CHOCOLATE AND NON- CHOCOLATE CONFECTIONERY CANADA ... · wafers, marshmallows, alfajores. cocoa powder and chocolate drinks. Sugar & gum confectionery • This market covers packaged

CHOCOLATE AND NON-CHOCOLATE CONFECTIONERY CANADA, FEBRUARY 2019

CHOCOLATE AND NON-CHOCOLATE CONFECTIONERY, CANADA - FEBRUARY 2019 2

© Mintel Group Ltd. All rights reserved.

OVERVIEW

What you need to know

Canadians have a sweet tooth. They show a love for chocolate and for non-chocolate candy by virtue of the sheer number of Canadians who claim they turn to these categories. While Canadians may like what’s familiar to them, there is also a notable portion that cite interest in options that are innovative when it comes to flavour, format and to an extent, functionality. While established brands don’t want to undermine the equity they’ve established with their consumers, feedback collected for this and other Reports show that there is opportunity to capitalize on prospective ‘white space’ opportunities.

Definition

For the purposes of this Report, Mintel has used the following definitions for chocolate and non-chocolate confectionery:

Consumer feedback

Chocolate confectionery:

• Individual chocolate bars (eg Kit Kat)

• Bags/boxes of chocolates (eg individually wrapped or non-wrapped)

• Larger format chocolate bars (eg tablets)

• Chocolate sold in loose form (eg bulk section of store)

• Other chocolate

Non-chocolate confectionery:

• Chewy candy (eg gummies)

• Hard candy

• Toffee/caramel/nougat

• Liquorice

• Lollipops

• Other non-chocolate candy

Market size definition

Chocolate confectionery:

• This market covers wrapped countlines, tablets, individually wrapped, non- individually wrapped, seasonal and other chocolate confectionery. Market size comprises sales through all retail channels including direct to consumer.

• Chocolate confectionery segment definitions

• Countlines: this comprises full size countlines such as Mars bars.

• Individually wrapped: this includes assortments such as Quality Street and bite sized wrapped.

• Non-individually wrapped: this includes assortments such as M&Ms, boxed chocolates (unwrapped) and bite sized unwrapped.

• Seasonal: this includes products which are available only at certain times of year such as Easter eggs, Christmas products such as chocolate Santa Claus.

• Tablets: this includes slabs where pieces can be broken off.

Chocolate confectionery exclusions:

• Unwrapped chocolate. Biscuits, cakes and cake bars including wafers, marshmallows, alfajores. cocoa powder and chocolate drinks.

Sugar & gum confectionery

• This market covers packaged chewing and bubble gum, fruit and other flavoured, mints, hard, functional and other sugar confectionery. Market size comprises sales through all retail channels including direct to consumer.

• Note: while gum is included in the overall market size estimates included in this Report, the scope of the overall Report, inclusive of consumer feedback, excludes gum

Sugar & gum confectionery segment definitions

• Bubble gum: this includes regular and sugar-free varieties.



• Fruit soft: this includes pastilles, gums, gummies, jellies and chews.

• Functional: this includes medicated and energy giving products.

• Hard: this includes boiled sweets and lollipops.

• Mints: this includes regular mints and breath fresheners.

• Other: this includes mixed assortments and other sugar confectionery.

• Other flavoured soft: this includes liquorice, marshmallows, fudge, toffee, caramels, nougat, Turkish Delight, Halva and others, such as éclairs and marzipan.

Sugar & Gum confectionery exclusions:

• Chocolate, breath freshening strips, loose unwrapped products.



REPORT CONTENT

OVERVIEWWhat you need to knowDefinition

Consumer feedbackMarket size definition

EXECUTIVE SUMMARYThe issues

Health-related concerns influencing category innovationFigure 1: Top areas of interest around chocolate and non-chocolate confectionery, November 2018Canada’s aging population represents a possible challenge to growthFigure 2: Chocolate and non-chocolate candy usage, November 2018

The opportunitiesConfectionery positioned as a ‘functional’ optionFigure 3: Interest in health-boosting ingredients, women 18-34-years-old vs overall, November 2018Candy’s not just for kids according to CanadiansFigure 4: Interest in adult-focused flavours, by age, November 2018Canadians equate chocolate with emotional well-beingFigure 5: Attitudes around chocolate supporting emotional well-being, by gender, November 2018Canadians are willing to pay more for better quality chocolateFigure 6: Willingness to pay more for higher quality chocolate, by age, November 2018

What it means

THE MARKET

WHAT YOU NEED TO KNOWChocolate value sales predicted to grow at a healthy clipChocolate tablets shows upward momentumSugar confection expected to grow at a tepid pace

MARKET SIZE AND FORECASTChocolate value sales predicted to grow at a healthy clip

Figure 7: Canada retail value sales and fan chart forecast of chocolate confectionery market, at current prices, 2013-23Figure 8: Canada retail value sales and forecast of chocolate confectionery market, at current prices, 2013-23Figure 9: Canada retail volume sales and fan chart forecast of retail chocolate confectionery market, 2013-23

Sugar confection expected to grow at a tepid paceFigure 10: Canada volume sales and fan chart forecast of retail sugar & gum confectionery market, at current prices, 2013-23Figure 11: Total Canada volume sales and forecast of retail sugar & gum confectionery market, at current prices, 2013-23Figure 12: Canada retail value sales of sugar confectionery vs gum confectionery, 2017

MARKET BREAKDOWNChocolate tablets segment shows upward momentum

Figure 13: Retail value sales of chocolate confectionery, by segment, 2015-17Figure 14: Retail volume sales of chocolate confectionery, by segment, 2015-17

Sugar confectionery’s growth broadly extends across segmentsFigure 15: Retail value sales of sugar confectionery, by segment, 2016-17Figure 16: Retail volume sales of sugar confectionery, by segment, 2016-17

Distinct differences in value versus volume sales by chocolate companyFigure 17: Percent share of retail value and volume sales of chocolate confectionery, by company, 2017

Sugar confectionery market concentrated in tree companiesFigure 18: Percent share of retail value and volume sales of sugar confectionery, by company, 2017



MARKET FACTORSCanadians strive to eat healthy most of the time…

Figure 19: Healthy eating, November 2016...but flexibility may be contributing to the high obesity rates

Figure 20: Body mass index, self-reported rate of being overweight or obese among Canadian adults, by gender, 2010-14Canada’s aging population may be a headwind for confections

Figure 21: Population aged 65 years and over in Canada, historical and projected (% of total), 1971-2061Canadians are more time-pressed

KEY PLAYERS

WHAT YOU NEED TO KNOWConfections’ edge being softened by ‘better-for-you’ innovationAlcoholic flavours can address demand for adult flavours

WHAT’S WORKING?Tablets’ growth a reflection of changing category dynamics

Figure 22: Share of retail value sales of chocolate confectionery, by segment, 2017

CHALLENGESConcerns around sugar a watch-out for the industry

WHAT’S NEXT?Alcoholic flavours a means to offer adult experiences

Figure 23: Raaka Bourbon Cask Aged Unroasted Dark Chocolate (USA), December 2018Figure 24: Raaka Cabernet Sauvignon Unroasted Dark Chocolate Bar (USA), December 2018Figure 25: Ghirardelli Chocolate Squares Dark Chocolate Bourbon Caramel (USA), June 2018Figure 26: Lolli & Pops Strawberry Champaign Chocolate Bar (USA), April 2018Figure 27: SugarfinaFigure 28: Project 7

Confections’ indulgent edge softened by examples of ‘better-for-you’ positioningFigure 29: EatingEVOLVED Original Keto Cups (USA), November 2018Figure 30: Theo Coconut Turmeric Chocolate Clusters (USA), March 2017Figure 31: Zazubean Hottie Chili & Cinnamon Dark Chocolate with Yerba Matte (Canada), January 2016Figure 32: Natierra Himalania Organic Dark Chocolate Covered Chia Seeds (Canada), January 2016Figure 33: Biena Foods Dark Chocolate Chick Peas (USA), July 2018

Chocolate in emerging snack-kit categoryFigure 34: Kraft Trios Snackfulls Extra Sharp White Cheddar Cheese, Dried Cranberries & Semisweet Chocolate (USA), July 2018Figure 35: Nestlé Kit Kat Snax Sweet & Salty Snack Mix (Canada), December 2018

THE CONSUMER

WHAT YOU NEED TO KNOWNearly all Canadians eat chocolate and candyConfections and TV are the most ideal match for CanadiansConfection made with natural sugars are of interest to CanadiansFlavour and brand are the top considerations for consumersConsumers look to chocolate for its emotional benefitsCandy is not just for kids

CHOCOLATE AND CANDY USAGENearly all Canadians eat chocolate and candy

Figure 36: Consumption of chocolate and non-chocolate confectionery in the past three months, November 2018Figure 37: Motts Fruitsations + Veggie Assorted Fruit Flavoured Snacks (Canada), August 2017Figure 38: Welch’s Jelly Beans (US), March 2018

Younger adults are more likely to eat confections, meaning opportunity exists to close-the-gap with older consumers.Figure 39: Consumption of chocolate and non-chocolate confectionery in the past three months, by age, November 2018Figure 40: Consumption of chocolate and non-chocolate confectionery (select) in the past three months, by parental status, November 2018



Figure 41: Consumption of chocolate and non-chocolate confectionery (select) in the past three months, Quebec vs overall population, November 2018Figure 42: Consumption of chocolate and non-chocolate confectionery (select) in the past three months, Chinese Canadians vs overall population, November 2018

Most Canadians eat chocolate and candy on a weekly basisFigure 43: Frequency chocolate or non-chocolate confectionery consumption, November 2018

Opportunity for candy to drive more frequent usage versus chocolateFigure 44: Frequency chocolate and non-chocolate confectionery consumption, November 2018Figure 45: Frequency non-chocolate confectionery, by parental status, November 2018Figure 46: Oreo Dippers, October 2017

CHOICE FACTORSFlavour and brand are the top considerations for consumers

Figure 47: Important factors when choosing chocolate or candy, November 2018Figure 48: Important factors when choosing chocolate and candy, November 2018

Making confections “easy to find” is more important for younger consumersFigure 49: Importance of “easy to find” when choosing chocolate or candy, November 2018

Ethically-sourced ingredients hold relatively limited appealFigure 50: Importance of “ethically sourced” when choosing chocolate or candy, women 35-44 vs overall, November 2018

Ingredients are the most impactful way to communicate healthFigure 51: Importance of “health-related factors” when choosing chocolate or candy, under-44s vs over-45s, November 2018

OCCASIONSConfections and TV are the most ideal match for Canadians

Figure 52: When chocolate or candy is eaten, November 2018Figure 53: M&M’S® Movies, April 2017Figure 54: Chocolate eaten “while watching TV” or “As a snack in the evening”, under-44s vs over-45s, November 2018

The afternoon is a ‘battleground’ occasion for confectionersFigure 55: Chocolate or candy as an afternoon snack, by age and population segment, November 2018Figure 56: Chocolate or candy is eaten “just after meals” or “when studying/working”, Chinese Canadians^ vs overall population, November 2018

Canadians are more likely to enjoy confections on their ownFigure 57: Chocolate or candy eaten “while alone” or “with other people”, by age, November 2018

INNOVATION OPPORTUNITIESConfection made with natural sugars of interest to Canadians

Figure 58: Areas of interest, November 2018Figure 59: Interest in “made with natural sugars” and “health boosting ingredients”, moms vs Overall, November 2018

Less is more with smaller portions equating to permissibilityFigure 60: Interest in “smaller portions, but richer flavours”, November 2018

Younger consumers show greater interest in ‘exploration’Figure 61: Interest in flavours innovation, by age, November 2018

Demand for vegan confections requires contextFigure 62: Interest in vegan option, women 18-34 vs overall, November 2018Figure 63: Endangered Species Chocolate Dark Chocolate with Raspberries (Canada), November 2018Figure 64: Free2b Sun Cups Minis Candy Filled with Creamy Sunflower Seed Butter (Canada), May 2018Figure 65: Theobroma Chocolat Organic 60% Cacoa with Orange Chunks (Canada), March 2018

ATTITUDES TOWARD CONFECTIONERYConsumers look to chocolate for its emotional benefits

Figure 66: Attitudes toward chocolate, women vs men, November 2018Figure 67: Stirs the Soul Honey Lavender Conscious Raw Chocolate (USA), January 2017Figure 68: Prana Organic Chocolate Matcha Magic Dark Chocolate Bark with Roasted Sesame, Crispy Rice & Matcha (USA), September 2018Figure 69: Two Moms in The Raw Soul Sprout (USA), January 2017

Candy is not just for kidsFigure 70: Attitudes toward candy, November 2018Figure 71: PUR Mints Mojito Sugar Free Mints, (Canada), December 2018



APPENDIX

DATA SOURCES AND ABBREVIATIONSData sources

Consumer survey dataConsumer qualitative research

Abbreviations and terms



DATABOOK CONTENT

Standard demographics you can expect to see tracked in our Databooks:

• Age and income• Age• Area• Born in Canada• Children in household• Education• Employment• Family structure• Financial situation• Gender and age• Gender and marital status• Gender• Generation• Household income• Household size• Housing situation• Language primarily spoken in the home• Marital status• Mobile device ownership• Number of children of any age in household• Number of children under 18 in household• Parental status by gender• Parental status• Race and origin• Race• Region• Sexual orientation• Social media• Visit social media websites daily• Visit social media websites weekly

Consumer ResearchQ1 Which of the following have you eaten in the past three months? Please select all that apply.Q1 Which of the following have you eaten in the past three months? Please select all that apply., by demographicsQ2 How often do you generally eat the following types of sweetsQ2 How often do you generally eat the following types of sweets, by demographicsQ3 How often do you generally eat the following types of sweets - NET - At least weeklyQ3 How often do you generally eat the following types of sweets - NET - At least weekly, by demographicsQ4 How often do you generally eat the following types of sweets - NET - Any chocolate or candyQ4 How often do you generally eat the following types of sweets - NET - Any chocolate or candy, by demographicsQ5 How often do you generally eat the following types of sweets - NET - Any chocolate or candy - At least weeklyQ5 How often do you generally eat the following types of sweets - NET - Any chocolate or candy - At least weekly, by demographicsQ6 When do you typically eat chocolate or candy? Please select all that apply.Q6 When do you typically eat chocolate or candy? Please select all that apply., by demographicsQ7 Which of the following factors are important to you when choosing chocolate or candy? Please select all that apply.Q7 Which of the following factors are important to you when choosing chocolate or candy? Please select all that apply., by demographicsQ8 Which of the following factors are important to you when choosing chocolate or candy? - NET - Any confectioneryQ8 Which of the following factors are important to you when choosing chocolate or candy? - NET - Any confectionery, by demographics



Q9 Which of the following options for chocolate or candy would you be interested in? Please select all that apply.Q9 Which of the following options for chocolate or candy would you be interested in? Please select all that apply., by demographicsQ10 Which of the following statements do you agree with? Please select all that apply.Q10 Which of the following statements do you agree with? Please select all that apply., by demographics

Demographics By DemographicsDemographics by demographics

RESEARCH METHODOLOGY

REPORT, US - YEAR MONTH 10

11CANADA RESEARCH METHODOLOGY

Canada Research Methodology

Mintel is an independent market analysis company that prides itself on supplying objective information on a whole range of markets and marketing issues.

There are six main sources of research that are used in the compilation of Mintel reports:

• Consumer research

• Social media research

• Desk research

• Trade research

• Statistical forecasting

Mintel reports are written and managed by analysts with experience in the relevant markets.

Consumer research

Exclusive and original quantitative consumer research is commissioned for almost all Mintel reports. In addition, qualitative research is also undertaken for a large proportion of reports in the form of online discussion groups. Mintel invests a considerable sum each year in consumer research, and the purchaser of a Mintel report benefits, as the price of an individual report is less than the cost of the original research alone. The research brings an up-to-date and unique insight into topical issues of importance.

Consumer research is conducted among a nationally representative sample of internet users in Canada and is carried out by Lightspeed. The results are only available in Mintel reports. Note that Mintel’s exclusive research is conducted online in both English and French.

Starting in July 2017, Mintel’s consumer

research has been conducted using a device agnostic platform for online surveys (ie respondents can now take surveys from a smartphone in addition to a computer or tablet). This methodology change may result in data differences from previous years; any trending should be done with caution.

Sampling

Online Surveys

Lightspeed

Founded in 1996, Lightspeed's double opt-in U.S. online consumer panel contains approximately 1.27 million U.S. consumers. Lightspeed recruits its panelists through many different sources including web advertising, permission-based databases and partner-recruited panels. Note: Lightspeed GMI was re-branded as Lightspeed in September 2016.

Mintel sets quotas on age and gender, region, and household income. Specific quotas for a sample of 2,000 adults aged 18+ are shown below.

Please note: these quotas are only representative of a standard General Population survey sample of 2,000 internet users aged 18+. Sample size, targets, and quotas may vary per report. Please see the Report Appendix for further details.

Age groups by gender % N

Male, 18-24 7.9 158

Male, 25-34 9.1 181

Male, 35-44 10.4 207

Male, 45-54 8.1 163

Male, 55-64 6.1 123Male, 65+ 7.4 148

Female, 18-24 6.9 139

Female, 25-34 8.8 177

Female, 35-44 9.4 188

Female, 45-54 8.7 174

Female, 55-64 8.6 172

Female, 65+ 8.5 170

Total 100 2,000

Region % NOntario 40.2 804Quebec 22.1 443

British Columbia 13.3 265

Alberta 10.7 214Saskatchewan 3.0 61Manitoba 4.8 95Atlantic Provinces (New Brunswick, Newfoundland/ Labrador, Nova Scotia, Prince Edward Island)

5.9 118

Total* 100 2,000

*Mintel does not include rural regions such as the Yukon

or the Northwest Territories (including Nunavut) in its

research. Thus the consumer research data does not

reflect opinions and behaviours of the population living in

those areas.

Household income % NLess than $25,000 14.0 281$25,000 - $49,999 20.8 416$50,000 - $69,999 15.0 300$70,000 - $99,999 17.8 356$100,000 and over 32.4 647

Total 100 2,000 Secondary Data Analysis


CANADA RESEARCH METHODOLOGY 12

In addition to exclusively commissioned surveys, Mintel gathers syndicated data from the most respected consumer research firms. This allows Mintel analysts to form objective and cohesive analyses of consumer attitudes and behaviour.Qualitative Research

Revelation by FocusVision

FocusVision provides Mintel with qualitative bulletin board software. This allows the creation of Internet-based, ‘virtual’ venues where participants recruited from Mintel’s online surveys gather and engage in interactive, text-based discussions led by Mintel moderators.

Further Analysis

Mintel employs numerous quantitative data analysis techniques to enhance the value of our consumer research. The techniques used vary form one report to another. Below describes some of the more commonly used techniques.

Repertoire Analysis

This is used to create consumer groups based on reported behaviour or attitudes. Consumer responses of the same value (or list of values) across a list of survey items are tallied into a single variable. The repertoire variable summarises the number of occurrences in which the value or values appear among a list of survey items. For example, a repertoire of brand purchasing might produce groups of those that purchase 1-2 brands, 3-4 brands and 5 or more brands. Each subgroup should be large enough (ie N=75+) to analyse.

Cluster Analysis

This technique assigns a set of individual people in to groups called clusters on the basis of one or more question responses, so that respondents within the same cluster are in some sense closer or more similar to one another than to respondents that were grouped into a different cluster.

Correspondence Analysis

This is a statistical visualisation method for picturing the associations between rows (image, attitudes) and columns (brands, products, segments, etc.) of a two-way contingency table. It allows us to display brand images (and/or consumer attitudes towards brands) related to each brand covered in this survey in a joint space that is easy to understand. The significance of the relationship between a brand and its associated image is measured using the Chi-square test. If two brands have similar response patterns regarding their perceived images, they are assigned similar scores on underlying dimensions and will then be displayed close to each other in the perceptual map.

CHAID analysis

CHAID (Chi-squared Automatic Interaction Detection), a type of decision tree analysis, is used to highlight key target groups in a sample by identifying which sub-groups are more likely to show a particular characteristic. This analysis subdivides the sample into a series of subgroups that share similar characteristics towards a specific response variable and allows us to identify which combinations have the highest response rates for the target variable. It is commonly used to understand and visualise the relationship between a variable of interest such as “interest in trying a new product” and other characteristics of the sample, such as demographic composition.

Key Driver Analysis

Key driver analysis can be a useful tool in helping to prioritise focus between different factors which may impact key performance indicators (eg satisfaction, likelihood to switch providers, likelihood to recommend a brand, etc). Using correlations analysis or regression analysis we can get an understanding of which factors or attributes of a market have the strongest association or “link” with a positive performance on key performance indicators (KPIs). Hence, we are able to identify which factors or attributes are relatively more critical in a market category compared to others and ensures that often limited resources can be allocated to focusing on the main market drivers.

TURF Analysis

TURF (Total Unduplicated Reach & Frequency) analysis identifies the mix of features, attributes, or messages that will attract the largest number of unique respondents. It is typically used when the number of features or attributes must be or should be limited, but the goal is still to reach the widest possible audience. By identifying the Total Unduplicated Reach, it is possible to maximize the number of people who find one or more of their preferred features or attributes in the product line. The resulting output from TURF is additive, with each additional feature increasing total reach. The chart is read from left to right, with each arrow indicating the incremental change in total reach when adding a new feature. The final bar represents the maximum reach of the total population when all shown features are offered.

Social Media Research

To complement its exclusive consumer research, Mintel tracks and analyses social media data for inclusion in selected reports. Using Infegy’s Atlas software, Mintel ‘listens in’ on online conversations across a range of social platforms including Facebook, Twitter, consumer forums and the wider web.

Atlas provides rich consumer insight via the analysis of commentary posted publicly on the internet. The system performs comprehensive and broad collection of data from millions of internet sources, working to ensure a faithful and extensive sampling of feedback from the widest range of individuals. The dataset contains commentary posted in real time, as well as a substantial archive dating back through 2007.

Trade research

Informal

Trade research is undertaken for all reports. This involves contacting relevant players in the trade, not only to gain information concerning their own operations, but also to obtain explanations and views of the strategic issues pertinent to the market being researched. Such is Mintel’s concern with accuracy that draft copies of reports are sent to industry representatives, to get their



feedback and avoid any misrepresentation of the market. These comments are incorporated into reports prior to final publication.

Formal Internally, Mintel’s analysts undertake extensive trade interviews with selected key experts in the field for the majority of reports. The purpose of these interviews is to assess key issues in the market place in order to ensure that any research undertaken takes these into account.

In addition, using experienced external researchers, trade research is undertaken for some reports. This takes the form of full trade interview questionnaires and direct quotes are included in the report and analysed by experts in the field. This gives a valuable insight into a range of trade views of topical issues.

Desk research

Mintel has an internal team of market analysts who monitor: government statistics, consumer and trade association statistics, manufacturer sponsored reports, annual company reports and accounts, directories, press articles from around the world and online databases. The latter are extracted from hundreds of publications and websites, both Canada and overseas. All information is cross-referenced for immediate access.

Data from other published sources are the latest available at the time of writing the report.

This information is supplemented by an extensive library of Mintel’s reports produced since 1972 globally and added to each year by the 500+ reports which are produced annually.

In addition to in-house sources, researchers also occasionally use outside libraries such

as Statistics Canada and the Canadian Grocer. Other information is also gathered from store and exhibition visits across Canada, as well as using other databases within the Mintel Group, such as the Global New Product Database (GNPD), which monitors FMCG sales promotions.

All analysts have access to Mintel’s Market Size and Macroeconomic Databases – a database containing many areas of consumer spending and retail sales as well as macroeconomic and demographic factors which impinge on consumer spending patterns..

The database is used in conjunction with an SPSS forecasting program which uses weighted historical correlations of market dynamics, with independent variables, to produce future spending scenarios.

Statistical Forecasting

Statistical modelling

For the majority of reports, Mintel produces five-year forecasts based on an advanced statistical technique known as ‘multivariate time series auto-regression’ using the statistical software package SPSS.

Historical market size data feeding into each

forecast are collated in Mintel’s own market size database and supplemented by macro- and socio-economic data sourced from organisations such as Statistics Canada, The Bank of Canada, The Conference Board of Canada and the Economist Intelligence Unit.

Within the forecasting process, the model searches for, and analyses relationships between, actual market sizes and a selection of key economic and demographic factors (independent variables) in order to identify those predictors having the most influence on the market.

Factors used in a forecast are stated in the relevant report section alongside an interpretation of their role in explaining the development in demand for the product or market in question.

Qualitative insight

At Mintel we understand that historic data is limited in its capacity to act as the only force behind the future state of markets. Thus, rich qualitative insights from industry experts regarding past and future events that may impact the market play a crucial role in our post statistical modeling evaluation process.



As a result, the Mintel forecast allows for additional factors or market conditions outside of the capacity of the data analysis to impact the market forecast model, using a rigorous statistical process complemented by in-depth market knowledge and expertise.

The Mintel fan chart

Forecasts of future economic outcomes are always subject to uncertainty. In order to raise awareness amongst our clients and to illustrate this uncertainty, Mintel has introduced a new way of displaying market size forecasts in the form of a fan-chart.

Next to historical market sizes and a current year estimate, the fan chart illustrates the probability of various outcomes for the market value/volume over the next five years.

At a 95% confidence interval, we are saying that 95 out of 100 times the forecast will

fall within these outer limits, which we call the best and worst case forecasts. These, based on the statistically driven forecast, are the highest (best case) and lowest (worst case) market sizes the market is expected to achieve.

Over the next five years, the widening bands successively show the developments that occur within 95%, 90%, 70% and 50% probability intervals. Statistical processes predict the central forecast to fall within the darker shaded area which illustrates 50% probability, i.e. a 5 in 10 chance.

A general conclusion: Based on our current knowledge of given historic market size data as well as projections for key macro- and socio-economic measures that were used to create the forecast, we can assume that in 95% of the time the actual market size will fall within the purple shaded fan. In 5% of all cases this model might not be correct due to random errors and the actual market size will fall out of these boundaries.

Weather analogy

To illustrate uncertainty in forecasting in an everyday example, let us assume the following weather forecast was produced based on the meteorologists’ current knowledge of the previous weather condition during the last few days, atmospheric observations, incoming weather fronts etc.

Now, how accurate is this forecast and how certain can we be that the temperature on Saturday will indeed be 15°C?

To state that the temperature in central London on Saturday will rise to exactly 15°C is possible but one can’t be 100% certain about that fact.

To say the temperature on Saturday will be between 13°C and 17°C is a broader statement and much more probable.

In general, we can say that based on the existing statistical model, one can be 95% certain that the temperature on Saturday will be between 13°C and 17°C, and respectively 50% certain it will be between about 14.5°C and 15.5°C. Again, only in 5% of all cases this model might not be correct due to random errors and the actual temperature on Saturday will fall out of these boundaries and thus will be below 13°C or above 17°C.

(To learn more about uncertainty in weather forecasts visit: http://research.metoffice.gov.uk/research/nwp/ensemble/uncertainty.html)


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CHOCOLATE AND NON- CHOCOLATE CONFECTIONERY CANADA ... · wafers, marshmallows, alfajores. cocoa powder and chocolate drinks. Sugar & gum confectionery • This market covers packaged