Well-founded sales planning with up-to-date market data

Update 2021 – Market data for Germany 2021

Karlsruhe, July 6th 2021: Various purchasing power data with data vintage 2021 are now available for Germany. Updates for numerous other European and international countries will follow shortly.

Whether for nationally or internationally operating companies – comparable homogeneous data are the basis for solid analyses and planning. An up-to-date database is essential for a well-founded decision-making process. For this reason, the globally consistent and comparable geographic and market data from Michael Bauer International GmbH are subjected to continuous quality controls and regularly updated to the latest data vintage.

Purchasing power as an important planning tool across all industries

In regional sales planning, purchasing power is the most frequently used indicator for the consumption potential of a region. The general purchasing power can be an important planning tool not only for the retail sector, but also for many other industries. In addition to the professional planning of new locations based on their prospects of success, purchasing power data can also be used to plan sales areas based on their sales potential. As part of the 2021 update, the MBI purchasing power data was updated to the 2021 data vintage in order to enable companies across all industries to plan in the best possible way based on the latest data.

Specific purchasing power indicators provide deeper insights into consumer behavior

In addition to general purchasing power, MBI offers other specific purchasing power indicators for the retail sector to provide even more detailed insights into consumer behavior. Retail spending is obtained at the place of residence and shows the proportion of purchasing power available for spending in retail. The retail turnover, on the other hand, is measured at the point of sale and describes the turnover of local retail trade. The retail centrality index puts those two indicators in relation and thus provides a measure of the attractiveness of a shopping location. The retail centrality index can be used to estimate the extent to which the local retail trade is able to attract supra-regional customers. With the help of these indicators, which were also recently updated to the 2021 data vintage, areas can be evaluated according to their strengths and weaknesses and unexploited potential can be localized. In addition, these specific purchasing power indicators allow marketing activities to be focused locally.

Online retail spending available for Germany for the first time

In recent years, there has been an increasing movement away from traditional brick-and-mortar towards online retail. The Corona pandemic further increased the importance of online retailing. Many consumers who previously preferred brick-and-mortar retail made their first purchases online. Even after the end of the pandemic, a large number of consumers will continue to shop online and there will be a general and permanent change in shopping behavior. In order to reflect this development, the new 2021 update also provides specific insights into online retail spending for Germany for the first time.

Consumer spending provides information about what the disposable income is being spent on

Consumer spending supplies potential data specifically tailored to the assortment. This provides companies with targeted insights into purchasing power for various product categories. It describes the amount of disposable income available to consumers in a region for spending in retail or proportionately online for the respective product range. Unlike general purchasing power, it not only includes total disposable income, but also what it is ultimately spent on. To optimize location analyses, improve advertising planning, and provide advantages in direct marketing, consumer spending for Germany has also been updated to the 2021 data vintage. For Europe, consumer spending by product groups provides information on 20 internationally consistent and comparable product groups. The data for this indicator is also updated regularly.

Purchasing power data can be visualized geographically – together with our postcode, administrative or microgeographic boundaries, they enable even more effective and efficient decision-making. Therefore, all studies are available at the level of municipalities or municipalities with 10,000 or more inhabitants and 5-digit postcodes as well as at the higher levels.

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The assessment of political risks – Orientation and security through MBI CONIAS Risk Intelligence

It’s a paradox: Political risks, including wars and political violence, are consistently ranked among the biggest risk factors for business managers[1]. Supply chains can be disrupted, inventories can be destroyed, sales markets can disappear. Nevertheless, the area of early detection and warning of political risks receives little attention from internationally operating companies. It is often assumed that crises and wars are too complex to be effectively predicted – but the scientifically based CONIAS approach was developed for precisely that purpose. One of the basic methods used to understand and more quickly classify the multi-layered risk situations is pattern recognition[2].

Pattern recognition is derived from general human approaches

For the complex field of political risks, pattern recognition is so well suited because it closely resembles general human behavior. An example of this is the following scenario: Two people, 20 and 50 years old, start their new job in a small company with ten employees on the same day. While the younger of the two tends to be quiet and reticent about the new situation, acting rather defensively and preferring to listen rather than speaking himself, the older one benefits from many years of professional experience and several job changes. Having experienced this situation many times before, the older person can therefore better and more quickly assess people he encounters in the new situation. He compares their behavior, their body language, the sound of their voice, but also their position with people he met on earlier “first days at work”. In doing so, the older person recognizes patterns that give him orientation in the new situation and derives conclusions for his behavior.

The MBI CONIAS database records non-violent early phases and other conflicts

People make use of pattern recognition – no matter whether through their own experience or through experience acquired through telling or reading – and thus orient themselves in new situations.  The CONIAS approach and the CONIAS database are also committed to this idea. Unlike conventional conflict databases, which only record wars or violent phases of conflict, the CONIAS database also records the non-violent early phases of these later wars[3]. In addition – and this is what makes the CONIAS approach so special – other conflicts that begin similarly to later wars but ultimately take a peaceful course are also recorded. Only in this way is it possible to make statements about the vulnerability of certain conflict constellations. This can be explained as follows: It is true that a large proportion of the few interstate wars since 1945 have been fought over territory. Examples include Iraq’s invasion of Kuwait (1991) or the conflict between Armenia and Azerbaijan over the Nagorny Karabakh region (2020). Nevertheless, conversely, it would be wrong to say that territorial or border disputes lead to war particularly frequently. Currently, there are about 120 recorded border disputes between states, almost all of which are settled without violence only at the diplomatic level. Other sources speak of an even higher number of unresolved border disputes[4].

Only a comprehensive collection of data allows to properly assess the risk potential of border disputes

In total, the CONIAS conflict database contains information on the course of more than 1,900 intra- and interstate, violent and non-violent conflicts since 1945. A large number of indicators are recorded for each conflict and actor involved, reflecting all dynamic changes in conflict resolution, but also in the socio-economic environment[5]. Thus, the CONIAS database provides millions of data points supplying statistical information on global conflict behavior. One of the most important findings of empirical conflict research can also be confirmed by CONIAS: Democracies do not wage wars against other democracies[6]. We have already integrated this “law” of democratic peace into our thinking to such an extent that, for example, even the most severe low blows in bilateral relations between Germany and the U.S. did not cause any fear of war even among the greatest pessimists.

Especially in areas not illuminated by other conflict databases, the CONIAS database reveals more points of reference

The database has shown, for example, that culturally driven conflicts have become significantly more important since the end of the Cold War in 1990 and especially after 9/11/2001[7]. At the same time, however, the CONIAS database shows that over time, it is not the number of different religions in a country that makes it vulnerable to intrastate violence, but the number of different languages spoken in the country[8].

The CONIAS conflict database is continuously maintained, and current conflict events continue to be recorded. Every quarter, knowledge about the evolution of conflicts around the world grows by tens of thousands of data points. Currently, the CONIAS team is working to better understand the links between political conflicts, human rights violations, and damage or destruction to natural livelihoods. The new supply chain law, as well as an ever-growing sense of responsibility for human rights and the environment, requires companies and ultimately every individual to act carefully in this regard. We would be pleased not only to provide you with points of reference, but also to support you with our comprehensive know-how and long-standing expertise. If you are interested, please contact our Sales Team.

About the author:
Dr. Nicolas Schwank
Chief Data Scientist Political Risk
Michael Bauer International GmbH

References:
[1] Allianz (Ed.): Allianz Risk Barometer, Various Years. Last 2021
[2] Trappl, Robert (Ed.) (2006): Programming for peace. Computer-aided methods for international conflict resolution and prevention. Dordrecht: Springer; Schrodt, Philip A. (2000): Pattern Recognition of International Crises Using Hidden Markov Models. In: Diana Richards (Ed.): Political complexity. Nonlinear models of politics. Ann Arbor: Univ. of Michigan Press, pp. 296.
[3] Schwank, Nicolas (2012): Konflikte, Krisen, Kriege. Die Entwicklungsdynamiken politischer Konflikte seit 1945. Baden-Baden: Nomos (Weltregionen im Wandel, 9); Schwank, Nicolas, et al. “Der Heidelberger Ansatz Der Konfliktdatenerfassung.” Zeitschrift Für Friedens- Und Konfliktforschung, vol. 2, no. 1, 2013, pp. 32–63.
[4] https://www.cia.gov/the-world-factbook
[5] Schwank, Nicolas (2012): Konflikte, Krisen, Kriege. (vide supra)
[6] Small, Melvin; Singer, J. David (1976): The war-proneness of democratic regimes, 1816-1965. In: The Jerusalem journal of international relations.  1 (4), pp. 50–69.
[7] Croissant, Aurel (2009) et al.: Kulturelle Konflikte seit 1945. Die kulturellen Dimensionen des globalen Konfliktgeschehens. 1st edition. Baden-Baden: Nomos (Weltregionen im Wandel, 6). Stiftung, Bertelsmann (2010): Culture and Conflict in Global Perspective. The Cultural Dimensions of Global Conflicts 1945 to 2007. Guetersloh: Verlag Bertelsmann Stiftung.
[8] Ibid.

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