Evolution of choice and innovation in the EU food sector

Transcription

1 Evolution of choice and innovation in the EU food sector Key findings October 2014

2 1 Scope and methodology Objectives of the study Assess the economic impact of modern retail on choice and innovation in the EU food sector Analyse the evolution of choice and innovation Identify the main potential drivers of choice and innovation (eg concentration) and measure their evolution Determine the impact of drivers on choice and innovation 1

3 1 Scope and methodology A comprehensive study, requiring outstanding resources A very broad scope to meet high expectations Wide scope in the EU Long period including pre-crisis Consumer standpoint: access to choice and innovation at local level in modern retail shops Quantitative (including econometrics) and qualitative analyses (6 case studies) Outstanding resources 2004 to 2012 (2 time periods per year) Selection of shops and countries based on availability of data on long period Data purchase / consolidation from different data sources Focus on local data: 343 shops, 9 Member states, 105 consumer shopping areas 23 product categories with a full set of information for each product A database of 11 million data records 2

4 1 Scope and methodology Expert workshops and literature review Validation of definitions: work process 1 st survey round List of initial thoughts about choice and innovation Emphasis on drivers and operationalisation Online focus group Validation of the first round Generation of consensus around choice and innovation Free discussion on each topic focusing on reactions and conclusions 2 nd survey round Validation of conclusions providing a joint definition and operationalisation of key concepts Emphasis on differences encountered throughout the study 3

5 1 Scope and methodology Expert workshops and literature review Measurement of choice Food choice Variety of products available in shops in consumer shopping areas Variety of packaging sizes Variety of prices Variety of suppliers Shop choice Variety of shops to which a typical consumer has access within a normal distance (consumer shopping area) 4

6 1 Scope and methodology Expert workshops and literature review Measurement of innovation Need to have objective and measurable KPI, excluding subjective qualitative qualifications Number of new SKUs* (excluding promotions) available for consumers at shop level Identification and measurement of innovation types New product Range extension New packaging New formulation Relaunch * SKU = Stock keeping Unit 5

7 1 Scope and methodology Expert workshops and literature review Innovation types (Mintel GNPD) New product: assigned when a new range, line, or family of products is encountered. This launch type is also used if a brand that already exists on GNPD, in one country, crosses over to a new sub-category New variety/range extension: used to document an extension to an existing range of products on the GNPD New packaging: determined by visually inspecting the product for changes, and also when terms like New Look, New Packaging, or New Size are written on pack. New formulation: determined by visually looking for key terms on pack like New Formula, Even Better, Tastier, Now Lower in Fat, New and Improved, Great New Taste.. Relaunch: some wording indicating that the product has been relaunched on the packaging or the product does not exist on the database but there is secondary source information (such as from a press release, magazine, trade show, website or a shop display) that the product has been relaunched 6

8 1 Scope and methodology Database construction List of shops Trade Dimensions Join by period and CSA List of EAN present on shelves for each 343 shops of the sample Join by period, MS and product category Supplier characteristics List of new products (EAN, type of innovation) Join by period and EAN Opus DATABASE CALCULATIONS Join by period, MS and product category Retails characteristics Sociodemographic statistics National and NUTS3 levels Join by period and CSA Shop level database Shop level database Shop level database Output tables for descriptive statistics Shop level database Database of 11 millions data records processed 7

9 1 Scope and methodology Database construction Selection of shops and consumer shopping area at local level Identification of consumer shopping areas thanks to geolocalisation Consumer point of view Geographical perimeter of each consumer shopping area (CSA) illustrative Travel time between the central point (city hall) and outer limit of the area 15 min travel time for large cities 20 min travel time for medium and small cities 25 min for a rural zone Figure 2: Example: Consumer shopping area Clichy-sous-Bois (FR) 8

10 1 Scope and methodology Database construction 343 shops in 105 consumer shopping areas GDP/Capita Low Medium - Medium + High Total Type of living Number of Number of Number of Number of Number of CSA CSA CSA CSA CSA Predominantly Rural (PR) Intermediate (INT) Predominantly Urban (PU) TOTAL Representativeness of sample vs EU27 population by standard and type of living categories 30% 25% 25% 25% 25% 26% 24% 22% 35% 40% 42% 38% EU27 23% 22% EU27 Sample Sample Low Medium- Medium+ High PR IN PU Sources: Eurostat, EY analysis 9

11 1 Scope and methodology Database construction Identification of catchment areas for each shop in the sample to assess local concentration The area includes, for a given shop, all modern retail shops in the area Shop point of view Geographical perimeter of each catchment areas illustrative Travel time between the central point (shop) and outer limit of the area 10 to 20 min for hypermarkets 5 to 10 min for supermarkets and discounters depending on area type Figure 2: Example: Consumer shopping area and catchment area Clichy-sous-Bois (FR) 10

12 1 Scope and methodology Database construction Database of 11 millions data records processed Belgium 23 categories Shop coverage 343 shops Hypermarkets Supermarkets Discounters All main retail groups and banners in Europe CSA coverage 105 Consumer Shopping Areas Country coverage Czech Rep. Denmark France Hungary Italy Poland Portugal Spain 51% of EU pop Product coverage Frozen pizzas/starter, frozen ready cooked meals, frozen vegetables Ice cream Milk, cheese, yoghurt, butter/margarine, desserts Bread, ham / delicatessen Baby food, canned vegetables, edible oil, savoury snacks Coffee, tea, Biscuits, cereals, chocolate Fruit juices, mineral water, soft drinks Time coverage Precrisis period Crisis period

13 1 Scope and methodology Descriptive analyses Aimed to distill the richness of the database into meaningful statistics Produced a consistent reporting pack covering choice, innovation and all potential drivers for each member state and at consolidated level, for short and long periods Approach allowed comparison between CSA, countries, CSA types, shop types, identifying wider trends as well as those unique to particular markets Informs the econometric analysis and provides hypotheses for testing 12

14 1 Scope and methodology Approach Econometric analyses measuring the impact of drivers on choice and innovation Analyse the historical evidence for the impact of potential drivers on various measures of choice and innovation, controlling for local and national influences Model the behaviour of each shop and the selection of products that it offers, with reference to various national and local drivers and shop characteristics 13

15 1 Scope and methodology Econometric analyses measuring the impact of drivers on choice and innovation [choice or innovation] s,p,t = f { shop type s, t shop size s, t private label share n/s,p,t retailers' concentration n/s,t suppliers' concentration n/s,p,t [or imbalance (retailer vs supplier concentration) n/s,p,t ] socio-demographic indicators c,t rural/urban category c or population density c product category turnover n,p,t economic prosperity/macroeconomic conditions c/n,t Member State n product category p year y season m new competitor shop opening s,t } 14

16 Main conclusions

17 Main conclusions: choice Choice available to consumers in local shops increased in terms of the number of alternative products, the number of different brand suppliers and the number of modern retail shops The increase was greater during than In addition to the shop types and shop size which have an obvious impact on choice, economic prosperity and product category turnover have been favourable factors for choice Competition in the form of a new shop opening in the local area improves the choice offered in existing shops There is no evidence that the concentrations of retailers or suppliers have been an economic driver of choice The impact of private labels on the evolution of choice was found to be negative but small. 16

18 Main conclusions: innovation The presence of innovative food products at retailers shop level has declined in Europe after 2008 Innovations focused on new packaging have become considerably more common over time in most European Member States Innovation slowed down, particularly because of the impact of: Economic drivers: booming unemployment impacting the purchase power of European households Increasing supplier concentration at national level However, the opening of new shops in the catchment areas had a positive effect on new products available to consumers, whereas greater concentration among modern retailers at a local level was associated with less innovation As for choice, shop type and shop size had an obvious impact on innovation available to consumers High share of private labels were associated with less innovation Evidence from the case studies suggested that, for fresh non-barcoded products, a keydriver for innovation was the organisation of the supply-chain. 17

19 Evolution of choice

20 2 Evolution of choice and innovation Choice in shops, alternative products and brand suppliers has increased in the majority of sampled MS Type of choice Component Trend Choice in alternative products** 7,9% 2,4% 5,1% Choice in packaging sizes 5,0% 2,0% 3,5% Food Choice Choice in alternative suppliers** 5,6% 1,5% 3,5% Choice in prices per product category Shop Choice Choice in shops* 1,8% 1,3% 1,6% + Positive CAGR; - Negative CAGR; ++ CAGR is twice as much as average growth value; -- CAGR is twice as less as average growth value 1 : Results need to be considered with caution because of inconsistency found in data 19

21 2 Evolution of choice and innovation Choice in alternative products Per product category Annual growth of total number of EAN by product category % 8% 7% 6% 5% 4% 3% 2% 1% 0% 8% 7% 7% 7% 7% 6% 6% 6% 6% 6% 5% 5% 5% 5% 5% 5% 5% 5% 4% 4% 3% 3% 3% 2% Source: EY analysis based on Nielsen Opus, at local level - FR-IT-PT-SP-HU-BE,

22 2 Evolution of choice and innovation Choice in alternative products Per shop type Annual growth of total number of EAN by shop type 0,76 1,26 1,76 2,26 2,76 3,26 3,76 10% 9,3% % 8,3% 8,0% % 6,8% 7% % 5,2% 5% 4,7% % 3,6% % 2,2% 2,6% % 1% % 0 Hypermarkets Supermarkets Discount Stores CAGR(04-08) CAGR(08-12) CAGR(04-12) 2004 values Source: EY analysis based on Nielsen Opus - FR-IT-PT-SP-HU-BE, 23 product categories 21

23 Evolution of innovation

24 2 Evolution of choice and innovation Innovation rate declined after 2008 Evolution of innovations 5% 4% Evolution of the number of innovations (new EAN) 3,8% 3% 2% 1% 0% -1% -2% -1,2% -3% -4% -5% -6% Total new EANs -5,3% CAGR(06-08) CAGR(08-10) CAGR(10-12) Source: EY analysis based on Nielsen Opus BE-FR-IT-PO-PT-SP 23

25 2 Evolution of choice and innovation over almost all product categories Per product category Annual growth in number of new EAN codes by product category % 2% 0% 3% 2% 1% 1% 1% 0% -2% -4% 0% 0% 0% -1% -1% -1% -1% -1% -2% -2% -2% -3% -3% -6% -8% -4% -4% -4% -5% -7% Source: EY analysis based on Nielsen Opus BE-FR-IT-PO-PT-SP 24

26 2 Evolution of choice and innovation A shift in innovation type from product innovation to packaging innovation Per type of innovation across product categories 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Dairy Fresh non dairy Frozen Savoury grocery Sweet grocery Beverage Relaunch 0% 4% 0% 3% 1% 4% 1% 4% 2% 5% 1% 3% Range extension 39% 32% 32% 38% 41% 40% 32% 28% 39% 34% 40% 24% Formula 3% 4% 5% 6% 6% 7% 5% 4% 5% 4% 2% 2% Packaging 6% 27% 7% 22% 4% 19% 6% 26% 5% 28% 16% 40% Product 51% 32% 56% 31% 48% 31% 56% 38% 49% 29% 40% 31% Source: EY analysis based on Mintel GNPD and Nielsen Opus Proportion of innovation types by product category 25

27 2 Evolution of choice and innovation The development of new packaging is stronger in France, Italy and Spain than in Belgium, Poland and Portugal Per type of innovation across MS 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Proportion of types of innovation by MS (local level) Belgium 04 Belgium 12 France 04 France 12 Italy 04 Italy 12 Poland 04 Poland 12 Portugal Portugal Spain 04 Spain 12 Relaunch 0,6% 0,0% 1,0% 0,1% 0,4% 0,6% 0,6% 0,3% 0,0% 0,0% 2,4% 0,5% Range extension 45,1% 35,6% 50,6% 33,6% 30,9% 28,5% 41,4% 39,0% 33,9% 26,6% 34,9% 36,4% Formula 3,1% 3,7% 5,7% 6,2% 2,9% 2,7% 1,7% 1,8% 0,0% 2,8% 3,9% 3,0% Packaging 8,6% 9,9% 8,4% 30,8% 7,1% 33,7% 2,9% 5,8% 3,1% 8,5% 10,2% 24,1% Product 42,0% 48,7% 34,1% 24,3% 58,5% 30,3% 53,1% 52,2% 62,2% 59,6% 48,6% 33,6% Source: EY analysis based on Mintel GNPD and Nielsen Opus

28 2 Evolution of choice and innovation New packaging development occurs across all product categories, and represents a large share of innovations for dairy products. Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Proportion of types of innovations: Dairy 0% Butter/margarine Cheese Dessert Milk Yoghurt Relaunch 0% 4% 1% 5% 0% 4% 0% 5% 1% 3% Range extension 34% 25% 35% 33% 38% 41% 33% 23% 57% 36% Formula 5% 4% 3% 2% 6% 8% 0% 2% 1% 6% Packaging 7% 31% 7% 29% 3% 19% 8% 35% 6% 23% Product 54% 35% 55% 31% 52% 28% 59% 35% 35% 32% Source: EY analysis based on Mintel GNPD and Nielsen Opus

29 2 Evolution of choice and innovation New packaging share remains weaker than product innovation and range extension for fresh non dairy products Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Proportion of types of innovations: Fresh non dairy 0% Fresh pre-packaged bread Ham/delicatessen Starters/pizzas Relaunch 0% 2% 0% 2% 0% 5% Range extension 30% 37% 36% 41% 29% 36% Formula 3% 8% 3% 4% 9% 7% Packaging 3% 25% 8% 21% 11% 19% Product 64% 28% 53% 31% 51% 33% Source: EY analysis based on Mintel GNPD and Nielsen Opus

30 2 Evolution of choice and innovation New packaging share remains weaker than product innovation and range extension for frozen products Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Proportion of innovation types by category: Frozen 0% Frozen vegetables Ready-cooked meals Ice cream Relaunch 0% 4% 3% 6% 0% 2% Range extension 44% 44% 44% 38% 35% 39% Formula 7% 6% 9% 10% 1% 4% Packaging 4% 21% 7% 19% 1% 15% Product 44% 25% 37% 27% 62% 40% Source: EY analysis based on Mintel GNPD and Nielsen Opus

31 2 Evolution of choice and innovation New packaging development occurs across all product categories for savoury grocery Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Baby food (ambient) Canned vegetables Edible oil Savoury snacks Relaunch 1% 9% 0% 3% 0% 2% 1% 4% Range extension 34% 25% 21% 36% 33% 21% 41% 31% Formula 9% 8% 8% 2% 0% 1% 1% 4% Packaging 1% 27% 21% 19% 0% 31% 3% 24% Product 54% 31% 50% 39% 67% 45% 53% 38% Source: EY analysis based on Mintel GNPD and Nielsen Opus Proportion of types of innovations: Savoury grocery 30

32 2 Evolution of choice and innovation New packaging development is quite strong for sweet grocery products. Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Proportion of types of innovations: Sweet grocery 0% Chocolate (Bar + Biscuits Cereals Coffee Tea Candies) Relaunch 1% 5% 3% 6% 1% 3% 2% 6% 3% 6% Range extension 40% 32% 26% 28% 49% 33% 38% 32% 45% 46% Formula 2% 4% 8% 7% 1% 3% 7% 2% 5% 4% Packaging 6% 27% 5% 31% 3% 26% 3% 35% 8% 21% Product 51% 33% 59% 29% 46% 36% 50% 25% 40% 22% Source: EY analysis based on Mintel GNPD and Nielsen Opus BE-FR-IT-PO-PT-SP 31

33 2 Evolution of choice and innovation New packaging represents the larger share of innovation types for beverages. Per type of innovation and product category 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Fruit juices (ambient) Mineral water Soft-drinks Relaunch 1% 5% 0% 2% 1% 3% Range extension 42% 31% 37% 14% 42% 27% Formula 1% 2% 6% 1% 0% 3% Packaging 11% 31% 26% 54% 12% 36% Product 45% 31% 31% 29% 45% 32% Source: EY analysis based on Mintel GNPD and Nielsen Opus Proportion of types of innovations: Beverage 32

34 Econometric results: drivers of choice and innovation 33

35 3 Econometrics results Positive impacts on choice Product category turnover Economic prosperity Shop size Shop type New shop opening in the local area No or low economic impact on choice Concentration drivers Private labels (-) Unemployment (+) Population density (-) 34

36 3 Econometrics results Product category turnover, economic prosperity, shop size and shop type are the most important drivers for choice Drivers for choice Product variety Product size variety Product supplier variety Product price variety impact Signif. Import. impact Signif. Import impact Signif. Import impact Signif. Import Product category turnover.. GDP per capita?.. Shop floor space.. Shop type N.A. N.A N.A? N.A New shop opening in the local area.... Positive impact Negative impact? Where the sign varies according to whether the parameter is estimated over the long or short data sets Significant at 5% level Significant at 1% level Impact of more than 5% Impact of more than 10% -- Not statistically significant or economically important according to these thresholds 35

37 3 Econometrics results Product category turnover, economic prosperity, shop size and shop type are the most important drivers for choice Product category turnover Choice in variety of EANS versus national product category sales turnover in 2010 period 1 in four Member States Statistical significance 1% level Direction of impact Positive Economic importance Large Sources: Analysis based on Nielsen Opus and Euromonitor International 36 Sources: Analysis based on Nielsen Opus and Euromonitor International

38 3 Econometrics results Product category turnover, economic prosperity, shop size and shop type are the most important drivers for choice GDP per capita Statistical significance 1-5% level Direction of impact Positive Economic importance Large Choice in variety of EAN codes versus GDP per capita Floorspace Statistical significance 1% level Direction of impact Positive Economic importance Large Sources: Analysis based on Nielsen Opus and Eurostat 37

39 3 Econometrics results Other drivers have no or low economic impact on choice Drivers for choice Product variety Product size variety Product supplier variety Product price variety Retail concentration at national level Retail concentration at local level impact Signif. Import impact Signif. Import impact Signif. Import impact Signif. Import Supplier concentration at national level Imbalance between retailers and suppliers at national level ? ? Private labels (local) Unemployment Population density.. Too few observations for conclusions to be drawn with confidence. 38

40 3 Econometrics results Little indication of an impact of national retail concentration on choice Retail concentration at national level Choice in variety of EAN codes in the sampled shops versus national retail concentration Very few observations from which to draw conclusions Statistical significance No, except product price variety Direction of impact Negative for product price variety Economic importance Large for product price variety Sources: Analysis based on Nielsen Opus and Planet Retail 39

41 3 Econometrics results There is no evidence that supplier concentration is an economic driver of choice Supplier concentration at national level Statistical significance No (except product size variety) Choice in variety of EAN codes versus national supplier concentration by product category, 2008 Direction of impact Positive for product size variety Economic importance Small Sources: Analysis based on Nielsen Opus and Euromonitor International 40

42 3 Econometrics results Other econometric results regarding factors driving choice Population density Statistical significance 1% level Direction of impact Negative Economic importance Small Unemployment Statistical significance Various Direction of impact Positive; negative for product price variety Economic importance Small Private labels Statistical significance 1% level Direction of impact Negative Economic importance Small Measure of imbalance between retailers and suppliers at national level Statistical significance Various Direction of impact Ambiguous for statistically significant cases Economic importance Moderate for product price variety 41

43 3 Econometrics results High shares of private labels tend to be associated with less choice in hypermarkets and supermarkets Share of private labels (in each product category) in each shop Choice in variety of EAN codes versus private labels share, by shop type Sources: Analysis based on Nielsen Opus and Euromonitor International 42

44 3 Econometrics results Measured impacts on innovation Product category turnover (+) Shop size (+) Shop type (+) New shop opening in the local area (+) Retailers business expectations (+) Unemployment (-) Population density (-) Local retailer and supplier concentration (-) Private labels (-) 43

45 3 Econometrics results Shop size, shop type, retailer business expectations and product category turnover are the most important positive drivers for innovation Drivers for innovation Opus innovations New products New packaging New formulations New line extensions impact Signif. Import. impact Signif. Import impact Signif. Import impact Signif. Import impact Signif. Import Shop size Shop type N.A. N.A. N.A. N.A. N.A. Retailer business expectations Product category turnover?......?? Positive impact Negative impact? Where the sign varies according to whether the parameter is estimated over the long or short data sets Significant at 5% level Too few observations for conclusions to be drawn with confidence. Significant at 1% level Impact of more than 5% Impact of more than 10% -- Not statistically significant or economically important according to these thresholds 44

46 Shop size, shop type, retailer business expectations and product category turnover are the most important positive drivers for innovation Shop size Statistical significance 1% level Direction of impact Positive Economic importance Large Very few observations from which to draw conclusions Statistical significance 1% level Direction of impact Positive Economic importance Large 3 Econometrics results Retailer business expectations Shop type Statistical significance 1% level Direction of impact Positive (larger formats offer a greater number of innovative products) Economic importance Large Opus innovations versus retailer business expectations Sources: Analysis based on Nielsen Opus and Eurostat 45

47 Statistical significance 1% level 3 Econometrics results Shop size, shop type, retailer business expectations and product category turnover are the most important positive drivers for innovation Product category turnover Direction of impact Generally positive Economic importance Various New EAN codes (innovations) versus national product category sales turnover in 2010 period 1 in four Member States Sources: Analysis based on Nielsen Opus and Euromonitor International 46

48 3 Econometrics results Other drivers have various impacts on innovation Drivers for innovation Opus innovations New products New packaging New formulations New line extensions Retail concentration at national level Retail concentration at local level Supplier concentration at national level Imbalance between retailers and suppliers at national level impact Signif. Import. impact Signif. Import impact Signif. Import impact Signif. Import impact Signif. Import? ?? Private labels New shop opening in the local area Too few observations for conclusions to be drawn with confidence. 47

49 3 Econometrics results Greater concentration among retailers at a local level is associated with less innovation in new packaging Retailer concentration at the procurement level Very few observations from which to draw conclusions Statistical significance 1% Direction of impact Positive except for new packaging (negative) and new formulations (ambiguous) Economic importance Large (for modern retail measure) Retailer concentration at the local level Statistical significance No (except for new packaging) Direction of impact Negative Economic importance Large for new packaging New EAN codes (innovation) versus national retail concentration Source: Analysis based on Nielsen Opus and Planet Retail New EAN codes (innovation) versus local retail concentration in two years Sources: Analysis based on Nielsen Opus and Nielsen Trade Dimensions 48

50 3 Econometrics results Greater concentration among suppliers at national level is associated with less innovation (some measures) Supplier concentration at the national level Opus innovations versus national supplier concentration by product category, 2008 Statistical significance 1% for several innovation indicators Direction of impact Mostly negative Economic importance Moderate to large Sources: Analysis based on Nielsen Opus and Euromonitor International 49

51 3 Econometrics results The finding for supplier concentration is also reflected in the finding for retailer supplier imbalance Imbalance between retailers and suppliers at national level Choice in variety of EAN codes versus imbalance between retailers and suppliers Statistical significance 1% for most innovation indicators Direction of impact Positive (i.e. a greater imbalance in favour of suppliers has a negative impact) (except new packaging where ambiguous) But remember that the sample does not have cases with high national retail concentration Economic importance Generally large Sources: Analysis based on Nielsen Opus and Euromonitor International 50

52 3 Econometrics results Other general economic drivers have low or negative impact on innovation Drivers for innovation Opus innovations New products New packaging New formulations New line extensions impact Signif. Import. impact Signif. Import impact Signif. Import impact Signif. Import impact Signif. Import Unemployment Population Population density

53 3 Econometrics results The rate of unemployment in the region has a generally important negative impact on innovation Unemployment Statistical significance 1% level (in long data set) New EAN codes (innovations) versus unemployment rate Direction of impact Negative (in long data set) Economic importance Large Sources: Analysis based on Nielsen Opus and Eurostat 52

54 3 Econometrics results Other econometric results regarding factors driving innovation Population and population density Statistical significance 1% (for population density for new packaging and new formulations) Direction of impact Negative (in those cases) Economic importance Large (in those cases) Private labels Statistical significance 1% level for three cases Direction of impact Negative Economic importance Large (in those cases) 53

55 3 Econometrics results High shares of private labels are associated with fewer innovative products in hypermarkets and supermarkets Share of private labels (in each product category) in each shop New EAN codes (innovations) versus private labels share, by shop type Sources: Analysis based on Nielsen Opus and Euromonitor International 54

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