Celiac disease affects 1 in 100 & the GFP MATRIX shows how demand is related to a country’s wealth.

Mar 24


Bruce Scott Dwyer

Bruce Scott Dwyer

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America's gluten free demand is less than Canada and Australia's but greater than Japan's. The Gluten free matrix shows how demand and the luxury value of gluten free food items change dependent on the community's wealth.


In this article gluten free research shows:

  • The definition of Tier 1 (very low) and Tier 3 (very high) gluten markets
  • Outliers to the standard trend called hyper and hypo markets
  • A linear relationship is shown between raw searches and Adjusted Celiac Searches
  • A logarithmic relation is shown between a countries adjusted searches and the wealth of the country (per person).

This research draws together all previous GFP Matrix research and articles on the GFP website and is based on analysis of Google search results for gluten products made in December 2008. The analysis compares communities in the Americas,Celiac disease affects 1 in 100 & the GFP MATRIX shows how demand is related to a country’s wealth.  Articles Europe and Asia. Communities are defined as specific language segments within a country. Most countries have the bulk of their market defined by their incumbent language searches and English language searches.

To assist analysis, gluten related search terms were divided into seven groups as shown below. Only the top 50 search terms were used for detailed statistical analysis, but in most cases, these fifty terms represent 95% of all terms.

Gluten Group Composition:

  • Generic GF Product: This group of search terms all involve the word gluten and are generic in nature, such a gluten, gluten free, gluten free products, gluten free meals.
  • Gluten Diet: These are terms that are related to the specifics of gluten free diets such as: gluten free diets, celiac diet.
  • Gluten Free recipe: Terms such as gluten free recipes, gluten recipe, gluten free baking, wheat free baking
  • Celiac related: These are terms related to information on the disease such as: celiac, celiac disease, gluten intolerance, gluten allergies
  • Wheat free: Terms such as: wheat free, wheat gluten, wheat allergy
  • GF Locations: gluten free stores, gluten free shopping, gluten free restaurant
  • Specific GF Foods: gluten free bread, gluten free pizza, gluten free cakes, gluten free muffins

Another main concept in this research was the development of a term called ‘Adjusted Celiac searches’. This value is based on raw gluten search term volumes and ‘adjusted’ (increased) to account for internet usage in a country as well as the probable total search engine gluten queries (using specific country Google Market share as the basis). This adjusted value thus estimates the total gluten searches as if 100% of a country had internet access and all search engine results were used. This intermediate value is then divided by 100 to gain a monthly ‘adjusted celiac search’ value. This assumes that the average celiac rate is approximately 1 in 100 people (diagnosed and undiagnosed). This hypothetical value thus estimates the average number of times that a celiac searched for gluten free products in a community per month – assuming that all celiac's search. Note that the main difference in English spelling in all countries is whether they use celiac (UK derived) or celiac (USA).

While it was found that a communities number of raw searches per population can act as a rough guide to its level of development, there are several exceptions. It was found that at the very low (Tier 3) and high raw searches positions (Tier 1), a pattern emerged in how the search groups are proportioned. See below:

TIER 1 characteristics

The primary classification of this group is high raw search results (searches / population). The group comprises: Australia (0.0218), US (0.0123); Canada English (0.0175); UK (0.0135); Singapore English (0.02730). Singapore’s standout result is partly due to the countries very high economic success (GDP per person) and its very high usage of telecommunications (see Asia GFP Matrix article).

At the high ‘raw search’ end of the spectrum, (AND high adjusted celiac searches) it was found that there are a high proportion of ‘generic gluten searches’ compared to other groups. It was also found that the majority of these searches were for CORE generic terms such as: gluten, gluten free, gluten free food, gluten free products etc

And of these generic searches, usually two terms comprise 90% or more of the total group’s searches. There are also usually more than ten generic terms in the top 50 search terms. While several tier 3 communities also have the top two terms being a high proportion of the group, the group usually comprises only five or less terms.

The second highest group is usually the celiac group. Like the generic group these terms are often CORE terms such as: celiac, celiac disease, gluten intolerance etc.

The specific food group on average has a low proportion of seven percent of the top 50 searches.

It is speculated that the reason for the top two groups comprise 70% TO 80% of top 50 searches are that in the generic group, these communities are in high internet penetration and have high economic wealth countries where high demand has created a high supply of gluten websites. The gluten specific websites act like gluten malls with internal search functions that people use to find the gluten info they are after. Whereas in less developed countries these large sites may not exist and people have learned to use unique and three or four word search terms to find what they want right from the search engine stage.

The high proportion of celiac group searches are most likely by newly diagnosed people and older diagnosed attempting to find if new medical breakthroughs have occurred in the disease. Again CORE terms usually dominate this group because there are enough large all encompassing celiac devoted sites that provide the answers people are seeking.

What foods to TIER 1 communities search for? The table below shows that bread (1.6); is almost always the first and/ or second searched for term. Then on average the next three most popular specific food items are cake (2.5), dairy (3.5) and pizza (3.8).


The primary classification of this group is very low raw search results per population. The group comprise communities such as: US Spanish (0.0011); Mexico Spanish (0.0004); Brazil Portuguese (0.006); China Simplified (0.009); India Hindi ( (0.0004); Indonesia Indo ( 0.0008).

As can be seen from the table, the average generic search proportion for the tier 4 communities was 32% (compared to tier 1, 58%). While compared to Tier 1 communities, the average proportion of the specific foods group was three times as high at 22%.

Of the specific foods, on average the highest average ranked tier four foods were: cake (1.3), pizza (2.0), cookies (2.3), flour (2.7), bread (3.3), corn (3.5), oatmeal (3.6)

Tier 2

These communities are developing gluten free markets. Their raw searches lie somewhere between tier 1 and tier 3 communities. In graph plots of raw or adjusted celiac searches versus GDP or other similar metrics these communities form the bridge between tier 1 and tier 3 communities.

While some tier 2 or tier 3 communities may appear to have high generic searches, often a high proportion of these are for non-core terms such as: gluten free breakfast, gluten free snacks, gf desserts, gf gifts. etc

Hypo markets

These are markets that exist in highly developed countries (like tier 1 communities) have very low raw gluten searches (like tier 3 communities) and very high proportions of generic gluten searches (like tier 1) markets.

Three classic examples of tier 2 communities are the incumbent language searches in: Germany, France & Spain.

It can be seen that the average raw search value is 0.0016, the average % of Core generic terms is 81% (much higher than the average tier 1 communities) and specific foods is 6% - very close to tier 1.

Typically the specific foods searched for are similar to tier 1 communities. For example the top three foods searched for by French speaking people in France were: gluten free Flour, oatmeal and rye flour.

Assuming that European communities have a typical proportion of celiac’s per population, the low raw searches are an anomaly. These communities have a high internet penetration and relatively high Google market share so the low share is likely to be caused by low diagnosis within the communities. These areas still have a relatively high level of wheat and gluten consumption via breads and pastas so either there is something else in their diet keeping celiac disease at bay, or they find their gluten products some other way.

Hyper Markets

These are ‘over performing’ markets.

On raw searches per population they appear like tier 3 communities, that is they have very low search values, however when adjusted for Internet penetration and Google share, they have over performing high values for ‘celiac searches’ per month.

Typically, unlike hypo markets, they are developing communities with relatively low GDP per person values. Prime examples of hyper markets are Russia English Speaking, China Traditional and Indonesia English speaking

The average raw search values are low size at 0.0035 but these convert to a very high celiac search value of 5.4. This means that these large population countries have relatively low searches, also have low internet penetration. When values are adjusted for internet penetration and Google market share they have some of the highest ‘per celiac’ searches of any communities.

Hyper markets also tend to have a lower proportion of generic searches than tier 1 and even tier 3 and the specific food searches proportion lies between that of tier 1 and tier 3.

These hyper communities are also often characterised by being smaller English speaking communities within larger non English lower economic wealth countries. It is likely that these enclaves have a higher than normal proportion of people who have access to enhanced health care (to be diagnosed in the first place) and that their relative wealth makes their gluten free tastes more exotic/ luxurious than even tier 1 communities. For example the top Russian English specific foods in order of search size are: pizza; gluten free beer; gluten free cakes; gluten free muffins.

Similarly Indonesian English speaking searches searched in order of size for: gluten free pizza, gluten free pasta; gluten free muffins then flour.


The raw search versus ‘adjusted celiac search’ plots shows a linear relationship between the two parameters. While this may be expected, this graph reveals Russia China and Indonesia as anomalies to this trend. As discussed previously, the things these places have in common are that they all have very large populations, are developing countries and have low internet penetration.

The reasons for these outliers are discussed in hyper markets section above however one of the most useful things is to see how so many developed and developing communities are clustered at the undeveloped market end of this graph. While Australia, US and Singapore markets are not anywhere near fully developed as yet (still a large undiagnosed amount of people - much less than the 1 in 100 forecast), it shows that there is a very large room for development in these markets also. As being a celiac is a disease and has severe medical consequences for its sufferers, it is amazing that very developed countries such as France, Germany and Italy should have such lowly developed gluten demand.

Even more remarkable is the implications of the plot for ‘Adjusted celiac values V GDP per person’ resulting in a strong logarithmic trend. Ignoring the effects of outlier hypermarkets such as Russia, China and Indonesia, it can be seen that most countries lie on a steadily decreasing arc as the celiac search values increase.

This clearly demonstrates that for the majority of countries analysed that wealth (GDP per person) is a clear indicator of gluten free demand and/ or celiac diagnosis. While India and Mexico might also appear slightly off the log trend, it should be noted that these countries have very low internet penetration and so the adjustment factors to convert tier raw values into celiac search values are much more sensitive to small changes in media estimations of internet and Google share.

The relationships also suggests that particularly for countries with at least moderate internet penetration, that knowing their GDP per person value may allow an estimate of their gluten free market development and/ or diagnosis level.

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