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    "result": {"data":{"allContentfulAsset":{"edges":[]},"contentfulFaqPost":{"seo":{"__typename":"ContentfulSeo","title":"MOE","description":"Latana MOE","noIndex":true,"ogImage":null}}},"pageContext":{"slug":"moe","markdown":{"nodeType":"document","data":{},"content":[{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Margin of Error (MOE)?","marks":[{"type":"bold"}],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"Margin of Error, or MOE, is a statistical measure that reflects the amount of random sampling error in survey results. A bigger MOE for a particular data point means a lower level of confidence in that value.","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"MOEs are usually represented as ±X%. This means if a data point is 10% with a MOE of ±2%, the true value of the datapoint is somewhere between 8-12%.","marks":[],"data":{}}],"data":{}}]},"images":[],"allPosts":[{"node":{"title":"Comparison chart","slug":"comparison-chart","category":"charts","content":{"content":"# __Comparison chart__\n\nThe Comparison chart is the most flexible in the app. It allows you to visualize your findings over time to identify trends across waves to ensure you are setting the right strategic brand objectives.\n\nYou can analyze each KPI across time periods, geographies, competitor brands, and segments.\n\n## Visual settings\nThe upper right area of the chart visualization contains options to change the format of chart display, data grouping, data format, and sorting.\n![chart appearance](//images.ctfassets.net/7so8go2zrvbw/4nFHcoGYQZuS3smpBsGroJ/8476c2007d3ceba04b9386c65e968f22/Screenshot_2025-06-20_at_10.00.40.png)\n\n### Chart average\nComparison charts include a Chart average toggle in the visual settings toolbar. Turn it on to add an Average benchmark calculated from the values currently shown in your chart.\n\nThis helps you quickly see whether your brand — or any other data point in the chart — is performing above or below the average for the brands, KPIs, waves, segments, or markets you have selected.\n\n### How to enable Chart average\n\n1. Open an existing comparison chart, or create a new one.\n2. In the upper right corner of the chart, find the visual settings toolbar.\n3. Toggle on Average (trending-up icon).\n4. Your preference is saved with the chart, so the average stays visible the next time you open it.\n\n## Table interpretation\nEvery comparison chart has a table interpretation below the visual part. It can be used for:\n- Data sorting by clicking table header.\n- Quick visualization of progress over time (delta) when used in time-based charts.\n- Hide/show bars and lines on the chart by clicking rows.\n![chart table](//images.ctfassets.net/7so8go2zrvbw/6lCWRN7XxRRNnCu3Qto42G/d99645e5144d9e4e95bfd14c99b49a77/Screenshot_2025-06-20_at_10.02.29.png)\n\n## Chart legend\nThe chart legend displays the names of the variables represented in the chart and can be used to show and hide bars in the chart.\n![chart legend](//images.ctfassets.net/7so8go2zrvbw/1HnECGzhIGvQWm1NCK1Nce/be6107496f87f1940c470f0a365263a1/Screenshot_2025-06-20_at_10.00.53.png)\n"}}},{"node":{"title":"How to export a csv of your data?","slug":"how-to-export-csv","category":"csv","content":{"content":"### __How to export a csv of your data?__\n\nIn addition to exploring the data in the Latana app, you also have the option to download this data as a CSV file. While all these insights are visible in the dashboard, there are  use cases for exporting the raw data:\n1. Displaying the brand insights in third party data visualization tools\n2. Correlating brand KPIs with other relevant marketing metrics (revenue, website traffic, search etc).\n\n__How to download data in a CSV__\n\n1. Access the latana App \n2. Click on the download button at the bottom of the navigation bar\n![select-download-csv-button](//images.ctfassets.net/7so8go2zrvbw/5zOth6QpUd30qaAtRt2ytt/a493f9e5fa83be73a04f830468bd491e/Screenshot_2025-06-05_at_16.31.20.png)\n3. Select the studies and waves you would like to download the data for and input your email address\n![select-studies-waves-csvexport](//images.ctfassets.net/7so8go2zrvbw/1lFaQQXeiXgB4EhwJzxFsF/54e974ed07e00a87424eb01e6e444565/Screenshot_2026-01-20_at_10.17.54.png)\n4. Click “Request export”\n5. The data will arrive in your inbox shortly\n\nAfter you download the CSV, you can import it to Google Sheets, Tableau or any other third party tool you’re using.\n"}}},{"node":{"title":"How to create a new chart","slug":"how-to-create-a-new-chart","category":"charts","content":{"content":"__1.__ Click on the “+” button in the navigation bar and select “Chart” from the menu\n\n![how_to_create_a_chart](//images.ctfassets.net/7so8go2zrvbw/1HPNmd9uEhpNEb28c2XDUO/1d4b641f34159ab9b978c68e070bc8e8/Screenshot_2025-05-29_at_16.17.51.png)\n\n__2.__ Select the Study you want to create a chart for in the pop-up\n\n![chart-creation-select-study](//images.ctfassets.net/7so8go2zrvbw/7q04RdSjNXLiw8MaT3KHeE/0955af9a02ea7e348da952a6c78ed3fa/Screenshot_2025-05-29_at_16.19.59.png)\n\n__3.__ Select the chart type in the next window. For more information on each chart type check out the other sections in this FAQ.\n\n![chart-creation-select-type](//images.ctfassets.net/7so8go2zrvbw/3ckKq6vq5RCGFDhdoafIMg/9843866d8d1de0ba45fe9b93feb7dc43/Screenshot_2025-05-29_at_16.20.22.png)\n\n__4.__ Add a title and sub-title to your chart and select the Geography, Time period, KPIs, Brands & Segments you want to have in the chart. Each chart comes with its own limitations. More on this in the chart-specific sections.\n\nFor the time period you will be able to select either months or calendar weeks depending on the frequency of delivery.\n\n![chart-creation-filters](//images.ctfassets.net/7so8go2zrvbw/3W0IPcZDH0rXBa8TpmxvMd/cb98099b4e5bb2f6952f82f0db8d3f9c/Screenshot_2025-08-04_at_16.19.25.png)\n\n__5.__ Once you have selected your chart filters, you can view the data by clicking \"Preview chart\" in the bottom right corner. The chart will only be saved, after you click \"Save\" in the top right corner."}}},{"node":{"title":"Brand consideration","slug":"brand-consideration-kpi","category":"kpis","content":{"content":"## __Brand Consideration__\n\nBrand Consideration KPI measures what share of an audience would consider buying/using your brand vs competitors in your industry in the purchase decision process. Insight into this KPI is __mostly relevant for your target audience__, seeing as it is so strongly tied to purchase decision behaviour. \n- *It is less relevant when looked at with a general population vision as consideration will then be skewed by those who are not even considering your wider category.*\n\nAs this KPI is lower in the brand stage funnel, we do not recommend brands who are earlier in their brand growth to track this KPI: this will only provide noisy and irrelevant data to your brand performance understanding. \n\n__Brands__ who have first __established higher levels__ of __Brand awareness__ and Brand Understanding will inevitably gain __more relevant insights__ from this KPI. Generally we advise that brands gain an awareness level of 35%+ amongst their target audience before beginning to measure and track their Brand Consideration levels. This will also help to achieve more reliable results with lower margins of error.\n\nBrand consideration can also give hint about the experience of the consumer with your brand so far and their likelihood of purchasing next time.\n\n### How do we ask it?\n\nBrand consideration KPI question is asked individually for every brand. Latana utilises two versions of this KPI to match your brand and industry:\n1. \"How likely are you to choose this brand when buying CATEGORY?\"\n2. \"How likely are you to choose this brand when using CATEGORY?\"\n\nThe options are on a 4-point scale with \"Not at all likely\", \"Not very likely\", Somewhat likely\", \"Very likely\".\n- *The 4-points scale is set based on tests performed by Latana research team that identified that this is the optimal design to produce the best, most reliable results*.\n\n![brand_consideration_silo_in-survey](//images.ctfassets.net/7so8go2zrvbw/6diPlmqVidXo991Km2KYQN/4e441bef4ee2e4d48f2b3d170e8596e6/Screenshot_2025-06-11_at_15.45.35.png)\n\n### What do you see in the app?\nBrand consideration is delivered as brand-specific KPI where the range of predictions varies from 0 to 100% for every brand. The percentage is the sum of the amount of respondents who have selected \"Somewhat likely\" and \"Very likely\". The highest percentage displayed among the five indicates the top ranking among selected competitors. \n\n### Latana's recommendations\nThere are two KPIs that can provide the most valuable insight addition to this KPI:\n\n__Driver associations__, when assessed alongside Brand consideration, can provide a crucial  narrative of your opportunities and gaps towards being considered at the purchase decision moment for your target audience. If you are missing the mark with your audience on key priorities that sway decision motivations, and furthermore are winning/losing out against competitors here, it can strongly explain why consideration is either high or low. \n\n__Brand preference__, when assessed alongside Brand consideration, can provide a crucial narrative of the loyalty strength amongst your target audience. These two KPIs highly complement each other, in that they provide understanding of your purchase or usage strength. \n\n- *To note, industries vary in the combination of these KPIs due to differing purchase decision motives. Therefore it is complementary to understand the competition level within the industry alongside the consideration and preference of a brand. Especially for those industries that do not foster multi-brand usage (banking, for example)*"}}},{"node":{"title":"How to log in","slug":"how-to-log-in","category":"members","content":{"content":"# How to log in\n\nFirst, visit the login page at [https://app.latana.com/login](https://app.latana.com/login).\n\n![login_page](//images.ctfassets.net/7so8go2zrvbw/4nJYWAMuVEfED4K526TiRm/589e9f4290fb2245a081dd61961913f8/Screenshot_2025-07-18_at_11.49.22.png)\n\nNow you have two options:\n\n1. You can use the \"Log in with Google\" button if your organisation uses google workspace to login or setup a new account\n\n2. You can enter your email and password and click \"Log in with email\". If you do not yet have an account, ask the admin of your organisation to invite you or reach out to the Latana team\n\nUse the \"Forgot password\" button if you have forgotten your password."}}},{"node":{"title":"Heatmap","slug":"heatmap","category":"charts","content":{"content":"### __Heatmap__\n\nThe Heatmap enables you to easily visualize and compare brands' performance across different segmentations, kpis or countries. The varying shades of blue help you identify patterns in the data so you can quickly gather valuable insights.\n\n![heatmap_visual](//images.ctfassets.net/7so8go2zrvbw/2vgtKrMBPO5Nu3BeDb7hEg/85970051456e5ebde4d81d0e04b10a41/Screenshot_2025-06-06_at_16.39.35.png)\n\nThe heatmap is used differently than other charts. Start by using the normal filter selection process to build your chart. Each chart allows you to select one wave and one KPI Within this chart, you can look at either one segment, brand, or country\n\n- If you choose a segment, you can select any number of brands and countries.\n- If you choose a brand, you can select any number of segments and countries\n- If you choose a country, you can select any number of brands and segments\n\nIn the visual, you can toggle between viewing total or branched percentage data and absolute values by clicking on the button in the upper right hand corner.\n"}}},{"node":{"title":"Growth performance","slug":"growth-performance","category":"charts","content":{"content":"# Growth performance\nThis chart allows you to get a quick overview on how several brands or KPIs have performed this month compared to one or three months ago.\n\nYou can switch between showing a mix of total or branched percentages and absolute values with the toggle in the upper right hand corner of the visual. You can sort the table in ascending or descending order across the columns by clicking on the column names.\n\n![growth_performance_visuals](//images.ctfassets.net/7so8go2zrvbw/4Rqcd4YIyrqXXI3ptQsXtR/47690f12649ac46af83fee0610bb3cbc/Screenshot_2025-06-06_at_17.03.25.png)"}}},{"node":{"title":"Ranking chart","slug":"ranking-chart","category":"charts","content":{"content":"# Ranking chart\n\nThis chart shows a simple, clean ranking of either locations, brands or segments for the same KPI. You can order the entries ascending or descending by clicking on the table column header. \n\nYou can toggle between showing total or branched percentages and absolute values in the upper right hand corner.\n\n![ranking_chart_visual](//images.ctfassets.net/7so8go2zrvbw/6P2n6g0pJ2DkiGVffTKz0a/a7d4e5b11c4817af8e35c6b50a93d497/Screenshot_2025-06-06_at_17.06.38.png)"}}},{"node":{"title":"Brand Perception Chart","slug":"brand-perception-chart","category":"charts","content":{"content":"# Brand Perception Chart\n\nThis chart is specifically designed to visualise the results of the brand perception KPI and help you gather insights from it easily. The chart’s focus is the net sentiment score, which is calculated by subtracting the negative from the positive sentiment score. The other columns show you how this score has changed compared to 1 and all months ago respectively. The first row shows an average across all selected brands while the other rows represent one brand each.\n\nYou can toggle between showing branched percentages or absolute values for the negative, positive and neutral indicators via the switch in the upper right hand corner.\n\n![brand_perception_visual](//images.ctfassets.net/7so8go2zrvbw/21G5BwbQY7FFquHbTyQTr1/ddd0257f56e02b381be9f1ba673b95f5/Screenshot_2025-06-06_at_17.17.19.png)"}}},{"node":{"title":"Chart appearance settings","slug":"chart-appearance-settings","category":"charts","content":{"content":"# Chart appearance settings\n\nUnder Settings > Appearance you can configure a few settings that impact the chart visualisations.\n\n1. Decimals: Toggle on or off whether you want to see the one decimal point for all of your data\n2. Always show bar value: Toggle on or off whether you want to view the value on top of the bar charts\n3. Brand color settings: Choose specific colors for yours and your competitor brands that will be utilised across all charts. Click to add a brand, then click on the field with the hex code to pick a new color. Then click \"Save color settings\" to confirm. \n\n![appearance_settings](//images.ctfassets.net/7so8go2zrvbw/6JkonqZyZ74jveyZPjzxFK/fda53e0f82e75d1287b4aab817b4194e/Screenshot_2025-06-11_at_17.01.02.png)"}}},{"node":{"title":"Brand awareness","slug":"aided-awareness-kpi","category":"kpis","content":{"content":"## __Brand awareness__\n\n__Brand Awareness KPI__ measures what share of the audience recognises your brand when prompted by the brand name and logo. This KPI is also measured amongst your key industry competitors to provide a crucial benchmark to your brand growth.\n\nUnderstanding brand awareness is the __most important ingredient__ to a __successful marketing strategy__; it is the first step in the marketing funnel and is a crucial foundation to gaining customers. \nBrands who are newer to an industry or market should focus on measuring their brand awareness amongst their target audience. For more established brands, measuring awareness amongst both their target audience and the wider general population is key to understanding and tracking positioning.\n\n### How do we ask it?\n\nOn the mobile-optimised survey, the user will see the brand's logo as a picture with the caption of the brand name underneath. The question “Have you heard of the of the following brand?” can be answered with the responses: No, Not sure or Yes.\n\n![brand_awareness_in-survey](//images.ctfassets.net/7so8go2zrvbw/4D7U98i59xaKwXjvfJzvsk/b55e0afff8f5ea48c6e08d9f15d3abca/Screenshot_2025-06-11_at_15.45.21.png)\n\n### What do you see in the app?\n\nBrand Awareness will be displayed as __a percentage__ of people of the desired population __who are aware__ of the brand."}}},{"node":{"title":"Purchase drivers","slug":"purchase-drivers-kpi","category":"kpis","content":{"content":"## Purchase drivers\n__Purchase Drivers KPI__ measures what purchasing priorities are most valued by consumers in the decision process for your category. \n\nThis KPI is not specifically tied to any brand in particular as a standalone KPI but does provide a key metric to understanding your target audience better and to help position your brand in alignment with their priorities and expectations. \n\nThis KPI is not directly linked to your own marketing spend ROI, but can greatly identify where and when to place your marketing spend. For example, if you have been placing large efforts on positioning your brand as being known for *Quality* but Purchase Drivers results reveal that actually *Price* is the driving factor for your target audience to consider a purchase, then you know you may have been missing the mark in building that essential resonation with your audience. \n\nThis KPI can not only help to steer your branding and positioning efforts to be more aligned with consumer priorities (and therefore ensure you have the best chance for purchase success), it can also provide effective feedback on your positioning against competitors in this area when measured as a module alongside __Brand Drivers__.\n\n### How do we ask it?\nQuestion text is adjusted based on the industry of a brand and is displayed to users in the following format: \"What is most important to you when deciding on [industry] brands?\n- *Possible variations can also include \"retailers\", \"apps\" instead of or together with \"brands\"*\n\nRespondents can select multiple drivers available in the question. Drivers options are limited to 5 with additional options \"None of these\" and \"I don't know\" that un-check all other options selection on the current questions screen.\n\nThe list of drivers is defined by Latana research team based on the industry specifics and using an established library of drivers.\n\n![purchase_drivers_in-survey](//images.ctfassets.net/7so8go2zrvbw/5VaPLOhwbdfdsxgKi5L1RF/bf193f9d572aa018fec1925c491cd954/Screenshot_2025-06-11_at_16.44.02.png)\n\n### What do you see in the app?\nPurchase drivers are delivered as independent KPIs where the range of predictions varies from 0 to 100%. The highest percentage displayed among the five indicates the top priority in that domain. \n- *We don't recommend using donut-like charts or any charts that represent a 100% for visualising all drivers together*\n"}}},{"node":{"title":"Brand understanding","slug":"brand-understanding-kpi","category":"kpis","content":{"content":"## __Brand Understanding__\n\n__Brand Understanding__ KPI provides a crucial addition to Brand awareness insights, helping to identify if your brand positioning and messaging is being recognised and understood.\n\nThis is an early-funnel KPI, that works in tandem with Brand Awareness to deepen your understanding of your audience and their recognition and knowledge of your brand.\n\nSimilarly to Brand Awareness, the audience insight on this KPI is mostly tied to your brand stage. Brands who are generally newer to a market or industry should focus on measuring this KPI amongst their target audience in order to effectively track their resonation growth. More established brands can also benefit from measuring the general population’s understanding of their brand to assess if their wider messaging and positioning is gaining the traction they expect.\n\nBrand Understanding gives insights about __effectiveness__ of __marketing__ and __branding__ __campaigns__, and makes it possible to improve the messaging according to the category. If people recognise the brand but don't know which products or services it offers, it might be a sign for branding efforts to change direction.\n\n__Brand Understanding__ allows brands to use their __marketing budget__ in a __more informed way__ and __maximise__ their __return__ on marketing spend based on data.\n\n### How do we ask it?\n\nThe respondent will see the brand's logo as a picture with the caption of the brand name underneath. The question “Do you know what products/services this brand offers?” can be answered by selecting either \"No\", \"Not sure\" or \"Yes\".\n\n![brand_understanding_in-survey](//images.ctfassets.net/7so8go2zrvbw/7IKIgnlxQ0zLYkOFjfip0a/4c165a77673b0b0932537d3aea4babd2/Screenshot_2025-06-11_at_16.38.31.png)\n\n### What do you see in the app?\nBrand understanding is delivered as a single KPI as the score of people who selected \"Yes\".\n- *All estimations are made only based on answers from respondents that had an opinion on a subject matter*\n- *We recommend looking at this KPI together with Brand awareness and using your target audience*\n"}}},{"node":{"title":"Driver associations","slug":"driver-associations-kpi","category":"kpis","content":{"content":"## Driver Associations\n\n__Driver Associations__ is a combination of 2 KPIs overlayed to provide deeper brand insight:\n1. __Purchase Drivers__ firstly measures what purchasing priorities are most valued by your target audience in the decision process for your category.\n2. __Brand Associations__ will then measure the alignment of your brand, and your chosen competitor brands, against these same drivers to reveal gaps and opportunities. This is a crucial step towards understanding your growth potential and positioning in the market.\n\nAs this KPI is lower in the brand stage funnel, we do not recommend brands who are earlier in their brand growth to track this KPI: this will only provide noisy and irrelevant data to your brand performance understanding. \n\n__Brands__ who have first __established higher levels__ of __Brand awareness__ and Brand Understanding will inevitably gain __more relevant insights__ from this KPI. Generally we advise that brands gain an awareness level of 35%+ amongst their target audience before beginning to measure and track their Driver associations levels. This will also help to achieve more reliable results with lower margins of error.\n\nThis KPI is strongly linked to usage or purchase behaviour, and therefore measuring this in line with marketing activity can provide an indication towards effectiveness of sale. If you are missing the mark in one or two core consumer priority areas this can provide an undoubtedly strong indication that your marketing spend may be misaligned with the outgoing message. \n\n### How do we ask it?\n\nBrand Associations KPI is asked alongside a brand’s name and logo, with drivers listed as multi-select below. Therefore, the results of this strongly tie with a consumer’s emotional and associative resonation with your brand at the purchase decision moment. \n__Question text__ of Brand association part of the __Driver association__ KPI is \"What do you most associate with this brand?\". __List of options__ used in Brand association part of Driver association KPI is reusing the same values used in Purchase drivers to allow for data comparability. Respondents can select multiple associations available in the question. Options are limited to 5 with additional options \"None of these\" and \"I don't know\" that un-check all other options selection on the current question screen.\n- *The list of drivers and associations is defined by Latana research team based on the industry specifics and using an established library of drivers and associations.*\n\n![driver_associations_in-surveys](//images.ctfassets.net/7so8go2zrvbw/5eM52kKNpTOWaIFkQ7Djf6/2b820547379353deb71073ac71c80e77/Screenshot_2025-06-11_at_15.46.20.png)\n\n### What do you see in the app?\nDriver associations are delivered as independent KPIs where the range of predictions varies from 0 to 100%. The highest percentage displayed among the five indicates the top priority in the driver. \n\n### Latana recommendations\n__Brand consideration__, when assessed alongside Driver associations, can provide a crucial narrative of the decision making process amongst your target audience. After gathering information on the attributes you either make or miss the mark with your target audience, Brand Consideration can take your insights one step further and directly indicate your positioning in the purchase decision process against your competitors. \n\n__Brand preference__, similarly to Brand consideration, when assessed alongside Driver associations, can provide understanding of your purchase or usage strength and brand loyalty. \n\nTo note, industries vary in the combination of these KPIs due to differing purchase decision motives. Therefore it is complementary to understand the competition level within the industry alongside the consideration and preference of a brand. Especially for those industries that do not foster multi-brand usage.\n"}}},{"node":{"title":"Brand preference","slug":"brand-preference-kpi","category":"kpis","content":{"content":"## __Brand preference__\n\n__Brand preference KPI__ measures what share of an audience considers a brand as their first choice, above all others presented to them. This is generally a deeper-level funnel KPI, that works in perfect tandem with Brand consideration, to deepen your understanding of consumer purchase intent and brand loyalty. \nInsight into this KPI is __mostly relevant for your target audience__, seeing as it is so strongly tied to purchase decision behaviour. \n- *It is less relevant when looked at with a general population vision as preference will then be skewed by those who are not even considering your wider category.*\n\n__Brands__ who have first __established higher levels of Brand Awareness__ and Brand Understanding will inevitably __gain more relevant insights__ from this KPI. Generally, we advise that brands gain an awareness level of 35%+ amongst their target audience before beginning to measure and track Brand preference levels. This will also help to achieve more reliable results with lower margins of error.\n\nBrand preference KPI is strongly linked to usage or purchase behaviour and to loyalty of a brand. It is almost certain that the consumer in this case has already had experience with the brand (and other competitors) and has decided their experience warrants repeated usage and continued commitment to purchase behaviour. \n\n### How do we ask it?\n\nBrand Preference KPI question is asked alongside competitor names and logos in a single-select list format. __Question text__ of Brand preference KPI is \"Which of these brands would you prefer?\". \nThe list of __options__ is limited to 6 brands in total and additional option \"None of these\".\n\n- *The limit of 6 brands is set based on tests performed by Latana research team that identified that this is the optimal number of brands that allows for __enough focus__ on respondents' side.*\n- *Keep in mind, that every different combination of brands in the list is perceived differently by respondents. If the list has all brands with very similar brand logos and names, it can be more difficult to parse and differentiate the visual information and can end up in slightly lower KPI values.*\n![preference_in-survey](//images.ctfassets.net/7so8go2zrvbw/2l5XOVR830tG8uKtLPQdIQ/18dadb2377871deb9f30ee28bed5946c/Screenshot_2025-06-11_at_15.45.53.png)\n\n### What do you see in the app?\n\nBrand preference is delivered as brand-specific KPI where the range of predictions varies from 0 to 100% for every brand. The highest percentage displayed among the five indicates the top ranking among selected competitors.\n\n### Latana's recommendations\n__Driver associations__, when assessed alongside Brand preference, can provide a crucial narrative of your opportunities and gaps towards being preferred over other options for your target audience. If you are missing the mark with your audience on key priorities that sway decision motivations, and furthermore are winning/losing out against competitors here, it can strongly explain why preference is either high or low. \n\n__Brand consideration__, when assessed alongside Brand preference, can provide a crucial narrative of the decision making process amongst your target audience. These two KPIs highly complement each other, in that they provide understanding of your purchase or usage strength. \n\nTo note, industries vary in the combination of these KPIs due to differing purchase decision motives. Therefore it is complementary to understand the competition level within the industry alongside the consideration and preference of a brand. Especially for those industries that do not foster multi-brand usage."}}},{"node":{"title":"Market sizing","slug":"market-sizing","category":"kpis","content":{"content":"## Market sizing\n__Market Sizing KPI__ measures the share of potential buyers or users of your industry within a chosen market.\n\nThis KPI is not specifically tied to any brand but provides a key metric to understanding the potential __growth opportunity__ of your audience and of your market. The audience insight on this KPI is strongly tied to demographics. The question itself is already identifying your target audience, so drilling this down further by age or gender can reveal your potential future audience opportunity.\n\nThis KPI can greatly __identify__ __where__ and __when to place__ marketing __spend__. For example, if you see a strong decline in your industry’s Market Size within an annual period then it is safe to assume that your marketing spend may not see the normal expected returns on other KPIs such as Brand Consideration or Brand Preference. Alternatively, if you see your Market Size greatly increase within an annual period, this might provide you with the desired proof needed to persuade a push in brand messaging and marketing to better compete with players in your industry to stay ahead of the curve.\n\n### How do we ask it?\nVariations of question text depend on the purchase frequency in a given industry and kind of an industry in general, but the idea is the same: to capture people who are considering buying a product/service.\n\nPossible variations of wording:\n- \"Are you considering using/buying {product/service} in the next {time} months?\"\n- \"Are you considering buying {product} on your next visit to supermarket?\"\n\nRespondents then have an option to provide a single answer from the following options: No, Not sure, Yes.\n\n### What do you see in the app?\nMarket sizing KPI is delivered as a single estimate that can be converted into absolute population units.\n\n### Latana's recommendations\nIn order to gain a fuller picture of your industry, we recommend two core KPIs to be measured alongside Market Sizing:\n\n1. __Purchase drivers__ will add the essential insight of revealing what these potential buyers/users prioritise most from brands within the industry when making crucial purchase decisions.\n2. __Brand awareness__, measured on a wide set of industry brands, provides insights into the competition of the industry and reveals your brand positioning amongst it."}}},{"node":{"title":"Brand perception","slug":"brand-perception","category":"kpis","content":{"content":"## Brand perception\n__Brand Perception__ measures how consumers perceive your brand from a holistic perspective. This KPI is designed to provide a holistic gut-feel response to whether or not consumers feel positive or negative towards your brand.\nYou can see how your brand perception score changes over time and what is happening to your key competitors at the same time.\n\n### How do we ask it?\nBrand perception KPI question is asked individually for every brand. __Question text__ of Brand perception KPI is \"What is your overall perception of this brand?\". Respondents can select one answer from __options__: Bad, Not sure, Good.\n![brand_perception_in-survey](//images.ctfassets.net/7so8go2zrvbw/7iSGVemFrMXPiI6Hi07Asz/c9e9c53cc43eef8acebaf4c5110a7a32/Screenshot_2025-06-11_at_15.28.40.png)\n\n### What do you see in the app?\nBrand perception KPI is delivered as a single KPI and shows positive perception of a brand. The negative perception is the opposite of the value you see in the app.\n- *All estimations are made only based on answers from respondents that had an opinion on a subject matter. They are aware of the brand*"}}},{"node":{"title":"How does Latana prevent false data?","slug":"how-does-latana-prevent-fraudulent-data","category":"data_collection","content":{"content":"### __How does Latana prevent false data?__\n\nProviding very high quality data that is precise and representative of the real world  is central to Latana’s methodology. While our non-incentivised sampling method already mitigates common survey biases and naturally protects against fraud, there are still some behaviours in our surveys that can produce false results, such as accidental misclicking or not being attentive.\n\nWe have developed a process that identifies such behaviours with a high level of precision which allows us to remove those respondents from the sample. Each of our respondents is assigned a “Quality score” which grades respondents from 0 to 1. This score is assigned by a machine-learning-trained model which takes into account indicators like the respondents’ speed, positive/negative answer patterns, screen touch patterns and answers to trap questions. Respondents below a healthy score are cleaned from the pool while the higher quality respondents remain.\n\nRespondents who have previously received a low quality score are blocked from participating in our surveys again and we block respondents from participating in the same tracker for 6 months to maximise the amount of unique respondents in our sample and avoid respondents answering differently because of having previously seen the same survey.  \n"}}},{"node":{"title":"How does Latana process open-ended questions?","slug":"how-does-latana-process-open-ended-questions","category":"data_collection","content":{"content":"### __How does Latana process open-ended questions?__\n\nFirstly, we design open-ended questions in a way that minimises nonsense answers in the data. There is an instruction to respondents included next to the question that advises respondents to answer “no” if they cannot think of any answers to the question provided to ensure we can correctly differentiate them from nonsense answers.\n\nOnce all responses are collected, we run a script to check for any nonsense answers we do get and clean anything out that is not a proper answer/looks like nonsense. We then sense-check the results following this cleaning, to ensure that no valid responses have been inadvertently cleaned and that no nonsense answers still remain in the clean data.\n\nLastly, we run a proprietary script that correctly categorises each answer allowing for some different spellings and misspellings before the answers are translated via our MRP process into the results visible in our App. "}}},{"node":{"title":"How does Latana recruit respondents?","slug":"how-does-latana-recruit-respondents","category":"data_collection","content":{"content":"### __How does Latana recruit respondents?__\n\nAt Latana, we use non-probability sampling to recruit non-professional survey respondents. \n\nWe place ads and banners to our surveys on partnering publisher websites and apps to invite opt-in, real-time respondents to take our brand surveys. We ensure distribution of our sources via daily and sample-related capping of our partnering publishers. This ensures we do not skew any wave of data for any client by allowing an abundance of traffic from one given source, and allows us to monitor and control the variability of demographics and the attribution of traffic.\n\nOnce a respondent’s opt-in is gained they will navigate through to a dynamic pre-screening survey in which we collect relevant demographic or characteristic profiling data, or screening questions based on a survey's specific targeting criteria. Once eligible for a survey, the respondent is routed to a relevant survey for completion.\n\nTo ensure we reach a maximum amount of unique respondents and avoid biasing respondents who have answered the same survey before, we block respondents via unique ids from answering a survey of the same tracker for six months.\n"}}},{"node":{"title":"Click-through surveys","slug":"click-through-surveys","category":"data_collection","content":{"content":"## Click-through surveys\nYou can access click-through ⚠️ __demo surveys__ by clicking the links in below.\n\n### Notes\n- All brands and questions used in the survey are selected just for __Demo purposes__ and have not been optimised for a specific use case\n- If you see a \"Thank you\" screen after answering the age question it means you have selected the \"Under 18\" option. Because we only collect respondents aged 18+ we terminate users who indicate that they are under 18\n- The segmentation questions are aligned with the US\n\n## Demo Surveys\n\n- __[Essential package](https://surveytags.latana.com/b5e487ed-82ff-4fbd-8d3f-d8adb428d025/preview)__  survey: Aided brand awareness, Market sizing KPI questions and segmentats\n- __[Pro package](https://surveytags.latana.com/f175edd2-2951-43f8-bdd7-1451f0f242a0/preview)__ survey: Aided brand awareness, Brand perception, Brand consideration and segments\n\n---\n\n### How to see surveys in Mobile view\n\nEither open the link on your phone or if you are using a laptop or desktop:\n\n__Chrome browser__\n1. Open the survey in Chrome browser\n2. Right-click any empty area on the web page\n4. Select \"Inspect\" in the menu\n5. You will see a Dock appears in the browser window\n6. Click \"Mobile view\" button in the Dock at the upper left hand corner of the side bar\n7. Select the device that you want to use for previewing the survey at the top of the window\n\n__Safari browser__\n1. Open the survey in Safari browser\n3. Look for the menu bar at the top of the browser application window.\n4. Make sure that the option \"Show Develop menu in menu bar\" is enabled in the advanced settings. You can check this option by opening the menu Safari > Preferences (or by hitting CMD + \",\" keys), and clicking the tab \"Advanced\"\n5. Click on the \"Develop\" name of the menu. \nnshot_2023-04-06_at_14.51.47.png)\n4. Click \"Enter Responsive Design Mode\" option in the menu\n5. Select the device that you want to use for previewing the survey"}}},{"node":{"title":"What are dashboards in Latana?","slug":"what-are-dashboards-in-latana","category":"dashboard","content":{"content":"# What are dashboards in Latana?\n\nDashboards are a collection of charts to be viewed at a glance. They are useful for gathering quick insights from several charts that you tend to need all of the time.\n\nWe recommend creating dashboards grouped by markets or KPIs or segment deep dives. \n\n![dashboard_screen](//images.ctfassets.net/7so8go2zrvbw/5RUPIy64XBb0RoPseuIXA9/db8e34ff1cc8508a49e4c8a8636dd346/Screenshot_2025-06-11_at_12.18.09.png)\n"}}},{"node":{"title":"Funnel Chart","slug":"funnel-chart","category":"charts","content":{"content":"# Funnel chart\n\nThe funnel chart provides you with an effective overview of your marketing funnel success across awareness and consideration either vs up to five competitors or across six markets or segments.\n\nThe percentages in the arrows on the right side of the funnel show which percentage of people converted from the previous to the next stage, while the absolute values on the side of the funnel show the absolute value for each stage.\n\nThe table below the visualisation shows in absolute values and percentages how awareness, consideration as well as the awareness-consideration conversion have changed compared to one and the maximum amount of months ago.\n\n![funnel_chart_visual](//images.ctfassets.net/7so8go2zrvbw/5jX5Q0w5zapzXwj8pqiEnl/b1eced8dc43b3c218dc5293532c29b92/Screenshot_2025-06-06_at_17.25.26.png)\n\nWith the toggle in the upper right hand corner you can sort the visual by either the name, Segment size, or awareness or consideration %.\n\n![market_funnel_sorting](//images.ctfassets.net/7so8go2zrvbw/4itLM6Gy0yUCcQC8v1u7GF/c235f6f134625223266ff9d0d33db784/Screenshot_2025-06-06_at_17.29.18.png)"}}},{"node":{"title":"How to create a dashboard?","slug":"how-to-create-a-dashboard","category":"dashboard","content":{"content":"## How to create a dashboard?\n1. Click on the create (+) button on the left hand side\n2. Select \"Dashboard\" to create a new dashboard\n![create_dashboard_button](//images.ctfassets.net/7so8go2zrvbw/6KNaRlkOo2xHketihVcyhv/ee944b66d908b8e6ede366c64774a3ab/Screenshot_2025-06-06_at_15.20.56.png)\n4. Add charts by clicking add charts in the center or the top right\n![add_charts_to_dashboard-button](//images.ctfassets.net/7so8go2zrvbw/4JymGCE2c36xe9YKy5Qz99/9c6b7419753ffdbe243e4cb710758f97/Screenshot_2025-06-06_at_15.23.11.png)\n6. Choose the charts by clicking on the tick boxes. One chart can only ever be added to one dashboard. A dashboard can contain charts from multiple studies. You can also add charts to dashboards via the action menu in the top right corner when you have the chart view open.\n![select-charts-for-dashboard](//images.ctfassets.net/7so8go2zrvbw/4hA55aFxhUG4P4MS0UxPSL/8c146a686cd19c0427bb0d30a2a5f44f/Screenshot_2025-06-06_at_15.26.28.png)\n9. Once added, you can resize (bottom right corner), re-order via drag and drop (top left corner) and remove (top right corner) your charts again. Below the chart title you can see which elements you have selected.\n![reorganise_charts_in_dashboard](//images.ctfassets.net/7so8go2zrvbw/44oEPygMWyUsTT7okoau4m/577b55fd33b7ec96030d78caa85267e4/Screenshot_2025-06-06_at_15.28.47.png)\n12. Finally, give your dashboard a name and don't forget to save it\n\n![name_and_save_dashboard](//images.ctfassets.net/7so8go2zrvbw/1TUuhiEgLpU3FssgIpxMXQ/39f2d1eefb2022e6daf518748588c0cf/Screenshot_2025-06-06_at_15.34.06.png)"}}},{"node":{"title":"Matrix chart","slug":"matrix-chart","category":"charts","content":{"content":"# Matrix chart\n\nThis chart allows you to effectively compare and contrast the relationship between two KPIs of either a group of segments or brands across several time periods. The relative position of the data points allow you to very effectively benchmark segments or brands in a two-dimensional goal space.\n\n![matrix_chart_visual](//images.ctfassets.net/7so8go2zrvbw/1vxRCYCkyzDGWEq7V9WMTa/40eeaafd3cb85d636e5744541fe1f33b/Screenshot_2025-06-06_at_16.46.16.png)\n\nWith the two controls at the top right hand corner you can turn on or off the trend over time and switch the axes."}}},{"node":{"title":"How to use MoEs in Latana","slug":"how-to-use-moes-in-latana","category":"moe","content":{"content":"## How to use MoEs in Latana\n\nMargins of Error can be visualised in the comparison chart They show as error bars on the charts, and green/yellow/red markers in the data tables to indicate high/medium/low levels of certainty.\n\nYou can enable/disable MoE bars by clicking the MoE toggle in the upper right corner in the chart view.\n![moe_toggle_comparison_chart](//images.ctfassets.net/7so8go2zrvbw/2K5kY2PWZWXQB88yNJMK5C/b50831b17c9f58e412b6d2898be9bddd/Screenshot_2025-06-06_at_15.44.07.png)\n\nYou can use the MoEs to determine whether or not a difference is significant. For example:\n- If Aided Awareness is 65% ± 3% in wave 1 and 77% ± 2% in wave 2, the change is significant and you can be confident it reflects a real-world change.\n- If Brand Consideration is 35% ± 3% in wave 1 and 33% ± 2% in wave 2, the change is not significant and it likely does not reflect a real-world change.\n"}}},{"node":{"title":"Gauge Chart","slug":"gauge-chart","category":"charts","content":{"content":"### __Gauge Chart__\n\nThis chart helps to provide a snapshot KPI visual on a given time period. This is useful if you want to focus on one specific value when monitoring and reporting on the data.\n\n![gauge_chart_visual](//images.ctfassets.net/7so8go2zrvbw/72dj5r6u8Sramr9HCNIyr3/ab09861b0464f3050ddb62d1c90f930a/Screenshot_2025-06-06_at_16.42.46.png)\n\nGauge chart supports the \"Brands average\" function that displays the average value of a KPI for all available brands as a second gauge above the original one. To enable this Average view in a comparison chart:\n\n1. Go to the page with your chart OR create a new chart\n2. Click Brand in filters area\n3. At the bottom of the screen, toggle on \"Show average for all available brands\"\n"}}},{"node":{"title":"How are the Certainty and Margin of Error calculated?","slug":"how-are-the-certainty-and-margin-of-error-calculated","category":"moe","content":{"content":"### __How are the Certainty and Margin of Error calculated?__\n\nWhen you click on a datapoint in the dashboard, you'll see the following pop-up, which includes information on the Certainty and Margin of Error for that datapoint.\n\n![results_details_popup](//images.ctfassets.net/7so8go2zrvbw/qwZegtuwQNA1WtxAf5C7n/1675a7cd183edd0e5b922a379ce91749/Screenshot_2025-06-06_at_15.47.38.png)\n\n### Certainty\n\nThe traffic light system (High = green, Medium = yellow, Low = red) is intended to help you understand the quality of the datapoint at a glance, based on the Margins of Error. \n\nThe thresholds are defined as follows: \n\n__High__: Margin of Error is less than or equal to 3%\n\n__Medium__: Margin of Error is greater than 3% and less than or equal to 10%\n\n__Low__: Margin of Error is greater than 10%\n"}}},{"node":{"title":"What is MoE?","slug":"what-is-moe","category":"moe","content":{"content":"### __What is MoE?__\n\nMoE, or Margin of Error, is a statistical measure that reflects the amount of random sampling error in survey results. A bigger MOE for a particular data point means a lower level of confidence in that value.\n\nMoEs are usually represented as ±X%. This means if a data point is 10% with a MoE of ±2%, the true value of the datapoint is somewhere between 8-12%."}}},{"node":{"title":"Studies","slug":"studies","category":"glossary","content":{"content":"# Studies\nStudies are collections of datasets that have the same data structure and represent one module of your tracker.\n\nIn most cases, one study represents one survey across multiple waves. Sometimes one survey is utilised in multiple studies. This is usually the case when the KPIs and segments for each study are different despite coming from the same survey. This may be necessary because the datasets need to be separated from one another to produce the most reliable and stable insights and not interrupt the comparability of the tracker.\n\nIn the Latana app, you will encounter studies when you are creating charts or segments. Each study has a separate set of charts and segments because each is built on a different foundation of data. You cannot combine data from separate studies in one chart but you can have charts from different studies in one dashboard\n"}}},{"node":{"title":"How to download a png or csv of an individual chart?","slug":"how-to-download-a-png-or-csv-of-an-individual-chart","category":"csv","content":{"content":"## How to download a png or csv of an individual chart?\nIf you want to download the data or visual for a specific chart only, you can do so by clicking the download or image button respectively in the upper right hand corner of your screen when the chart view is open. \n\n![click-per-chart-png-csv](//images.ctfassets.net/7so8go2zrvbw/2aTN49RnXkwwzMcqwtJ86O/e86ba3d4c13bf9aed092f49a398feaa7/Screenshot_2025-06-05_at_16.40.37.png)\n\nAfter clicking the button, the download will start immediately in your browser.\n"}}},{"node":{"title":"Creating audience segments","slug":"creating-audience-segments","category":"segments","content":{"content":"### __Creating Audience Segments__\n\nThe standard segments based on age, gender, education, income and location are already available in the app for your use. To create your own segments, follow these steps:\n\n__1.__ To navigate to your segment library - click  on the segments icon in the navigation bar on the left side. Alternatively you can click the “+” button on the left side, choose “Segment” and choose the study in the next pop-up.\n\n![create_new_segment](//images.ctfassets.net/7so8go2zrvbw/5K6e9CY2YSHNc0ttRxV1JX/77bf1d9c42303e0a828fa50785d4bafb/Screenshot_2025-05-29_at_14.28.32.png)\n\n__2.__  Navigate to “Custom segments” via the tab at the top. Select the study you want to create a segment for via the dropdown. Click “Add segment” .\n\n__3.__ Select the audience characteristics that you want for building your segment. Leave all characteristics as “All responses” that are irrelevant.\n\nGive your segment or audience a suitable name - this could be a description of the segment or a persona (i.e. “Urban High Income” or simply “Kim”)\n\n![create_new_custom_segment](//images.ctfassets.net/7so8go2zrvbw/6cYuap4ofPYJ0i70bTY6dW/da8d11e25624dd865a94860fe43aa164/Screenshot_2025-05-29_at_14.31.11.png)\n\n__4.__ Click “Save” to save your segment. This segment is now available for all charts."}}},{"node":{"title":"Education segmentation","slug":"what-do-the-characteristics-in-the-education-section-of-the-segment-builder","category":"segments","content":{"content":"## How are the education segments defined?\nThe three levels in the education section of the segment builder are based on the international standard classification of education (ISCED) levels from low - no completed high school education, through medium - completed high school education, to high - completed a university degree.)\n\nFor example in the US, the levels are mapped as follows:\n\n- __Low education__: I don't have a formal education / I have some primary or secondary education\n- __Medium education__: I have completed high school or obtained an equivalent degree\n- __High education__: I have completed university or obtained an equivalent degree / I have completed a postgraduate degree (masters or doctorate)\n\nThe exact question options are based on the location where the survey is running and adjusted per that related education system.\n\n![Education question](//images.ctfassets.net/7so8go2zrvbw/2HN62rd7p6d3y2YFmowqcu/e3df7135f8306581cb3bd2f68cec3a7e/Screenshot_2023-04-04_at_18.58.50.png)"}}},{"node":{"title":"Segmentations in Latana surveys","slug":"segmentations-in-latana-surveys","category":"segments","content":{"content":"## Segmentations in Latana surveys\n\n### Visual examples\n\n__Age__\n![Age](//images.ctfassets.net/7so8go2zrvbw/nvtSaSSFl3asBbY24Z2gg/69650430c15d93fa7c81dc5804dea963/Screenshot_2023-04-04_at_19.20.55.png)\n\n__Gender__\n![Gender question](//images.ctfassets.net/7so8go2zrvbw/6HgX6iNoFJTW1vijFDkgMA/a47c3da02146c2790fae08b8d6833ade/Screenshot_2023-04-04_at_19.20.41.png)      \n\n__Location__\n![Location segmentation](//images.ctfassets.net/7so8go2zrvbw/QKPJ5MFDWkfPwl3jaNmyu/ed945af7e3369b3bc232352f66aff61e/Screenshot_2023-04-04_at_19.20.48.png)\n\n__Education__\n![Education](//images.ctfassets.net/7so8go2zrvbw/2n4BNikUVBwSafDpM9knKV/763a3c2b8b5b179ac8ca3b3402f6c921/Screenshot_2023-04-04_at_19.21.01.png)\n\n__Income__\n![Income](//images.ctfassets.net/7so8go2zrvbw/1nQwr4DOGMGVOwQSJzal26/a524a93f387be45c417f3f61e73ebf8b/Screenshot_2023-04-04_at_19.21.09.png)\n"}}},{"node":{"title":"How to create a new user","slug":"how-to-create-a-new-user","category":"members","content":{"content":"### __How to create a new user__\n\n__1.__ Go to your Latana account\n\n__2.__ Select __settings__ on the bottom left-hand side of your dashboard\n\n__3.__ Select __members__ \n\n__4.__ Input __email address__ and select __user permission level__\n\n__5.__ Click __invite__"}}},{"node":{"title":"User permission levels","slug":"user-permission-levels","category":"members","content":{"content":"### __Dashboard user permission levels__\n\n__Guest:__ Has the ability to view the data but cannot make any edits.\n\n__Editor:__ Has the ability to view, create and edit charts, dashboards, folders and segments.\n\n__Admin:__ Has the ability to view, create, edit charts, dashboards, folders and segments in addition to editing user permissions and creating user logins.\n"}}},{"node":{"title":"Statistical Significance","slug":"statistical-significance","category":"glossary","content":{"content":"__Statistical Significance __\n\nA measurement to evaluate whether a result is due to chance or likely to be real. "}}},{"node":{"title":"Respondent","slug":"respondent","category":"glossary","content":{"content":"__Respondent__\n\nA respondent is a person who meets your targeting criteria and participates in your survey."}}},{"node":{"title":"Absolute Value","slug":"absolute-value","category":"glossary","content":{"content":"__Absolute Value__\n\nThis is the actual population number, i.e. amount of people, representing the KPI and segment combination."}}},{"node":{"title":"Acquiescence Bias","slug":"acquiescence-bias","category":"glossary","content":{"content":"__Acquiescence Bias__\n\nAlso known as agreement bias, is a category of response bias common to survey research in which respondents have a tendency to select a positive response option or indicate a positive connotation disproportionately more frequently. \n\nAny questions with just Yes/No answer options for example are particularly vulnerable to this bias. "}}},{"node":{"title":"How to manage folders","slug":"how-to-manage-folders","category":"folders","content":{"content":"### __How to manage folders__\n\nAs you open up access to the Latana app for various teams within your company, things can start to get a little hectic. Especially when multiple team members have editing access and begin to create the charts and dashboards they need. \nThis is where our folder feature comes in handy! \n\nFolders help you keep things tidy and organised. To create a new chart or dashboard folder, you can follow two paths:\n\n__A.__ On the __charts__ or __dashboard__ page click on the New folder button in the upper right hand corner, specify a name and click __Create folder__.\n![create new folder](//images.ctfassets.net/7so8go2zrvbw/5mDpxDCtZ2OIkEruYhhErC/826a1b94452389192555f7c34310dec0/Screenshot_2025-05-29_at_09.05.14.png)\n\n__B.__ Click on the __+__ button in the navigation bar anywhere in the APP. Select __Folder__, toggle to select whether you want to create a chart or dashboard folder, specify a name and click __Create folder__\n![create new folder with plus button](//images.ctfassets.net/7so8go2zrvbw/4lPsS9z8JMeF1Ai88H6RHN/080e7dd93cf9b7036b8588e946f69edf/Screenshot_2025-05-29_at_13.42.35.png)\n![specify new folder name](//images.ctfassets.net/7so8go2zrvbw/2tYh0xzM0iWAiKZcBJxfhg/a6d67682bfcf4728e1c1b370b97be696/Screenshot_2025-05-29_at_13.44.51.png)\n\nTo rename or delete your folder __click__ the three white dots on the right hand side of your folder name:\n![rename or delete folders menu](//images.ctfassets.net/7so8go2zrvbw/4pRuUhp67v55MyYCD8aTfL/da8b0e7a03fd803eaefab24e9f5e8121/Screenshot_2025-05-29_at_13.46.05.png)\n\nTo add charts or dashboards into folders you can either __drag and drop__ them into any folder or use the three white dots in the overview or in the charts or dashboard view. The number next to the three dots indicates how many items are in the folder.\n\n![move_folder_actions_menu](//images.ctfassets.net/7so8go2zrvbw/1XY1HB5yBh67hMwW7RzIlX/1c477c2c466398b5b5f997a4b0122716/move_folder_actions_menu.png)\n\nIf you use the menu to move the item to a folder, select the correct folder in the dropdown.\n![move_folder_folder_selection](//images.ctfassets.net/7so8go2zrvbw/4chy07cybKQZAKtfdtVI2M/8517f4133aab855b4769ca04e284ae36/Screenshot_2025-05-29_at_13.51.30.png)\n\nYou can enter a folder by clicking on them. In the folder, you can use the same actions menu to remove charts from the folder again. You can do the same in the charts or dashboard view. Then they will reappear on the main page.\n\n![remove_from_folders_acitons_menu](//images.ctfassets.net/7so8go2zrvbw/32rPLtSS0INulc0PldkQL9/3935f70b8f4d70897cb8d4e20fc4917f/remove-from_folder_action.png)\n"}}},{"node":{"title":"Market Research Panel","slug":"market-research-panel","category":"glossary","content":{"content":"__Market Research Panel__\n\nA market research panel is a panel made up of people who have either applied, or been invited, to become panel members. Panel members are then invited to take part in research projects that are aligned to their profile and registered interests. Respondents are often incentivised with monetary or non-monetary (e.g. gaming credits) incentives."}}},{"node":{"title":"Data Cleaning","slug":"data-cleaning","category":"glossary","content":{"content":"__Data Cleaning__ \n\nRemoving unqualified, biased or inattentive responses from a survey. This process improves the data quality and protects against survey bias."}}},{"node":{"title":"Why bother with MRP?","slug":"why-bother-with-mrp","category":"data_collection","content":{"content":"## __Why bother with MRP?__\n\n### Traditional Quota Sampling\n\nQuota sampling can often give us a rough idea of our KPI of interest but may suffer from noise. \n\nNoisy data can often be identified by large fluctuations up and down over time. The problem with such noise is that it gets difficult to say exactly how high or low our KPI actually is, and which increases or decreases are legitimate.\n\nOne consequence of this is larger margins of error (MoEs) in quota sampling. These difficulties exist at the general population level, and get worse as we look into more niche audiences, especially those with smaller sample sizes.\n\n__MRP__\n\nLatana uses MRP models to address these problems in a couple of ways. The first comes from not treating data as isolated in time, like quota sampling does. Opinions and awareness take time to form and change, meaning that brand and consumer behaviour KPIs tend to evolve smoothly. \n\nLatana’s MRP models consider data from all waves up to a point in time simultaneously to construct the story which makes the most sense across time. Waves with more data give us more information and help to anchor the narrative. Those waves with less data are more informed by the surrounding waves so that noise does not result in a misleading conclusion.\n\nThis is not just a trend line drawn across the quota sampling estimates. This method of connecting data through time is applied at the most fine-grained level to estimate the KPIs for every audience simultaneously. Due to the coherence of all these estimates, we still get a plausible story after aggregating to our target audience.\n\nBy working with the data in its entirety, any KPI fluctuations are greatly reduced, so real changes are easier to identify. The margins of error are significantly narrower, which means higher confidence in the data, and the ability to look at segments with confidence."}}},{"node":{"title":"Income segmentation","slug":"how-are-the-income-questions-defined","category":"segments","content":{"content":"### __How are the income questions defined? __\n\nIn regard to the income level question, we categorise the answers into 4 various currency brackets depending on the specific country (in the respective currency, of course). \n\nThe currency brackets are based on official statistics for the median income level in developed and developing countries. In the Segmentation builder in the app, these will be consequently categorised into low, medium or high. \n\nVisual example of the question (based on USA data):\n![Income question](//images.ctfassets.net/7so8go2zrvbw/5EOX6lsNIpo5wGb94RverJ/a07e2d8c95b266d33da21e2e41057462/Screenshot_2023-04-04_at_19.03.28.png)"}}},{"node":{"title":"Understanding segmentation","slug":"understanding segmentation","category":"segments","content":{"content":"### __What is segmentation?__\n\nMarket segmentation is the process of dividing the total markets into important segments. Before identifying your target audience, it’s important to map the market through segmentation. \n\nWhile traditional quota sampling has proven to be fairly ineffective in accurately predicting consumer attitudes due to a limited respondent sample for niche audiences, Latana’s advanced statistical modelling, MRP, allows you to do this with much higher precision.\n\n__What are audience characteristics?__\n\nAudience characteristics are the building blocks for your segmentation. They’re demographic, sociographic, psychographic or behavioural criteria you can use to frame and analyse your audience. \n\nThe Latana dashboard offers the standard demographic characteristics of age, gender, income, education, and location, to build your audiences. Each audience characteristic represents an answer from a segmentation question asked in the survey.\n\n__What are custom audience characteristics?__\n\nCustom audience characteristics are like standard audience characteristics except they’re unique to your brand or category. They enable you to filter, analyse and compare your brand’s performance across your brand’s audiences. \n\nWith custom audience characteristics, you can compare between the general population and your target audience, and discover and explore new potential audiences for your brand."}}},{"node":{"title":"Evolution Chart","slug":"evolution-chart","category":"charts","content":{"content":"### __Evolution chart__\n\nThis chart helps to visualise granular evolutions of KPIs wave-on-wave. This is especially useful for inspecting smaller movements on more stable and non-changing KPIs over time.\n\n![Evolution Chart](//images.ctfassets.net/7so8go2zrvbw/3hRaZEa5GrnYz7ql358jut/e3a7fac6a37395ff3999061518995eff/Screenshot_2022-04-11_at_12.17.54.png?w=300)"}}},{"node":{"title":"Radar chart","slug":"radar-chart","category":"charts","content":{"content":"### __Radar Chart __\n\nA radar chart provides a quick snapshot to help you visualise which characteristics respondents associate with your brand across different time periods, markets and brands. \n\nTo create your radar chart:\n\n__1.__ Click the __+__ symbol on the right hand side of __charts__\n![Create Radar](//images.ctfassets.net/7so8go2zrvbw/1Vqw8h2KZYspr5en9AWw6H/17cd636fb8824ce703e10744943c3808/Screenshot_2022-03-25_at_16.36.00.png?w=300)\n\n__2.__ All brand association characteristics are selected by default. You can adjust the selection but a minimum of 3 associations need to be selected in order to create your radar chart. \n\n__3.__ Then proceed to set the filters as you wish.\n![Radar chart](//images.ctfassets.net/7so8go2zrvbw/5ATA5NWzAreeRkJqNYYptm/f8e8fa6a7420696501206cbefa2f1c22/Screenshot_2022-03-25_at_16.34.43.png?w=300)\n__Nb:__ Like all other charts, you can only have multiple in two filters at any given time. Since you need to have multiple brand associations selected, this allows you to choose having multiples in either the geography, time period, brand or segment filter."}}},{"node":{"title":"How MOEs are calculated in Latana","slug":"how-moes-are-calculated-in-latana","category":"moe","content":{"content":"### __How MOEs are calculated in Latana__\n\nWhen you’re calculating MOEs for traditional survey data, you typically use a formula based on sample size, standard deviation, and confidence level.\n\nAt Latana, our approach to MOEs is based on how we use our MRP model to generate estimates. With [MRP](https://knowledge.latana.com/what-is-mrp) (Multi-level regression and poststratification) we are using data from the entire population to improve the quality of data, especially for hard-to-reach groups. The MRP model generates 100 estimates of each KPI for each slice of the population, and then we use the mean of those estimates as the value for the KPI.\n\nFor example, the MRP model generates 100 estimates of Aided Brand Awareness for high-income, high-education, urban, millennial females. The mean of those estimates is 45%, and that’s what we show as the value for Aided Brand Awareness for that segment. The lowest value of the 100 estimates is 42% and the highest value is 48%, which means it has an MOE of ±3%."}}},{"node":{"title":"What is MRP?","slug":"what-is-mrp","category":"data_collection","content":{"content":"### __What is MRP?__\n\nMRP, or to give it its full name, Multilevel Regression and Poststratification, is a form of advanced data science made popular by Professor Andrew Gelman. Professor Gelman first used it for election forecasts, while Latana is the first to use MRP for brand tracking.\n\nMRP creates a model and uses this model to generate estimates for responses in a survey. This model, when given a set of respondent characteristics, can produce an estimate for how that type of respondent would answer a survey question. Following that, MRP organizes the respondent’s characteristics into groups. By doing so, they can better capture how the variables interact in real life. Finally, MRP takes weighted averages of all the predictions. This is to ensure that the model has a fair sample of respondents.\n\n__In a nutshell, MRP does the following:__\n\n__1.__ Uses past data to correct for fluctuations in the data over time.\n\n__2.__ Leverages all the data in the sample and utilizes weighting techniques to ensure findings are more accurate and representative.\n\n__What is the value in this?__\n\n__1.__ Gain insights of a higher level of accuracy than quota sampling.\n\n__2.__ Control for inaccurate fluctuations or “noise” in the data over time.\n\n__3.__ Measure brand performance among ultra niche audiences with high confidence bounds."}}},{"node":{"title":"FAQs ","slug":"faqs-data-collection","category":"data_collection","content":{"content":"### __FAQs__\n\n__Q. When can I see the data in my dashboard?__\n\nThe timeline we provide takes the moment when we have agreed on all details of the survey as the starting point. \n\nAfter that, the survey will go through 4 main stages: \n- Setup \n- Translation \n- Fieldwork \n- Post-processing \n\nThe length of each stage varies depending on your contract. \n\n__Q: How do you incentivise respondents?__\n\nHere at Latana, we utilise two channels to collect respondents, the incentivised channel (market research panels), and non-incentivised (DSP).\n\nThe incentived channel is a reward-based channel where participants sign up and belong to a panel. Rewards can range from gaming credits, vouchers to monetary value. Latana does not directly incentivise respondents who complete our surveys. Instead, any incentivisation is taken care of by our panel partners.\n\nOn our DSP (demand-side platform) channel, there is no paid incentivisation involved. Latana deploys advertising to engage with respondents/casual survey takers. As there is no incentivisation or obligation for a respondent to complete a survey, the level of effort here can be significantly higher but the scale here is much better than via incentivised channels due to the ability to target anyone that has access to a mobile device and advertising.\n\n__Q: How do you ensure representativity in your data?__\n\nThe MRP process involves steps to ensure results are representative of a target population. Unlike quota sampling which uses targeted cell collection to fill specific nat rep quotas, MRP instead applies the post-stratification weighting to the MRP model predictions. The post-stratification weights are based on Barro Lee census population data (age, gender, regional, education) and are extended to more variables when necessary using methods such as raking.\n\nBarro Lee census data is widely accepted and trusted in the field of market research and is often applied as the base target for quota sampling methodologies too."}}},{"node":{"title":"Our tips for finding your target audience","slug":"our-tips-for-finding-your-target-audience","category":"research","content":{"content":"### __Our tips for finding your target audience/s__\n\nIf you're a growth brand and are still in the process of figuring out your target audience, we've put together a brief guide on how to do this. If you've found your target audience yet wish to explore new audiences that will likely deliver a high ROI, the below tips still apply.\n\n__1. Make sure you have the right custom segmentations__\nThis is very important. You'll need all the relevant characteristics in your dashboard in order to assess their value for your brand. If you're unsure which custom characteristics are right for your brand or category, check out our help article here.\n\n__2. Create all relevant segments in the dashboard__\nNow that you have all your characteristics showing in your dashboard, create segments for all of your relevant audiences using Latana's [segment builder](https://faq.latana.com/creating-audience-segments).\n\n__3. Evaluate each audience__\nThe final step! Now that you've built all of your possible segments, you can start evaluating their value in terms of ROI for your brand. Check out the audience framework shown below:\n\n![Target audience framework](//images.ctfassets.net/7so8go2zrvbw/26FAoWaHerUHLjkm5IC3x/457d18764f0c0e02994f78ea384d3c5b/Target_audience_framework.png?w=300)\n\n__Audience Finder__\nDoes the audience you're looking at have high awareness and high brand strength? This is a strong indication they should be one of your key audiences! \n\nDo they have high awareness, low brand strength? This generally means you've invested in this audience in the past, however you're not seeing a positive ROI on your marketing spend. \n\nLow awareness and high brand strength? This probably means you're not capitalising on a promising audience.\n\nWe define brand strength as a combination of key KPIs - Brand Consideration, Brand Preference and Brand Usage. At the core of this framework is the idea that your best audience is one that has a strong need for your product, shows purchase intent, prefers you to the competition and is already using your brand."}}},{"node":{"title":"Choosing the right segmentation/audience characteristics","slug":"choosing-the-right-segmentation-audience-characteristics","category":"research","content":{"content":"### __Choosing the right segmentation/audience characteristics__\n\n__The importance of audience characteristics__\nAudience characteristics allow you to filter, analyze and compare your brand’s performance across audiences. Choosing the right ones means you can build your segments and personas, compare between the general population or target audiences, and discover and explore new potential audiences for your brand.\n\n__Identifying the right custom audience characteristics__\nStuck on which custom audience characteristics are right for your brand? Consider the below tips:\n\n__1. Identify your target audience.__ \nIf you know this already, it should be easy to identify their custom characteristics.\n\n__2. Ask what’s unique to your brand.__ \nIf you’re still figuring out your target audience, think what separates your brand from competitors. For an online bank whose value proposition is free international ATM withdrawals, their target audience might be “Frequent Flyers”. In this case, frequent flyers - yes/no would be two audience characteristics in your dashboard.\n\n__3. Think about your market more broadly.__ \nIf you’re just starting to build your brand identity, try using category specific audience characteristics. For example, “interested in using online fitness apps”, “owns a smartphone”, and “has a gym membership” would be relevant characteristics for an online fitness brand. Even if you have a distinct brand, this is a common approach to segmentation if you’re looking to cast your net wide and target the whole market of your category.\n\n__4. Set it and forget it!__\nYou can’t measure change if you change the measure! \n\nWith brand tracking, it’s important to ensure your survey stays relevant over time. As long as you’re not looking to radically transform your business in a year, selecting brand or category specific audience characteristics should do the trick.\n\n![Custom audience characteristics](//images.ctfassets.net/7so8go2zrvbw/VqX6RYlcJQ0yVr59eNdrV/04253dd86ac9a80cca6e01e0bd0d22dc/Screenshot_2022-04-11_at_18.33.06.png)\n\n__Note:__ A segmentation question should usually be a yes/no question, which will translate into two custom audience characteristics in the dashboard. We can, however, support up to 5 different audience characteristics per segmentation question."}}},{"node":{"title":"Survey design","slug":"survey-design","category":"research","content":{"content":"### __Survey design FAQs__\n\n__Q. Are your surveys mobile-optimised?__\n\n__A.__ Yes\n\n__Q. How do you display our competitor brands?__\n\n__A.__ At Latana, we do not utilise brand lists (which has been known to bias results), instead, we display one brand (logo + brand name) at a time to respondents as well as deploying randomisation.\n\n__Q. Why do you add a third answer choice “unsure” to your surveys?__\n\n__A.__ Extensive research shows that a forced binary response for the aided awareness question can lead to acquiescence bias which can potentially cause slight over-inflation of awareness levels. By reformatting the answer options to allow the third option (unsure), it gives the respondent the opportunity to answer more honestly without being forced into a yes/no only scenario. \n\nFor example, a respondent may have been exposed to your products/services/branding efforts at some point in time but has not been exposed at a high enough frequency for them to confidently answer yes they are 100% aware of the brand, and recall may not be particularly strong but also not poor enough to answer a hard no.\n\n__Q. Is it worthwhile measuring unaided awareness?__\n\n__A.__ Unaided awareness is a KPI best suited for brands with high awareness levels e.g. Coca Cola, Nike. Brands with low levels of awareness (<20%) will see marginal rates of unaided awareness (between 0-0.5%). At this low rate, movements can be interpreted very incorrectly because absolute percentages move in a much more varied manor even based on one more mention per wave. This makes for a very unreliable measure and cannot be used to make clear decisions from.\n\nOur recommendation here is to start with tracking your aided awareness so we can gather a baseline to assess whether or not your aided awareness levels are strong enough to capture the much more granular unprompted recall. "}}},{"node":{"title":"FAQs","slug":"faqs-research","category":"research","content":{"content":"### __FAQs__\n\n__How do I know if my results are statistically significant?__\n\nStatistical significance testing is not compatible with MRP (bayesian models), so we are not able to currently offer this feature. \n\nHowever, instead, we provide you with margins of error readings - this is the compatible model to identify error bounds within the data. Using MoE readings (which are provided directly in the dashboard on every data point, including segments), you are able to identify the accuracy and confidence levels in the dataset and provide reliability in the data readings across waves and by segments.\n\n__Q. What is the difference between campaign impact measurement and brand health tracking?__ \n\nCampaign impact measurement and brand health tracking are two different types of tracking. \n\nWhilst your brand tracker is designed to track more KPIs over a longer period of time to enable you to see directional trends, with campaign impact tracking, we are primarily interested in measuring the uplift in awareness, the simplicity in the way we structure our surveys is to ensure reliable data wave over wave. \n\n__Q. Is Google Trends data comparable with Latana’s results?__\n\nIn short, the answer is no. Google Trends data source and Latana data source are not comparable primarily due to the underlying audiences they both capture. Google Trends is measuring active interest within a market, while Latana is assessing brand health within the category of content uploaders:\n\n__Google Trends data__ is essentially a combination of Unaided Awareness + Brand Consideration - it is detecting unprompted interest in your brand. \n\nThis is a very different indicator of consumer drive to purchase, due to the fact people only actively search for a brand that they intend to either research or actually buy from. Ultimately, anyone who searches for a brand online already knows that brand, and furthermore has an active and unprompted interest in it, which limits a wider understanding of brand share and potential growth opportunities.\n\n__At Latana__ we are assessing your brand’s wider brand health (such as awareness levels and perception) within a population that is more representative of the category itself (screened for content uploaders only). \n\nThere is no pre-disposition to knowing the brand or having any former interest in it when giving an opinion on it. Here we are trying to capture: how many people within the category know your brand, how many would consider your brand, what do people think of your brand and measure this growth over time.\n\n__What is the difference of incidence rate (IR) between country/region/city?__\n\nGenerally, IR's will vary greatly by region, but as you can imagine the general rule is that the IR is highest by country level, and becomes narrower the smaller the region you wish to target. \nE.g:\nUSA = Whole country (population is 328.2m = 100%)\nUSA, Northeast = Region (population is 55.9m = 17%)\nUSA, New York City = City (population is 8.42m = 2.6%)\n\n__Can I have a sample size of less than n=500?__\n\nA sample size lower than n=500 would not provide reliable and representative data and will be at risk of high fluctuations across waves, therefore we consider a minimum sample size of n=500 per wave to be a robust sample size to ensure a reliable reading of data. \n\nHowever, this is also dependent on the level of segmentation required within the data. \n\nIf only standard measures of age, gender, education, region and income are required for analysis of the data (no custom segmentations), then n=500 per wave is the minimum we accept. For any custom segmentation requirements, we will require a wider sample size to accommodate for this and ensure reliability of the segmented data."}}},{"node":{"title":"Audience Characteristics","slug":"audience-characteristics","category":"glossary","content":{"content":"__Audience characteristics__\n\nAudience characteristics are the building blocks for your segmentation. They are demographic, sociographic, psychographic or behavioural criteria you can use to frame and analyse your audience."}}},{"node":{"title":"Brand Tracking","slug":"brand-tracking","category":"glossary","content":{"content":"__Brand Tracking__\n\nBrand tracking is the continuous monitoring of your brand’s health over a period of time. By tracking key metrics such as brand awareness and perception, it provides a means to understand if your target audience knows of your brand and how they feel about your brand."}}},{"node":{"title":"Designated Market Areas (DMA)","slug":"designated-market-areas-dma","category":"glossary","content":{"content":"__DMA__\n\nA designated market area (DMA), is a region of the United States that is used to define television and radio markets. There are 210 DMAs covering the whole United States and are usually defined based on metropolitan areas, with suburbs often being combined within. \n\nDMAs are determined by the Nielsen Company and impact the cost of advertising in a specific area. The more viewers in a particular DMA, the more an advertisement will cost. "}}},{"node":{"title":"Drop off rate (DOR)","slug":"dor-drop-off-rate","category":"glossary","content":{"content":"__Drop Off Rate__\n\nThe __DOR__ is the percentage of respondents who did not complete the survey."}}},{"node":{"title":"DSP","slug":"dsp","category":"glossary","content":{"content":"__DSP__\n\nA demand-side platform (DSP) is an automated programmatic advertising platform where marketers can purchase and manage ad inventories from multiple ad sources. \n\nDSPs work by using programmatic advertising, which is the buying and selling of ads in real-time through an automated system. With real-time bidding, ad placements are auctioned off in milliseconds. "}}},{"node":{"title":"Fieldwork","slug":"fieldwork","category":"glossary","content":{"content":"__Fieldwork__\n\nIn market research, fieldwork is the term used for the collection of primary data from external sources. "}}},{"node":{"title":"Incentivised Channel","slug":"incentivised-channel","category":"glossary","content":{"content":"__Incentivised Channel__ \n\nA channel such as a research panel where respondents are incentivised with monetary or non-monetary rewards in exchange for their time to take part in a survey."}}},{"node":{"title":"Incidence Rate","slug":"incidence-rate","category":"glossary","content":{"content":"__Incidence Rate__\n\nThe incidence rate is defined as the number of respondents from a sample pool that will qualify for your study. It is often synonymous with the qualification rate in market research.\n\nFor example, if you have 100 random people who are willing to participate in your survey, and only 3 actually qualify based on your screening criteria, your incidence rate would be 3%.\n"}}},{"node":{"title":"Key Performance Indicators (KPIs)","slug":"key-performance-indicators-kpis","category":"glossary","content":{"content":"__KPI__\n\nA Key Performance Indicator (KPI) is a measurable value that demonstrates how effectively a company is achieving key business objectives. High-level organization KPIs are metrics that are measurable over time, like Revenue and Retention.\n\nBrand KPIs work the same way, however, they are specific to the brand."}}},{"node":{"title":"Length of Interview (LOI)","slug":"length of interview","category":"glossary","content":{"content":"__Length of Interview__\n\nLOI is the time it takes a respondent to complete the survey."}}},{"node":{"title":"Margin of Error (MoE)","slug":"moe","category":"glossary","content":{"content":"__Margin of Error (MOE)?__\n\nMargin of Error, or MOE, is a statistical measure that reflects the amount of random sampling error in survey results. A bigger MOE for a particular data point means a lower level of confidence in that value.\n\nMOEs are usually represented as ±X%. This means if a data point is 10% with a MOE of ±2%, the true value of the datapoint is somewhere between 8-12%.\n"}}},{"node":{"title":"MRP","slug":"mrp","category":"glossary","content":{"content":"__MRP__\n\nMRP, or to give it its full name, Multilevel Regression and Poststratification, is a form of advanced data science made popular by Professor Andrew Gelman. Professor Gelman first used it for election forecasts, while Latana is the first to use MRP for brand tracking.\n\nMRP creates a model and uses this model to generate estimates for responses in a survey. \n\nThis model, when given a set of respondent characteristics, can produce an estimate for how that type of respondent would answer a survey question. Following that, MRP organizes the respondent’s characteristics into groups. \nBy doing so, they can better capture how the variables interact in real life. Finally, MRP takes weighted averages of all the predictions. This is to ensure that the model has a fair sample of respondents.\n"}}},{"node":{"title":"Net Promoter Score (NPS)","slug":"net-promotor-score-nps","category":"glossary","content":{"content":"__Net Promoter Score (NPS)__\n\nA value calculated by asking respondents “How likely are you to recommend this brand, product or service to a friend?”. The classic question asks them to rate this on a scale from 0 to 10 where 0 means not at all likely and 10 means extremely likely. Respondents who answer 9 or 10 are referred to as Promoters, those who answer 7 or 8 are referred to as Passives and those who answer 0 to 6 are referred to as Detractors. \n\nThe NPS is the percentage of Promoters minus the percentage of Detractors."}}},{"node":{"title":"Non-Incentivised Channel","slug":"non-incentivised-channel","category":"glossary","content":{"content":"__Non-Incentivised Channel__\n\nA channel such as via ad-formats, where respondents answer survey questions without receiving an incentive for doing so. "}}},{"node":{"title":"Open-Ended Question","slug":"open-ended-question","category":"glossary","content":{"content":"__Open-Ended Question__\n\nOE questions allow respondents to reply in their own words. There are no pre-set answer choices e.g. Unaided awareness KPI."}}},{"node":{"title":"Sample","slug":"sample","category":"glossary","content":{"content":"__Sample__ \n\nA subset of the population of interest."}}},{"node":{"title":"Screen Out","slug":"screen-out","category":"glossary","content":{"content":"__Screen out__\n\nA screen out refers to a disqualified or terminated respondent due not fitting the criteria of the screening question asked.\n"}}},{"node":{"title":"Screening Question","slug":"screening-question","category":"glossary","content":{"content":"__Screening Question__\n\nScreening questions or screeners are questions at the beginning of the survey. Depending on the respondents' answer, it will determine if they qualify to move through the rest of the survey or not. "}}},{"node":{"title":"Segmentation","slug":"segmentation","category":"glossary","content":{"content":"__Segmentation__ \n\nSegmentation is the process of dividing markets into groups with people or occasions that are similar to each other, but different to the other groups. \n\nThere are a number of different ways to segment such as consumer segmentations. This type of segmentation is used to understand which consumers to target and service with distinct marketing propositions, or to tailor brands, products, pricing, communication to specific groups and make more effective use of marketing resource."}}},{"node":{"title":"Weighting","slug":"weighting","category":"glossary","content":{"content":"__Weighting__  \n\nThe process of adjusting the value of survey responses to account for over-or under-representation of different categories of respondents. \n\nWeighting is used where the sample design is disproportional or where the achieved sample does not accurately reflect the population under investigation."}}}]}},
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