Market Intelligence for decision makers: chapter overview

1. An introduction to this blog

2. Debunking myths about market intelligence (intro)

2.1. Market Intelligence is expensive
2.2. Market knowledge should be 100% accurate
2.3. Market Intelligence is complex
2.4. You need external agencies to collect market intelligence data
2.5. Market Intelligence reporting is cumbersome and extensive
2.6. Market Intelligence insights are unambiguous
2.7. Interpreting Market Intelligence insights is easy

3. Examples of how to build market insights for strategic decision making... all by yourself! (intro)

3.1. Monitoring your business environment
3.2. Prioritizing new market investments
3.3. Assessing your competitive position
3.4. Innovation based on megatrend assessments
3.5. Account planning and benchmarking

4. Establishing a market intelligence practice in your company (intro)

4.1. Market Intelligence... a full-time job or not?
4.2. Who should your market intelligence function report to?
4.3. Which frequency of reporting for your market insights?
4.4. Which communication means for your market insights?


(...stay tuned for more...)

Book reviews:

• Nate Silver "The signal and the noise - The art and science of predictions"
• William Higham "The next big thing, Spotting and forecasting consumer trends for profit"
• Lawrence Friedman "Go-to-market strategy, Advanced techniques and tools for selling more products, to more customers, more profitably""
• Eric Ries "The lean startup, How constant innovation creates radically successful businesses"
• Frederic De Meyer "The impact of megatrends on your business, How to assess the impact of long-term trends on your company and adapt your strategy to it"

Adapting your go-to-market strategy based on new trends

I’ve been through some material on Go-to-market strategies lately, for a specific project I’m working on. One of the most interesting books I’ve been through is I’ve been through some material on Go-to-market strategies lately, for a specific project I’m working on. One of the most interesting books I’ve been through is
'Go To Market Strategy: Advanced Techniques and Tools for Selling More Products to More Customers More Profitably' by Lawrence Friedman. In it, he explains different steps to target the right markets and choosing the right (integrated) channel, and provides a very handy ’90-days’ roadmap for determining a new channel model.

But the most useful part for me was the way he maps channel go-to-market models. In a simplistic form, this gives for instance these 2 models:

The beauty of this simplification is that you can quickly model alternative routes-to-market (by playing around with the dots), and in a way assess their complexity and feasibility in the same graphic.

I’ve tried to apply this on my company’s business and found plenty of ways to go much deeper, by both expanding the ‘Sales Cycle’ elements on the Y axis to, for instance, key influencers; and by extending the list of alternative players on the vertical axis.

Each industry, and even each company, would have its own list of potential players in their go-to-market model. However, it might be useful to cross-check this with the findings of the Megatrend exercise I talked about on this blog, or make the impact on the go-to-market strategy a key discussion point in the Megatrend Think Tanks (see previous posts).

It might be worthwhile to include Crowdsourcing, for instance, or an online, Amazon-style affiliate program (which in effect is an example of crowdselling). Or, if you’re a Health Care company, it would make sense to input ‘governments’ in your model, since if the trend toward the ‘market state’ continues to amplify, it might well be that you’ll have to partner with governments in order to go-to-market, in a much more sophisticated way than what currently might be the case.

Friedman’s model definitely provides some interesting perspectives once you start playing with it.

Assessing the impact of megatrends on your business

Are you prepared to take the maximum out of long-term trends?

In this original work, Frederic De Meyer gives multiple examples of how major societal shifts are impacting on all types of businesses, often indirectly and subtly. This book provides concrete, practical advice on how to use megatrends to generate innovative ideas and understand the challenges of your customers and how to best benefit from this knowledge.

Some preliminary reactions to the book:

“Frederic offers a unique insight of how global changes translate into new business opportunities. This book is an essential tool for any future-oriented manager or entrepreneur and anyone involved in innovation strategies” Philippe De Ridder, co-founder, Board of Innovation

“Designing and implementing a good strategy is quite a challenge. In an increasingly complex world, it is becoming increasingly difficult to recognise the core from the noise. This book on megatrends will help you do this. It will give you the necessary insights to focus on the themes that are crucial to the future of your company. A must read!” Jeroen De Flander, co-founder, The Performance Factory and author of Strategy Execution Heroes

“This excellent book is a comprehensive  overview of the major trends and also offers a methodology to better assess the future reality and master its consequences. The author provides an essential guide for any strategy exercise” Peter Corijn, Vice-President, Procter & Gamble

Frederic De Meyer is the founder of the Institute for Future Insights, a company focused on understanding the impact of long-term trends on companies and helping companies to adapt to these shifts. For the last 10 years, Frederic De Meyer has lead the European market intelligence team of technology company Cisco. 

What every business can learn from startups...

Can ‘entrepreneurship’ be prone to scientific rigor? It sounds counter-intuitive, for sure. In my eyes, entrepreneurship is much more associated with intuition, guts and inspiration, rather than processes and rules.

Eric Ries’ ‘The lean startup’ is a brave attempt at bringing entrepreneurship to a scientific level. Did he succeed? Hell, no. The ‘methodology’ proposed in his book is nothing close to scientific, it is more an abstraction  of the things he learned as (a consultant of) entrepreneur. Nevertheless, he learned a great deal of valuable insights, and shares it without restriction in this incredibly rich book.

Concepts like ‘validated learning’, ‘minimal viable product (MVP)’, ‘pivot moments’ and the ‘5 why’s’ will definitely linger on in my thoughts about business strategy. But perhaps the strongest learning for me was the concept of ‘long batch’ versus ‘short batch’.

I’ve had a traditional economic education, where I was taught that splitting workflows in small pieces, each performed by specialized people (the long batch), is the most efficient way to produce a product. According to Ries’ experience, it is not. At least not for everyone.

I’ve made a quick attempt to put his arguments in a chart:

Thing is, if you obtain experience from selling a product (or service) early on in the process, even with an imperfect product (MVP), you quickly obtain insights (validated learning) on what the market exactly want, and can adapt your product instantly to it. It’s easier said than done, since this requires many specialized teams to collaborate –each with their own agenda- but it is definitely more efficient since you can take corrective measures earlier on compared to the long-batch.

Furthermore, your resources –especially human resources- will be working on these products all of their time (instead of waiting for measurement feedback or bottlenecks to be resolved). In that way, the short-batch concept would potentially unleash the innovation capabilities in each of these resources, since ‘if we stop wasting people’s time, what would they do with it?’ (E. Ries).

A capital question…

Spotting and forecasting consumer trends for profit (book review)

The next big thing – Spotting and forecasting consumer trends for profit, William Higham

To be fair: originally I bought this book hoping to find loads of new trends affecting consumers. There are a couple indeed, but the book is more about understanding the nature of trends, where they originate from, how they grow and, perhaps more importantly, how to build a process in the company in order to assess these trends and make decisions based on them.

Perfect! I  got much more than I expected !

The book starts with a couple of examples of consumer trends, which Highman often provides with a compelling name: boundary blurring (consumers are active participants in the market), gender blurring, ‘trading up’ (H&M asking Karl Lagerfeld to make a collection, Carlsberg exclusive ‘900’ brand), ‘Come together’, etc.

Highman then develops an argument for a ‘trend marketer’ function within a company (not only responsible for trend spotting, but also decision making and implementation). The idea is appealing, and I’d certainly apply for any such job –although it’s in fact another name for 50% of what I’m doing in my current role.
The main part of the book, however, is dedicated to understanding the nature of trends:

• How they start: pretty much –a bit to my surprise- a matter of  PESTEL factors (Political, Economical; Societal; etc.). I say to my surprise, since I do think that with that the author omits the Psychological factor, though he might argue that this factor is dependent on all the other PASTEL factors. We could argue for years before getting clear with that one…;
• Sorts of trends: behavioral vs. attitudinal; micro vs macro trends; … I’m thinking of developing a classification of trends myself, though Highman gives a good basis, I do think it can do with some more granularity.;
• Where trends occur;
• How to collect trends;
• How trends spread (opinion formers; 6 degrees; etc);
• Mapping trends: Causal analysis / assistance attribute / needs attribute / passive drivers… a very good aid to assess trends!
• How to draw conclusions and decisions based on the work above: ‘trend audits’, innovation workshops and scenario planning..

Though here and there the ideas might feel a bit light (the chapter on ‘how to collect trends’, for instance, doesn’t go much further than basic research), the beauty of this book resides in the fact that all these ideas taken together, they form a pretty solid process with which companies can deal and benefit from the trend-marketer exercise.

Pretty compelling and convincing at the end.

The annex is worth mentioning: Highman explains the full process he himself has gone through (or helped with) in discovering and assessing three major consumer trends: ‘traditionalizing ‘(how people get back to more traditional ways of living); ‘come together’ (where people will increasingly look for ways to meet and share events ‘live’) and ‘the new old’ and ‘the new old’ (changing spending patterns of ageing society –well, after all not changing at all).

All in all a very enriching read.

The art and science of prediction...

“This book is less about what we know than about the difference between what we know and what we think we know”, writes Nate Silver somewhere at the end of his book. He could not have phrased it more beautifully. Our brain is a remarkable instrument that, when it comes down to predicting almost anything, contains a substantial amount of techniques to mislead us. Of course, these techniques often helped us to survive in tougher times, but they fail us miserably in the area of future-watching. Just as an example: apparently we have a bad habit of neglecting certain risks just because they’re too hard to measure…. Not sure how far this will get us in terms of survival, however…

The solution? Statistics. According to Nate Silver, that is. He’s not saying statistics can predict everything (or, for that matter, anything) –‘all statistic models are faulty, but some are useful’- but at least it can provide us with a more accurate picture of the (possible) future(s).

Chapter by chapter Nate applies this thought on very specific subjects, ranging from the American elections (his specialty), to weather forecasting, earthquakes, economic growth and climate change, to more tangible topics such as poker, chess, or beating the stock market (which, he concludes, is impossible).

Fascinating material, and some conclusions linger on for quite some time. For instance: the level of uncertainty of prediction increases when the system we study is non-linear and dynamic (which sounds intuitive at first, but is very often forgotten, for instance when governments predict next year’s GDP), or when the data we start with is not accurate enough (like with weather forecasting). We also need to watch out not to ‘overfit’ our models (adapting it with the data we have at hand), as we often do with predicting earthquakes. Sometimes we can use models developed for one system onto another system, like applying the power-law distribution we use for earthquakes onto the event of future terrorist attacks (which results in a somewhat bewildering conclusion).

The central thought Nate Silver uses across his book is the formula of Bayes which, for the curious people among you, looks like this:

[xy] / [xy + z(1-x)]

Where

X = the initial likeliness you would attribute to an event.

Y = the ‘positive possibility’ (the likeliness of an event without taking x into account)

Z = the ‘negative possibility’ (the likeliness of the event not taking place).

Believe it or not, but with this formula you can even calculate the likeliness that your partner is cheating on you! The key figure in the formula is the ‘x’, the likeliness you grant to an event accuring –which implies that the overall likeliness gets more accurate as you gather more experience or knowledge about the event (if your partner cheated on you in the past, the ‘x’ would increase, and hence the overall likeliness in a particular event as well).

Strangely enough this thought process can be applied to virtually any system (or prediction), though dependent on a number of factors it can produce totally different outcomes, with different levels of accuracy. The world will never be predictable, but with a sensible use of statistics it can become a little more so…

Fascinating reading…

(4) Establishing a market intelligence practice in your company

I

n the previous chapters we demonstrated that building market insights is difficult nor costly. In fact, as we have shown, many insights can be obtained with little effort and with virtually no investment. Nevertheless, chances are that you as a decision maker do not have any time to invest in this activity. You will need to rely on your employees to provide you with the insight, and if your need for market insights crosses a certain threshold you should probably consider putting dedicated resources to the task.

Easier said than done. Fact is: such a function remains in the category of overhead costs, so this naturally gives rise to a number of questions about its exact nature and the soundness of such an investment. In this chapter we will try to provide an answer to the most pressing questions that arise from this decision.