March 29, 2017
In this article, I discuss how the classic VRIN model can be used to evaluate modern web platforms.
It’s one of the most cited models of the resource-based view of the firm. Essentially, it describes how a firm can achieve sustainable competitive advantage through resources that fulfill certain criteria.
These criteria for resources that provide a sustainable competitive advantage are:
By gaining access to this type of resources, a firm can create a lasting competitive advantage. Note that this framework takes one perspective to strategy, i.e. the resource-based view. Alternative ones are e.g. Porter’s five forces and power-based frameworks, among many others.
The “resource” in resource-based view can be defined as some form of input which can be transformed into tangible or intangible output that provides utility or value in the market. In a competitive setting, a firm competes with its resources against other players; what resources it has and how it uses them are key variables in determining the competitive outcome, i.e. success or failure in the market.
In each business environment, there are certain resources that are particularly important. An orange juice factory, for example, requires different resources to be successful than a consulting business (the former needs a good supply of oranges, and the latter bright consultants; both rely on good customer relationships, though).
I first give a general overview of the VRIN dimensions in online context. This is done by comparing online environment with offline environment.
The term ‘value’ is tricky because of its definition: if we define it as something useful, we easily end up in a tautology (circular argument): a resource is valuable because it is useful for some party.
The specific resources for online platforms are discussed later on.
One of the key preoccupations in economic theory is scarcity: raw materials are scarce and firms need to compete over their exploitation.
Offline industries are characterized by rivalry – once oil is consumed, it cannot be reused. Knowledge products on the web, on the other hand, are described as non-rivalry products: if one consumer downloads an MP3 song, that does not remove the ability for another consumer to download as well (but if a consumer buys a snickers bar, there is one less for others to buy). Scarcity is usually associated to startups so that they are forced to innovate due to liability of smallness.
This deals with how well the business idea can be copied.
in “traditional” industries, such as manufacturing, patents and copyrights (IPR) are important. They protect firms against infringement and plagiarism. without them, every innovation could be easily copied which would quickly erode any competitive advantage. Intellectual property rights therefore enable the protection of “innovations” against imitation.
Imitation is less of a concern online. In most cases, the web technologies are public knowledge (e.g., open source). Even large players contribute to public domain. Therefore, rather than being something that competitors could not imitate, the emphasis on competition between web platforms tends to be on acquiring users rather than patents. (There are also other sources of resource advantage we’ll discuss later on.)
The difference between imitation and substitution is that in the former you are being copied whereas in the latter your product is being replaced by another solution. For example, Evernote can be replaced by paper and pen.
However, I would argue the source of resource advantage comes from something else than immunity of subsitution: after all, there are tens of search-engines and hundreds of social networks but still the giants overcome them.
‘Why’ is the question we’re going to examine next.
Here’s what I think is important:
Knowledge means holding the “smartest workers” – this is obviously a highly important resource. As Steve Jobs said, they’re not hiring smart people to tell them what to do, but so that the smart workers could tell Apple what to do.
Storage/server capacity is crucial for web firms. The more users they have, the more important this resource is in order to provide a reliable user experience.
Users are crucial given that the platform condition of critical mass is achieved. Critical mass is closely associated with network effects, meaning that the more there are users, the more valuable the platform is.
Content is important as well — content is a complement to content platforms, whereas users are complements of social platforms (for more on this typology, see my dissertation).
Complementors are antecedents to getting users or content – they are third parties that provide extensions to the core platform, and therefore add its usefulness to the users.
Algorithms are proprietary solutions platforms use to solve matching problems.
Company culture is a resource which can be turned into an efficient deployment machine.
A great company culture may be hard to imitate because its creation requires tacit knowledge.
Financing is an antecedent to acquiring other resources, such as the best team and storage capacity (although it’s not self-evident that money leads to functional a team, as examples in the web industry demonstrate).
Finally, location is important because can provide an access to a network of partner companies, high-quality employees and investors (think Silicon valley) that, again, are linked to the successful use of other resources.
A location is not a rare asset because it’s always possible to find an office space in a given city; similarly, you can follow where your competitors go.
What can be learned from this analysis?
First, the “value” in the VRIN framework is self-evident and not very useful in finding out differences between resources, UNLESS the list of resources is really wide and not industry-specific. That would be case when exploring the ; here, the list creation was
My list highlights intangible resources as a source of competitive advantage for web platforms. Based on this analysis, company culture is a resource the most compatible with the VRIN criteria.
Although it was argued that substitutability is less of a concern in online than offline, the risk of disruption touches equally well the dominant web platforms. Their large user base protects them against incremental innovations, but not against disruptive innovations. However, just as the concept of “value” has tautological nature, disruption is the same – disruptive innovation is disruptive because it has disrupted an industry – and this can only be stated in hindsight.
Of course, the best executives in the world have seen disruption beforehand, e.g. Schibstedt and digital transformation of publishing, but most companies, even big ones like Nokia have failed to do so.
Let’s take a look at the three big: Google, Facebook and eBay. Each one is a platform: Google combines searchers with websites (or, alternatively, advertisers with publisher websites (AdSense); or even more alternatively, advertisers with searchers (AdWords)), Facebook matches users to one another (one-sided platform) and advertisers with users (two-sided platform). eBay as an exchange platform matches buyers and sellers.
It would be useful to assess how well each of them score in the above resources and how the resources are understood in these companies.
I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: http://goo.gl/QRc11f
March 29, 2017
Many workers are concerned about “robotization” and “automatization” taking away their jobs. Also the media has been writing actively about this topic lately, as can be seen in publications such as New York Times and Forbes.
Although there is undoubtedly some dramatization in the scenarios created by the media, it is true that the trend of automatization took away manual jobs throughout the 20th century and has continued – perhaps even accelerated – in the 21st century.
Currently the jobs taken away by machines are manual labor, but what happens if machines take away knowledge labor as well? I think it’s important to consider this scenario, as most focus has been on the manual jobs, whereas the future disruption is more likely to take place in knowledge jobs.
This article discusses what’s next – in particular from the perspective of artificial intelligence (A.I.). I’ve been developing a theory about this topic for a while now. (It’s still unfinished, so I apologize the fuzziness of thought…)
My theory on development of job markets relies on two key assumptions:
The idea here is that while it is relatively easy to replace a job taken away by simple machines (sewing machines still need people to operate them), it is much harder to replace jobs taken away by complex machines (such as an A.I.) providing higher productivity. Consequently, less people are needed to perform the same tasks.
By “development cycles”, I refer to the drastic shift in job market productivity, i.e.
craftmanship –> industrial revolution –> information revolution –> A.I. revolution
Another assumption is that the labor skills follow the Gaussian curve. This means most people are best suited for manual jobs, while information economy requires skills that are at the upper end of that curve (the smartest and brightest).
In other words, the average worker will find it more and more difficult to add value in the job market, due to sophistication of the systems (a lot more learning is needed to add value than in manual jobs where the training requires a couple of days). Even currently, the majority of global workers best fit to manual labor rather than information economy jobs, and so some economies are at a major disadvantage (consider Greece vs. Germany).
Consistent to the previous definition, we can see the job market including two types of workers:
The former create the systems as their job, whereas the latter operate them as their job. For example, in the sphere of online advertising, Google’s engineers create the AdWords search-engine advertising platform, which is then used by online marketers doing campaigns for their clients. At the current information economy, the best situation is for workers who are able to create systems – i.e. their value-added is the greatest. With an A.I, however, both jobs can be overtaken by machine intelligence. This is the major threat to knowledge workers.
The replacement takes place due to what I call the errare humanum est -effect (disadvantage of humans vis-à-vis machines), according to which a machine is always superior to job tasks compared to human which is an erratic being controlled by biological constraints (e.g., need for food and sleep). Consequently, even the brightest humans will still lose to an A.I.
Consider these examples:
(Some of these figures are a bit outdated, but in general they serve to support my argument.)
Therefore, the ratio of workers vs. customers is much lower than in previous transitions. To build a car for one customer, you need tens of manufacturing workers. To serve customers in a super-market, the ratio needs to be something like 1:20 (otherwise queues become too long). But when the ratio is 1:1,000,000, not many people are needed to provide a service for the whole market.
As can be seen, the mobile application industry which has been touted as a source of new employment does indeed create new jobs , but it doesn’t create them for masses. This is because not many people are needed to succeed in this business environment.
Further disintermediation takes place when platforms talk to each other, forming super-ecosystems. Currently, this takes place though an API logic (application programming interface) which is a “dumb” logic, doing only prescribed tasks, but an A.I. would dramatically change the landscape by introducing creative logic in API-based applications.
Many professional services are on the line. Here are some I can think of.
1. Marketing managers
An A.I. can allocate budget and optimize campaigns far more efficiently than erroneous humans. The step from Google AdWords and Facebook Ads to automated marketing solutions is not that big – at the moment, the major advantage of humans is creativity, but the definition of an A.I. in this paper assumes creative functions.
An A.I. can recall all laws, find precedent cases instantly and give correct judgments. I recently had a discussion with one of my developer friends – he was particularly interested in applying A.I. into the law system – currently it’s too big for a human to comprehend, as there are thousands of laws, some of which contradict one another. An A.I. can quickly find contradicting laws and give all alternative interpretations. What is currently the human advantage is a sense of moral (right and wrong) which can be hard to replicate with an A.I.
An A.I. makes faster and more accurate diagnoses; a robot performs surgical operations without flaw. I would say many standard diagnoses by human doctors could be replaced by A.I. measuring the symptoms. There have been several cases of incorrect diagnoses due to hurry and the human error factor – as noted previously, an A.I. is immune to these limitations. The major human advantage is sympathy, although some doctors are missing even this.
4. Software developers
Even developers face extinction; upon learning the syntax, an A.I. will improve itself better than humans do. This would lead into exponentially accelerating increase of intellect, something commonly depicted in the A.I. development scenarios.
Basically, all knowledge professions if accessible to A.I. will be disrupted.
Actually, the only jobs left would be manual jobs – unless robots take them as well (there are some economic considerations against this scenario). I’m talking about low-level manual jobs – transportation, cleaning, maintenance, construction, etc. These require more physical material – due to aforementioned supply and demand dynamics, it may be that people are cheaper to “build” than robots, and therefore can still assume simple jobs.
At the other extreme, there are experience services offered by people to other people – massage, entertainment. These can remain based on the previous logic.
I can think of a couple of ways.
First, learn coding – i.e. talking to machines. people who understand their logic are in the position to add value — they have an access to the society of the future, whereas those who are unable to use systems get disadvantaged.
The best strategy for a worker in this environment is continuous learning and re-education. From the schooling system, this requires a complete re-shift in thinking – currently most universities are far behind in teaching practical skills. I notice this every day in my job as a university teacher – higher education must catch up, or it will completely lose its value.
Currently higher education is shielded by governments through official diplomas appreciated by recruiters, but true skills trump such an advantage in the long run. Already at this moment I’m advising my students to learn from MOOCs (massive open online courses) rather than relying on the education we give in my institution.
At a global scale, societies are currently facing two contrasting mega-trends:
It is not hard to see these are contrasting: less people are needed for the same produce, whereas more people are born, and thus need jobs. The increase of people is exponential, while the increase in productivity comes, according to my theory, in large shifts. A large shift is bad because before it takes place, everything seems normal. (It’s like a tsunami approaching – no way to know before it hits you.)
I can think of a couple of ways:
By adopting a Marxist approach, we can see there are two groups who are best off in this new world order:
Others, as argued previously, are at a disadvantage. The phenomenon is much similar to the concept of “digital divide” which can refer to 1) the difference of citizens from developed and developing countries’ access to technologies, or 2) the ability of the elderly vs. the younger to use modern technology (the latter have, for example, worse opportunities in high-tech job markets).
There are some relaxations to the arguments I’ve made. First, we need to consider that the increase of time people have as well as the general population increase create demand for services relating experiences and entertainment per se; yet, there needs to be consideration of re-distribution of wealth, as people who are unable to work need to consume to provide work for others (in other words, the service economy needs special support and encouragement from government vis-à-vis machine labor).
While it is a precious goal that everyone contribute in the society through work, the future may require a re-check on this protestant work ethic if indeed the supply of work drastically decreases. the major reason, in my opinion, behind the failure of policies reducing work hours such as the 35-hour work-week in France is that other countries besides these pioneers are not adopting them, and so they gain a comparative advantage in the global market. We are yet not in the stage where supply of labor is dramatically reduced at a global scale, but according to my theory we are getting there.
Secondly, a major relaxation, indeed, is that the systems can be usable by people who lack the understanding of their technical finesse. This method is already widely applied – very few understand the operating principles of the Internet, and yet can use it without difficulties. Even more complex professional systems, like Google AdWords, can be used without detailed understanding of the Google’s algorithm or Vickrey second-price sealed auctions.
So, dumbing things down is one way to go. The problem with this approach in the A.I. context is that when the system is smart enough to use itself, there is no need to dumb down – i.e., having humans use it would be a non-optimal use of resources. Already we can see this in some bidding algorithms in online advertising – the system optimizes better than people. At the moment we online marketers can add value through copywriting and other creative ways, but the upcoming A.I. would take away this advantage from us.
It is natural state of job markets that most workers are skilled only for manual labor or very simple machine work; if these jobs are lost, new way of organizing society is needed. Rather than fighting the change, societies should approach it objectively (which is probably one of the hardest things for human psychology).
My recommendations for the policy makers are as follows:
Because the problem of reducing job demand is not acute, these changes are unlikely to take place until there is no other choice (which is, by the way, the case for most political decision making).
Up to which point can the human labor be replaced? I call it the point of zero human when no humans are needed to produce an equal or larger output than what is being produced at an earlier point in time. The fortune of humans is that we are all the time producing more – if the production level was at the stage of 18th century, we would already be in the point of zero human. Therefore, job markets are not developing in a predictable way towards point of zero human, but it may nevertheless be a stochastic outcome of the current development rate of technology. Ultimately, time will tell. We are living exciting times.
March 29, 2017
I’ve been doing a lot of Facebook advertising. Compared to Google AdWords, Facebook Ads is missing a lot of features, and has annoying bugs. I’m listing these problems here, in case anyone working at Facebook would like to have an advertiser’s opinion, and that people working with programmatic ad platforms see how difficult it is to create — if not perfect, then at least a satisfactory system.
A caveat: although I’m updating the list from time to time, it might be some bugs are already corrected and the missing features added. The ones fixed have been pointed out by strike-through.
Acknowledgments: A big thanks goes to Mr. Tommi Salenius, who is my right hand in digital marketing.
Problems in Page Insights:
Want to contribute? Send me bugs and/or missing features and I’ll list them here.
Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.
Contact email: [email protected]
March 29, 2017
I keep forgetting this stuff, so noting it down for myself (and others).
1. Don’t ask “would you” questions, ask “did you” questions. People are unable to predict their behavior.
2. Don’t ask about your product, ask about their problem. Wrong question: “We have this product A – would you use it?”. Right question: “Do you ever have this problem B?” [that you think the product A will solve]
3. Only in the very end introduce your solution. Then ask openly what he or she thinks about it: “What do you see problematic about it?” Also ask if they know someone who would like this solution.
4. Listen, don’t pitch. Pitching is for other times – you DON’T need to sell your product to this person, you only need to hear about his or her life.
5. Repeat what he or she says – many times people think they understand what the other person is saying, but they don’t. Only by repeating with your own words and getting them to nod “That’s right” you can make sure you got it.
6. Make notes – obviously. You don’t want to forget, but without notes you will.
7. Make “many” interviews. Many = as long as you notice there are no more new insights. In research, this is called saturation. You want to reach saturation and make sure you’ve identified the major patterns.
8. Avoid loaded questions. False: “Is this design good?” Correct: “What do you think of this design?”
9. Avoid yes/no questions. What would you learn from them? Nothing.
10. Focus more on disproving your idea rather than validating it. In philosophy of science, this is called falsificationism. It means not claim can be proved absolutely true, but every claim can be proved wrong. Rather than wanting to prove yourself right (at the risk of making a false positive), you want to prove yourself wrong and avoid wasting time on a bad idea. Remember: most startup ideas suck (it’s true – I’ve seen hundreds, and most will never amount to business – be very very critical about your idea).
As hinted in the previous, customer developing is like doing real research. You want to avoid false positives – i.e., getting the impression your idea is good although it sucks; and false negatives which is to conclude the idea is bad although in reality it’s not.
In general, you want to avoid respondent bias, recall bias, and confirmation bias. These are fancy names meaning that you want people to tell you honestly what they think, and you want to interpret it in an objective way, not being too fixed on your initial assumption (i.e., hypothesis). Be ready to change your opinion, like Gandhi advised.
About non-interview methods, i.e. testing via landing pages.
a. Force customers to pay from the beginning – this way you see if the thing has value to anyone.
b. Needless to say: MVP. Create first the non-scalable, bare minimum solution. This is not even a product, it’s a service. Use manual labor over technology and get the user information through free tools like Google Forms.
c. If you get a high dropout, you need to make sure people understand the USP. For this, you CAN ask your friends’ opinions: “Do you get it?” But prefer friends without prior knowledge on the project, because they have fresh eyes.
Before conducting any interviews or tests, do some market research based on facts. Yes, I know Steve Blank says to “go out of the building” straight away and forget about traditional market research, but he’s not a marketing expert. Think a bit before you fly out the door: Who are your customers? Why them? Do they have money? Do they want to buy from you? etc.
You can use this spreadsheet for segmentation (not my doing, just copied it from Sixteen Ventures):
Example questions from Cindy Alvarez:
More points from Cindy (she’s a real specialist):
Here are some useful links:
If you have to read one book about this topic, read this one: http://www.amazon.com/Interviewing-Users-Uncover-Compelling-Insights-ebook
If you want to read another book, then it’s this one: http://www.amazon.com/Lean-Customer-Development-Building-Customers-ebook
If you need to read a third book, then you should stop doing a startup and become a researcher 🙂
March 29, 2017
Here’s an example on how to do PR for a crowdfunding campaign. It should be sent at least a couple of weeks prior to launch.
this is [yourname] from [yourcompany].
We are preparing to release a new product in [yourplatform], and I wanted to give you heads-up since you wrote about [a competitor] six months ago. Our product is similar, but better 😉
Here’s why it is better:
Here’s a link to press material including pictures and more information: [link]
The campaign will be launched on [date], so I hope you’d publish an article about us at around that time.
In the meantime, I’m of course available for any questions / comments!
Have a nice day,
[yourname] from [www.yourwebsite.com]