Search Density per Head

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Chris Jones

Author: Chris Jones, Independent Multichannel Retail Consultant

 

Comparing Countries?

 

In my previous two articles (Global SEM Costs and Global SEM Costs Part 2) I’ve looked at two ways of forecasting the expected efficiency of your SEM, in terms of cost-per-click, in various countries where you might consider targeting some spending at as part of your international ecommerce strategy. In both cases, I tried to find ways of eliminating “language” bias: in the first I compared some global brand terms, and in the second, I compared generic womenswear terms by using the global website-menu structure of H&M.

 

An interesting by-product of these investigations is that the Google Keyword Planner tool has also given me estimated search volumes (what Google calls “average monthly searches” based on the last 12 months). I’ve then used these to make a very brief comparison at the “search density per head” in each of the countries I captured data for.

 

Methodology

 

The first thing we need to do is to adjust these volumes because Google’s market share is variable in different countries.

 

In the IMRG/eCommerce Worldwide guide to taking your e-commerce international “A Nation of Shopkeepers”, we showed the following chart of search-engine market share in different countries:

 

eCommerce Worldwide Cross-Border Search Engine share by Country

 

I’ve therefore used the underlying data behind this chart to adjust the search volumes I captured in proportion to Google’s share of total search in each country.

 

I’ve then expressed the search volumes calculated as “monthly searches per head of population” (using country population data taken from the CIA World Factbook).

 

Finally I’ve expressed the searches-per-head as a percentage of the baseline figure for the UK, so we can make some comparisons.

 

Results

 

Here is the resulting chart:

eCommerce Worldwide Cross-Border relative search density by country

 

While I wouldn’t want to overstate the significance of conclusions drawn from a very small sample of keywords, there are still some interesting observations to be made.

 

  • By far the most important observation is to notice how strong the “generic” density is in Sweden, which is H&M’s home country of course. What this is saying very clearly is that copying your home-country menu structure intact across to a new language/country is not the optimal approach. H&M are likely to be missing out on a lot of natural search, especially in countries like France where they seem to have done a rather poor job. Given how important key category-pages are to a website, this is an area in which it probably pays to really focus your localisation efforts.
  • The consistency in behaviour (apart from Belgium) between language groups is worth noting. Germany and Austria have pretty much the same relationship as UK and USA/Australia for example. So, incidentally, do Japan+Korea and Spain+Italy. This again points to the importance of getting the translation/localisation right for crucial terms, and of constantly analysing and optimising your top-level taxonomy structures.
  • The gap between branded and generic in Japan and Korea gives a feel for the importance of non-roman scripts – approximately double (although this isn’t reproduced for Russia).
  • Analysing Belgium as a single country was a mistake! If you’re tackling Belgium, split your analysis into French- (south + Brussels) and Vlaams- (the rest) speaking areas early on, and do your planning with this split in place (this observation is true in the detail of the data as well; I wish I’d left out Belgium with hindsight).
  • Ignoring the strange outlying data points, there is actually a pretty good correlation here between search density and ecommerce penetration. The UK is top, places like USA, Germany and Sweden pretty close behind, Brazil is fairly distant. This tends to suggest that the overall analysis is at least plausible.

 

Conclusion

 

Overall, my main conclusion would be that “translated clones” are probably not going to be optimal in terms of marketing and traffic-driving. There is then of course a difficult business decision to make about the cost of maintaining different customer experiences versus the value of doing so.

 

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