Archive for the english category

Joni

I hate to see investors coming into a growing startup… here’s why

english

I hate to see, from a customer’s perspective, investors coming into a growing Web startup.

Because it only means rising prices.

The logic is this: 1) the investors need a positive return, and 2) the startup is growing because it has created something valuable, in most cases significantly more valuable than what it is charging from the customer.

Therefore, the investor logic is to raise the price and narrow down the extant value gap, i.e. charge according to the value provided (or, closer to it). However, most customers will still stay, because they keep getting more value than what they pay, even with increased prices, and therefore the startup can maximize its revenue. In addition, there would be a switching cost associated with finding a new provider, such as learning the new tool, configuring it, exporting/importing data, etc. So basically, this strategy is a form of value transfer from the customer to the startup — or, more correctly, to the investor.

Next, we’ll explore what this means for investors and founders.

1. Implications for investors

The major implication for an investor of course is that it makes sense to identify startups which are growing fast but have not optimized the value capture part of their business model.

However, a major difference lies in between having some revenue and not having revenue at all; in the latter case, the growth might be just an indicator of popularity, not business potential. (See my dissertation on startup dilemmas for a thorough elaboration of this topic.)

2. Implications for founders

The major implication to a startup is that if you seek funding, price your product well below the value provided, thereby sacrificing unit-level profitability for growth. But if you want to stay away from investors, experiment with rising prices – that way, it’s only you keeping the surplus. Obviously, this is “ceteris paribus”, so it excludes the potential revenue uplift from scaling with investor money. As we know, the only reason to bring in an investor is to grow the size of the business and thereby also increasing the founder’s personal profit, regardless of stock dilution.

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]

Joni

The Basics of Dilemmas

english

Introduction

By definition, a dilemma is a trade-off situation in which there are two choices, each leading to a negative outcome.

General solution

A general solution, then, is to weigh the outcomes and compare them against one another.

For example:

choice A: -1
choice B: -2

In this example, choice A has smaller negative effect, so we’d pick that one.

Complications

However, there are complications.

Consider the above would in fact be the short-term outcomes, but there are also long-term outcomes. For example

choice A: -1, -3
choice B: -2, -1

This leads us into payoff functions, so that the outcomes (payoffs) consist of many variables. In the example, the long-term negative effects outweigh the short-term effects, and we would  change our choice to B.

However, the choice can also be arbitrary, meaning that neither choice dominates. In game theory terms, there is no dominant strategy.

This would be the case when

choice A: -1, -2
choice B: -2, -1

As you can see, it doesn’t matter which choice we take since each gives a negative outcome of equal size. There is an exception to this rule, namely when the player has a preference between short- and long-term outcomes. For example, if he wants to minimize long-term damage, he would pick B, and vice versa.

How to apply this in real life?

In decision-making situations, it’s common to make lists of + and -, i.e. listing positive and negative sides. by assigning a numerical value to them, you can calculate the sum and assign preference among choices. in other words, it becomes easier to make tough decisions.

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

Joni

Organic reach and the choice of social media platform

english

(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

Joni

About moral hazard and banking crises

english

Introduction

The struggle against moral hazard in banking is constant and real. There’s no turn-key solution for eliminating it, but it must be kept in mind at all times by policy makers.

Consider the following citation from Wikipedia:

“The role of the lender of last resort, and the existence of deposit insurance, both create moral hazard, since they reduce banks’ incentive to avoid making risky loans. They are nonetheless standard practice, as the benefits of collective prevention are commonly believed to outweigh the costs of excessive risk-taking.”

This structural problem, similar to that of the problem of the commons, drives individual bankers into competing with risks. It’s an escalating situation in which one banker takes a slightly larger risk; after seeing this one fares okay, another banker takes again a marginally increased risk position, and so on. As a result, the overall risk position of the market escalates (little by little), until one trigger event causes a collapse.

Because there is a lender of last resort, the risks for the bankers are mitigated (as long as there are enough bankers who participate in “bidding up” the risks). Because there is deposit insurance, the risk for the private individuals is eliminated as well, so they continue putting their money on the “roulette table” of the (rationally) greedy banker. The lender of the last resort will impose some more regulation, and the bankers promise to behave nicely.

However, there are no fixed threshold rules in how the financial markets work and so “boiling the frog” begins all over again.

What to do?

In my opinion, the best way to counter this effect of excessive risk taking is to move the collective risk at individual risk level, so that bankers would be privately responsible for their bank’s rescue – this would take the form of losing banking license and/or private assets.

This might lead, in cases of crises, in exchange of an entire generation of bankers, but this would only be fair; at good times, they are healthily compensated, so at bad times of their own doings, they must bear the consequences. Moral hazard, by definition, arises when there is a potential that the interests of the agent and the principal differ – by aligning the interests, the problem perishes. The same effect works in reverse; in this case aligning the cost of reckless behavior.

The author is a university teacher at the Turku School of Economics. His “hobby” is to keep track of the euro-crisis.

Joni

Big data is not enough data

english

There is a big data fallacy

My argument here is simple – even though it’s a common argument that “everything is tracked”, marketers face a big data fallacy when assessing their ability to predict consumer behavior.

The reason is explicated here [1]:

“On any given occasion, everything from personal factors such as how well a person has slept the night before, current mood, hunger, and previous choices, to environmental variables such as the weather, the presence of other people, background music, and even ceiling height can influence how a customer responds. Algorithms can use only a handful of variables, which means a lot of weight is inevitably placed on those variables, and often the contextual information that really matters, such as the person’s current physical and emotional condition or the physical environment in which the individual is tweeting, Facebooking, or buying online, isn’t considered.”

Therefore, what is known is simply not enough to accurately predict an individual consumer’s behavior. On average, however, given the limitation of computable variables, marketing algorithms can enhance marketing performance. But data will never make marketing “perfect” – just simply because there’s not enough of it.

Endnotes

[1]: Dholakia (2015) https://hbr.org/2015/06/the-perils-of-algorithm-based-marketing

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

Joni

How to measure offline marketing with online metrics?

english
How to measure offline marketing with online metrics?

Introduction

The issue with offline marketing is tracking. For many offline marketing efforts, such as exhibitions and networking events, it’s hard to track results.

Participation in these events is often expensive, and the results are evaluated on a qualitative basis. Although qualitative evaluation is better than nothing, quantitative data is obviously better. And in many cases, we can do that – all we need it the measuring mindset and a little bit of creativity.

The bottom line is: If you’re spending a lot of money into offline marketing, you have to justify its performance. Otherwise you don’t know how well the money turns into desired outcomes, let alone how well event A compared with event B in terms of performance.

The simple solution

The issue can be solved by using metrics. For example, if we are selling in a trade fair, I can use performance metrics like these:

  • sales (€, qty)
  • number of catalogs and/or flyers distributed
  • number of emails gathered via a lead-generation contest (“give us your email – win prize x”)

Of course, knowing the cost of participation, we can now calculate composite metrics such as:

  • Direct ROI = (sales – cost) / cost
  • Cost per lead (email) = cost / number of emails
  • Cost per catalogue distributed = cost / number of catalogues distributed

These can be now measured against digital channels, and evaluated whether or not we’d like to participate in the event in question again, say, next year.

Comparing offline and online performance

During my time as a marketing manager, I’ve come up with different ways to standardize the offline metrics, that is to say calculate offline marketing activities so that they are comparable with digital channels.

Here are three ways we’ve been using.

1. Cost per card

  • CPCa = cost of participation / number of business cards collected
  • Compare with: CPL

Networking is an important part of the sales cycle, especially in B2B markets. By quantifying the results, you are able to compare one event against another, as well as compare the results with lead generation (CPL) through digital channels
(for this, only include the business cards of potential customers).

2. Cost per catalog

  • CPCat = cost of distribution / number of catalogues distributed
  • Compare with: CPC

In Finland, I’ve found that catalog distribution inside magazines is a cost-effective form of marketing. This metric I compare with Google CPC, i.e. the cost of average paid user via Google. The rationale is that since the catalog is inside the customer’s favorite magazine, she will surely take a look at it (during the reading
session you tend to have more time).

3. Cost per festival contact

  • CPF = cost of participation / number of visitors
  • Compare with: CPM

Summer festivals are hot in Finland. Every year, there is more than a dozen big festivals across the country. We’re participating in some of them together with our suppliers. Festivals most often provide you with the number previous year’s visitors. I find it best to compare this metric with CPM, since the visitors are just
hypothetical contacts.

Of course, we can use several metrics, so for festivals I use CPF to evaluate which ones are the most cost-effective ones (that’s one, but the not the only criterion, since the match between us and the target audience is more important). Then, to evaluate how well we did, I’ll use the other metrics, mainly cost per lead (email) and cost per catalog distributed.

Hopefully this article gave you some useful ideas. If you have something to share, please write in in the comments. Thanks for reading.

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

Joni

A Few Interesting Digital Analytics Problems… (And Their Solutions)

english

Introduction

Here’s a list of analytics problems I’ve devised for a class I was teaching a digital analytics course (Web & Mobile Analytics, Information Technology Program) at Aalto University in Helsinki. Some solutions to them are also considered.

The problems

  • Last click fallacy = taking only the last interaction into account when analayzing channel or campaign performance (a common problem for standard Google Analytics reports)
  • Analysis paralysis = the inability to know which data to analyze or where to start the analysis process from (a common problem when first facing a new analytics tool 🙂 )
  • Vanity metrics = reporting ”show off” metrics as oppose to ones that are relevant and important for business objectives (a related phenomenon is what I call “metrics fallback” in which marketers use less relevant metrics basically because they look better than the primary metrics)
  • Aggregation problem = seeing the general trend, but not understanding why it took place (this is a problem of “averages”)
  • Multichannel problem = losing track of users when they move between online and offline (in cross-channel environment, i.e. between digital channels one can track users more easily, but the multichannel problem is a major hurdle for companies interested in knowing the total impact of their campaigns in a given channel)
  • Churn problem = a special case of the aggregation problem; the aggregate numbers show growth whereas in reality we are losing customers
  • Data discrepancy problem = getting different numbers from different platforms (e.g., standard Facebook conversion configuration shows almost always different numbers than GA conversion tracking)
  • Optimization goal dilemma = optimizing for platform-specific metrics leads to suboptimal business results, and vice versa. It’s because platform metrics, such as Quality Score, are meant to optimize competitiveness within the platform, not outside it.

The solutions

  • Last click fallacy → attribution modeling, i.e. accounting for all or select interactions and dividing conversion value between them
  • Analysis paralysis → choosing actionable metrics, grounded in business goals and objectives; this makes it easier to focus instead of just looking at all of the overwhelming data
  • Vanity metrics → choosing the right KPIs (see previous) and sticking to them
  • Aggregation problem → segmenting data (e.g. channel, campaign, geography, time)
  • Multichannel problem → universal analytics (and the associated use of either client ID or customer ID, i.e. a universal connector)
  • Churn problem → cohort analysis (i.e. segment users based on the timepoint of their enrollment)
  • Data discrepancy problem → understanding definitions & limitations of measurement in different ad platforms (e.g., difference between lookback windows in FB and Google), using UTM parameters to track individual campaigns
  • Optimization goal dilemma → making a judgment call, right? Sometimes you need to compromise; not all goals can be reached simultaneously. Ultimately you want business results, but as far as platform-specific optimization helps you getting to them, there’s no problem.

Want to add something to this list? Please write in the comments!

[edit: I’m compiling a larger list of analytics problems. Will update this post once it’s ready.]

Learn more

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

Joni

Digital Marketing Laws (work in progress…)

english

Hi,

this is work in progress – I’ll keep updating this list as new moments of “heureka” hit me.

Digital marketing laws

  1. The higher the position in a SERP, the higher the CTR
  2. The more a mixed platform gains demand-side popularity, the more it restricts the organic reach of supply-side
  3. Search-engine traffic consistently outperforms social media traffic in direct ROI
  4. People are not stupid (yes, this is why retargeting is not a stairway to heaven)
  5. “it is almost always much cheaper to retain satisfied customers and turn them into repeat business than it is to attract a new, one-time customer.”

Want to add something? Please post it in the comment section!

Joni

Using the VRIN model to evaluate web platforms

english

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

Joni

The difference between business logic and strategy

english

Introduction

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.

Conclusion

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: http://goo.gl/QRc11f