Pan Emea Digital Campaign


How to extrapolate and describe your audience behaviour from a given population using Webber’s assumption of class/background distinction
 
 

Company Background

Our Client is a leading Japanese automotive manufacturer. During 2007, they produced almost 1.3 million vehicles for global sales. The majority of these (nearly 1 million) cars were produced in the company’s Japanese plants, with the remainder coming from a variety of other plants worldwide.

Client request

Our Client request included a design of a common EMEA platform for all its websites coupled with a Pan EMEA online marketing strategy, which needed to be easy-to-customised with localization contents. The first objective was reached clustering all markets on the basis of selective constraints and whilst to be able to design a “common” online mkt strategy we needed to group countries according to sales potential and similarities in consumers’ behaviour. 

Problems experienced

Consumers’ behaviour differs significantly not only among countries, but among social classes too. Level of education, social and economic status, disposable income, level of access to information, culture, religion, style and fashion, exposure to external inputs were just some of the constraints we had to face.   

To estimate of the company’s sales potential, we needed to collect products and markets specific data related to:

  1. Competition > both online and offline – strength and likely to react to new entry.
  2. Markets – strength of barriers.
  3. Consumers Online Buying Cycle – their ability and willingness to buy offline/online, coupled with their readiness in using online tools.
  4. Product – degree of relative advantage, compatibility, complexity and communicability.  
  5. Channel structure – access to retail level and supply chain analysis.
  6. Technology – Internet, broadband access, CRM/Siebel technology. 
  7. Segments to be marketed.
  8. Media Landscape.

We used information gathered from diverse sources though which we clustered the EMEA markets using a spider graphical representation which allowed us to show similarities/divergences in market entry, technology, internet sophistication, supply chain optimization weighted by Internet Usage, willingness and ability to buy, brand perception.

All countries/markets were also grouped on the basis of social classes, culture and aesthetic taste.

Results

We segmented the overall market considering 6 clustering profiles based on online manner of speech, schooling, leisure habits and many other factors assuming that the outcome produced by “horizontal” classification is more marketing/selling effective than the simple “vertical” differentiation. 

For each market we were able to define competitors benchmarking indexes weighting factors such as market presence, dominant positioning, barriers to entry, mkt messaging, frequency and marketing mix as well as consumers’ preferences towards detailed automotive specifications and market characteristic (rules and standards, barriers to entry, economic growth, income inequalities and technology development). 

We were able to market different countries using similar marketing campaigns with minor adjustment on country basis, thus saving costs and effort on both planning and creative stages. The deep market knowledge allow us to always keep in touch with every local market adjustment, plus the awareness of different technology stages in which countries might be allow us to easily switch marketing channels accordingly. 

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