Does your B2B startup need a Product Manager?

As we've mentioned before, product managers play different roles depending on the type and maturity of the business they operate in. In this post we're going to tackle what enterprise product managers should be doing and why you need one...or don't!

 

There are enterprise, or B2B, products that don't benefit from a product manager, e.g. long-lead time, well specified products with heavy technical emphasis and little user interaction (hint: think "waterfall-y" products). Space X rockets, for example, are unlikely to have product managers. A team of technologists and project managers is likely all that is needed to launch a product successfully based on well identified, stable specifications.

 

However, most software and hardware startups do have user interfaces, don't have stable specs and are being launched into young and changing markets. These characteristics lend themselves to having product owners to be the "parent" of the product. That parent should be considering both market needs and technical capabilities.

 

So what should you expect your product manager does;

  • They ensure the product vision, aka roadmap, is clear to everyone in the business.

  • That clarity of product vision comes from a strategy and vision that is commonly understood across your business. If the strategy isn't understood, the product manager forces the company to create one, or creates it and gets agreement. This is the oft-misunderstood process of creating a roadmap.

  • They optimize the product to deliver mid-term revenue by productizing features, as opposed to delivering a feature to close a deal.

  • They supports the sales teams in finding ways to close deals without compromising repeatable revenue.

  • They think in products and customer value, rather than technical components.

 

So, does a product manager, and your product organization have to manage every "product"?

No! Examples of products that they don't need to manage might include;

  • A product that is only used internally, that another organization could own and manage. An example might be a customer support portal.

  • A "product" that is a one-off item for a customer, is developed outside the company and will be managed by another department. An example might be an integration to a customer's business system that your professional services organization will manage.

 

What are "products" that should be managed by a product manager?

  • Products that are sold to more than one customer.

  • Product that have users.

 

Alright, these Product Manager people you speak of seem great. But, are there downsides to having a product manager? Well, actually there is one. B2B Product Managers don't optimize for near-term revenue. As a result of thinking beyond this quarter there are compromises they will make make between delivering what a single customer wants now, versus what the market wants more generally.

Of course, great Product Managers can dance the dance, and manage to keep both sales and their customer happy, while keeping the product "on-roadmap"!

Artificial Intelligence powered Products - Differentiation and Defensibility (Part 1)

At Intersection X we have the privilege of talking to many companies, working on great products, powered by awesome new technologies. Unsurprisingly the technology conversations tend to concentrate around the hot, disruptive technologies making their way up the hype cycle.

 

Our job, however, is to decode the technology hype and mine for real value that can be productized from these technologies. For 2013 and 2014, the hype and conversations were dominated by Drones and UAVs.  In 2015 and 2016 the winner has easily been Artificial Intelligence (AI).  For our inaugural research-focused post we're going to share our insights into differentiation & defensibility of AI products. We'll leverage the patterns we’re observing across the opportunity of “AI”. As a teaser we’ll touch on everything from machine learning to bots!

 

Before we launch head long into this, we should warn you that it’s a long post. We’ve added a handy little table of contents below so you can read it in chunks.

-       Product Value Models

-       The AI value model

-       Bots, OCR and Stuff

To kick things off, we're going to frame our conversation to establish a common understanding. In our workshops and in our client engagements we use something we call a Product Value Model (PVM). It's a conceptual model that illustrates the components that both deliver the value to the customer, and line up with technology components. It’s a really powerful tool for linking business value and the technology's contribution to that value.

 

Imagine for a second that you were the Product Manager for Amazon Prime before it existed, and you wanted to explain to the execs what were the important parts of the product you wanted to build and at the same time explain the business value. We use a product value model for exactly that. Here’s a PVM for the new Amazon Prime product;

The model highlights the things Amazon prime needs to do well to have a successful product; acquiring users effectively, enticing them to quickly and easily select and purchase something, and finally delivering purchases flawlessly, consistently and rapidly to the customer.

 

We can use the Product Value Model to think about the AI opportunity. We’ll call it an Opportunity Value Model (OVM) instead because we’re not talking about a specific product. The OVM represents the pieces of value that combined represent a generalized AI product. We'll use that model to explore what constitutes those elements of value and, in our second post, as a lens to differentiate AI opportunities.

So, with no further-to-do, please welcome our Artificial Intelligence Opportunity Value Model;

We build products to deliver value, so before we break the model down, let’s discuss the types of value AI products generate for users today.

As humans, we generally experience two types of value from products; utility value and emotional value. For the most part, the value derived from the AI products we're seeing and working with is utility. Some simple examples of utility-enabling AI products are automated executive assistants, bots to help you book travel, or cars that drive you (instead of the other way around!). These cases are simple examples of using an AI product to deliver utility-value by saving time.

On the flip-side, deriving emotional value, for example through an personal-coach chat bot, appears to be a bit further out[1],[2].

With that context, let’s break down the OVM starting with the interface;

Interface To derive value from a product, there has to be some type of interface to interact with the product and receive the value. A key driver for the growth of AI is its potential to make technology behave in a more human-like manner and in doing so make it simpler. 

A nice example of simplified UI in action is the current interface trend in AI chat-bots. Most chat-bots are not being delivered as new stand-alone apps, rather existing UIs (Facebook Messenger, Slack, etc) are being used as the human interface to the bot.

Message Bots are simply extracting insights from data and models and delivering that information via lightweight interfaces. There’s no longer a need for a native app or web app to be custom created, rather the bot leverages an existing interface, e.g. SMS or Facebook Messenger.

Note If AI is enabling products to no longer need an interface, why is interface part of our AI Opportunity Value Model? Interface is relevant because there is a better interface than your thumbs. And AI is enabling it. We humans long-ago developed a cool interface called speech. Teaching a computer to interface with humans via speech (aka Automatic Speech Recognition (ASR)) is advancing very rapidly due to large investments made by Amazon, Google and Apple. Imagine what our world will look like when we just talk to our technology and it finally understands the intent as well as another person might. Plucky startups like Capio, a client of ours, are making high-performing ASR accessible by driving cost for high accuracy solutions way down. As a result high accuracy ASR is coming to the mass market now, and interfaces are changing!

Model There are many reasons the time is now for AI but perhaps the biggest reason is the advancement of the algorithms necessary to simulate human intelligence. Machine-learning, computer vision and natural language processing are all terms that are familiar to many of us and they are used to reference algorithms that process data. In simple terms, algorithms are used to process raw data into an insight. That insight is added to the other insights and a model of the world that we’re interested in is created. You can think of the model as an answer machine for a particular topic. The model represents the simulation of human intelligence and the better your model is, the better the answers you get from the machine. (P.S. If you have 20 minutes, this video is a great illustration suitable for the lay-person of a modeling technique called neural networks.)

Datasets, aka knowledge. And now comes the most important part. In fact, if it’s missing there is no AI product. It is the dataset. The value that AI products deliver is through mining some form of knowledge.  That knowledge might be a dataset representing a digital map or it might be hundreds of thousands of images of skin abnormalities or purchasing responses to pricing changes. The value of our model (the answer machine) is primarily a function of that dataset’s quality. The quality of dataset can be measured in different ways, but for the sake of simplicity we can assume the larger the dataset the larger the quality.  As an aside, this is the allure of big-data. Large data-sets hold the keys to many things and we’re now in an age where it is easier to mine those (big) datasets.

Infrastructure. I know I said the dataset was the most important, but coming in a close second is infrastructure. AI’s current rise is powered by the availability of the computational power needed to power the CPU-cycle hungry algorithms. Algorithms and models get complex very quickly and often they are dealing with combinatorial explosion that requires horsepower to process. Thankfully the hardware industry has been hard at work to bring ever more powerful, application specific hardware to market for us, and at a reasonable price. Anyone with an AWS account now has access to the computational power necessary to power sophisticated models.


Alright, lets summarize; Most AI-products are delivering utility value, rather than emotional value, via ever simpler interfaces. The back-end of these products is leaning on two key components; data (a source of knowledge) and a model (an answer machine). These new products need very powerful infrastructure to do their work. If AI-products were a form of transport they would be a rocket ship because they consume vast amounts of rocket fuel (computing power). Thankfully people have been working to create cheaper rocket fuel making it possible to build AI products more cost effectively.

Ok, we now have a common understanding through which to analyze our AI products. Let’s try it out with a couple of examples ahead of the second post; the cheat sheet for understanding AI opportunity quality and defensibility. We’ll try the OVM out on a messaging bot and an old-school document scanning (OCR) solution.

Messaging Bot; Product Hunt recently launched Kittybot, an AI messaging bot to uncover new products. Here’s how it work; if I want to find out about the latest products in IOT, Kittybot will surface them for me as soon as they are on Product Hunt.  Product Hunt cleverly decided to use Slack as KittyBot’s interface.

Ok, so lets break it down;

·      Value; The end-user value is that I get finely curated information directly to an interface that already has my attention.

·      Interface; Product Hunt leveraged an existing, widely available interface; Slack.

·      Model; (We’re assuming) the algorithm they are using is a simple one; search.

·      Dataset; Product Hunt are leveraging their own proprietary dataset of quickly growing new products that the crowd has already characterized.

·      Infrastructure; Again we’re assuming this a relatively simple hosted solution like AWS.

 

Document Scanning; OCR is not a new technology at all. In fact it has been around for a long time trying to solve something that most humans find easy; reading documents. It is a form of Artificial Intelligence though. Let’s test out the value model on a generic office document scanning solution.

·      Value; The end-user value is that I get digitized data from a paper document that I can now act on, e.g. identify the address on a package to know where to route the mail.

·      Interface; If all goes well, and the mail is routed correctly, there is no human interaction required. When there are errors the human will need to use a lightweight admin interface to solve the issue.

·      Model; OCR is generally driven off a rules-based model that is built over time and is context sensitive, e.g. a text block in the top right corner is likely an address.

·      Dataset; The OCR system learns by seeing lots, and lots, and lots of documents.

·      Infrastructure; There is both hardware and software infrastructure required. The software and computing infrastructure is relatively simple and uses commoditized equipment. The hardware is very, very specific to get thousands of odd-shaped envelopers scanned quickly.


We can see that the model is really useful to parse both the technology and the business value of these wildly different applications.

We hope you enjoyed this first post. Stay tuned for post #2 where we’ll discuss how to identify the characteristics of AI products that drive differentiation and competitive advantage for the long run!

P.S. While you’re waiting for Post#2 you might enjoy our post on what AI means for product managers

[1] Our working hypothesis for this is that a) utility is more targetable and b) emotional value doesn’t make “rational” sense so it’s harder to target.

[2] Utility can beget emotional value, e.g. I sent my friend in Australia a whatsapp message about a friend getting engaged (utility) and I re-connected with them about our time in school together (emotional value).

Customer Discovery - Beware the Polite Smile.

Whether you're a nascent startup, or a large enterprise, the investment required to launch a new product is immense.  Thankfully due to the success of the Lean Startup, the Business Model Canvas and their ilk, we realize there are ways to de-risk the process of going to market with a new product.

OK, so what's the biggest risk we want to mitigate? Building the wrong product of course! So how do you know that customers will want your product? I can hear lots of people shouting at the screen “Ask your customers, ask them!”.

You absolutely must, unequivocally go out into the world and talk with your prospective customers. But beware of one practical problem; humans don’t tell you what they want. They tell you want you want to hear!

For the first 18 months of billFLO, a company I founded, I asked potential customers the question “Would you use billFLO?”. In return I got lots of polite smiles accompanied by comments like “YES! We’ll try it”.

I’d get back to the team all full of smiles “We got another beta customer. We’re on a roll!”. We’ve got customer validation now I thought. Next step…lets scale this baby! A few weeks later those customers had disappeared, nowhere to be found.

Polite smiles are not enough to build a business on. Don’t fool yourself. Once they’ve used it you need to get the customer to say irrefutably positive statements about your product. Or, if they haven't tried it yet, they should be begging you for it. I’d go so far as to say don’t invest in a new product until people are beating down your door to get it.

 

The robots are coming, the robots are coming!

Honda's Asimo Robot, the most advanced humanoid robot.

Honda's Asimo Robot, the most advanced humanoid robot.

Everywhere we looked in 2015 there seemed to be another headline about how artificial intelligence (AI) is going to take over the world and eradicate the human race. The fear that the media is playing off even has its own buzzword: AI anxiety.

If you're not up to speed with the potential of AI, it's worth investigating to form your own conclusions on the impact on the human race. The basic premise is that the ability to combine sufficient computing power (hardware) with intelligence (software) to rival the human brain will be possible, for sub-$1,000, somewhere between 2035 and 2055. What that means for the human race and our society is the subject of much debate. We at Intersection X won't be so audacious as to present an opinion on that grand topic, but rather, we'll focus on what it means for a subset of the human race: productizers!  

Let's familiarize ourselves with the concepts to get started. Perhaps the most considered article (and easily our favorite of the various pieces we've read) is from the fine folks at WaitButWhy. We're going to lean on a nice framework they outlined to help us answer the question "Why should productizers care about AI?"

The WaitButWhy folks talk about three categories of AI: Narrow AI, General AI and Super AI.  Narrow AI is the category of AI that can automate tasks of a very focused nature. General AI builds on Narrow AI and could, in the future, replicate the general intelligence of humans. The theory says that there comes a time when the general intelligence of AI will improve to the point that it surpasses human intelligence and it becomes Super AI. Super AI is what's getting lots of people wrapped around the axle.

As productizers, we like to tout that we can imagine the future of products and help bring them to life. But in the context of AI, we're very shortsighted; we rarely think more than one to five years down the line because our goal is to deliver revenue as soon as possible! That means Super AI, at 20-plus years away, isn't relevant for productizers today. What about General AI? Well, that's a good question. At Intersection X, we are lucky to be exposed to leading-edge AI work being carried out by a range of clients, and everything we have seen suggests General AI is not on the radar in the next 24 months.

So that leaves us with Narrow AI. Let's discuss an example to get a sense of its power and how it's already impacting daily life unbeknownst to us. When I emailed an investor friend of mine to meet for lunch, he added his admin, Amy, to the conversation to find a time to meet. Amy and I emailed back and forth and in a few minutes found a mutually workable time to meet. I was marveling at Amy's efficiency when I noticed the domain of her email address: x.ai. After some quick Googling, I discovered that Amy is an AI executive admin powered by the startup x.ai. Nothing about the experience had suggested to me that Amy was a computer; it was simply her email address that tipped me off. I was blown away. For reference, this happened in January 2014. Yep, well over a year ago... eeek!.

Let's look at another Narrow AI example from Building System Planning (BuildingSP), a client of ours. If you're sitting in an office right now, look above your head. Can you see all those pipes and ducts hanging from the ceiling? Did you know that a group of highly paid CAD drafters and engineers had to sit at a computer and laboriously route the water, the electric conduits, HVAC pipes and ducts through the building?  It's not uncommon for each routing (water, HVAC, etc) to be done by a separate draftsperson and for it to take months to complete for one building. BuildingSP is leveraging the power of AI to eliminate the human time needed to route the pipes. Imagine the architect simply describing the requirements - "Route the water and electrical ducts, give the water pipes precedence and optimize the routing for cost. And make sure everything is within 10-inches of the ceiling". Thirty seconds later BuildingSP's artificial intelligence has generated the optimal solution. They're revolutionizing the building and construction industry, which will lower the cost of building for everyone.  And BuildingSP is delivering this value today.

Ok, so Narrow AI is living and working in our midst today. Back to our question though: Why should productizers care about AI? Well, what's consistent in both of our examples is that the user experience has changed. More specifically, the user interface has (almost) been eliminated. We no longer need to learn how to use an application to achieve our goals. We simply tell the system what we want. Let that settle in for a second. You no longer need to invest time in order to learn how to use a product. Powerful, right?

The skeptics amongst you are probably pointing out that "telling" an application to do something doesn't always work so well. We've all been the angry customer shouting at our bank's phone-based system: "I said connect to an agent, grrr!". The reason that experience is so common is that speech recognition traditionally requires large specialized teams, huge data sets and a big investment to get to usable accuracy. Only the likes of Google and Facebook have that mix of resources. However, it probably won't surprise you to hear that AI is helping here too. Capio, an Intersection X client, creates AI-powered, self-learning voice recognition and conversational systems. They can get to usable accuracy in 48 hours, and their models get better over time by testing themselves against human-provided reference data. What once required a huge time and money investment is now becoming available to the (software-developing) masses.

It's time to put the nail in the coffin of user interfaces! AI is eliminating them and allowing users to get to the value they seek much more quickly. That's a huge shift for the products of the future. Essentially, we're no longer constrained, or we're at least significantly less constrained, by the limitations of clunky, non-organic interfaces like a keyboard or touch screen. It's a little difficult to fathom the possibilities of what might emerge from this transition.

So, it's time to ask yourself, what would your product be like without a user interface?

P.S. Just in case you're not freaked out enough already by the power of AI, we've created a list of the articles we've read to keep the paranoia going. Enjoy!

If you remember one thing about user testing, remember this.

User testing is a painful process. For the entrepreneur it means putting your baby out there for ridicule by strangers. If you’re a professional product manager it’s a little less personal but, at a minimum, it’s a great way to get completely demoralized by how much more work you still have to do. It’s worth it though, right? I don’t need to convince you, do I? OK, good.

The organization of user testing is the first big challenge. You have to find a stranger (usually) to try out your product. The good news is that tools like usertesting.com and fresheyes.co can help you find your victim. Here’s the rub though. In their haste to get started, too many people rush to check the checkbox by grabbing the first human that walks by. No, don’t do it! It seems too obvious to me, but I have to say it. The rule is to find the person that resembles your customer the closest. And not just any customer, the one that will understand what the feature is trying to do. The bus manufacturer doesn’t ask the bus passenger to test the steering wheel. Yes, the passenger uses the bus but I think you’ll get better feedback from the driver. 

Adios, Dear Product Manager

Screen Shot 2016-02-11 at 8.15.36 AM.png

At Intersection X, we've come to an interesting realization over the last year. It's a big realization so take a breath. Ready? Here it comes.

It's time to say goodbye to the the title of Product Manager.

Say Whaaaaaat? Why? Well put simply, the Product Manager title has lost its meaning. It means too many different things to too many different people. It is simultaneously too broad and yet too narrow a term.

In large organizations the Product Manager role is now often indiscernible from the Project Manager role. With longer development cycles that come with more mature products, the focus swings from product definition to product delivery. On-time delivery becomes the predominant measure of success and Product Managers get sucked into it's vortex. The Product Manager's effort is solely focused on the efficiency of delivery and removing obstacles to delivery. Sounds a lot like a Project Manager, right? 

At the other end of the spectrum in startup-land (where we live!) the term Product Manager is too narrow a definition for the role being played. Life as a startup Product Manager is so much more that managing a product. Aside from the fact that frequently the product doesn't exist yet (!),  startup product managers are responsible for wide-ranging efforts from roadmap (product strategy), to product positioning (product marketing), to on-boarding (customer acquisition).

Ok, so if "Product Manager" is too narrow a term for startup-land then what's the right moniker? Well, let's generalize a typical Intersection X engagement to help us understand the role better. 

StartupCo has engaged Intersection X to be their product department.  They have just launched their MVP of a photo sharing app. It leverages cool new machine learning technology to rapidly sift through your photos and generate printable mosaics of your face made up of your photos (Note: this is a completely made up business!). StartupCo has great engineering resources, an awesome head of sales (also their CEO), a handful of early customers, 6 months of runway and they want to be successful.

Our challenge, and the startup Product Manager's, would be to now mix up a recipe with the company's resources (MVP + Eng + Sales + Runway) that creates a product that the market wants, can buy and StartupCo can deliver. There will be many questions; what's the right price? What's the right price unit? How do we position it? Do we have enough time to deliver that cool new feature that we think increases stickiness? What's the next feature after that?....The journey to the end-state will have the product manager passing through every department at StartupCo as well as the market, and pulling all those threads together to work out what the right recipe is. Traditional product management best practices simply won't suffice.

At Intersection X we would draw upon our product management, product marketing, product strategy, customer acquisition and entrepreneurial experience to find the right recipe for StartupCo. With tight timelines, dynamic markets and less than perfect information the secret to success is having that breadth of knowledge and understanding. Again, the title of Product Manager doesn't get anywhere close to connoting what the startup Product Manager is actually doing!

<drags-over-the-soap-box/>The real issue here is that we, as product managers, have a product marketing issue.  People don't understand what we do at Startups. It's time we reposition our own product (ourselves) to better articulate what we do!

To do that we must, with a tinge of sadness, say adios dear Product Manager! Farewell good friend!

And with that, in rushes a gust of fresh air. We can now recognize that our real value is in taking the resources available to the company and working creatively within the constraints to bring to market the best product we can.  We are in fact Productizers! Say it one, say it all, "We are Productizers". Our productizing task is to package what our company can offer into a product that the market wants, can understand, compare, purchase and consume. 

So there it is. We encourage all you startup Product Managers out there to drop the old-school job title and embrace your new title of Productizer! 

 

 

Road-mapping is a contact sport.

For the un-initiated, putting together a roadmap always seems like it should be a simple process. A roadmap is just a single piece of paper after-all. How hard can it be?

We've got some news. When done properly it should be hard. In fact road-mapping should be a full-contact-sport. What do we mean by that? Road-mapping is where the rubber (your strategy) meets the road (the market). It should put the top decision makers in a room to make company impacting decisions. For the best results, it shouldn't be a comfortable experience.  It should be agony. Ok, maybe not agony, but road-mapping should be a high-energy debate to ensure the right decisions are made. 

"Ok, I got it", I hear you say, "Let's talk about how to make it a contact-sport." 

Right, let's do that....actually, one thing before we get started. Let's talk quickly about why we recommend roadmap's even for the earliest of startups. Road-mapping is a process of assigning your resources to deliver on the most important things you've decided you need. When you're a 5-person product team with a 20-person dev team, spread across 4 scrum teams it becomes a complex process. But when you're three guys in a garage, why bother? Well, by skipping formal roadmap discussions what you're really missing is the goal setting process for your startup. As we'll discuss, road-mapping (or at least proper road-mapping) forces you to decide what the business needs most. The temptation for early startups is to skip the roadmap and thus skip the awkward goal setting conversation. Not good.

Ok, back to our regularly scheduled blog post. Let's talk about 3 ways to make sure you get the productive full-contact road-mapping we're talking about.

Be the google maps of your startup

Coming to the road-mapping table without a common understanding of strategic goals across your decision makers is a fundamental error. It's akin to an explorer sitting down with their team to chart a course for their adventure and not agreeing on their destination. While it's a fundamental error, it's an easy mistake to make because many startups don't have a clear understanding of where they need to be at the end of the current planning horizon. Much like google maps forces us to enter a destination before optimizing the route for us, the product leader needs to ensure strategic goals are in place. They need to be the google maps of their company. Sometimes it can feel like pushing a rope trying to get this alignment. However, there is no point passing go on road-mapping until you have agreement on the strategic goals.

P.S. As Des Traynor over at Intercom points out, you can see this go wrong when the work on your roadmap doesn't look anything like the strategic vision you use to describe your company. Oops!

P.P.S. The other current "when-roadmaps-go-bad story" is the bloating of the Evernote platform.

Frame, frame, frame.

OK, so I know we said that road-mapping is a contact sport, but like every sport it needs rules of engagement to enable constructive conflict, rather than destructive. We see many VPs of Product making the mistake of not framing the road-mapping process, i.e. they don't set the rules of engagement. The rules of engagement break down into two topics - the prioritization process and the current proposal. There are any number of different feature prioritization methodologies to chose from, but it's the product person's job to find the right one for their company and propose it (Note: Daniel at foldingburritos has collected a really great portfolio of them here).

The other part of framing is to put forth your proposal for the roadmap you believe is most likely to achieve the strategic goals (remember those goals you all agreed on before!). In the absence of a proposal, your road-mapping peers will have nothing to push against. This creates two problems. First, you'll look silly because as the product person you haven't come up with a roadmap(!), and more importantly your ever-helpful peers will head straight into freestyle road-mapping. Freestyle road-mapping is when either the hipo or the squeeky-wheel drive the road-mapping decisions. It's a disaster and really hard to curb once started. Frame the conversation and you'll be good.

Be a player-coach

Ok, you've gotten everyone aligned on the strategic goals and you've made a proposal for this roadmap cycle. It's time to let the game begin. It's time for the players, I mean decision makers, to go head-to-head and debate the features and make all the agonizing tradeoffs. Your role as coach is now complete and it's time to join the fray with your strongly held opinions and spark the debate. As the head of product you have a strong opinion, right? Remember, you're the product person and it's your job to have the best information and form opinions based on that. However, you don't have all the information so it's time to play ball and get your team to articulate their opinions so you can reach consensus on the best roadmap.

Now,  you have our permission to pop back into the coach role if the conversation is going side-ways. And if that happens, you have framed the conversation already so you can fall back to the strategic goals already agreed upon, the process agreed upon and the current topic of conversation (your proposal).

And....you're done.

You got your folks in the room and the game commenced. You likely argued, battled and agonized over some very tough decisions. There might even have been some raised voices. That's ok. You've achieved your goal; engaged decision makers battling toe-to-toe on important decisions. You have a roadmap. Nothing left to do now except deliver on it!

User delight and why it’s too late for your product to have it.

We’ve spent much of 2015 with startups across a multitude of industries from wearables to artificial intelligence to enterprise software. All of them want to build a product that delights their users and thus dominates the market. A few are on their way to doing that, most are not.

Apple. Uber. Product Hunt. Amazon Prime. Zappos.  They all have it. Products that delight. We’re often struck by how simple the element of delight is. Why didn’t other startups think of free, no-hassle returns for shoes or a laptop that just works or free shipping on my internet purchases. Because other startups were too dumb. Ok, they weren’t too dumb, they were too busy.

Why were they too busy? Because startups spread themselves and their business across multiple product opportunities making them too busy. Although its counter-intuitive, placing a single product bet has a much higher probability of success than spreading yourself across multiple opportunities. A single bet allows you to focus all your energies and resources on that one path. Allowing yourself the time to focus creates the space for ingenuity, attention to detail and creativity to take hold. Resisting the urge to try other bets in parallel protects that productive space. And it’s that productive space that leads to light bulb moments, and more importantly, the time to act on them. The technology behind the Uber experience or the work of gathering up high quality new products for Product Hunt is not simple. The hard work those companies invested results in a simple experience that delivers delight. An absence of focus means no delight will happen. And delight almost guarantees success for a startup.

Want to know if your startup is building a delightful product for your users? Ask yourself this question; From CEO down to the most junior employee, does everyone know exactly what you need to do in the next 4 weeks? If you respond with something like “Yes, we need to test the assumption that ….” then you are likely on the right path. If you respond by saying “We are looking to raise a seed round to bring our new product to market” then it’s already too late to create user delight for your product….and your startup might even be doomed.

Stay tuned for my next post on a process to work out which bet to place.