While
I was working as a Market and Business Intelligence manager at an American
multinational, I received plenty of such type of questions. I remember one of them
very clearly, since it posed some very particular challenges. It was on a
Friday late afternoon when a colleague called me for an urgent question –I
found out most of the urgent questions tend to be asked on a Friday afternoon,
strangely enough. My colleague has just been appointed to head a team that
would introduce a new product on the European market. Nice challenge, except
that he was given only two sales people and a very limited marketing budget.
You can probably guess what his question to me was: which countries should he
focus on to maximize the success of this introduction.
Oh,
I forgot to mention: he needed to take the decision the next Tuesday, and he
had no particular budget to spend on getting data to support this decision.
The
technology being too new, we could obviously not rely on any existing market
research to help us out. So what could we base our decision on? Actually, there
were a couple of things we did know, and could use to build our market insights
on. For instance, the new technology was specifically relevant for only a
number of industries, and we did have some good insights in the IT budgets of
these industries. Based on our previous experience, we also knew in which
countries our sales channels were best prepared to introduce new technologies
to their clients. Based on the same experience from past introductions of new
technologies, we had a good knowledge of which countries were fastest in
adopting new technologies.
Very
soon we came to the conclusion that the information we did have available could
be categorized in three types. The first category, let’s call it ‘Business Context’,
contained information about the business environment of the European countries,
like economic growth projections, addressable market growth for our existing
technologies and IT budget spending. The
second category, let’s call it the ‘Internal Readiness’, contained internal
information that provided indications of how successful the introduction of a
new technology could be in the European countries, like the pace at which
previous technologies were adopted by the countries, or how many sales channels
we could leverage to introduce the new technology. The third category, let’s
call it the ‘Industry Relevance’ contained information about the potential of
the industries for which the new technology was designed for.
We
ended up with following list of metrics we could use:
(‘Technology
X’ in the list designs the technology we needed to introduce. The technology
was already available in the US and, while there was no special emphasis on it,
already sold in the European markets. So we had some insights in the early
adoption of the technology)
These
three categories answered three crucial questions we had to answer to make our
prioritization question:
- Which markets have the most favorable business environment in which the introduction of our new technology would be successful?
- Which markets have shown in the past to be the quickest adopters of new technologies?
- In which markets are the industries we focus on the biggest and most promising?
But
how could we use all the metrics above in order to answer these three
questions? It is important to keep in mind here that our single aim was to
prioritize the countries in which to introduce our new technology. In other
words: we were trying to measure their relative
attractiveness for this technology. Hence, in the initial stage at least, we
could rank the countries for each of these metrics.
For
the first category for instance, this provided us with following view:
This
table compared countries to one another based on how well they scored on the
business environment metrics. It is a ranking, so a ‘1’ indicates the best
performing country. For instance: Germany is the biggest European economy and
hence had the score of 1. The composite Leading Indicator of the OECD (an
indication of how well the economies will perform in the near future) indicated
that Greece had the best economic growth prospects, hence Greece was given a
score of 1. Before you ask: this analysis was performed well before the 2008
financial crisis and its subsequent economic troubles.
By
taking the averages of the scores for the three categories, we obtained a solid
view of the different countries’ attractiveness for the new technology, and
where our investments would have the highest return. At this point, each
country had three scores, which we could then map on a single chart, for
instance a bubble chart (ideal when working with three dimensions). For
instance, we could map the scores for the business environment in the X-axis,
the scores related to the internal readiness on the Y axis, and the bubble size
could represent the industry relevance metric. This would provide us with a view in which we can recognize four
country segments, each with their specific conclusion for how to go-to-market
with our new technology:
“Ideal
world”
Countries
in this segment are ideally suited to introduce the new technology in. They
have a promising economic climate and have proven in the past to be eager
adopters of new technologies. To realize their full potential these markets
need to be addressed with sufficient focus and, eventually, budget. In our
case, this would mean putting our sales resources on these markets.
“Seed”
The
countries in the top –left segment need a different approach. These are
countries that benefit from a favorable business environment, but have proven
to be slow adopters of new technologies (at least ours). Sales activities in
these countries are not likely to succeed quickly, at least not in comparison
to the countries in the “ideal world” segment. We should rather sell the bigger
picture in these markets, trying to evangelize the need for and the benefits of
new technologies, and investigate which arguments might make them more
favorable to these new technologies. One could argue this is a challenge for
the marketing department, so perhaps we should reserve some of the marketing
budgets for these countries.
“Harvest”
The countries in the bottom-right segment have
shown to be smooth adopters of new technologies in the past, but the current
unfavorable business environment might hinder them to be early adopters of the
new technologies, at least for the time being. Though we need to prepare these
markets for when the market conditions turn favorable, we should not seek to
invest too much efforts in sales right now, at least not by dedicated sales
people. Perhaps we need to single out top potential customers in each of these
markets, and have them covered by a business development resource, or by
internal sales.
“Wait”
Countries
in the top-right segment endure difficult market circumstances and have proven
to be slow or lagging new technology adopters. Given the limited resources at
hand, we would not put any particular emphasis on these markets.
We
now obtained some insights to base our decision on:
In
our specific case we might conclude to put the two sales resources on the
German and British market, if at all possible in conjunction with the Italian
market. We should use our marketing budget to increase the adoption rate of
Sweden, France, Netherlands and Switzerland , and invest in informing the
Danish, Spanish, Belgian and Greek customers about the introduction of our new
technology, perhaps through the existing sales force (which would require some
additional training).
Of
course you will argue that the lines we have drawn to define the segmentation are
completely subjective. And, indeed, if we would have had access to four sales
people instead of two, we could have drawn the vertical line somewhat more to
the right. The separation line is subjective, and should adapt to the resources
at hand. Do not forget this exercise was aiming at comparing countries in terms
of the success with which we could introduce a new technology. The chart above
provides an unambiguous answer (although the metrics themselves might lead to
ambiguity, dependent on which ones you chose to include).
Working
with aggregated indexes, like we have done in this example, has the advantage
of including a large and varied array of arguments and parameters into our
decision equation. We could refine it further by giving different weights to
our parameters, favoring those we are sure to have an impact on our decision
over ones that are only vaguely related to our subject.
I
do realize that many decision takers will not feel too comfortable with these
aggregated indexes. Many will rather have the full list of parameters with
their scores, eventually put in a ‘heat map’ in which they can quickly judge on
the overall attractiveness of a country –and the reasons behind it. Fair
enough, both views can coexist since they basically tell the same story, and
the overall conclusion will invariably be the same with both views. My
preference for the aggregated score originates in the simplicity the ultimate
view offers, the fact that so many arguments come together in a limited number
of factors.
Also,
many decision takers might feel uncomfortable with this exercise since it
offers no guarantee of accuracy. However, we need to keep in mind the initial
request when building this type of insights. The aim of this exercise was to
prioritize countries in which to invest, in other words: to compare countries
based on specificities that undoubtedly are relevant to our decision. Putting
them together does not alter the accuracy of the picture we are building, and
certainly does not diminish the degree of confidence with which we take our
decision. Quite on the contrary, if we find a sufficient amount of metrics, and
if these are sufficiently related to our decision, we will increase our
confidence, since mistakes or misjudgments will be countered by the many other
metrics that are accurate. In a way this reduces your error margin.
And,
as was the case with this exercise, sometimes it is the only type of insights
one can build for a decision.
Did
this example debunk the myths about market intelligence?
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