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Tag: business strategy

Modern Market Research Methods: A Startup Perspective

Published by Joni on March 29, 2017

EDIT: Updated by adding competitive analysis, very important to benchmark competitors.

EDIT2: Updated by adding experimentation (14th April, 2016)

Introduction

Somebody on Quora was asking about ‘tools’ for validating viability and demand for a startup’s products.

I replied it’s not a question of tools, but plain old market research (which seems to be all too often ignored by startup founders).

Modern market research methods

In brief, I’d include the following options to a startup market research plan:

  1. market statistics from various consultancy and research institution reports (macro-level)
  2. general market (country, city) statistics generated just for your case (macro-level à la PESTLE)
  3. competitive analysis, i.e. benchmarking existing solutions — will help you find differentiation points and see if your “unique idea” already exists in the market
  4. (n)etnography, i.e. going in-depth to user communities to understand their motivations (micro-level, can be done offline and online)
  5. surveys, i.e. devising a questionnaire for relevant parties (e.g., customers, suppliers) to understand their motivations (just like the previous, but with larger N, i.e. micro-level study)
  6. customer development, which is most often used in B2B interviews as a presales activity to better understand the clients’ needs. Here’s an introduction to customer development (Slideshare).
  7. crowdfunding, i.e. testing the actual demand for the product by launching it as a concept in a crowdfunding platform – this is often referred to as presales, because you don’t have to have the product created yet.
  8. experimentation, i.e. running different variations against one another and determining their performance difference by statistical testing; the tests can relate to e.g. ad versions (value propositions, messages) or landing pages (product variations, landing page structure and elements). Here’s a tool for calculating statistical significance of ad tests.

So, there. Some of the methods are “old school”, but some — such as crowdfunding are newer ways to collect useful market feedback. Experimentation, although it may appear novel, is actually super old school. For example, one of the great pioneers of advertising, Claude Hopkins, talked about ad testing and conversion optimization already in the 1920. (You can actually download his excellent book, “Scientific advertising“, for free.)

How to combine different methods?

The optimal plan would include both macro- and micro-level studies to get both the “helicopter view” and the micro-level understanding needed for product adoption. Which methods to to include in your market research plan depends on the type of business. For example, crowdfunding can be seen as a market validation method most suitable for B2C companies and customer development for B2B companies.

The punchline

The most important point is that you, as a startup founder, don’t get lured into the ‘tool fallacy’ — there’s no tool to compensate for the lack of genuine customer understanding.

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]

How to prevent disruption from happening to you? AKA avoiding the “Vanjoki fallacy”

Published by Joni on March 29, 2017

Introduction

A major issue of corporations is how they can avoid being disrupted. This is a commonly established issue, e.g. Christensen discusses it in his book “Innovator’s dilemma”. But I’m going to present here a simple solution for it.

Here it is.

Rule Number 1: Don’t look at absolute market shares, look at growth rates

I call this the “Vanjoki fallacy” which is based on the fatal error Vanjoki did while in Nokia, namely thinking that “Apple only has 3% of market share, we have 40%. Therefore we are safe”, when the guy should have looked at growth rates which were of course by far in Apple’s favor. Looking at them forces you to try and understand why, and you might still have a chance of turning the disruption around (although that’s not guaranteed).

“How can I do it?”

So, how to do it? Well, you should model your competitors’ growth – as soon as any of the relevant measures (e.g., revenue, product category, product sales) shows exponential growth, that’s an indicator of danger for you. Here’s the four-step process in detail.

First, 1) start out by defining the relevant measures to track. These derive from your industry and business model, and they are common goal metrics that you and your competitor share, e.g. sales.

Second, 2) get the data – easy enough if they are public companies, since their financial statements should have it. Notice, however, that there is a reporting lag when retrieving data from financial statements, which plays against you since you want as early knowledge of potential disruptors as possible. You might want to look at other sources of data, e.g. Google Trends development or some other proxy of their growth.

Third, 3) model the data; this is done by simply fitting the data into different statistical models representing various growth patterns — remember derivation at school? It’s like that, you want to know how fast something is growing. Most importantly, you want to find out whether the growth resembles linear, exponential growth, or logarithmic growth.

How to interpret these? Well, if it’s linear, good for you (considering your growth is also at least linear). If it’s exponential growth rate, that’s usually bad for you. If it’s logarithmic, depends where they’re at in the growth phase (if this seems complicated, google ‘logarithmic growth’ and you see how it looks). Now, compare the competitor’s growth model to yours – do have reason to be concerned?

Finally, 4) draw actionable conclusions and come up with a strategy to counter your opponent. Fine, they have exponential growth. But why is that? What are they doing better? Don’t be like that other ignorant Nokia manager Olli-Pekka Kallasvuo who publicly said he doesn’t have an iPhone, and that he will never get one. Instead, find out about your competitors products. Here is a list of questions:

  • What makes them better?
  • What makes their processes better?
  • What makes their brand better?
  • What makes their business model better?
  • What makes their employees better?

Find out the answers, and then make a plan for the best course of action. You may want to identify the most likely root causes of their growth, and then either imitate, null (if possible) or counter-disrupt them with your next-generation solution.

Conclusion

In conclusion, don’t be fooled by absolute values. The world is changing, and your role as a manager or executive is to be on top of that change. So, do the math and do your job. The corollary to this approach, by the way, is to create a some kind of “anti-disruption” alert system — that would make for a nice startup idea, but it’s a topic for another post.

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]

Online ad platforms’ leeching logic

Published by Joni on March 29, 2017

I and Mr. Pitkänen had a discussion about unfair advantage in business – e.g., a gift card company’s business model relying on people not redeeming gift cards, investment banker’s relying on monopoly to take 7% of each new IPO, doctor’s controlling how many new doctor’s are educated, taxi driver’s keeping the supply low through licenses, governments inventing new taxes…

It seems, everywhere you look you’ll find examples of someone messing with the so-called “free market”.

So, what’s the unfair advantage of online ad platforms? It’s something I call ‘leeching logic’. It’s about miscrediting conversions – channel x receives credit for a conversion while channel y has been the primary driver to it.

Let me give you two examples.

EXAMPLE 1:

You advertise in the radio for brand X. A person likes the ad and searches your brand in google. He clicks your search ad and buys.

Who gets credited for the sale?

radio ad – 0 conversions
google – 1 conversion

The conclusion: Google is leeching. In this way, all offline branding essentially creates a lift for search-engine advertising which is located at a later stage of the purchase funnel, often closing the conversion.

EXAMPLE 2:

You search for product Y in Google. You see a cool search ad by company A and click it. You also like the product. However, you need time to think and don’t buy it yet. Like half the planet, you go to Facebook later during that day. There, you’re shown a remarketing ad from company A but don’t really notice it, let alone click it. After thinking about the product for a week, you return to company A‘s website and make the purchase.

Who gets credited for the sale?

Google – 1 conversion (30-day click tracking)
Facebook – 1 conversion (28-days view tracking)

In reality, Facebook just rides on the fact someone visited a website and in between making the purchase also visited Facebook, while they learned about the product somewhere else. They didn’t click the retargeting ad or necessarily even cognitively processed it, yet the platform reports a conversion because of that ad.

For a long time, Facebook had trouble in finding its leeching logic, but now it finally has discovered it. And now, like for other businesses that have a leeching logic, the future looks bright. (Good time to invest, if the stock’s P/E wasn’t somewhere at 95.)

So, how should marketers deal with the leeches to get a more truthful picture of our actions? Here are a few ideas:

  •  exclude brand terms in search when evaluating overall channel performance
  • narrow down lookback window for views in Facebook — can’t remove it, though (because of leeching logic)
  • use attribution modeling (not possible for online-offline but works for digital cross-channel comparisons)
  • dedupe conversions between channels (essentially, the only way to do this is by attribution modeling in 3rd party analytics software, such as GA — platforms’ own reporting doesn’t address this issue)