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Using the VRIN model to evaluate web platforms


In this article, I discuss how the classic VRIN model can be used to evaluate modern web platforms.

What is the VRIN model?

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:

  • valuable
  • rare
  • imperfectly imitable
  • non-substitutable

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.

How it applies to web platforms?

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).

So, what kind of resources are relevant for online platforms?

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.

  • critical for offline: yes (but which resources?)
  • critical for online: yes (but which resources?)

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.

  • critical for offline: yes
  • critical for online: no

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.

  • critical for offline: yes
  • critical for online: no

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.

  • critical for offline: yes (depends on the case though)
  • not so critical for offline: yes (see the example of Evernote)

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.

Important resources for online platforms

Here’s what I think is important:

  1. knowledge
  2. storage/server capacity
  3. users
  4. content
  5. complementors
  6. algorithms
  7. company culture
  8. financing
  9. HQ location

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.

  • valuable: yes
  • rare: no (comes in abundance)
  • imperfectly imitable: no
  • non-substitutable: yes

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.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

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.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

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).

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: yes

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.

  • valuable: yes
  • rare: no (depends)
  • imperfectly imitable: yes
  • non-substitutable: no (can be replaced by in-house activities)

Algorithms are proprietary solutions platforms use to solve matching problems.

  • valuable: yes
  • rare: no (depends)
  • imperfectly imitable: no
  • non-substitutable: yes

Company culture is a resource which can be turned into an efficient deployment machine.

  • valuable: yes
  • rare: yes
  • imperfectly imitable: yes
  • non-substitutable: yes

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).

  • valuable: yes
  • rare: no (for good businesses)
  • imperfectly imitable: no
  • non-substitutable: no (bootstrapping)

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.

  • valuable: yes
  • rare: no
  • imperfectly imitable: no
  • non-substitutable: no

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.

How to go deeper

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:

The difference between business logic and strategy


I started thinking this question today when reading my students’ exam answers. The questions was “Define business logic and give an example of it”, and many answers actually defined strategy. At that point, I realized it’s not so easy to see a difference between these two concepts.

So, what would I see as the main difference between strategy and business logic?

What is business strategy?

First, strategy in my opinion involves competition – it’s firm-related decision-making in which we try to gain a competitive advantage, i.e. apply a strategy that helps us win; or, more particularly, to achieve a goal, such as grabbing market share, become profitable, grow, etc. Hence, strategy is closely associated with reaching a pre-defined goal – in company terms, we usually set a vision of where we want to take the company in a certain time-frame (say five years from now), and then create an overall strategy that should take us towards that ideal state. When the firm’s vision is based on some shared principles or values, this is called mission.

As a concept, strategy is much older than business logic and has its roots in military thinking (hence the competitive dimension). For example, Ceaser, Napoleon and Clausewitz are seen as classics of strategy.

What is business logic?

Business logic, then again, would be a description of “why” — why are customers paying us money? It’s much more focused on value / benefit / utility than strategy. I would say business logic is an explanation as to why an organization can remain viable – e.g., it can transform some form of resources (raw material) into output (products). Or, it can be based on exploiting people’s vice (such as the Finnish liquor monopoly Alko) or market inefficiencies, or it can create markets for other players (e.g. Google AdWords).

It seems the two concept involve some overlap – the description of business logic approach strategy when we think how the firm combines resources to produce something customers perceive attractive enough to buy. I’d also say both are applicable to many organizations, not just firms – consider a university, for example. The strategy of a university revolves around ways of attracting the best students and teachers (it’s like a two-sided market), but its business logic is to transform education resources into courses and monetize that either through tuition fees (e.g. US) or state money (e.g. Finland).

As I said to my students, it’s an eye-opening experience when you start seeing either of these concepts “bare” — at that point you truly understand the core of particular choices firms make, and why things are the way they are.


In sum, I’d say strategy is a barebone description of how to compete in a market, whereas business logic is a barebone description of how to make money. If both were games, strategy would be Risk and business logic Monopoly.

What do you think? Please share your thoughts on the topic!

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas:

How to use Facebook in marketing segmentation?


This article discusses the potential of segmentation in Facebook advertising.

Why is segmentation needed?

Segmentation is one of the most fundamental concepts in marketing. Its goal is to identify the best match between the firm’s offering and the market, i.e. find a sub-set of customers who are most likely to buy the product and who therefore can be targeted cost-effectively by means of niche marketing rather than mass marketing.

There are some premises as to why segmentation works:

  • Not all buyers are alike.
  • Sub-groups of people with similar behavior, backgrounds, values and needs can be identified.
  • The sub-groups will be smaller and more homogeneous than the market as a whole.
  • It is easier to satisfy a small group of similar customers than to try to satisfy large groups of dissimilar customers.

(The list if a direct citation from the Essentials of Marketing by Jim Blythe, p. 76.)

While segmentation is about dividing the overall market into smaller pieces (segments), targeting is about selecting the appropriate marketing channels to reach those customer segments. Finally, positioning deals with message formulation in the attempt of positioning the firm and its offerings relative to competitors (e.g., cheaper, better quality). This is the basic marketing model called STP (segmentation, targeting, positioning).

How to apply segmentation in Facebook?

I will next discuss three stages of Facebook campaign creation.

1. Before the campaign

There are a few options for creation of basic segments.

  • generate marketing personas (advantage: makes you assume customer perspective; weakness: vulnerable to marketer’s intuition, i.e. tendency to assume you know your customer whereas in reality you don’t)
  • conduct market research (advantage: suited to your particular case; weakness: costly and takes time)
  • buy consumer research reports (advantage: large sample sizes, comprehensive; weakness: the reports tend to be very general)
  • use Facebook Audience Insights (advantage: specific to Facebook; weakness: gives little behavioral data)

The existence of weaknesses is okay – the whole of point of segmentation is to gather REAL data which is stronger thana priori assumptions.

Based on the insights you’ve gathered, create Saved target groups in Facebook. These incorporate the segments you want to target for. If you are using an ad management tool such as Smartly, you can split audiences into smaller micro-segments e.g. by age, gender and location. Say you have a general segment of Women aged 25-50; you could split it into the following micro-segments by using an interval of five years:

  • women 25-30
  • women 31-36
  • women 37-42
  • women 43-48

The advantage of micro-segments is more granular segmentation; however, the risk is going too granular while ignoring the real-world reason for differences (sometimes the performance difference between two micro-segments is just statistical noise).

After creating the segments in Facebook (reflected in Saved target groups), you want to test how they perform — so as to see how well your assumptions on the effectiveness of these segments are working. For this, create campaigns and let them run. In Power Editor, go to the Custom audiences (select from the sliding menu), select the segments you want to test and choose to create new ad groups. (See, now we have moved from segmentation into targeting, which is the natural step in the STP model.)

NB! If you particularly want to test customer segments, keep everything else (campaign settings, creatives) the same. In Power Editor, this is fairly simple to execute by copy-pasting the creatives between ad groups. This reduces the risk that the performance differences between various segments are a result of some other factor than targeting. Finally, name the ad sets to reflect the segment you are testing (e.g. Women 25-31).

2. During the campaign

After a week or so, go back to check the results. Since you’ve named the segments appropriately, you can quickly see the performance differences between the segments. To make sure the differences are statistically valid (if you are not using a tool such as Smartly), use a calculator to determine the statistical significance. I created one which can be downloaded here.

When interpreting results, remember that the outcome is a combination of segment and message (and that the message is a combination of substance and tone, i.e. what is said and how it is said). In other words,

Result = segment x message, in which message = substance x tone, so that
Result = segment x (substance x tone)

Therefore, as you change the message, it reflects to performance across various segments. This means that you are not actually testing the suitability of your product to the segment (which is what segmentation and targeting is all about), but the match between the message and the target audience. Although this may seem like semantics, it’s actually pretty important. You want to make sure you’re not getting a misleading response from your segment due to issues in message formulation (i.e. talking to them in a “wrong way”), and so you want to make sure it reflects the product as well as possible. Ideally, you’d want to tailor your message based on your ideas of the segment, BUT this is prohibited in the early stage because we want to make sure the message formulation does not interfere with the testing of segment performance.

How to solve this problem, then? Three ways: first, make sure the segments you are testing are not too far apart – i.e. women aged 17 and men aged 45 subjected to the same message can create issues. Second, try to formulate a general message to begin with, so it doesn’t exclude any segments. Third, you could of course make slight modifications to the message while testing the segments — here I would still keep the substance (e.g. cheap price) stable across segments while maybe changing the tone (e.g. type of words used) depending on the audience – for example, older people are usually addressed in a different tone than the younger audience (yo!).

Finally, one extra tip! If you want more granular data on how different groups within your segment have performed, go to Ad reports and check out the data breakdowns. There is a wealth of information there which can be used in creating further micro-segments.

3. After the campaign

What to do when you know which segments are the most profitable? Well, take the results you’ve got and generalize them into your other marketing activities. For example, when you’re buying print ads ask for demographic data they have on readers — it has to be accurate and based on research, not guesses — and choose the media that matches the best performing segments according to your Facebook data. In my opinion, there is no major reason to assume that people in the same segment would act differently in Facebook and elsewhere (strictly speaking, the only potential issue I can think of is that Facebook-people are more “advanced” in their technology use than offline-people, but this is generally a small problem since such a large share of population in most markets are users of Facebook).

There you go – hopefully this article has given you some useful ideas on the relationship between segmentation and Facebook advertising!

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas:

A.I. – the next industrial revolution?


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…)

 Theory on development of job markets

My theory on development of job markets relies on two key assumptions:

  1. with each development cycle, less people are needed
  2. and the more difficult is for average people to add value

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:

  • workers who create
  • workers who operate

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:

  • Facebook has one programmer per 1.2 million users [1] and one employee per 249,000 users [2]
  • Rovio has one employee per 507,000 gamers [3]
  • Pinterest has one employee per 400,000 users [2]
  • Supercell have one employee per 193,000 gamers [4]
  • Twitter has one employee per 79,000 users [5]
  • Linkedin has one employee per 47,000 users [6]

(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 [7], 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.

Which jobs will an A.I. disrupt?

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.

2. Lawyers 

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.

3. Doctors 

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.

Which jobs will remain?

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.

How can workers prepare?

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.

What are the implications for the society?

At a global scale, societies are currently facing two contrasting mega-trends:

  • the increase of productivity through automatization (= lower demand for labor)
  • the increase of population (= higher supply of labor) (everyone has seen the graph showing population growth starting from 19th century [8])

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.)

What are the scenarios to solve the mega-trend contradiction?

I can think of a couple of ways:

  1. Marxist approach – redistribution of wealth and re-discovery of “job”
  2. WYSIWYG approach – making the systems as easy as possible

By adopting a Marxist approach, we can see there are two groups who are best off in this new world order:

  • The owners of the best A.I. (system capital)
  • The people with capacity to use and develop A.I. further (knowledge capital)

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:

  • decrease cost of human labor (e.g., in Finland sometime in the 70s services were exempted from taxes – this scenario should help)
  • reduce employment costs – the situation is in fact perverse, as companies are penalized through side costs if they recruit workers. In a society where demand of labor is scarce, the reverse needs to take place: companies that recruit need to be rewarded.
  • retain/introduce monetary transfers à la welfare societies – because labor is not enough for everyone, the state needs to pass money from capital holders to underprivileged. The Nordic states are closer to a working model than more capitalistic states such as the United States.
  • push education system changes – because skills required in the job market are more advanced and more in flux than previously, the curriculum substance needs to change faster than it currently does. Unnecessary learning should be eliminated, while focusing on key skills needed in the job market at the moment, and creating further education paths to lifelong learning.

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).

Open questions

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.










Briiffi mediamyyjien kanssa toimimiseen

Jokainen yrittäjä ja markkinointipäällikkö joutuu puhelinmyynnin kohteeksi. Siinä missä yksityishenkilöille kaupataan kaikkea lehdistä boksereihin, bisnesmarkkinoilla yleisin kauppatavara on mainostila eli media.

Ensimmäinen vinkki: Suurin osa mediamyyjien tarjouksista on huonoja, eikä niihin kannata lähteä mukaan. Tässä artikkelissa avaan tekijöitä, joihin itse kiinnitän huomiota markkinointipäällikkönä toimiessani.

Miten siis toimia mediamyyjien kanssa?

1. Selvitä todellinen maantieteellinen näkyvyys

Millä paikkakunnilla ja missä paikoissa mainos näkyy? Tämän kriteerin hyödyllisyyteen luonnollisesti vaikuttaa se, haetaanko valtakunnallista vai paikallista näkyvyyttä. Joskus kohdistettu paikallinen täsmäisku on parempi vaihtoehto, mutta tällöinkin pitää tietää tarkasti jakelu – esim. pääkaupunkiseudulla sillä on useimmissa tapauksissa merkitystä jaetaanko pelkästään Espooseen tai Vantaalle, tai molempiin.

2. Selvitä todellinen näkyvyys paikan päällä

Missä mainokset tarkalleen ottaen näkyvät (esim. ostoskeskuksen sisällä tai golf-kentällä)? Joskus sijoittelu on huono: piilossa tai kaukana kulkureitiltä taikka klutteroituneena (=hukkuneena muihin mainospaikkoihin). Selvitä myös kuinka monta muuta mainostajaa on? Joskus niitä on monta ja sitten yksittäisen mainostajan huomioarvo laskee. Mitä enemmän muita mainostajia, sen pienempi on sinun huomioarvosi.

Todellisen näkyvyyden selvittämiseksi kannattaa vierailla paikan päällä. Jos se ei ole mahdollista, kannattaa pyytää kuvia mainosten sijoittelusta ja niiden välittömästä ympäristöstä.

3. Selvitä mainosformaatti

Tärkein kriteeri on koko – isompi parempi. Toinen kysymys: onko formaatti digitaalinen? Digitaalisessa on hyvää mahdollisuus liikkuvaan kuvaan; huonoa taas rotatointi, eli voi olla monta mainostajaa – tällöin selvitä kuinka monta. Analogisessa eli paperi tms. ratkaisussa sinä omistat koko pinnan, rotatoivassa digitoteutuksessa jaat sen muiden kanssa (näitä voi olla esim. 10 kpl, silloin näkyvyys on 1/10). Jälleen: Mitä enemmän muita mainostajia digitaulussa on, sen pienempi on sinun näkyvyytesi.

4. Selvitä todellinen kohderyhmä

Onko mediamyyjällä tarkempaa tietoa mainoksen näkijöistä? Tyypillistä, aivan täysin tavallista, on se, että mediamyyjän mukaan tämä on JUURI OIKEA kohderyhmä. Yksikään mediamyyjä ei ole koskaan minulle sanonut, että kohderyhmä on väärä; kaikkien mielestä se on aina oikea. Tämä on vähän huono myyntitaktiikka, mutta näin ne yleensä toimivat. Tarkemmin yksityiskohtia kysymällä voi kuitenkin kriittisesti päästä selvyyteen asiasta – jotkut kohderyhmät vain ovat parempia.

5. Selvitä todellinen hinta

Tämän operaation tarkoituksena on verrata mediakustannusta muihin kanaviin. Esimerkiksi meillä ElämysLahjoissa saamme Facebookissa neljä miljoonaa näyttökertaa runsaalla kolmellasadalla (CPM ~8 senttiä); kävijöistä Googlen kautta maksamme n. 20 senttiä; näitä lukuja pitää verrata mediamyyjän tarjoaman median yksikkölukuihin. Lopputuloksena “halpa” absoluuttinen hinta voikin olla suhteessa kallis ja vastaavasti paljon euroja maksava kampanja liidikohtaisesti edullinen.

Sen sijaan pelkälle “näkyvyydelle” ei kannata antaa suurta preemiota – jos Kauppalehden etusivun bänneri maksaa 25 € per tuhat näyttökerta ja Facebook-mainos 1 € per tuhat näyttökerta, ei Kauppalehden mainos varmasti tule olemaan 25 kertaa tehokkaampi (jos et usko, kokeile niin näet). Facebookin ja Googlen hintoihin eivät useimmat suomalaiset mediat vain pysty – silloin niitä ei kannata valita.


Vielä lopuksi vinkki mediamyyjille (jos heitä on lukijoiden joukossa): Olkaa kiinnostuneita mittaamisesta. Yksikään mediamyyjä, joka minulle on soittanut, ei nimittäin ole ollut. Joskus ne kysyvät “Miten kampanja meni?” ja sitten kun kerron. Tämä on sellainen asia, jossa analogiset mediamyyjät häviävät digille, ja se täytyy ottaa tosissaan. Rakenna asiakkaiden kanssa siis tapoja mittaamiseen sen sijaan että sanoisit “ei voi mitata”. Sellaiselta mediamyyjältä en ainakaan itse osta mitään.

Kirjoittaja opettaa digitaalista markkinointia Turun kauppakorkeakoulussa ja toimii ElämysLahjojen markkinointipäällikkönä.

The Digital Marketing Brief – four things to ask your client

Recently I had an email correspondence with one my brightest digital marketing students. He asked for advice on creating an AdWords campaign plan.

I told him the plan should include certain elements, and only them (it’s easy to make a long and useless plan, and difficult to do it short and useful).

Anyway, in the process I also told him how to make sure he gets the necessary information from the client. These four things I’d like to share with everyone looking for a crystal-clear marketing brief.

They are:

1. campaign goal
2. target group
3. budget
4. duration

First, you want to know the client’s goal. In general, it can direct response (sales) or indirect response (awareness). This affects two things:

  • metrics you include as your KPIs — in other words, will you optimize for impressions, clicks, or conversions.
  • channels you include — if the client wants direct response, search-engine advertising is usually more effective than social media (and vice versa).

The channel selection is the first thing to include into your campaign plan.

Second, you want the client’s understanding of the target group. This affects targeting – in search-engine advertising it’s the keywords you choose; in social media advertising it’s the demographic targeting; in display it’s the managed placements.

Based on this information, you want to make a list (of keywords / placements / demographic types). These targeting elements are the second thing to include into your campaign plan.

Third, the budget matters a great deal. It affects two things:

  • how many channels to choose
  • how to set daily budgets

The bigger the budget is, the more channels can be included in the campaign plan. It’s not always linear, however; e.g. when search volumes are high and the goal is direct response, it makes most sense to spend all on search. But generally, it’s possible to target several stages in customers’ purchase funnel (i.e., stages they go through prior to conversion).

Hence, the budget spend is the third thing to include into your campaign plan.

The daily budget you calculate by dividing the total budget with the number of channels and the duration (in days) of the campaign. At this point, you can allocate the budget in different ways, e.g. search = 2xsocial. It’s important to notice that in social and display you can usually spend as much money as you want, because the available ad inventory is in effect unlimited. But in search the spend is curbed by natural search volumes.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas:

The Bounce Problem: How to Track Bounce in Simple Landing Pages


This post applies to cases satisfying two conditions.

First, you have a simple landing page designed for immediate action (=no further clicks). This can be the case for many marketing campaigns for which we design a landing page without navigation and a very simple goal, such as learning about a product or watching a video.

Second, you have a high bounce rate, indicating a bad user experience. Bounce rate is calculated as follows:

visitors who leave without clicking further / all visitors

Why does high bounce indicate bad user experience?

It’s a proxy for it. A high bounce rate simply means a lot of people leave the website without clicking further. This usually indicates bad relevance: the user was expecting something else, didn’t find, and so leaves the site immediately.

For search engines a high bounce rate indicates bad landing page relevance vis-à-vis a given search query (keyword), as the user immediately returns to the SERP (search-engine result page). Search engines, such as Google, would like to offer the right solution for a given search query as fast as possible to please their users, and therefore a poor landing page experience may lead to lower ranking for a given website in Google.

The bounce problem

I’ll give a simple example. Say you have a landing page with only one call-to-action, such as viewing a video. You then have a marketing campaign resulting to ten visitors. After viewing the video, all ten users leave the site.

Now, Google Analytics would record this as 100% bounce rate; everyone left without clicking further. Moreover, the duration of the visits would be recorded as 0:00, since the duration is only stored after a user clicks further (which didn’t happen in this case).

So, what should we conclude as site owners when looking at our statistics? 100% bounce: that means either that a) our site sucks or b) the channel we acquired the visitors from sucks. But, in the previous case it’s an incorrect conclusion; all of the users watched the video and so the landing page (and marketing campaign associated with it) was in fact a great success!

How to solve the bounce problem

I will show four solutions to improve your measurement of user experience through bounce rate.

First, simply create an event that pings your analytics software (most typically Google Analytics) when a user makes a desired on-page action (e.g. video viewing). This removes users who completed a desired action but still left without clicking further from the bounce rate calculation.

Here are Google’s instructions for event tracking.

Second, ping GA based on visit duration, e.g. create an event of spending one minute on the page. This will in effect lower your reported bounce rate by degree of users who stay at least a minute on the landing page.

Third, create a form. Filling a form directs the user to another site which then triggers an event for analytics. In most cases, this is also compatible with our condition of a simple landing page with one CTA (well, if you have a video and a form that’s two actions for a user, but in most cases I’d say it’s not too much).

Finally, there is a really cool Analytics plugin by Rob Flaherty called Scrolldepth (thanks Tatu Patronen for the tip!). It pings Google Analytics as users scroll down the page, e.g. by 25%, 75% and 100% intervals. In addition to solving the bounce problem, it also gives you more data on user behavior.


Note that adding event tracking to reduce bounce rate only reduces it in your analytics. Search-engines still see bounce as direct exits, and may include that in their evaluation of landing page experience. Moreover, individual solutions have limitations – creation of a form is not always natural given the business, or it may create additional incentive for the user; and Scrolldepth is most useful in lengthy landing pages, which is not always the case.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas:

Assessing the scalability of AdWords campaigns


Startups, and why not bigger companies, too, often test marketing channels by allocating a small budget to each channel, and then analyzing the results (e.g. CPA, cost per action) per channel.

This is done to determine a) business potential and b) channel potential. The former refers to how lucrative it is to acquire customers given their lifetime value, and the latter to how well each channel performs.


However, there is one major issue: scaling. It means that when we pour x dollars into the marketing channel in the test phase and get CPA of y dollars, will the CPA remain the same when we increase the budget to x+z dollars (say hundred times more)?

This issue can be tackled by acquiring enough data for statistical significance. This gives us confidence that the results will be similar once the budget is increased.

In AdWords, however, the scaling problem takes another form: the natural limitation of search volumes. By this I mean that at any given time, only a select number of customers are looking for a specific topic. Contrary to Facebook which has de facto an unlimited ad inventory (billions of ad impressions), Google only has a limited (although very large) ad inventory.


Here’s how to assess the scalability of AdWords campaigns:

1. Go to campaign view
2. Enable column called “Search impression share” (Modify columns –> Competitive metrics)

This will tell you how many searchers saw your ad out of all who could have seen it (this is influenced by your daily budget and bid).

In general, you want impression share to be as high as possible, given that the campaign ROI is positive. So, in general >80% is good, <10% is bad. (The exception is when running a long-tail strategy aiming for low-cost clicks, in which case <10% is okay.)

3. Calculate the scalability as follows:

scalability = clicks / impression share

For example, if you have an impression share of 40 % with which you’ve accumulated 500 clicks, by increasing your budget and bids so that you are able to capture 100% impression share, you will accumulate 1250 clicks (=500/0,40) which is the full potential of this campaign.


Note that the formula assumes the CTR remains constant. Additionally, increasing bids may increase your CPA, so improving quality score through better ads and relevance is important to offset this effect.

The ROI of Academic Publishing

Problem of ROI in publishing

The return on investment (ROI) of academic publishing is absolutely terrible.

Think of it – thousands of hours spent correcting formatting, spellings, rephrasing, and so on. All this after the actual insight of the research has been accomplished. In all seriousness, 10% of time spent doing research and 90% writing and rewriting cannot be thought of anything else but waste.

Why should we care?

The inefficiency of the current way of doing it – as in combining doing research and writing about it under the same name of “doing research” – is horrible waste of intelligence and human resources. It inflates the cost of doing research, and also makes scientific progress slower than if 90% was spent on research and 10% on writing.

Root cause

Some might say it’s a perverse outcome of letting staff go – nowadays even professors have to do everything  by themselves because there is so few assistants and administrators. Why is this perverse? Because at the same time more people need work. It’s also perverse, or paradoxical, because letting the help go is done to increase efficiency but in the end it actually decreases efficiency as the research staff shifts their use of time from doing research to fixing spelling errors. There is a large misunderstanding that letting people go would lead to better efficiency – it may save costs but exactly at the cost of efficiency.

My experiences

The thought for this article came to mind when me and my colleague received yet again some minor edit requests for an article to be published in a book – the book material was ready already last year, but all these people are working to fix small minor details that add zero substance value. What a waste!

And I’m not alone in this situation; most if not all academics face the same problem.


Two solutions readily come to mind:

  • report the data and that’s it
  • use editors to fix all minor errors instead of forcing the high-thinkers to waste their time on it

The latter one is much better, as the first option misses the importance of interpreting the results and theorizing from them (the whole point of doing research).

What is ROI of research?

Efficiency, such as ROI of research, should be defined as learning more about the world. This will never be accomplished by writing reports but going out to the world. At the same time, I don’t mean to undermine basic research – the ROI of research is not the same as its immediate usefulness, let alone its immediate economic potential. ROI in my argument simply refers to the ratio of doing research vs. writing about it, not the actual quality of the outcome.

The author works as a university teacher in the Turku School of Economics

Startup syndromes: “The Iznogoud Syndrome”

1. Definition

The Iznogoud Syndrome can be defined as follows:

A startup strives to disrupt existing market structures instead of adapting to them.

In most industries, existing relationships are strong, cemented and will not change due to one startup. Therefore, a better strategy is to find ways of providing utility in the existing ecosystem.

2. Origins

The name of this startup syndrome is based on the French comic character who wants to “become Caliph instead of the Caliph“, and continuously fails in that (over-ambitious) attempt. Much similarly, many startups are over-ambitious in their attempt to succeed. In my experience, they have an idealistic worldview while lacking a realistic perspective on the business landscape. While this works for some outliers – for example Steve Jobs – better results can be achieved with a realistic worldview on average. The world is driven by probabilities and hence it’s better to target averages than outliers.

3. Examples

I see them all the time. Most startups I advise in startup courses and events aim at disintermediation: they want to remove vendors from the market and replace them. For example, a startup wanted to remove recruiting agencies by making their own recruiting platform. Since recruiting agencies already have the customer relationships, it’s an unrealistic scenario. What upset me was that the team didn’t even consider providing value to the recruiting agencies, but intuitively saw them as junk to be replaced.

Another example: there is a local dominant service providing information on dance events, which holds something like 90% of market (everyone uses it). Yet, it has major usability issues. Instead of partnering with the current market leader to fix their problems, the startup wants to create its competing platform from scratch and then “steal” all users. That’s an unrealistic scenario. All around, there is too much emphasis put on disintermediation and seeing current market operators either as waste or competitors as oppose to potential partners in user acquisition, distribution or whatever.

Startups should realize they are not alone in the market, but the market has been there for a hundred years. They cannot just show up and say “hey, I’m going to change how you’ve done business for 100 years.” Or they can, but they will most likely fail. This is all well for the industry in which it doesn’t matter if 9 out of 10 fail, as the one winning brings the profits, but for an individual startup it makes more sense to get the odds of success (even average one) greater. So you see, what is good for the startup industry in general is not the same as what is good for your startup in particular.

4. Similarity to other startup syndromes

The Iznogoud syndrome is similar to “Market education syndrome”, according to which an innovation created by the startup falls short in consumer adoption regardless of its technical quality – many VC’s avoid products requiring considerable market education costs. Whereas the Market education syndrome can be seen a particular issue in B2C markets, the Iznogoud syndrome is more acute in B2B markets.

5. Recommendations

Simply put, startups should learn more about their customers or clients. They need to understand their business logic (B2B) or daily routines (B2C) and how value can be provided there. In B2B markets, there are generally two ways to provide value for clients:

  • help them sell more
  • help them cut costs

If you do so, potential clients are more likely to listen. As stated previously, this is a more realistic scenario in doing business than thinking ways of replacing them.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: