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Tag: platforms

Organic reach and the choice of social media platform

(This is work in progress.)

Introduction

It is a well-established fact that the organic reach in a dominant platform decreases over time, as the competition over users’ attention increases. There is thus an inverse relation:

The more competition (by users and firms) in a user’s news feed, the less organic visibility for a firm.

The problem

How would a firm willing to engage in a social media activity approach this matter?

In particular,

  • how should it divide its time and marketing efforts between alternative platforms?
  • when does it make sense for it to diversify?

The analysis

The formula behind the decision is u * o, in which

u = fan base
o = organic reach

  • all else equal, the larger the organic reach, the better
  • all else equal, the larger the fan base, the better

But, even in a drastically smaller platform a large o can offset the relative fan base advantage.

For example, consider a firm has presence in two platforms.

platform A
500M users, 5,000 fans

platform B
10,000 users, 100 fans

By first look, it would make sense to invest time and effort in platform A, given that both the overall user base as well as the fan base are significantly larger. However, now consider the inclusion of factor o.

platform A
500M users, 5,000 fans
organic reach 1% = 50 users

platform B
10,000 users, 100 fans
organic reach 90% = 90 users

It now makes sense to shift its social media activities to platform B, as it gives better return on investment in terms of gained reach.

(it is assumed here that post-click actions are directly proportional to the amount of website traffic, and thus do not interfere in the return calculation).

Conclusion

More generally,

as organic reach decreases in platform A, platform B with relatively better organic visibility becomes more feasible

Implications

Firms are advised to consider their social media investments in the light of organic reach, and not be fooled by vanity metrics such as the total user base of a platform. Relative metrics, such as share of organic visibility matter more.

Entrant platforms can encourage switching behavior by promising firms larger degree of organic reach. At early stages this does not compromise utility of the users, as their news feeds are not yet cluttered. However, as the entrant platform matures and gains popularity, it will have an incentive of decreasing organic reach.

This effect may partially explain why a dominant platform position is never secure; entrants can promise better reach for both friends’ and firms’ posts, thereby giving more feedback on initial posts and a better user experience which may increase multi-homing behavior and even deserting dominant platforms, as multi-homing behavior has its cost in time and effort.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: http://goo.gl/QRc11f

Using the VRIN model to evaluate web platforms

Introduction

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.

Value:

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.

Rarity:

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.

Imitability:

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.)

Substitutability:

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.

Conclusions

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: http://goo.gl/QRc11f

A.I. – the next industrial revolution?

Introduction

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.

Examples

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.

Recommendations

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.

References:

[1]: https://www.facebook.com/notes/facebook-engineering/facebook-engineering-bootcamp/177577963919

[2]: http://royal.pingdom.com/2013/02/26/pinterest-users-per-employee/

[3]: http://www.rovio.com/en/news/press-releases/284/rovio-entertainment-reports-2012-financial-results

[4]: http://www.gamesindustry.biz/articles/2014-02-11-clash-of-clans-daily-revenue-at-5.15-million-hacker

[5]: http://www.statista.com/statistics/272140/employees-of-twitter/

[6]: https://press.linkedin.com/about-linkedin

[7]: http://www.visionmobile.com/product/uk-app-economy-2014/

[8]: http://www.susps.org/images/worldpopgr.gif

Bugs and problems in Facebook Ads [UPDATED 10/08/2016]

Introduction

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.

[UPDATED 10/08/2016]

  • add ‘like disavow tool’ (cf. Google’s link disavow)
  • ‘Facebook marketing partner’ –> expanding to smaller agencies (cf. Google Partners)
  • save target groups when making targeting in ad creation tool
  • add possibility to exclude saved audiences
  • ads receive an unequal number of impressions; if many ads in one ad set, most of them receive zero impressions
  • de-duping target group frequency across campaigns (overlapping audiences: avoid inflation of total frequency by de-duping)
  • distribute budget automatically between campaigns and ad sets
  • Split option in Power Editor does not split an existing audiences, but actually creates a new (complementing) one
  • add possibility to exclude age groups (could be done with exclusion of saved audiences)
  • sorting columns does not work in Power Editor reports section
  • sorting based on conversions does not work properly in Ads Manager columns (it calculates some sort of average)
  • re-position image in Power Editor –> not possible to see preview
  • in web interface impossible to make advance connection with parameter OR – now it uses AND – for example, fans of my page AND friends of fans makes target group impossibly small
  • does not show total budget (or any totals) in campaign view (UPDATE: partly fixed for some metrics, but total budget still not visible)
  • impossible to target competitors’ fans (what are the barriers for making this happen?)
  • breakdowns not possible based on e.g. education level (more breakdown possibilities)
  • possibility to set budget at campaign level
  • no possibility to filter campaign (cf. adwords) –> trying to find a campaign quickly is a pain
  • utm tagging missing –> impossible to track from 3rd party analytics
  • shared budget feature is missing –> you should copy this feature from AdWords
  • when copying campaigns, impossible to change goal (really stupid, cannot test performance with different goals)
  • campaign reporting –> no trends, no graphs –> impossible to assess long-term development of campaigns (compared to AdWords)
  • campaign page –> no possibility to change metric for graph (much better in AdWords where two metrics can be freely chosen)
  • no frequency cap (again, possible in AdWords)
  • no ‘compare to previous time period’ option in reports (unlike AdWords)
  • no possibility to delete images in image gallery –> wtf, makes it very difficult to manage
  • too small image size in image gallery –> again, hard to manage images
  • not possible to copy numbers in power editor (!!!) –> sometimes, you’d want to copy numbers between campaigns or into excel
  • power editor loses text field content when changing ad (field)
  • power editor does not enable image variation
  • web version does not show all image variation ads in first pageload
  • unable to copy ad sets in web interface –> impossible to make quick new versions targeting e.g. newsfeed vs. right column
  • doesn’t show pause status in ads while in review
  • power editor does not copy ad statuses while duplicating ad sets
  • rotate evenly option missing –> compare to AdWords
  • cta not possible to be removed in powereditor once put into ads
  • unable to revert to suggested image in web interface after choosing image from gallery
  • facebook ads no sound in video preview
  • missing bid modifiers: e.g. for ad placement, e.g. -50 %, right column

Problems in Page Insights:

  • inability to answer standard questions such as: what are the all-time most liked posts? how many posts did we do last month?

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]

Notes on Customer Development

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):

https://docs.google.com/spreadsheet/ccc?key=0ArHFxUyqbcmHdHp5VEY2eXNLby0zaHFKSDhpc0xEdkE&usp=sharing

Example questions from Cindy Alvarez:

  • How is your customer currently dealing with this task/problem? (What solution/process are they using?)
  • What do they like about their current solution/process?
  • Is there some other solution/process you’ve tried in the past that was better or worse?
  • What do they wish they could do that currently isn’t possible or practical?
  • If they could do [answer to the above question], how would that make their lives better?
  • Who is involved with this solution/process? How long does it take?
  • What is their state of mind when doing this task? How busy/hurried/stressed/bored/frustrated? [note: learn this by watching their facial expressions and listening to their voice]
  • What are they doing immediately before and after their current solution/process?
  • How much time or money would they be willing to invest in a solution that made their lives easier?

More points from Cindy (she’s a real specialist):

  • Abstract your problem by a level. For example, if you want to know whether someone will use a healthy lunch delivery service, ask about “lunch”
  • Start with an open-ended “Tell me about how you…” question. i.e. “Tell me about how you deal with lunch during the workweek”
  • Shut up for 60 seconds. This is a LONG, LONG time and it feels awkward. It also forces the person to go beyond the short (and probably useless) answer and go into detail.
  • Whenever you hear emotion in the person’s voice, prolong that line of conversation.
  • (You can prolong conversations by asking why/how often/who/where questions. It may take 2 or 3 or more of these follow-up questions to get at the interesting detail.)
  • Avoid yes/no questions. Whichever one the person chooses, it’s probably not useful for you.
  • Whenever the person starts complaining listen (and encourage it!) People are more specific with complaints than praise, and specificity is where you learn.
  • Challenge your pre-existing hypotheses by referencing the mythical “other person”. For example, “I’ve heard from other people that ______. Do you agree?” It’s easier for people to disagree with an anonymous third party than to disagree with YOU.
  • Avoid talking about your product or your ideas until the end – but then DO give the person the opportunity to ask you some questions. This is NOT a chance for you to sell your idea, it’s just an equalizer. You’ve been asking questions the whole time, now it’s their turn.
  • Thank them profusely and reinforce one concrete point that you learned.
    • Alwaaaaaayyyyys ask for referrals to 2-3 other friends who are roughly in the target market so you can interview them.

Here are some useful links:

http://www.quora.com/Customer-Development/What-are-your-favorite-methods-for-doing-problem-interviews-during-Customer-Discovery

https://blog.kissmetrics.com/26-customer-development-resources/

http://sixteenventures.com/startup-customer-development-hacks

http://practicetrumpstheory.com/how-to-interview-your-users-and-get-useful-feedback/

http://giffconstable.com/2011/07/12-tips-for-customer-development-interviews-revised/
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 🙂

Crowdfunding pitch to media – an example

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.

Hi [name],

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:

  • [reason 1]
  • [reason 2]
  • [reason 3]

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]

Tel. [telephone]

Skype: [Skype]

Email: [email]