To log into your Growth Scope portal via the following link: www.growthscope.com.au/growthscope You will be directed to log into Tableau, follow the prompts and use the username and password provided to you by your account manager. If you have any issues logging in, please contcat your account manager. When you have successfully logged in, the first page you will see is the navigation page for the Growth Scope portal. You will use this page regularly, so it is best to spend some time reviewing the module overview guide as well as the individual module guides to get a good understanding of each of the modules. To navigate through the portal simply click any button on the navigation page. Every page within the portal contains a "HOME" button at the top left, which will bring you back to the navigation page at any time. there is also a "FILTERS" button which will take you to the filters page at any time. The category filters (if more than one category included in your subscription) can be managed directly from the navigation page by clicking the relevant category button. To change categories, simply click a different category, to click "RESET". You can also control the category filters from the "Filters" page. The footer of every page of the portal contains details of the data filter settings: Category / sub-category Occasion Date range Based on the selected filters, the sample base (n = xxx) is also displayed, so you know at any point in time what data view you are looking at and how large the sample size is. PLEASE NOTE: Any data points with a sample of n<30 are automatically supressed. User licence: Each user is provided with their own licenced access to the Growth Scope portal. Your name is linked to your login details and is displayed on the bottom of every page of the portal. DO NOT share your login details with anyone else, if additional licences are required, please contact your account manager.

There are currently 12 modules in the Growth Scope platform, plus a filters control page. Additional modules may be added over time. There are eight base modules that provide robust insights into the Australian liquor market. There are four additional modules that provide even greater granularity and ability to cross tab data to provide richer insights at the click of a button. This guide will provide a high-level summary of each module to get you started, for more detail (including important questionnaire methodology and analysis nuances which are crucial for interpretation accuracy), please review the detailed guide for each module. FILTERS This is the starting point for any exploration of the Growth Scope portal. Use the filters page to select which view(s) of the data you want to see in all other modules. Only the category(ies) or channel(s) you have subscribed to will be assessable in your portal. Once your files are selected, a description of the filter will appear at the bottom of every page in the portal as well as the base sample size (n), so you will know at any point in time what filters are being applied to the view. You can return to the filters page at any time by clicking the “FILTERS” link at the top right of any page in the Growth Scope portal. BASE MODULES CATEGORY SUMMARY The category summary module provides a very high-level summary of the 5 W’s and 1 H (who, what, when, where, why and how much) of the category selected in the filters. It is intended to simply provide a snapshot of what is happening in the category. MARKETING METRIX The ‘Marketing Metrix’ module provides an overview of the key size metrics used in Growth Scope. The size of the market, category, channel or segment being filtered to is a function of incidence metrics and intensity metrics. Incidence metrics are made up of penetration (how many people) and frequency (how often a similar occasion occurs per month), while intensity metrics are made up of intensity (how many serves consumed per occasion) and price premium (relative price premium paid on the occasion). In combination, these four metrics tell us the total value that the category, sub-category, channel or segment being filtered to contributes to the total alcohol market, indexed such that the total market = 100. Definitions of each metric are provided at the bottom left of the page. TRACKING The tracking module takes the five metrics outlined in the ‘Marketing Metrix’ module and tracks them over time. You can choose whether to see tracking by month, quarter or year, and you can select which metric you wish to view on the tracking charts. BRAND The brand module provides a simple view of the % of respondents who consumed each brand. PORTRAIT PAINTERS There are 4 portrait painter modules, one for each stage of the Growth Scope funnel. Some of the same data repeats across multiple modules. Click into any portrait painter module to view a summary dashboard. Each module dashboard is structured around the 5 W’s and 1 H of marketing: who, what, when, where, why and how much. Clicking into any of the 5 W’s or 1 H will take you into a view with more detail and additional measures that do not fit onto the summary page. Occasion Portrait Painter A view of the details of the occasions on which the filtered data set was consumed. Consumer Portrait Painter A view of the consumers relating to the filtered data set. Shopper portrait painter A view of the shoppers relating to the filtered data set. Product Portrait Painter A view of the products relating to the filtered data set. See the individual module user guides for more detail being each portrait painter module. ADDITIONAL MODULES The additional modules cover select data contained elsewhere in the portal, but present them in ways that enable you to derive additional insight. FUNNEL The funnel is structured around categories and shows to what degree a category was available, suitable, considered, consumed and the main consumed alcohol type on the occasions set in the filters. LIGHTHOUSE The lighthouse module enables you to draw a line of sight between consumer/occasion behaviour and the associated shopper behaviour. It recognises that the majority of the purchase decision is driven by the nature of the consumption occasion and therefore allows you to see how different occasions and occasion variables drive different shopping activity. MARKETING METRIX DRILLDOWN A deeper dive on the ‘marketing metric’ module, enabling you to cross tab the marketing metrix by additional variables. TRACKING DRILLDOWN A deeper dive of the tracking module, allowing you to cross tab the tracking metrics by additional variables.

The category filters (if more than one category is included in your subscription) can be managed directly from the navigation page by clicking the relevant category button. When you select a category, the filters will automatically set to that category, it will remain coloured, while all other categories will become greyed out. To change categories, simply click a different category, to click "RESET". You can also control the category filters from the "Filters" page. If all categories are selected, then the data set will be filtered to the aggregate of all categories. Click into the “FILTERS” page from the main navigation page. Similar to the main navigation page, category filters can also be controlled on the filters page. The date range filters appear on the top right of the filters page. Click the date under either “FROM” and/or “TO” to open up a calendar view, you can select the exact date ranges you want included in your filter set. Once you select a date, the calendar view will lose and your selected date will apepar in the erlevant date field. Note: data collection for Growth Scope began on 28 June 2020. Additional future dates will automatically appear in the filter range as data updates are made from the back end of the system. The “Current selections” area of the filters page tells you what filters are currently set to in words. The filtered sample size area tells you two different sample sizes: Sample: this is the total sample size of respondents based on yoru selected filters. You can this of this as the total numbr of occasions included in the filtered data st. Shopper sample: this is the total number of respondents in the filtered data set who were either the shopper, or had sufficient knowledge of the shopping occasion to be included in the shopper data set. In most cases this will be a smaller number than the ‘sample’ n, however in cases where the selected filters are shopepr based filters then this will be the same as the ‘sample’ n. A coloured flag is used to warn you if the sample sizes are getting too small to depend on. Green = sample sizes are large enough, so proceed with confidence. Yellow = sample size is borderline, proceed with caution Red = sample size is too small – do nto proceed, instead we recommend that you adjusut your selected filters to include more data. In addition to categories, you can choose up to two additional optional filters. Select the dropdown to view the filter variables currently available. Choose the filter variable you which to use and the relevant filter options will appear below. Simply click on your desired filter from the list that appears and it will be selected. The description will appear in the text descriptions under “CURRENT SEELCTIONS” above, and the “FILTERES SAMPLE SIZE” n’s will update accordingly. You can reset your filters at any time by clicking the “RESET” buttons”. At the bottom of the page are benchmark filters. These are used in the “COMPARE” charts in the portrait painter modules. You can choose a benchmark category or sub-category to use to compare against. The date range filter automatically applies to the benchmark set. If you leave the benchmark filters both set to “All” your comparison charts will show your chosen filter in green, and in grey will show all other data EXCEPT your chosen filtered data set. If you select a category and/or sub category for the benchmark filter, then your compare charts will show your selected filtered data set in green, and the grey bar will show the selected benchmark category only (excluding the filtered data set if it is a subset of the benchmark set). Once you are happy with your chosen filters, click “Apply and continue” at the top right of the page to apply your filters and take you back to the main navigation page. The footer of every page of the portal contains details of the data filter settings: Category / sub-category Occasion Date range Based on the selected filters, the sample base (n = xxx) is also displayed, so you know at any point in time what data view you are looking at and how large the sample size is.

Below is a glossary of all the key filer options available. Subcategory: The subcategory consumes on the most recent consumption occasion If a category filter is selected, only the relevant subcategories will be available from the subcategory filter menu. If no category is selected, then ALL subcategories will be displayed. Selecting a sub-category to filter by focuses the data set on all respondent sin the specified date range who drank the chosen sub-category on their most recent consumption occasion. Please note: these consumers may have also consumed another alcohol sub-category in addition to the selected filtered subcategory. Age: The age of the respondent, split into broad age groups Gender: The gender of the respondent State: The state of domicile of the respondent Premise: Whether the occasion occurred on premise or off premise. Please note: there are a few consumption locations that do not fall into either on or off premise e.g. on an airplane, these are listed under ‘other’ premise. Channel: The channel where the alcohol consumed on the most recent occasion was purchased This includes both on and off premise channels, aggregated into larger groups. Note: BYO occasion that occurs at a restaurant but was purchased from a big box retailer would be classified as an on premise location, but the channel would be classified as big box. Retailer: The retailer banner group whether the liquor was purchased (off premise only) These are aggregated up to banner group, with the exception of Endeavour Group where Dan Murphy’s has been pulled out separately due to the sheer size of the retailer. Brand: The master brand of the product consumed Only brands with sufficient sample sizes are displayed and available for filtering Manufacturer: The manufacturer brand of the product consumed Product brands are aggregated up to manufacturer brand level for this filter. Occasion reason: The underlying reason for the consumption occasion occurring These reasons are not mutually exclusive i.e. a particular occasion can occur for more than one reason. The available occasion reason filter option are aggregated up to larger groups of similar occasions. Occasion location: a breakdown of the location of the consumption occasion beyond just on premise and off premise. At the moment only the largest locations are displayed for sample size reasons, all smaller locations are aggregated under ‘other’. Emotional need: The underlying emotional needs the consumer was trying to satisfy on their most recent consumption occasion. These needs are not mutually exclusive i.e. a particular occasion can tap into more than emotional need. The available emotional needs filter option are aggregated up to larger groups of similar needs. Functional need: The underlying functional needs the consumer was trying to satisfy when choosing the products/brands they consumed on their most recent drinking occasion. These needs are not mutually exclusive i.e. a consumer may be looking to satisfy more than more than functional need at a time. The available functional needs filter option are aggregated up to larger groups of similar needs. Life Stage: One axis of the Growth Scope Demand Zone framework, grouping individual consumers into different life stages (age and presence of children) Social context: One axis of the Growth Scope Demand Zone framework, grouping occasions based on the size of the gathering and the nature of the relationships between group members. Demand zone: The overlay of Life stages and social contexts to create demand zones. The intersect between life stages and social contexts has been aggregated up to 13 distinct demand zones which total to provide a picture of the entire occasion landscape. These 13 demand zones can be used to segment the consumer landscape and inform key where to play choices. Custom filters: Custom filters are available as a customisation for subscribing organisations – if you have subscribed to custom filters, they will appear at the bottom of the filters list.

The category summary module provides a very high-level summary of the 5 W’s and 1 H (who, what, when, where, why and how much) of the category selected in the filters. It is intended to simply provide a snapshot of what is happening in the category. The name of the category is shown at the top of the page. Select metrics for each of who, what, when, where, why and how much for the named category are shown. WHO % of filtered respondents who were male % of filtered respondents aged under 30 WHAT The top 3 sub categories based on % consumption on the last drinking occasion Also shown is the % who consumed the sub category in the last month WHEN The % for whom the consumption occasion occurred in the last week WHERE The % of filtered respondents for who the occasion occurred on or off premise WHY % of the filtered data set who indicated the occasion occurred for each reason. Top 3 reasons listed. HOW MUCH The average number of serves consumed per occasion for the filtered data set

The ‘Marketing Metrix’ module provides an overview of the key size metrics used in Growth Scope. The size of the market, category, channel or segment being filtered to is a function of incidence metrics and intensity metrics. Incidence metrics are made up of: Penetration: the % of respondents who are part of the filtered data set. In this case, 42% of respondents in the date range selected consumed beer on their most recent drinking occasion. Frequency: the average number of similar drinking occasions per month for this filtered data set. In this case, an average of 9 similar occasions per month. Intensity metrics are made up of: Intensity: The average number of serves consumed on the most recent drinking occasion by the filtered data set. In this case an average of 3.7 serves. Price premium: The claimed price paid per serve on the occasion indexed against the market average, where the market average = 100. In this case, a price premium of 109 means that the filtered data set paid 9% per serve above the market average for the alcohol consumed on their most recent drinking occasion. TOTAL VALUE INDEX In combination, these four metrics tell us the total value that the category, sub-category, channel or segment being filtered to contributes to the total alcohol market, indexed such that the total market = 100. In this case, a total value index of 39 means that the filtered data set contributed 39% of the total alcohol of alcohol in the date range selected. Definitions of each metric are provided at the bottom left of the page.

The tracking module takes the five metrics outlined in the ‘Marketing Metrix’ module and tracks them over time. Use the ‘date level’ drop down on the top left of the screen to toggle the tracking view between monthly, quarterly or yearly. Where the sample size for a given date range is too small, the data will be supressed and show on screen as blank. Increase the date range to a higher order group to display groupings with larger sample sizes. Use the “Metric to track … ‘ drop down on the top right of the screen to choose which metric you wish to display on the tracking chart. These metrics correspond to those on the Marketing Metrix module. In addition, a ‘percent’ metric is also available. This reports the raw % of respondents who make up the filtered data set as a % of the total respondent base within the specified date range. Please note: Percent is not always the same as penetration. See the ‘penetration in detail’ user guide for more information on this topic.

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In its simplest terms, penetration = the % of respondents who consumed the specified product/brand. However, it can be a bit more complex than this at times. This is because the default denominator and numerator used to calculate penetration varies depending on the nature of the filters and profiling variables selected. Filters are set on the filters page. Profiling variables are set within the marketing metrix drilldown and tracking drilldown modules. For the Marketing Metrix and Tracking modules (no profiling variables), the following rules apply If the filter includes a category/sub-category selection ONLY, then: Denominator = all respondents Numerator = number of respondents who consumed the category/sub-category on their most recent drinking occasion E.g. Filter: Category = Beer Penetration = % of all respondents who consumed beer on their most recent drinking occasion If the filter includes another variable (e.g. age, state, occasion reason etc.), then: Denominator = all respondents who fell into the selected filter variable Numerator = number of respondents who consumed the category/sub-category on their most recent drinking occasions who were also within the selected filter variable, Example 1 Filter: Category = Beer AND Age = 70+ Penetration = % of 70+ consumers who drank beer on their most recent drinking occasion Example 2​​​​​​​ Filter: Category = All categories AND Age = 70+ Penetration = % of 70+ consumers who drank anything on their most recent drinking occasion (100%) For the marketing metrix drilldown and tracking drilldown modules (where profiling variables are also included) additional rules apply For these modules, the nature of the profiling variable also influences the penetration calculation. If the profiling variable is a category or sub-category, then: Denominator = the number of respondents who make up the selected filter set (EXCLUDING cat category/sub-category filters) Numerator = the number of people within the selected filter set who consumed the category/sun category shown in the profiling variable Example: Filter: Category: Beer, State= Victoria Profiling variable = sub-category Denominator = number of people in Victoria Numerator = number of people in Victoria who consumed each Beer sub-category Penetration: the % of each beer sub-category consumed by people located in Victoria If sub-category Craft Beer has a penetration score of 12% based on the above filters and profiling variables, this means that 12% of Victorians consumed craft beer on their most recent drinking occasion. If the profiling variable is something other than category or sub-category (e.g. an occasion, consumer or shopper variable) then: Denominator = the number of respondents who make up the selected filter set AND the profiling variable Numerator: the number of respondents within the selected filter set AND profiling variable who consumed the category/sub-category Example: Filter: Category: Beer, State= Victoria Profiling variable = Age Denominator = number of people in Victoria who fall into each age group Numerator = number of people in Victoria within each age group who consumed each Beer sub-category Penetration: the % people located in Victoria within each age group who consumed beer on their most recent drinking occasion If age: 70+ has a penetration score of 25% based on the above filters and profiling variables, this means that 23% of Victorian’s aged 70+ consumed beer on their most recent drinking occasion. The above examples and rules outline the DEFAULT calculation for penetration. The reason for the calculation being different based on the filters and profiling variables being used is that interpretation of the results is naturally different depending on the way the data is being filtered or profiled. We have attempted to make the default the MOST common/logical view for each instance. Advanced users/analysts can override the default penetration calculation by clicking the “MORE” tab at the top right-hand corner of the “TRACKING”, “MARKETING METRIX DRILLDOWN” and “TRACKING DRILLDOWN” modules. Clicking this button will take you to a detailed information page that provides more technical information about the marketing metrix and the penetration calculation. On the right-hand panel of this screen are custom penetration calculation overrides and an explanation of each option. The default in the dropdown of this panel is set to “Use recommended base” this will default to whichever situation is outlined above. If you wish to override this calculation, you can choose from one of five options. Make your selection from the drop-down menu and click “BACK”. PLEASE NOTE: The “BACK” button from this page always reverts you back to the “MARKETING METRIX DRILLDOWN” module, if you do not wish to view this module, or you did not arrive here from this module, simply click “HOME” and navigate back to the original module you came from to see the data based on the new penetration calculation. Here is a detailed explanation of each of the custom penetration override options with worked examples. 1. Within filters Penetration is calculated as the % within the selected non-category filters Denominator = all filters (EXCLUDING category/sub-category/brand) Numerator = number of respondents who consumed the category/sub-category/brand that fell within the filtered data set AND who fell into the profiling variable group. Example Filter: Category = Beer, State = Victoria Profiling variable = Age Penetration = % all Victorian drinking occasions where the respondent fell into each age group AND they were drinking beer If age 70+ has a penetration score of 5% based on the above filters and profiling variables, this means that 5% all Victorian drinking occasions involved beer being consumed by those aged 70+ 2. Within filters and category selections Penetration is calculated as the penetration of the category/sub-category/brand within the selected non-category filters Denominator = all filters INCLUDING selected category/sub-category/brand Numerator = number of respondents who fell into each profiling variable group. Example Filter: Category = Beer, State = Victoria Profiling variable = Age Penetration = % of all beer drinking occasions that occurred in Victoria where the respondent fell into each age group If age 70+ has a penetration score of 20% based on the above filters and profiling variables, this means that 20% of Victorian beer drinking occasions were conducted by those aged 70+ 3. Within profiling Penetration is calculated as the penetration as the % that consumed the filter category/sub-category/brand within the selected occasion, consumer or shopper profiling variable Denominator = the profiling variable only (EXCLUDING category/sub-category/brand) Numerator = the number of respondents who consumed the category/sub-category/brand Example Filter: Category = Beer, State = Victoria Profiling variable = Age Penetration = % of each age group who were in Victoria AND consuming beer on their most recent drinking occasion. If age 70+ has a penetration score of 10% based on the above filters and profiling variables, this means that 10% of 70+ year olds said they live in Victoria AND drank beer on their most recent drinking occasion 4. Within filters and profiling Penetration is calculated as the penetration as the % that consumed the filter category/sub-category/brand within the selected non category/sub-category/brand filters AND within the selected occasion, consumer or shopper profiling variable Denominator = All filters (EXCLUDING category/sub-category/brand) AND profiling variables Numerator = the number of respondents within the filter and profiling variables who consumed the category/sub-category/brand. Example Filter: Category = Beer, State = Victoria Profiling variable = Age Penetration = % of Victorians of each age group who consumed beer on their most recent drinking occasion. If age 70+ has a penetration score of 35% based on the above filters and profiling variables, this means that 35% of Victorian based 70+ year olds drank beer on their most recent drinking occasion. 5. Within all occasions Penetration is calculated as a % within all occasions inside the selected date range. Denominator = all occasions Numerator = all who fall within the selected filters AND profiling variables. Example Filter: Category = Beer, State = Victoria Profiling variable = Age Penetration = % of all occasions where ethe respondent was based in Victoria, drank beer on their most recent drinking occasion AND fell into each age group.