They usually mean that their site is not that useful until there’s a certain number of people on it, and when you cross the critical mass point, then the site becomes engaging. So let’s talk about this idea in an engagement context.
As discussed above, there’s an idea that for a user-generated content site, you have an early bootstrapping problem. If you’re YouTube, but have no content, then no users will stick around. Yet if you have no users, then you have no one to upload content. So you need to break out of this local minimum until you cross some threshold – this is the critical mass point.
It takes careful thought to figure out what network your product is really built on. It’s very common to see companies that are primarily targeting purely online friends build features that are really meant for people that know each other offline.
Similarly, even within a type of network, it’s important to consider the level of adoption within that network. You could argue that there’s a concept for a “minimum social group” that represents the smallest number of friends within the appropriate network, before a social tool is useful. This minimum social group concept is interesting because some applications only need a small number of friends to get off the ground, and others need more:
- Skype: 2 minimum
- Mailing list: 4-5 minimum
- Forum: 10 minimum
- Social network: 10? 15? 20?
So I’d encourage anyone building a social site to really consider what type of network they are building for, and how many people they need at the local level. Once you can figure that out, then the next goal is to aggregate these smaller groups into a larger one.
So to review:
- Critical mass is defined by what type of network your social product operates on, and how many users you need on that network before the product becomes useful.
- Thus, critical mass is a product-by-product discussion – there’s no one-size-fits all.
- Similarly, people that use your product go through a collection of “phases” – from ranging from passive usage where there isn’t enough content to consume, to the point where they are very active and creating content themselves. The threshold point between the phases is a local observation of critical mass.
- Sites that are useful for “online friends” and don’t require too many people are the easiest to get off the ground (but have other issues, like they might be too niche).
- Site that are useful only for large numbers of “real life friends” (local review sites are a good example) are the hardest to get off the ground, yet are hugely useful if you can get people on board.
Viral Coefficient: What It Does and Does NOT Measure
Metrics like the viral coefficient give you understanding of:
- For every user coming into your site, how many friends do they bring?
However, they don’t give you an understanding of:
- How long will it take for you to saturate the entire network of users?
- Do your customers love your product? Does it stimulate other positive emotions?
- Is your product sticky? Does it generate a lot of pageviews?
- Where does your traffic monetize well? And what methods of monetization work best?
- When and how does your product fit into the lives of your customers?
- Is your market big enough? Can your startup grow to be a billion+ business? etc.
How Do I Balance User Satisfaction Versus Virality?
If you find yourself mostly thinking about balancing satisfaction versus virality, you’re probably doing it wrong.
You have features in your product that either drive growth or don’t, and you have features in your product that either really help the value proposition, or don’t. These are actually pretty independent factors and you can build product features that hit each different quadrant. For example, if you are building a product like Skype, finding your friends and sending invites is clearly a high value prop, high virality action. After all, you can’t use Skype by yourself. But if you take the exact same feature, and try to bolt it onto a non-viral product like, say, a travel search engine, then you’re just creating spam. There’s really no great reason to “find friends” in a travel product, though it might be useful to share your itinerary. A feature that’s high-value in one product is spam in the other. And if you think about each quadrant, you get something like this:
Let’s talk about each bucket:
- Awesome features grow your product and also people love them. The Skype “find friends” feature is a great one, but so is Quora’s “share to Twitter” feature.
- Do it anyway features are just the core of your UX. Writing on walls on Facebook may not be inherently viral in themselves, but it’s important to the product experience, keeps people coming back, and indirectly helps drive the virality of the product. The more people you have coming back, the more changes you have for them to create content or invite people
- Spam features are high virality actions that your users don’t really want to do, and don’t add to the product value prop. I think this is the bucket that the tradeoff lives of a question like, “should I be viral, or offer a great product?” If you are spending a lot of time in this quadrant, then you are shaky ground.
- WTF needs no explanation Ideally, you want to pick a proven product category that’s naturally viral and highetention, for instance communication, publishing, payments, photos, etc. – and then spend as much time building awesome features that both drive growth and also make your users happy. Stay away from spam features as much as you can, or use them sparingly lest your product becomes spam.
Simple Is Marketable
Simple products aren’t only better designed, they’re easier to market, too.
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 percent 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 percent 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. However, if you have a simple product, where almost 100 percent 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.
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 percent of new users’ experiences.
Keeps the focus on top of funnel rather than low-impact add-ons
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. (In some outliers, lengthening signup flows with the right steps can help, too.)
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.
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.
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.
Don’t Compete on Features
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?
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.
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.
- 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.
- 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.
- 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.
Does Every Startup Need a Steve Jobs?
What does Steve Jobs really do to create the amazing design culture at Apple? And more importantly, can a startup hope to even start to capture the same kind of culture? Well, let me give you my best guess:
IDEO’s product framework for Desirability, Feasibility, and Viability
The idea is that all products ultimately come from an epic struggle between three perspectives: Desirability, Feasibility, and Viability.
Here’s the diagram included in their HCD toolkit:
The way this was retold to me is that these factors map into functional parts of a business:
- Viability = Business focus (marketing, finance)
- Feasibility = Engineering focus (technologies, agile process, etc.)
- Desirability = Design focus (customers, aesthetics, etc.)
Business-focused product perspective: Viability
For business-oriented products, the focus might be on any of the following:
- “Hot markets”
- Making money
- Funding potential
- Distribution metrics
The idea there is that you get to a product via one of these first-order items. A business-oriented entrepreneur might identify a market, then try to come up with a product within the market.
Engineering-focused product perspective: Feasibility
For technology-oriented products, the focus might be on the following:
- Programming language and development stack
- Cool technologies or libraries
- Engineering processes (agile or otherwise)
For people who use this as a first-order filter, you might end up with a line of thinking like, “BitTorrent is really cool, how do we build a business around it?”
Design-focused product perspective: Desirability
For design-focused products, the focus might be on:
- Context, culture, and goals
- Customer goals and product experience
- Design aesthetics and interactions
The first-order filter in this case might be “Sick people go to hospitals and have a terrible experience – how do we improve that?” The tools employed at this initial stage might include user research, development of personas and user goals, and rapid prototyping to explore many product concepts.
How business and engineering goals encroach on the desirability of a product
From the perspective of making a sexy, highly desirable product, you’ll find lots of objections from business or engineering:
- “Spending money on visual design is too expensive”
- “Polishing a product will make the process too slow”
- “This product is boring to implement”
- “Can you redesign this product so we can build it in 1 week sprints?”
- “This target user is great, but we want the product to be more powerful and support more audiences”
- “But Zynga doesn’t do this, can you just copy them?”
- “Why build so many prototypes that get thrown away? That’s costly and slow”
- “If you added X to this product, it would put us into strategic market Y”
How do you handle questions like the above? All of them are great questions, and of course the right answer means you have to find a balance in the approach. But what is the expense towards the core of your product experience?
Back to Steve Jobs – what does he really do?
Long story short, my hypothesis is that Steve Jobs is one of the rare CEOs who is very focused on product desirability. In battles with the business and technology goals, desirability will almost always win out.
So his role isn’t that of a designer, but rather Chief Design Advocate. This means:
- He makes it clear that products should be “insanely great.”
- He recruits a top design team and protects them from competing goals.
- He is willing to spend money and adjust technology processes for the goal of highly desirable products.
- He convinces financial analysts, industry pundits, etc. that product design is very important.
To me, the amazing part about this is: Any company can do it.
Maybe not as good as Jobs, but they can decide to make it a priority – but few companies do. With the pressure of quarterly earnings, what competitors are doing, and employee aspirational desires, the focus moves off of killer experiences for customers – that’s no good.
If the above is true, then any of us can be the Steve Jobs of our team. Start by prioritizing design and desirability, and place it on a better footing relative to engineering and business goals. Learn the tools, develop your own religion, and start building great product experiences.
Minimum Desirable Product
A hypothesis-driven approach to product development dictates that you build as much as you need to test our your product, but not more and not less. But what are you “testing” your product for?
One possibility, as lean startups guru Eric Ries has stated, is to test your product for “viability.” He’s coined an important term, called Minimum Viable Product, and I’ll excerpt his excellent blog post below:
[quote]The idea of minimum viable product is useful because you can basically say: our vision is to build a product that solves this core problem for customers and we think that for the people who are early adopters for this kind of solution, they will be the most forgiving. And they will fill in their minds the features that aren’t quite there if we give them the core, tent-pole features that point the direction of where we’re trying to go. So, the minimum viable product is that product which has just those features (and no more) that allows you to ship a product that resonates with early adopters; some of whom will pay you money or give you feedback.[/quote]
Viability is certainly one bar you can test for, but a related (and overlapping concept) is around testing product desirability.
Viable versus Desirable
The idea here is that different companies often pursue products with different primary lenses – a business-driven company might try to assess viability upfront, thinking about metrics and revenue and market sizes. A feasibility (engineering) oriented organization might try to pick a super hard technology first (P2P! Mapreduce! Search!), then try to build a business around it. And a desirability-focused team might focus first and foremost on the target customer, their context and behavior, and build a product experience around that.
Thus, a Minimum Viable Product tends to center around the business perspective – what’s the minimum product I have to build in order to figure out whether or not I have a business? You might do that from testing signups on landing pages, try to sell products before they exist, etc. Putting up price points and collecting payment info is encouraged, because it helps assess the true viability of a product.
But what if you come from a human-centered perspective, and you want to build the Minimum Desirable Product? I think this is a subtle difference with big implications. A minimum desirable product (MDP) would focus primarily on whether or not you are providing an insanely great product experience and creating value for the end user.
Let’s define it as such: Minimum Desirable Product is the simplest experience necessary to prove out a high-value, satisfying product experience for users (independent of business viability)
To build an MDP, you will have to actually deliver the core of a product experience so that your customers can make a full assessment, rather than simply providing a landing page. Instead of measuring YOUR conversion rates and revenue generated, instead you might figure out the metrics of what benefits you are providing to the user.
You could view the the Minimum Desirable Product as the simplest product that has a credible shot at providing that product/market fit.
Minimum Feasible Product?
The last thought I will leave you with is: perhaps there are markets where the engineering portion is the most important – and thus the most important concept of Minimum Feasible Product.
For example, for a drug company curing cancer, the focus wouldn’t be on minimum viable product because if you have a cure for cancer, you’ll be viable. Similarly, you may not focus on desirability, because your product would clearly have pull from the market. You don’t need to do landing pages or user-centered research to figure out that curing cancer is a big deal from a business and user point of view.
Instead, the focus would be on Minimum Feasible Product – what is the smallest amount of work necessary to field a credible candidate for an “in lab” solution to the product?
Why Companies Should Have Product Editors, not Product Managers
Product managers: One of the toughest and worst defined jobs in tech
The role of “product manager,” “program manager,” “project manager,” is one of the toughest and worst defined jobs in tech. And it often doesn’t lead to good products. The various PM roles often have no direct reports, but you have the responsibility of getting products out the door. It often becomes a detail-oriented role that is as much about hitting milestones and schedules as much as delivering a great product experience.
Bad ideas are often good ideas that don’t fit
In the context of literature, books, and newspapers, it’s the job of the editor to pick the good stuff and weave it into a coherent story. You remove the bad stuff, but “bad” can mean it’s a good idea but just doesn’t fit into the story. It’s a compelling and important distinction for consumer internet.
Cohesion and consistency is difficult. When you have an organization with lots of very smart people all with their own good ideas, it’s difficult to decide which path to take. So often, products are compromised as the product “manager” doesn’t feel the responsibility to build up that cohesion as an ends in itself, and instead just tries to do as much as possible with the product given some set time frame. Focus, people!
In a recent talk at Stanford, Jack Dorsey describes his idea of editors:
“I’ve often spoken to the editorial nature of what I think my job is, I think I’m just an editor, and I think every CEO is an editor. I think every leader in any company is an editor. Taking all of these ideas and editing them down to one cohesive story, and in my case my job is to edit the team, so we have a great team that can produce the great work and that means bringing people on and in some cases having to let people go.”
Lead with product
What’s compelling to me about this is that it really orients the role of product to be about cohesive experiences first and foremost. Okay, yes, there’s still schedules first, but it doesn’t drive the thing – great products drive the process.
Why Low-Fidelity Prototyping Kicks Butt for Customer-Driven Design
Low-fidelity prototyping can be really useful because it aids an iterative, customer-focused approach rather than one where the Great Designer comes up with something directly from his brain.
Here’s a couple of the main advantages:
- Get better and more honest feedback
- It’s great for A/B testing
- Make the cost of mistakes cheap, not expensive
- Refine the page flow, not the pages
- Figure out the interaction design rather than the visual design
One of the hidden benefits of having a low-fidelity prototyping process is that it makes changing directions much easier, which naturally facilitates a collaborative design discussion. When you’re using a customer-driven product philosophy that incorporates a lot of outside metrics and qualitative feedback, you’ll probably get multiple people involved in the design process.
One of the highest leverage design decisions you can make is not about the look of an individual page, but what happens before and after it. For example, you can take a multi-step process and condense it onto one page, or change the ordering of something so that you do something and then register, rather than the other way around. These kinds of design decisions ultimately focus on the order and flow of the user, rather than the look or interactions of any specific page. If you go with a low-fidelity, then it’s easy to draw lots of small pages and link them up in a flow, and do things like cross pages off, change the ordering of a funnel, and lots of things that feel natural when the prototype is very rough.
Tools I recommend for paper prototyping
- Number 2 Pencil
- Giant art pad for drawings – you can get these at an art shop or office store
- Balsamiq (check out the video on the linked page)
- Macromedia Fireworks
Strive for Great Products, Whether by Copying, Inventing, or Reinventing
This last weekend, I watched Steve Jobs: The Lost Interview. It’s great for many, many reasons, and I wanted to write an important point I seized upon during the talk.
That phrase is one of the most confusing things about the Apple philosophy, and I think it is commonly misinterpreted. Product designers often use it as an excuse to endlessly work on their product, with no release date or eye on costs. It becomes the reason why people want to focus on building completely new products and avoid copying competitors. Apple has done a lot of stealing and reinventing.
Great products, regardless of source
To me, the way to reconcile this is that Steve Jobs cares first and foremost about great products. Sometimes the way to get there was to steal. Sometimes you reinvent and reimagine. And sometimes, you have to invent.
The point is, building a great product is about curating from the entire space of possible features you could build. Shamelessly steal ideas when they are the best ones. Ignore bad ideas even if they’re commonplace. Don’t think you have to build something totally different to make a great product.
The goal of building great products is for you to deliver something great to the customer, not to impress your designer friends on what new layout or interaction you’ve just developed.
Make it insanely great, even while you copy, steal, reinvent, or invent whatever you need to make that happen.
Know the Difference Between Data-Informed and Data-Driven
Data is powerful because it is concrete. For many entrepreneurs, particularly with technical backgrounds, empirical data can trump everything else – best practices, guys with fancy educations and job titles – and for good reason. It’s really the skeptic’s best weapon, and it’s been an important tool in helping startups solve problems in new and innovative ways.
Ultimately, metrics are merely a reflection of the product strategy that you already have in place and are limited because they’re based on what you’ve already built, which is based on your current audience and how your current product behaves.
Being data-informed means that you acknowledge the fact that you only have a small subset of the information that you need to build a successful product. After all, your product could target other audiences, or have a completely different set of features. Data is generated based on a snapshot based on what you’ve already built, and generally you can change a few variables at a time, but it’s limited.
This means you often know how to iterate towards the local maximum, but you don’t have enough data to understand how to get to the best outcome in the biggest market.
This is a messy problem, don’t let data falsely simplify it
So the difference between data-informed versus data-driven, in my mind, is that you weigh the data as one piece of a messy problem you’re solving with thousands of constantly changing variables. While data is concrete, it is often systematically biased. It’s also not the right tool, because not everything is an optimization problem. And delegating your decision-making to only what you can measure right now often de-prioritizes more important macro aspects of the problem.
Let’s examine a couple ways in which a data-driven approach can lead to weak decision-making.
Data is often systematically biased in ways that are too expensive to fix
The first problem with being data-driven is that the data you can collect is often systematically biased in unfixable ways.
It’s easy to collect data when the following conditions are met:
- You have a lot of traffic/users to collect the data
- You can collect the data quickly
- There are clear metrics for what’s good versus bad
- You can collect data with the product you have (not the one you wish you had)
- It doesn’t cost anything
This type of data is good for stuff like, say, signup percent s on homepages. They are often the most trafficked parts of the site, and there’s a clear metric, so you can run an experiment in a few days and get your data back quickly.
In contrast, if you are looking to measure longetention rates, that’s much more difficult. Or long-term perceptions of your user experience, or trying to measure the impact of an important but niche feature (like account deletion).
Oftentimes these metrics are exactly the most important ones to solve.
Not everything is an optimization problem
At a more macro level, it’s also important to note that the most important strategic issues are not optimization problems. Let’s start at the beginning, when you’re picking out your product. You could, for example, build a great business targeting consumers or enterprises or SMBs. Similarly, you can build businesses that are web-first (Pinterest!) or mobile-first (Instagram!) and both be successful. These are things where it might be nice to have a feel for some of the general parameters, like market size or mobile growth, but ultimately they are such large markets that it’s important to make the decision where you feel good about it. In these cases, you’re forced to be data-informed but it’s hard to be data-driven.
These types are strategy questions are especially important when the industry is undergoing a disruptive innovation,
Leverage data in the right way
It’s important to leverage data the same way, whether it’s a strategic or tactical issue: Have a vision for what you are trying to do. Use data to validate and help you navigate that vision, and map it down into small enough pieces where you can begin to execute in a data-informed way. Don’t let shallow analysis of data that happens to be cheap/easy/fast to collect nudge you off-course in your entrepreneurial pursuits.
5 Steps Towards Building a Metrics-Driven Business
Given my history blogging about viral marketing, I’m occasionally approached by folks who ask me, “For product X, how would you promote it and make it viral?” I think there’s an expectation that there’s a playbook which you can directly apply to every situation.
Unfortunately, there’s no real answer to this – ultimately, I think any advancements that can be made to your business function based on the fact you make very gradual improvements based on creating goals, measuring subcomponents, making hypotheses, and testing them. There’s no better way to do this than to just do it.
Step One: Create clear, measurable goals
First off, it’s important to identify the main goals of your business, based on current strategy. What are you focused on now? Is it total users acquired, is it number of photos uploaded, is it revenue generated? Whatever it is, you want to focus on something that’s not too soft (“increase brand recognition!”), but also not too tactical (“increase page views per session!”). I usually prefer something that’s a qualified overall metric. I don’t care about total registered users, for example, but I do care about total registered users that come back at least 2 times.
Step Two: Make an uber-model that breaks down key variables
Now that you have an overall goal in mind, you want to focus on breaking that down into key variables that you have control over.
In my perspective, a lot of what you’re aiming for is a “flow-based” model for users. You want to focus on separating out variables so that if you get 1000 new users one day, you know how many are coming in via being invited from active friends versus how many are coming in from ads. The more you break this down, the sooner you’ll get into variables that you can control.
Step Three: Collect both quantitative and qualitative data
One of the biggest headaches when you’re generating quantitative models on your business is that after the key variables are broken down, it’s difficult to figure out how to improve a particular metric. Oftentimes, the surest ways to improve end up as local maxima, whereas the highest yield increases are only offered as hazy global maxima.
Let’s take an example where you’re a photo-sharing site, and you need more people to upload their pictures. Local maxima could be reached by doing things like:
- A/B testing your upload page to make people more likely to upload
- Delivering a ton of email notifications prompting users to upload
- Using switch-and-bait tactics like information-hiding, creating false incentives, etc.
- Creating a gimmicky points system to upload photos
I think the quantitative side lends itself well to the above approaches, yet you rapidly hit diminishing returns.
Compare this to much harder (but higher payout) approaches like:
- Repositioning the product for a higher resonating value proposition
- Going after a different kind of audience to target their needs
- Recalibrating the “core mechanic” of the product to make uploading photos a natural part of using the product (like HotOrNot, for example)
These qualitative approaches are much higher risk, because you can’t collect significant amounts of data to validate your responses. You end up doing lots of user interviews, conducting ethnographic studies, and other methodologies that generate lots of data, but it’s still up to you as the entrepreneur to figure it out. Not easy!
Ultimately, I think you have to combine the above approaches, to make sure you have views of the local maxima as well as potential paths into global maxima.
Step Four: Generate hypotheses around key variables and variable combinations
Another key effort is to be able to follow the scientific method: Observe the data, generate many different hypotheses, and figure out what metrics are influenced. Build out an experiment, and conduct it!
In general, the more hypotheses you brainstorm the better – not all of them can be directly measurable, but sometimes you can figure out things that are related or proportional to what you’re trying to accomplish.
Step Five: Execute test and control methods, and don’t confuse correlation with causality!
Finally, it’s important to execute your scientific approach with proper test and control methods:
The entire point is: you have to separate out the variables that CAUSE the positive effects you’re looking for, versus merely related things. The only way to separate these variables out is via A/B testing.
What are the tools you’ll need to do this?
In general, the first two steps (creating goals and breaking down variables) can just be done using spreadsheet models and talking. It’s just figuring out how metrics really plays into your business – and even if you can’t measure anything right away, it’ll start to solidify how everything fits together.
Similarly, the hypothesis generation stage is all about getting in a conference room and doing brainstorms. The entire point of those discussions is just to generate ideas, with the constraint that the assertions have to be falsifiable.
For quantitative data collection, I typically do NOT recommend Google Analytics.
- Create clear, measurable goals
- Make an uber-model that breaks down key variables
- Collect both quantitative and qualitative data
- Generate hypotheses around key variables and variable combinations
- Execute test and control methods, and don’t confuse correlation with causality!
How to Use A/B Testing for Better Product Design
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.
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 percent 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 percent 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.”
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 percent 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?
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 percent of your users invite their friends, or how many friends they invite, etc.
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 percent 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.
How to Generate Awesome Test Candidates for A/B Testing
I’ve gather a couple rules of thumb in helping you generate good candidates for A/B testing below:
- Brainstorm the RIGHT way
- Dive down into potential customer motivation
- Go for high variance approaches
- Test big things first, smaller things later
Brainstorm the RIGHT way
First off, not all brainstorming is created equal – you want to make sure you are going for lots of quantity, that the most senior person in the room doesn’t “run” the whiteboard, and a bunch of other guidelines that you can find in this article on IDEO’s brainstorming techniques.
Dive down into potential customer motivation
One important issue is that every product and every page within your product likely caters to multiple needs. Influence, the classic book on persuasion by Robert Cialdini, enumerates many of them. Is it:
- Commitment and consistency
- Social proof
Or alternatively, you may have specific ideas about value propositions or user emotions
Who knows which feature is king? The point is, each one of these potential customer motivations and values probably deserves at least one, if not several, test candidates in your A/B test. The fundamental emotions driving your product have a huge likelihood chance of altering the outcomes of your split tests.
Go for high variance approaches
Similarly, life is too short for the safe stuff. Because of the fact that you throw away all the bad candidates and keep the good ones, it’s in your best interest to try to make the good ones as good as possible! As a result, make sure you try to go for extremely polarizing, high-variance approaches.
For example, make sure you try candidates that:
- … are aggressive and in your face
- … use different graphical elements like videos versus text versus audio
- … are varied in length, like very very long or very short
- … may offend certain subsets of your audience
- … are commanding and direct the user
Test big things first, smaller things later
Similarly, make sure that you prioritize the your tests so that you aren’t testing subtitles and paragraph copy when you could be trying out even more extreme stuff.
Things like the user flow, the layout, “hero shots,” and other factors are usually much more important than smaller things like icons or specific sub-labels for forms.
As a result, oftentimes the best thing to do is to rush out some forms to test, then make things prettier and more finalized from there.
Why You Should Make It Easy for Users to Quit Your Product
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.
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.
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 data points 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.
Talk to Your Target Customer in 4 Easy Steps
Consumer internet companies are often overly dependent on quantitative data like Google Analytics, but without understanding the qualitative parts – the consumer psychology that actually goes into making purchase decisions. It’s a good idea to balance out the data aspects, particularly if you are not your target customer.
How to recruit target customers to talk to, in four easy steps
It’s very very easy to talk to people on the internet. You really don’t have to do much work. Here’s what I will often do, in order to get some opinions about a particular set of products, or to deeply understand user behavior (like gifting! or decorating), or to get a better picture of what people do day to day.
Step 1: Write a recruiting survey
First off, go to Wufoo.com or a similar site (Surveymonkey.com works well too).
The most important part is to title the survey “Get a $20 Amazon gift certificate for 1 hour on the phone” or something similar.
Make a survey that includes the following questions:
- First name (text)
- Phone number (phone number)
- Email so we can send you a gift certificate (text)
- Best time to call, morning, afternoon, evening, weekend (multiple choice)
- Tell me about yourself! (textarea)
That is usually a good base, and you should make all the entries required. Then you also want to provide a couple questions that can help you screen or otherwise prioritize your questions. For example, for a Facebook app you might ask:
- What types of games do you like (multiple choice)
- What kind of phone do you have? (multiple choice)
- Why do you like game X? (textarea)
- Have you ever spent money on a game? (multiple choice) etc.
Step 2: Recruit your participants
Now that you have a survey set up, then you can take the URL and start getting people to fill it out. There are a couple obvious areas to recruit people, and I typically do the following:
- Link the survey from your product (if it’s out there)
- Buy ads on Facebook and send traffic to your link
- Post your survey on Craigslist
- Buy ads on Google Adwords and send clicks to your survey
For the ad-based solutions, I will usually limit the buy to $50 per day, and spend $0.50 or so per click. I usually find that it costs about $1-2 per survey completion. After I recruit a couple dozen, then you can start moving forward with the call.
Step 3: Do your phone interviews and learn something!
This where you’ll learn the most – you can just pick up the phone and start talking. I usually structure the interviews into a couple distinct sections, depending on what I’m trying to learn.
The first section I usually try to learn about basic internet usage:
- Tell me about yourself
- What’s your typical day like?
- Tell me about your computer setup – what do you have? When do you use it?
- What are your favorite internet sites? What sites do you use every day?
Then depending on the topic, I’ll usually drill into 3 or 4 different areas with a couple questions each. The entire point is to ask open-ended questions without leading them too much. I will do as many of these calls as makes sense until I am hearing the same information over and over. Then I’ll start tweaking things and changing the interview to adjust.
Also, I will usually not show them a product unless the entire discussion is focused on that – the point of these conversations for me is usually qualitative understanding, not usability.
Step 4: Buy your interviewees a gift card
When you’re done, don’t forget to send your interviewees a gift certificate – $20 card from Amazon is a good idea – to thank them for their time.
One of the best things is that once you get some relationships going with the best interviewees, you can go back to them for updates or to identify some of the most extreme cases.
Growth Hacker Is the New VP Marketing
Growth hackers are a hybrid of marketer and coder, one who looks at the traditional question of “How do I get customers for my product?” and answers with A/B tests, landing pages, viral factor, email deliverability, and Open Graph. On top of this, they layer the discipline of direct marketing, with its emphasis on quantitative measurement, scenario modeling via spreadsheets, and a lot of database queries. If a startup is pre-product/market fit, growth hackers can make sure virality is embedded at the core of a product. After product/market fit, they can help run up the score on what’s already working.
The process of integrating and optimizing your product to a big platform requires a blurring of lines between marketing, product, and engineering, so that they work together to make the product market itself. Projects like email deliverability, page-load times, and Facebook sign-in are no longer technical or design decisions – instead they are offensive weapons to win in the market.
These skills are invaluable and can change the trajectory of a new product. For the first time ever, it’s possible for new products to go from zero to 10s of millions users in just a few years. Great examples include Pinterest, Zynga, Groupon, Instagram, Dropbox. New products with incredible traction emerge every week. These products, with millions of users, are built on top of new, open platforms that in turn have hundreds of millions of users – Facebook and Apple in particular.
Before this era, the discipline of marketing relied on the only communication channels that could reach tens of millions of people – newspaper, TV, conferences, and channels like retail stores. To talk to these communication channels, you used people – advertising agencies, PR, keynote speeches, and business development. Today, the traditional communication channels are fragmented and passe. The fastest way to spread your product is by distributing it on a platform using APIs, not MBAs. Business development is now API-centric, not people-centric.
The role of the VP of Marketing, long thought to be a non-technical role, is rapidly fading and in its place, a new breed of marketer/coder hybrids have emerged.
What Does a Growth Team Work on Day-to-Day?
Day-to-day, I would break down what a growth team does into two major buckets: 1) Planning/modeling and 2) Growth tests.
But first, you need a great product
Let me note that if people aren’t using your product, then you’re wasting your time spending too much time optimizing growth. You need a base of users who are happy and then your job is to scale it.
Planning and model building
The planning/modeling side of things is really about understanding, “Why does growth happen?” Every product is different.
- You might find that people find you via SEO and then turn into users that are retained via emails
- You might find that people come to your site via web and then cross-pollinate to mobile, and that’s the key to your growth.
- You might find you need to get them to follow a certain # of people.
- You might realize they need to clip a certain # of links to get started.
You can come up with a model by looking at your flows for how users come into the site, by talking to users, and by understanding similar products. You can look at successful users and unsuccessful ones.
Ideally you can model a lot of this in a spreadsheet so you can do scenario-planning around what works and what doesn’t.
The goal is to create some kind of feedback loop that results in sustained growth. Maybe you buy ads, make money, and then reinvest even more in ads. Maybe you get people to create content, driving SEO, which brings in more people that create content. Or maybe you have something invitation based. The important part is to model this process and its component parts.
Once you have a model for how to drive your growth, the next part is to actually come up with a bunch of project ideas that can make those numbers go up and to the right. Ideally you can do lots of A/B tests for pretty short ideas that prove out the concept. If it works out, then keep investing.
Because the majority of A/B tests don’t do what you want (maybe the number is <30 percent ) as a result, you’ll want to have many, many A/B tests going at the same time so that you get a couple winners every week. Sometimes people do one or two A/B tests per week and then complain that it doesn’t work for them – they probably need to five to ten times their A/B test output in order to get a win or two per week.
To execute each growth project, you may also need to develop some instrumentation around tracking where users come from, and what they do. This can be a bunch of SQL databases and reporting at first, but might move to something fancier later on.
Eventually, the results of these tactical projects feed back into the uber model – you have to constantly reevaluate your priorities and understand which places in the product are the most leveraged in driving growth. So there’s a feedback loop of jumping from the strategic to the tactical, and back.
To summarize the above:
- Have a solid product where your users are happy
- Coming up with a model for how your site grows
- Trying out ideas and deploying them as A/B tests
- If the site grows, then try out more ideas. If it doesn’t, rethink the model in step 1 because it might be broken
You Don’t Need a Growth Hacker
Startups don’t need growth hackers – at first. They need products that are really working in the market. This means users love it, that there’s lots of retention and engagement, even at small numbers.
The reason for this is that ultimately working on scalable growth is an optimization problem. And it’s a combined product management and technical function, to boost an already positive growth curve into something even bigger. The analysis needed to drive user growth require a baseline of usage, whether they are A/B tests, cohort analyses, or lifetime value calculations, and the changes that make those numbers go up are product changes. The more data you have, the faster you can iterate and generate more growth.
When you are pre-product/market fit, and you only have dozens of friends and family using the site, you don’t have enough usage to create a baseline. What you need here is a lot of lead bullets, not one silver bullet. This is where PR, community management, partnerships, and other forms of hard-to-scale growth techniques are great. This is where you need to iterate on the product based on your own expert intuition of what it needs to be. And once you have enough usage and your product is working, then you can use some of the more quantitatively driven growth techniques.
So again, I repeat- startups need product/market fit, not growth. Growth comes as a result of having achieved fit, and a growth team is built to optimize the curve. The real question is, how do you get to product/market fit, given that most startups fail to get there?
If you’re a startup with minimal users and weak usage, keep iterating on product and doing the hard work of building an initial community.
At some point you’ll have enough usage to think about optimizing easy things, like signup or sharing flows. The goal is to move fast and ship a lot of product iterations to get to that usage level. But until then, it’s a waste of time to build a huge analytics system for A/B testing when you don’t have to.
Eventually, if you beat the Trough of Sorrow, you’ll start to find evidence that your product is working. Qualitatively, you’ll see the same users over and over, and they’ll tell you how much they love your product.
The first steps of working on growth are often super easy – figure out the critical flows in your site, like signing up and sharing, and what factors turn users into successful and active ones. Now start optimizing for that, starting with a few people working on a small number of A/B tests at a time. Based on how that goes, you can ramp it up over time.
Try to start optimizing growth too early, and you may not have the product in place to become a long-term success.
In every startup’s pursuit of growth, it’s important to remember that first and foremost we’re looking to create something that’s sustainable. Building something big and impactful takes years, and your distribution strategy will need to weather the passage of time. If you slash and burn your customers, your platform, or your product design, it’s a matter of time before your active users curve jumps the shark.
This means that your growth strategy has to be “polite” and be considerate of all the parties involved:
Don’t try to force people to do what they don’t want to do, all in the first session. You’ll burn out your audience, fail to retain an active userbase, and while that might look good in the first few months, over time your churn will beat your growth rate. That leads to a rapid decline, which you don’t want.
Each platform wants something different from you, and you have to learn to play by the rules to have a lasting relationship with them. Obviously this means you can’t burn their users – that’s the worst thing to do. Dumb, too. Some platforms want more engagement and user-generated content, and others want ad revenues. Learn what it is that they want, and make sure your product helps them as much as it helps you.
And finally, it’s important that your growth mechanics don’t compromise the design of your product.