MILK & NON-DAIRY MILK CANADA, JUNE 2019 · Dairy Milk Usage Dairy milk’s popularity with Canadians is undeniable Figure 26: Types of dairy milk purchased in past three months, April

MILK & NON-DAIRY MILKCANADA, JUNE 2019

MILK & NON-DAIRY MILK - CANADA - JUNE 2019 2

© Mintel Group Ltd. All rights reserved.

OVERVIEW

What you need to know

Dairy milk is consumed by the vast majority of Canadians. Aside from water (including tap water), there is no other drink that Canadians consume more of. That said, people in Canada are drinking less milk from retail than they used to and this is likely the result of multiple factors, including the rise of non-dairy milk (eg almond milk). Non-dairy milk can no longer be considered to be a niche product with nearly half of Canadian adults claiming to have purchased some type of non-dairy milk in the past three months. Understanding this dynamic, there are several questions to answer. Is there room for both dairy and non-dairy milk in Canadians’ diets? What’s important to Canadians when deciding what to drink or use? Where are their opportunities for innovation?

Definition

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

Consumer data

Dairy milk:

• 2% cow's milk

• 1% cow's milk

• Flavoured cow's milk (eg chocolate)

• Whole cow's milk (eg 3.25%)

• Lactose-free cow's milk products

• Organic cow's milk

• Goat's milk

Non-dairy milk:

• Almond milk

• Soy milk

• Coconut milk

• Cashew milk

• Rice milk

• Pea milk

• Quinoa milk

• Flax seed milk

Milk market size definitions (diary-milk)

Total:

• This market covers liquid, powdered and flavoured cow's milk. It excludes condensed, evaporated, sour and cultured milk. Market size is based on retail and non-retail sales.

Retail:

• This market covers liquid, powdered and flavoured cow's milk. It excludes condensed, evaporated, sour and cultured milk. Market size is based on retail and non-retail sales.

Segment definitions (at retail):

• Flavoured: this includes ready-to-drink (RTD) and powders where usually cold/tepid water is added to make up flavoured milk.

• White -powdered: powders where usually water is added to make up white milk.

Inclusions:

• Retail: all liquid and powdered (white and flavoured) pasteurised milk; unpackaged milk for consumers. Others: Horeca and any supplies to the food/other industries.

Exclusions:

• Total: soy, milk specifically for babies, drinking yogurts/fermented milks/milk drinks, sour milk, liquid cultured milks. Powders where usually hot/boiling water or milk is added to make up hot drinks (e.g. Horlicks, Milo, etc).

• Retail: soy, milk specifically for babies, drinking yogurts/fermented milks/milk drinks, sour milk, liquid cultured milks.



REPORT CONTENT

OverviewWhat you need to knowDefinition

Consumer dataMilk market size definitions (diary-milk)

Executive SummaryThe issues

Canadians are drinking less dairy milk from retailFigure 1: Dairy milk volume consumption per capita at retail (litres), 2010-18Canadians are more likely claim that they are buying more non-dairy milkFigure 2: Percent of consumers who are buying more, the same or less dairy and non-dairy milk versus last year, April 2019

The opportunitiesMany Canadians turn to both dairy and non-dairy milkFigure 3: Dairy and non-dairy milk purchased in past three months, by dairy and non-dairy milk drinkers, April 2019Focusing on specific attributes across different milk types can promote more relevant messagingFigure 4: Correspondence analysis – Symmetrical map – Perceptions, April 2019Non-dairy milk stands to benefit more from the use of claimsFigure 5: Claims that encourage Canadians to drink more milk, dairy vs non-dairy milk, April 2019“Made in Canada” positioning matters to CanadiansFigure 6: Importance of being “made in Canada” when choosing milk, April 2019

What it means

The Market

What You Need to KnowMilk dollar sales continue to soften at retailThe rise in the number of births represents potential opportunity for whole milkCanada’s aging population represents a potential challenge for milk’s growth

Market Size and ForecastMilk dollar sales continue to soften at retail

Figure 7: Total Canada retail value sales and fan chart forecast of market, at current prices, 2013-24Figure 8: Total Canada retail value sales and forecast of market, at current prices, 2013-24Figure 9: Total Canada retail volume sales and fan chart forecast of market, at current prices, 2013-24Figure 10: Total Canada volume sales and fan chart forecast of market, 2013-24Figure 11: Total Canada volume other sales and fan chart forecast of market, 2013-24

Market BreakdownDownward trend at retail influenced by multiple types of dairy milk

Figure 12: Total Canada volume retail sales of market, white liquid vs flavoured milk, 2014-18Figure 13: Total Canada volume retail sales of market, by fat content, 2014-18

Saputo remains the market leader, but “others” show growthFigure 14: Total Canada volume retail sales share of market, by company, 2017-18



Market FactorsCanada’s aging population represents a potential challenge for milk’s growth

Figure 15: Trended percentage of Canadian population by age, 0-14 vs Over-65, 1950-2036 (f)Figure 16: Population aged 65 years and over in Canada, historical and projected (% of total), 1971-2061

The rise in the number of births represents potential opportunity for whole milkFigure 17: Number of births annually, 2001-18

Immigration represents potential for dairy and non-dairy milkFigure 18: Distribution of immigrants living in Canada by region of birth, 2011 (estimated) and 2036 (projected according to high scenario)

Key Players

What You Need to KnowMore Canadians say they’re drinking more milk than less milkAlmond milk remains popular, but does its popularity come at a cost to the environment?Will Canadians buy into added value milk?

What’s Working?More Canadians say they’re drinking more milk than less milk

Figure 19: Percent of consumers who are buying more, the same or less dairy and non-dairy milk versus last year, April 2019Figure 20: Percent of consumers who are buying more, the same or less dairy and non-dairy milk versus last year, parents vs non-parents, April 2019

ChallengesRetail sales of dairy-milk continue to slide

Figure 21: Dairy milk volume consumption per capita at retail (litres), 2010-18Figure 22: Dairy milk volume consumption per capita in total market (litres), 2010-18

Uncertain trade environment adds an element of uncertaintyAlmond milk remains popular, but does its popularity come at a cost to the environment?

What’s Next?Will Canadians buy into added value milk?

Figure 23: Fairlife 2% Reduced Fat Ultra-Filtered Milk with DHA Omega-3 (US), August 2018Figure 24: A2 Whole Milk (US), December 2018Figure 25: Diary Home Strawberry Drinking Yogurt (Thailand), July 2018

‘Mooove’ over. Does dairy milk have to come from a cow?

The Consumer

What You Need to KnowDairy milk’s popularity with Canadians is undeniableRoom exists for dairy and non-dairy milk in the Canadian marketThree considerations are top-of-mind for consumersDifferent attributes are important when considering different types of milkModerating sugar in milk resonates with Canadians

Dairy Milk UsageDairy milk’s popularity with Canadians is undeniable

Figure 26: Types of dairy milk purchased in past three months, April 2019Dairy milk usage varies among different age groups

Figure 27: Type of dairy milk purchased in past three months (select), 18-45s vs over-45s, April 2019Figure 28: Type of dairy milk purchased in past three months (select), by parental status, April 2019Figure 29: Type of dairy milk purchased in past three months (select), Chinese Canadians vs Overall Population, April 2019Figure 30: Type of dairy milk purchased in past three months (select), born in Canada vs not born in Canada, April 2019



Non-dairy Milk UsageRoom exists for dairy and non-dairy milk in the Canadian market

Figure 31: Dairy and non-dairy milk purchased in past three months, dairy and non-dairy milk drinkers, April 2019Almond milk remains the clear favourite for Canadians

Figure 32: Type of non-dairy milk purchased in past three months, April 2019Oat milk represents an emerging opportunity

Figure 33: Oatly! The Original Oat-Milk (US), March 2019Age and background impact the likelihood of non-dairy milk purchases

Figure 34: Non-dairy milk purchased in past three months, by age and parental status, April 2019Figure 35: Types of non-dairy milk purchased in past three months, by parental status, April 2019Figure 36: Types of non-dairy milk purchased in past three months, 18-34s vs over-45s, April 2019Figure 37: Types of non-dairy milk purchased in past three months, Chinese Canadians vs overall, April 2019Figure 38: Types of non-dairy milk purchased in past three months (select), urban vs suburban and rural regions, April 2019

FactorsThree considerations are top-of-mind for consumers

Figure 39: Important factors when choosing milk, April 2019Figure 40: Importance of “no artificial hormones” as a factor when choosing milk, by gender, April 2019

Whether milk is ultra-filtered is of concern to a minority of consumersFigure 41: Importance of “best before date” as a factor when choosing milk, by age and parental status, April 2019

Health-related claims are ‘secondary’ considerationsDifferent between dairy and non-dairy milk are evident

Figure 42: Factors important to dairy and non-dairy milk purchasers, April 2019

AttributesDifferent attributes are important when considering different types of milk

Figure 43: Correspondence analysis – Symmetrical map – Perceptions, April 2019Age impacts how Canadians view different types of milk

Figure 44: Health-related associations with non-dairy milk, by age, April 2019Figure 45: Experience-related associations with non-dairy milk, by age, April 2019Figure 46: Associations of dairy and non-dairy milk with “tasty”, Chinese Canadians vs overall, April 2019

Chocolate milk is not just a treat for kidsFigure 47: Associations related to being “good for a treat” and “kids like it” with flavoured dairy milk, by parental status, April 2019Figure 48: Association related to “kids like it” with whole cow’s milk, by ages of children in household, April 2019

Encouraging Milk ConsumptionModerating sugar in milk resonates with Canadians

Figure 49: Claims that encourage Canadians to drink more milk, April 2019Figure 50: Claims that encourage Canadians to drink more milk, dairy vs non-dairy milk, April 2019

Added functionality encourages a quarter of Canadians to drink more milkFigure 51: Rebbl Banana Nut Protein Drink (US), November 2018

Less commonly considered benefits appeal to a subset of CanadiansFigure 52: Beauty benefits as a claim that encourages Canadians to drink more milk, women 18-24 vs overall, April 2019Figure 53: Yakult Beauty Plus+ Drinking Yogurt Skin (South Korea), June 2018

Appendix

Data Sources and AbbreviationsData sources

Consumer survey dataSales dataFan chart forecastCorrespondence analysis

Abbreviations and termsAbbreviations



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 Research

Q1 Which of the following dairy milks have you purchased in the past three months? Please select all that apply.Q1 Which of the following dairy milks have you purchased in the past three months? Please select all that apply., by demographicsQ2 Who in your household drank/used dairy milk in the past three months? Please select all that apply.Q2 Who in your household drank/used dairy milk in the past three months? Please select all that apply., by demographicsQ3 Are you buying more, the same, or less dairy milk now in comparison to last year?Q3 Are you buying more, the same, or less dairy milk now in comparison to last year?, by demographicsQ4 Which of the following non-dairy milks have you purchased in the past three months? Please select all that apply.Q4 Which of the following non-dairy milks have you purchased in the past three months? Please select all that apply., by demographicsQ5 Who in your household drank/used non-dairy milk in the past three months? Please select all that apply.Q5 Who in your household drank/used non-dairy milk in the past three months? Please select all that apply., by demographicsQ6 Are you buying more, the same, or less non-dairy milk now in comparison to last year?Q6 Are you buying more, the same, or less non-dairy milk now in comparison to last year?, by demographicsQ7 Which of the following factors are important to you when choosing dairy or non-dairy milk? Please select all that apply.



Q7 Which of the following factors are important to you when choosing dairy or non-dairy milk? Please select all that apply., by demographicsQ8 Which of the following attributes do you associate with each of these types of milk? Please select all that apply per type.Q8 Which of the following attributes do you associate with each of these types of milk? Please select all that apply per type., by demographicsQ9 Which of the following attributes do you associate with each of these types of milk? - NETQ9 Which of the following attributes do you associate with each of these types of milk? - NET, by demographicsQ10 Which of the following claims would encourage you to drink more dairy or non-dairy milk? Please select all that apply.Q10 Which of the following claims would encourage you to drink more dairy or non-dairy milk? Please select all that apply., by demographics

Demographics By Demographics

Demographics by demographics



SOCIAL MEDIA CONTENT

Based on exclusive consumer research, Mintel shows you how people use the internet to interact with the brands they buy, the leading topics of interest and how these play out across categories. We look at opinions shared on social networking sites such as Facebook and Twitter, via online review sites and forums, and offline. This reporting allows you to see which brands and topics generate the most conversations online and why.

Fitness enthusiasts reject added sugar

With consumers identifying “no added sugar” as the top claim that would encourage them to drink more milk, it’s no surprise the phrase registers high amounts of joy and trust within online discussions.

Taste is top priority for millennials drinking non-dairy milk

83% of Millennials have purchased non-dairy milk in the last 3 months, more than any other generation. Taste is the most important factor for these consumers when discussing non-dairy milk.

Oat milk on the rise

Weekly social mentions of almond milk—Canada’s most popular milk alternative—increased by just 4.4% over the last year compared to oat milk’s 100% weekly trend increase.

Coffee and plant-based milk go hand-in-hand

Within mentions of coffee over the last year, plant-based milk was far more prevalent than dairy milk.

RESEARCH METHODOLOGY


10CANADA 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 11

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)


© 2019 Mintel Group Ltd. All rights reserved.Confidential to Mintel.

Published by Mintel Group Ltdwww.mintel.comemail: [email protected]

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MILK & NON-DAIRY MILK CANADA, JUNE 2019 · Dairy Milk Usage Dairy milk’s popularity with Canadians is undeniable Figure 26: Types of dairy milk purchased in past three months, April

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