Data Returned by Statistics API
The API returns market sizes for the geographies and products which you have specified.
Every row of market sizes data has the following attributes:
Geography - given by country name and a Euromonitor-specific country code (eg Japan)
Category - given by product category name and a Euromonitor-specific CategoryID (eg Beer)
Data type - given by a data type name (eg retail volume)
Unit and multiplier - given in text form in a single field eg "Million litres" and also split out into a unit column and a numeric multiplier column
Below are available links:
Manipulating the Data Outside Passport
When using the data from the Passport API you should be aware that Passport provides a mixture of lowest level and aggregate data. Within the context of Passport everything that is presented is always pre-calculated correctly. If you want to take the Passport data into other systems and perform your own manipulations, it is essential to be aware of what can and cannot be summed together.
Geography
The "CountryName" field returns the name of a country or region. Please be aware that in addition to data for 210 countries, there is also data for the following aggregate levels:
World
Asia-Pacific
Australasia
Eastern Europe
Latin America
Middle East and Africa
North America
Western Europe
If you want to perform your own calculations on the data then you should avoid summing countries with their own regions.
In addition to the name there is a CountryID field that represents each unique country. This is a Euromonitor-specific code but it may be useful when manipulating the data.
Product Category
The "CategoryName" field returns the name of a product category eg "Green Tea", "Vodka" or "Frozen Pizza". Please note that this column includes lowest level researched categories and also aggregate levels such as total Tea, Spirits, or Packaged Food.
In addition to the Category Name there is a Category ID field which represents each unique product category name, which may be useful when manipulating the data. This is a Euromonitor-specific ID which may be subject to change in future.
Data Type
The "DataType" field returns the name of the type of data to which the row refers. The concept of data type covers two different aspects of a product category:
Type of sales measured - eg in drinks industries, off-trade vs on-trade (ie retail vs foodservice)
What attribute of a product sales are we measuring - eg volume or value, or in some industries how many outlets, transactions, or the selling space of a retailer.
Three different ways of presenting financial data are also encompassed within the concept of a data type. Namely:
Data in current or constant terms - ie current (nominal) data includes inflation while constant (real terms) data is fixed on the latest research year prices, to exclude inflation.
Data in local currency terms, or one of the five standard unified currencies (US Dollar, Euro, British Pound, Japanese Yen, or Swiss Franc).
Data in a unified currency (ie all local currencies converted to a single currency eg US$) is available using year-on-year exchange rates and fixed rates from the latest research year.
In addition to this for many industries we present value sales both in RSP (retail sales price) and in MSP (manufacturer sales price) terms. A few industries have only one or the other.
Thus for a single product category, such as Shampoo, Euromonitor Passport has data for one type of sales - retail sales - but we measure this in retail volume (ie millions of litres) and retail value. That retail value data exists in the following terms for both RSP and for MSP:
Local currency (constant)
Local currency (current)
US$ (constant, fixed exchange rates)
US$ (current, fixed exchange rates)
US$ (constant, year-on-year exchange rates)
US$ (current, year-on-year exchange rates)
Euro (constant, fixed exchange rates)
Euro (current, fixed exchange rates)
Euro (constant, year-on-year exchange rates)
Euro (current, year-on-year exchange rates)
GBP (constant, fixed exchange rates)
GBP (current, fixed exchange rates)
GBP (constant, year-on-year exchange rates)
GBP (current, year-on-year exchange rates)
CHF (constant, fixed exchange rates)
CHF (current, fixed exchange rates)
CHF (constant, year-on-year exchange rates)
CHF (current, year-on-year exchange rates)
JPY (constant, fixed exchange rates)
JPY (current, fixed exchange rates)
JPY (constant, year-on-year exchange rates)
JPY (current, year-on-year exchange rates)
So for any single product, category, if it has data in value terms then many rows will be returned for each unique product. The first version of the Euromonitor Passport API simply returns all data types so that you can then choose which data type you want to use. In future we intend to add the ability to specify and filter on particular data types.
Avoiding Calculation Errors
Please remember the following guidelines to avoid any errors in calculating data outside Passport:
Never add together two different data types. There are just a few cases where it makes sense to sum up two data types for the same product (eg off-trade volume plus on-trade volume to get total volume) but in these cases the aggregated data type will already be present in the data you pull from the API.
When adding data together for multiple geographies, ensure that you never sum a country plus its region. You can add together regions, you can add together countries, and you could add a country to a different region (eg North America plus Japan) but you should never inadvertently sum overlapping data (eg North America plus Canada).
When adding financial data for multiple geographies, make sure you choose one of the unified currency data types (USD, EUR, GBP, CHF or JPY) rather than summing up local currency data. Summing local currency data on multiple countries would never make sense unless you were summing just Eurozone countries. Note that countries which are modelled in Passport have data only in unified currencies, and their "local currency" data is in fact expressed in USD.
When adding data for multiple products, you need to be very well aware of the product hierarchy for the industry you are working with. Only add together lowest levels of research, or you can add together higher level products so long as they do not overlap. For example, it is legitimate to add total Fragrances plus total Skin Care as the two categories are separate, but it makes no sense to add total Fragrances plus total Premium Beauty & Personal Care, because this would double-count Premium Fragrances.
The Data Slice object helps you to avoid such double-counting by giving you the option of returning just lowest levels only - at least for market sizes.
Pay attention to units and their multipliers when summing up data - some products such as Skin Care or Surface Care have sub-categories with a mixture of units (eg litres, kg, units) and as such it is impossible to aggregate a single row of data. In other cases the units might be compatible but the multipliers differ - eg when summing Beer plus Spirits you should be aware that Beer is in millions of litres while Spirits is in thousands of litres. Consequently the data can be summed but only after some basic multiplication.
In addition to being careful about product hierarchies within an industry, you also need to be careful when combining data from different industries. A few examples:
Health and Wellness data is essentially a deeper break out of Packaged Food, Soft Drinks, and Hot Drinks. You should not add Coffee (from Hot Drinks) plus Organic Coffee (from Health and Wellness) because the data is already included.
Retailing data overlaps with the majority of other industries provided in Passport. You should not add sales of Supermarkets (from Retailing) plus sales of Packaged Food because you would then be double-counting Packaged Food sales through Supermarkets.
Luxury data largely overlaps with a number of other industries so it does not make sense to add, for example, Designer Apparel (from Luxury) plus Apparel (from Apparel and Footwear) as you would be double-counting.
Pay attention also to different data types between industries. For example, Retailing value data excludes sales tax, Packaged Food and most other industries include sales tax.
Pay attention to different time series between industries. If you are summing up a long time series between different industries, make sure you only sum the years which both industries have in common.
Pay attention to what is the latest research year between different industries. It is possible that at one point in time, one industry has fixed prices and exchange rates in 2017 and another has them fixed in 2018. You can avoid this problem by working with current / year-on-year data.
We are working on providing richer meta-data in a future version of the API to help users to avoid making any mistakes when manipulating Passport data outside Passport. For the short term it is worth remembering that every row of market sizes you can see on Passport is available through the API so the safest way to go, if possible, is to avoid making your own calculations and rely on the pre-calculated data. If in doubt, please do not hesitate to contact your account manager.