Think pricing

Startups: It’s Time to Think Pricing. Here’s How.

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Out of 7 startups that joined us just a few weeks ago for StartupYard Batch 7, only 2 are currently selling a product to real customers. Those 2 have just a handful of customers each. Most of our startups are very early stage; you have to have something to sell, before you can sell. But it surprises many of them how early it pays to think pricing. 

While we expend days and weeks and months of effort discussing features and USP, design and everything else, it’s surprising to me how difficult it really can be to talk to startups about pricing. Talking about pricing is kind of hard. People don’t want to think about it. They panic at the thought of raising prices, and they cower in fear of having prices too low. It can be a rollercoaster.

Of course, pricing is a sensitive subject. As Tom Whitwell writes in his insightful medium piece on pricing psychology, “Prices are a shortcut to our most sensitive emotional responses.” Pricing is a deeply primal part of consumer psychology, and as Whitwell shows, leaves consumers surprisingly, sometimes shockingly, susceptible to manipulation or suggestion.

I suggest you go and read that piece: The First Rule of Pricing,  to find out why. I’ll wait.

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Hello! Now that you’re back, this piece is going to build on Whitwall’s, to talk about what all that means for early stage startups, and how they should actually approach pricing their products for the first time, or through the first few iterations.

Your Customers Don’t Know What They Want (Or How Much They Would Pay)

As Malcolm Gladwell explored in his best-seller Blink, and associated Ted Talk “On Spaghetti Sauce,” it has been known in retail since the early 1980s that optimum sales results could not be achieved by finding the ideal single product and price point. For decades, product companies had been simplifying their offerings in the hopes of reducing costs while optimizing their sales around best-selling lines of products.

 

The logic was simple. The attractiveness of products could be graded on a bell curve. An ideal point was where most customers would be willing to buy, whether or not any of them were completely satisfied. Simple product lines also made advertising easier, reducing the need to target advertising to specific audiences, because increasingly, products were targeted at the vast middle of the market.

As he explains, beginning in the early 80s, big food companies, and later other product companies, discovered that this tendency to optimize around single products was hurting their profitability. Instead of selling one popular product that was a mix of the qualities most customers wanted, producers began to develop products that catered to “clusters” of customers who had distinct preferences.

Importantly, research showed that customers were not well equipped to predict what they would enjoy or what they would buy. As Gladwell notes, “For years and years, the standard practice when you wanted to find out what customers would want to buy… was to ask them.”

But customers routinely used experience as a reference point for future behavior. People are bad at imagining a future that isn’t similar to the present. Likewise, they are not good at predicting their future behaviors, because they assume their behaviors will remain consistent.

Experimental field research discovered that “hidden preferences” in consumer behavior were powerful, and almost completely unknown. By testing products with “value added” features, researchers found that price tolerance was much more flexible than previously believed.

For example, about ⅓ of US consumers enjoyed “Extra Chunky” spaghetti sauce. And yet no major brand offered such a product. Customers failed to state, when asked, that they wanted “chunky spaghetti sauce,” but experiments showed that when given the choice, they readily bought it and paid more for it.

Think Pricing

The post 80s flourishing of product segmentation was slow to be adopted for the digital economy. Driven by the technical difficulty of offering and maintaining more diverse product offerings at different pricing points, and the difficulty of marketing each individually in the online space, software and online companies often adopted the old model.

But today, tiered pricing has seen a major comeback. Customers are again comfortable with the concept applied to digital products. Thus instead of we have “9.99 for Standard, 14.99 for HD,” or the “Good, Better, Best” pricing model, in which features and functionalities are limited or exclusive to different products.  

So what does this mean for your own pricing? First, there is no optimum pricing strategy- at least not in the sense that most startups tend to think. There is no perfect price, but rather a continuum of price and feature combinations, into which most customers fall somewhere. The work of a product company is to identify where pricing and feature expectations align for different categories of customers– what Gladwell calls “clustering.”

If you aren’t consistently testing the limits of your pricing and the feature expectations of your customers, then you will likely leave money on the table. Whitwell uses the example of The Times of London. Beginning in 2014, The Times began asking customers whether they would pay X amount for different combinations of features. They produced a range of prices and feature sets, to test different “flavors,” of plan to sell to their customers.

What they found shocked them. Although a minority of their customers would choose to pay more for certain features, the actual revenue to be gained from offering those features at a different price point far outweighed the lower number of paying users. They found that customers would gladly pay up to 3 times more than they currently did to retain only a portion of the same features they enjoyed at the old price. By throwing in features that customers had not needed at lower price points, The Times had co-opted its ability to upsell those features later.

The Freemium Trap

“Freemium” is generally taken to mean a product which can be used free of charge indefinitely, but which is limited in comparison with a premium version, either in offered features, or capacity (such as storage), or in other ways.

It’s not always a bad idea to have a Freemium model. Particularly, products that provide a long-tail value that is hard to see at the beginning may have to be freemium. Most casual games use freemium these days. Dropbox is also a freemium service, which makes sense, because customers typically don’t have a need to buy up to 1TB of storage in one go- instead, they collect data slowly. Slack is another example: a small team doesn’t always need unlimited message history, storage, and all the bells and whistles on day one.

It’s hard to get someone to pay for something of uncertain value. It’s even harder to get someone to pay for something for which a ready and free replacement already exists.

But on the other hand, many, many startups who use a freemium model shouldn’t. When you provide a product aimed at customers who easily understand the value, and who moreover really need what you offer, then offering them a Freemium experience may simply be giving them a handout. And addicting your customers to the free product can make it even harder to sell the Premium version.

One of our startups, 2016’s Satismeter, experienced exactly this problem. As Co-Founder and CEO Ondrej Sedlacek told me recently:

“Switching from a freemium model to free trial and ditching cheaper plans was a big improvement for us. The truth was that people who needed our product were ready to pay for it.

Freemium ended up being a barrier to selling to some customers, because they would get used to just making do with the free version. When we eliminated our free plan, we saw only a slight reduction in signups, and we increased sales overnight. Plus, free users were ironically the most demanding for support. Paying customers invest their time to understand the product and set up the whole process to get the most value out of it”

Customers who understand your product’s value are inherently better customers in the long run. Attracting people who don’t believe in your product might be necessary at the beginning, but it should be viewed as a means to an end.

Price is about Positioning

In his piece, Whitwell calls attention to this with reference to Apple (itself discussed in another piece: Why You Should Never Ask Customers about Price). When unveiling the iPad, for example, Steve Jobs had basically two options, assuming that he couldn’t actually change the price of the product significantly.

First, he could sell the iPad as an expensive version of the iPhone (something many internet trolls did anyway), or second, he could sell the iPad as a cheaper and better version of a netbook computer. He chose the latter- making a point to talk about the features of a netbook in comparison with those of an iPad, before revealing the iPad’s original price point- at $999.

Voila: the Ipad wasn’t a very expensive phone. It was instead a cheaper and better netbook- one with all the features of an Iphone, and the power of a real computer.

In pricing psychology, this is called “anchoring,” and it’s hard not to notice once you know what it is. Retailers will routinely display their best selling items next to items which are significantly more expensive, and items that are significantly cheaper, in order to give the customer the feeling that she is getting the best deal.

Often products are offered that are far more expensive than is actually justified by features. The logic is plain enough: a few customers might buy the Deluxe Collector Edition, but it’s really just there to make the more popular product look cheap in comparison. That’s how you get a $10,000 Apple Watch, or a fully loaded Mustang Cobra. Buying the next best thing is almost aspirational- the customer is invested in a product category where prices run very high, giving them a sense that they are in the “big game.”

By the same token, restaurants may list the most profitable wine on the menu in second place, just above the cheapest wine, and just below a significant jump in prices. This plays off of a human tendency to “reality check” prices based on other available evidence. $25 for a bottle of wine seems like a lot if the options are $5, $15 and $25, but it seems reasonable if the prices start at $15, and reach over $100.

In sum, pricing can function as a way of positioning a product in the market. Too cheap, and the product may not be taken seriously enough. Too expensive, and it may flash a warning to a potential customer that the product is simply not for them.

Think About Pricing: Cost and Value

There is no formula for pricing. One of the hardest lessons that many startups learn is that the value of a product as they understand it, can be very different from its value to a paying customer.

Thus, cost and value are only loosely correlated. This is why it costs $10 to use the Wifi in an airport. The cost is negligible, but the value to a traveler is worth the price. Most commonly, startups should learn much more about their own customers, in order to understand the value of their products to those customers.

That doesn’t necessarily mean doing what your customers want. But it does mean understanding what your customer’s needs and priorities really are. Anyone who has angrily paid an obscene price for a bottle of water on a train, or for a dongle they simply must have for their Mac, knows that pricing is correlated with need.

Most importantly: think about your pricing more. It rarely fails that, when asked about their pricing, startups lack key insights that would potentially allow them to make the difference between a profit and a loss. Absent a clear picture of the value of their products to customers, startups simply guess at what people will be willing to pay- and more often than not, they guess wrong.

An exit is not a vision

An Exit is Not a Vision

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I attended a pitching competition this weekend, as I do many times each year. This one was not unlike many others.

Most of the pitches were very interesting, and I liked many of the ideas. But I noticed something I didn’t like. Aside from the usual little foibles like “we’re the Uber of X” (probably not), and “$400 Billion Market!” (kind of not really), I heard, several times, detailed digressions into exit strategies.

Ok, there’s nothing inherently wrong with thinking about an exit strategy. But I do find something offputting about a company that is trying to raise seed-level investment, talking about selling out within a couple of years. Exit strategy is not part of our program at StartupYard, because an exit is a natural extension of success- it doesn’t need to be the focus.

An Exit is Not a Vision

an exit is not a vision

We like to ask people what they hope their company will be doing in five years. That’s not because we think they really know what will happen in that time (they never do), but because we want to know the scope of their vision for the future.

You should know where you want to be in five years, because if the answer is “doing something else,” then building a startup might not be the best path. This isn’t Wall Street- there are no golden parachutes at early-stage startups.

Which would you rather hear? “I need $300K to build a great company that’s going to be changing the way people do X in five years–” or, “I need it to build a company that’s going to be bought by Google 18 months from now?”

One of those two is a vision. The other is at best a strategy (and at worst a delusion). Again, I’m sure it would be great if a startup could promise it definitely would sell to Google in 18 months, but if that’s your vision, and it doesn’t work (because it probably won’t), what then? If your greatest hope is to cash in a lottery ticket, then what kind of a sales pitch is that?

As Frédéric Mazzella, founder of BlaBlaCar, recently said in his comments for The State of European Tech, by Atomic Ventures, “Growth isn’t like an elevator, it’s like building a set of stairs.” Meaning, every step on the path towards growing a large company has to be taken individually. There is no straight line to the top.

Founders Focusing on Ambition, Not Passion

This is indeed something I’ve been taking more note of recently. It seems to me that I am hearing more about startup founders’ ambitions, and less about their actual passions. I’m getting a pitch about a person, instead of about the idea they care about. The cliche of “make the world a better place,” is at least a nod to social responsibility and building a sustainable business.

But this focus on exits, which I’m sure some startups do in their pitches, seems to me to be crass and opportunistic.  Even more perversely, I’ve actually heard this phrase more than once: “I have a passion for growth.” Which uses the words that founders know we want to hear, but is pretty twisted when you think about it.

Maybe this will sound incredibly touchy-feely, but I don’t think the best and brightest would be in the tech business if it was just about the money. Why we have to tell ourselves that it is, in fact, all about money is a mystery to me.

The sad part, at least for me, about such pitches is that they completely alienate me, and I suspect many other investors, and betray a focus on money that is unhealthy for an early stage company, still trying to find product/market fit.

As we say, “If it was easy, everyone would do it.” And yet I notice founders trying to make their paths toward profitability seem easy. A breezy growth spurt, followed by an acquisition, champagne raining from the sky. I suspect though, that this is a combination of self-deception and poseur behavior. Sound like you believe it, the reasoning goes, and the audience will think you have it covered.

But at the end of the day, if it’s something Google is going to buy for a cool $100 Million, they’ll be buying it because doing it themselves is hard. The value is in the difficulty of the work, along with the opportunity it represents. And yet I hear “$100 Billion market,” far more often than I hear: “here’s how we can do what nobody else can do.”

As I sometimes say to startups: “Do you want to be something- or do you want to do something?” Being a hyper-growth startup in a huge market is an ambition. Doing the best work you can, no matter what business you’re in, is a passion.

Ambition Isn’t Enough

an exit is not a vision

Of course, at StartupYard we talk to a lot of startup founders, and many, even most, will never realize their ambitions. That’s not a bad thing. Ambition is important, but it can’t be everything. Sometimes people fail because they aren’t smart enough, or don’t care enough, or don’t have the timing right. But sometimes it’s because their ambitions are far too great for their actual passion.

We’ve seen that first hand, and the end is always the same. The founder who is all ambition does just enough to satisfy the ego, and never enough to really drive the company forward in a meaningful way. Progress, according to ambition, is to be seen as a winner. Passion is for winning- for being the best, even if no one knows it yet.

Ambition is important. You must have it if you want to try to do things no one else has tried. Ambition drives people to succeed. But naked ambition leads nowhere. It must be paired with a strong passion to do good work.

These are hard lessons that must be learned. Still, I wish that as accelerators, incubators, investors, and mentors, we would be more clear on what we value most- which is passionate founders who are ambitious in a healthy way.

We like ambition. But ambition is not ever enough. Ambition doesn’t drive you to do the right thing for your fellow man. It doesn’t make you unique, or creative, or better than anyone else.

Passion is the thing that can’t be taught. You can develop someone’s ambition, and we often do just that. But we cannot develop their passion. As investors, it’s always tempting for us to be sold on a founder’s ambition. But in the end, passion always wins, and our best startups are the ones doing things that only they can do best. Why? Because they love it. Because they couldn’t imagine doing anything else.

And if they make boatloads of money from it, I can virtually guarantee, it will be a side effect of that passion, not a result of their ambitions.

Either you have passion for something, or you don’t. If you’re thinking of starting a business, I can only encourage you: do something you really care about, even if that something isn’t sexy, or isn’t going to make you very rich. If you’re really good at it, then it will make you rich enough.

Neuron Soundware, StartupYard, Startup Roku

Exclusive Interview: Neuron Soundware Wins Yet Another Award

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Neuron Soundware: Winning Awards and Customers

Since leaving StartupYard in this year, Neuron Soundware has made “soundwaves” in the startup community in Europe, winning multiple awards, including Vodafone’s Idea of the Year, and now, this week, Ceska Sporitelna’s Startup of the Year.

The company has come a long way in a year– from a small team that was able to demonstrate, at SY Demo Day 2016, a machine learning algorithm that could learn to mimic a human actor, to a company that provides machine learning diagnostic software to large equipment operators. They’ve received considerable press coverage. Already, they count both Siemens and Deutsche Bahn among their customers. 

I caught up with Pavel Konecny, Co-Founder and CEO of Neuron Soundware, to talk about what the team has been through since leaving StartupYard, and where they’re going in the near future:

Hi Pavel, a lot has happened for Neuron Soundware since you left StartupYard. Can you tell us what you’ve been up to since the program?

Pavel Konecny, of NeuronSoundware, talks about machine learning and sound.

Pavel Konecny, of NeuronSoundware, talks about machine learning and sound.

We were very busy of course. We have presented Neuron Soundware at international startup and advance engineering conferences in US, UK, Germany and Czech Republic. We got a lot of contacts, which we are going to leverage. We are also proud that we found our first paying customers including companies such as Siemens and Deutsche Bahn.

What are you providing for those new customers?

We provide sound analytics algorithms as a service – an early warning of the coming mechanical issues of machines such as wind turbines, escalators, etc.

Towards the end of StartupYard 2016, your team decided to focus on diagnosing mechanical issues for machinery. Can you tell us a bit more about how this works?

Neuron Soundware - StartupYard Alumni

Complex machinery with moving parts always has multiple points of potential failure. There are basically two ways to solve that issue: either you wait until something breaks, or you proactively monitor the parts you know are likely to break, and fix them before they do.

Waiting for a failure can be expensive, and even dangerous. We can’t wait for an airplane engine to just stop working. You can’t have a printing press suddenly fail an hour before the trucks arrive. The loss in business alone makes it a major vulnerability.

Why can’t humans do this kind of work? Why is a machine more effective?

I’ll give you a real world example: just google “failed wind turbine”. You would find scores of different pictures and videos from all over the world. Wind turbines are giant and very fast moving machines. If the blade breaks a part in the full speed, you can find the pieces miles away and this can be quite dangerous. Preventing these events is a huge challenge.

Currently they do exhaustive physical checks. What we found was that sound, the sound of a machinery operating normally, or machinery nearing a failure, was a very important source of data that was not being employed fully.

Wind Turbine, Neuron Soundware

Photo Courtesy of Kyoto Prefecture, Japan

If you can understand a machine by the sounds it produces, you can reduce the risk of sudden failures, and increase the effectiveness of maintenance, since repairs are directed according to some available data about what’s working and what isn’t.

A machine learning algorithm can learn to connect data points that a human would ignore. A particular sound or a particular frequency may lead to a particular failure at a higher rate. Many of these tasks are above the capability of a human, who has a limited attention span, and limited memory.

There are also practical ways in which a machine is more effective: nobody can listen inside an airplane engine while it’s flying. Nobody can consistently diagnose a mechanical failure based on auditory clues that humans can’t actually detect. You need machines and machine learning for that, and that’s the breakthrough we’ve made.

How does Neuron Soundware learn?

Some issues can be simulated and some just appear time to time and you need to be ready to record them.

Hence we have developed our IoT device equipped with several types of microphones, which we use for the initial data collection. The device is mounted to the machines, continuously listening and transferring audio files to our central server. When we collect enough samples, we use them as an input to our learning algorithm. The machine health monitoring is done using the same IoT device.

You’ve now conducted some pilots as well, how was the experience, and what have you learned that surprised you and your team?

We were surprised several times of the effectiveness of deep learning technology. It works with all type of sounds. If we collect enough samples, we can achieve quality of recognition above 99.5%. And that would get even better as the system would collect more data.

Already, our approach can detect and diagnose mechanical faults that human diagnosticians cannot.

What has been Neuron Soundware’s biggest challenge since leaving StartupYard?

Neuron Soundware, Napad Roku, StartupYard

The Neuron Soundware team wins Vodafon’s Idea of the Year

We are travelling a lot. So the most of the communication happens via Slack and Hangouts. We meet in-person as the whole team only once or twice a week. That’s an intense time, when we need to sort-out a lot of items quickly. It was very refreshing, when (Co-Founder) Filip got married in October and we were all together and not discussing business matters. So we went to (3rd Co-founder) Pavel’s band’s concert last weekend as keeping friendly team spirit is very important to us.  

You recently recommended another deep-tech startup for our program. Why did you recommend StartupYard? What do you think has been the most positive outcome of acceleration for your team?

We would not be where we are now, without StartupYard. We started with a long list of ideas, where to apply AI technology, and we end-up with The idea of the Year (awarded by Vodafone Foundation)- and now Startup of the Year (from Ceska Sporitelna).

So we would like to thank again the many mentors we met during the first month of the program. It also changed our mindset in several ways: how to validate the business potential; how to pitch our product. Rather talk to people than flood them with documentation.

I used to start a meeting by passing out a complicated document, outlining everything I wanted people to know. What I learned along the way is that it’s equally important for people to get to know me and my team as people. Business is about making a personal connection- and that was an important lesson.

You’ve been talking with investors recently. What have you discovered during this process? What are you planning to do with the funds when you raise them?


It takes much longer than anticipated. They all stated how simple it is. It looks nice as starts with an interview, a short two page document. Then you follow with more meetings and committee board presentations, longer documents and the whole process of due diligence.

It is difficult to imagine, even for me, what we could be capable of doing in two or three years with our self-learning AI technology. And how much value and money we can make. We will use the investment to expand our business. With a larger development team, we could quicker complete the self-service sound analytics platform we are working on. That would make our business highly scalable and we could ramp-up our sales team.


Neuron Soundware’s core technology has a lot of interesting applications. Where do you see your team focusing its efforts within the next few years?

We are working on a way to combine effectively the different datasets we are collecting.

That would practically allow us to skip the phase of training as the neural network would be already pre-trained to recognize a wide set of potential issues. This is basically the way a human mind operates: you use past experiences to gain insight on new situations, even if they are very different. A machine can be taught to do the same thing, once given enough data.

The goal then, would be to start shipping a small smart IoT device in large volumes, ready to be used within any machine. Imagine a kind of silent digital mechanic, always sitting and monitoring complex equipment, all the time, and getting better, and better at the job every hour of every day. That’s really the future we are building with Neuron Soundware.