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    "result": {"data":{"allContentfulAsset":{"edges":[]},"contentfulKnowledgePost":{"seo":{"__typename":"ContentfulSeo","title":"How are income level questions defined?","description":"How are income level questions defined?","noIndex":null,"ogImage":null}}},"pageContext":{"slug":"how-are-income-level-questions-defined","markdown":{"nodeType":"document","data":{},"content":[{"nodeType":"heading-1","content":[{"nodeType":"text","value":"How are income level questions defined?","marks":[],"data":{}}],"data":{}},{"nodeType":"paragraph","content":[{"nodeType":"text","value":"In regard to the income level question, we categorize the answers into 12 various currency brackets depending on the specific country (in the respective currency, of course). The currency brackets are based on official statistics for the median income level in developed and developing countries. In the dashboard, these will be consequently categorized into low, medium or high. So the first 5 categories will be marked as ‘low’, the 6th and the 7th as ‘medium’, and the last 5 as “high”. This guarantees a fair comparison for the various countries.","marks":[],"data":{}}],"data":{}}]},"images":[],"allPosts":[{"node":{"title":"The radar chart visualizes brand association KPIs.","slug":"the-radar-chart-visualizes-brand-association-kpis","category":"using-latana","content":{"content":"# The radar chart visualizes brand association KPIs.\n\nYou can select three or more characteristics associated with your brand and compare them with competitor brands.\n\nYou can also visualize which characteristics respondents associate with your brand across different time periods or in different countries\n\n![Radar chart type](//images.contentful.com/7so8go2zrvbw/6qtQPwSY3bU0XTRBiIczQ/9c1e28839bbe26b921b277f3afe0674b/Screenshot_2021-12-02_at_12.01.04.png)\n\n## How does it work?\n\nThe creation process for the radar chart is similar to the comparison and funnel charts:\n\nClick on the CREATE button and chooses \"Radar\".\n\n![Building a radar chart](//images.contentful.com/7so8go2zrvbw/2GjaZMn4HjojOa1NlJR5vN/2eec09791411989304cf3bc4b9059cf8/Screenshot_2021-12-02_at_12.04.15.png)\n\nThen set the filters as you see fit.\n\n![Filters selection](//images.contentful.com/7so8go2zrvbw/15KiCQbd8RSH1wvWAlR2p7/7b6132924bb36f413661b791c58fd833/Screenshot_2021-12-02_at_12.04.51.png)\n\n*Nb: all brand association characteristics are selected by default. You can adjust the selection. At least 3 associations need to be selected.*"}}},{"node":{"title":"Compare Brand Tracking Data across Time","slug":"compare-brand-tracking-data-across-time","category":"using-latana","content":{"content":"# Compare Brand Tracking Data across Time\n\n## Why compare data over time?\n\nTracking your performance across time is essential for measuring any changes to your brand’s health and the success of your campaigns. The ideal frequency with which you track your brand depends on how often you’re running your campaigns and how quickly you would like to spot trends in your brand’s health.\n\n## Observe changes in the chart visualizations\n\nUsing the time slider located below the chart visualizations in the “Brand Funnel” and “All KPIs” sections of the dashboard, select the month you wish to track and then drag it left or right depending on the period of time you wish to cover. As shown in the mock data below, between Jan 2020 until Feb 2020, February's performance on the right in white  shows 4.93% Aided Awareness and the rate of increase on the left in blue was 0.02%. In this example, we didn’t see a significant change month on month.\n\n![Brand Funnel](//images.ctfassets.net/7so8go2zrvbw/7z0XsbgKqjCg6U8f1Lj7Pt/9aafe66d8230082b539f588a6648d95e/image5.png)\n\n## Observe changes in the table\n\nFor a full breakdown of changes across time, look at the table at the bottom of the dashboard. Select “Segment” to lock in one particular target audience or “Brand” to lock in your brand or a competitor. The time slider will also determine what you see in this format. As shown in the mock example below, the table allows you to see how much each brand or segment increased/decreased for each KPI:\n\n![Data Table](//images.ctfassets.net/7so8go2zrvbw/AMmmy5EkCAmzeTAbMk8i3/4f1beb14a2aad67214e2ab342c17265c/image3.png)\n\nIn this case, we see 0.02% to 4.44% increases on each KPI between November 2019 and February 2020.\n\n## Using the Comparison Graph\n\nThe Comparison Graph section allows you to visualize your findings over time. Similar to the table, you can analyse each KPI across competitor brands and segments. In the mock example below, you can see Brand Consideration increasing 13.67% across the general population from September 2019 to February 2020.\n\n![Comparison Graph](//images.ctfassets.net/7so8go2zrvbw/3IXORKQqVbXYMJx5YJg6hQ/4ce2a560ca93cd3e56c327d6557e3b0e/image7.png)\n\nUse the Comparison Graph to spot trends across waves to ensure you’re setting the right strategic brand objectives. In the above case, we see some brands increasing in Consideration while competitors remaining stagnant over the same period. "}}},{"node":{"title":"Create Audience Segments","slug":"create-audience-segments","category":"using-latana","content":{"content":"# Create Audience Segments\n\nBefore you start creating your segments, you should already have your segmentation filters setup in the dashboard. If you’re missing important audience characteristics in your dashboard, check out our article on [Choosing the Right Segmentation](https://knowledge.latana.com/choosing-the-right-segmentation). See below some steps on how to create your audience segmentation in the Latana dashboard:\n1. Click the + symbol in the top right-hand corner of your dashboard and then select “Segment”\n2. Select the standard audience characteristics that you want for building your segment (if one of the audience characteristics are irrelevant, simply leave it set as “All Responses”)\n3. Select your custom audience characteristics you want for this segment\n4. Give 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”)\n5. Repeat for all of your audience segments\n\nFor a basic definition of segmentation and its value, check out our [Understanding Segmentation](https://knowledge.latana.com/understanding-segmentation) article.\n"}}},{"node":{"title":"All Things Brand Funnel","slug":"all-things-brand-funnel","category":"using-latana","content":{"content":"# All Things Brand Funnel\n\n## What is the Brand Funnel?\n\nThe brand funnel is a conceptual model that represents the journey consumers take from discovery to purchase. Your brand funnel should resemble something similar to the following:\n\n![brand funnel](//images.ctfassets.net/7so8go2zrvbw/4yQqjr18vu9HVdoF08UGXp/ad7f2de8b6c303877f99e37f4be00be8/brand_funnel.png)\n\nThe funnel indicates at what stage of the customer journey you’re converting and losing potential customers among audiences. The stages are as follows:\n\n### Unaided Awareness\n\nA stand-alone metric that asks an open-ended question about whether a respondent can recall your brand unprompted (% of total audience).\n\n### Aided Awareness\n\nHow many people have heard of your brand (% of total audience).\n\n### Consideration\n\nHow many of those who are aware of your brand would consider using it (% of Aided Awareness).\n\n### Preference\n\nHow many people prefer your brand over the competition (% of Aided Awareness).\n\n### Usage\n\nHow many people have used your brand in recent months (% of Aided Awareness).\n\n*__Tip__: If you’re interested in knowing what your custom funnel is measuring for each KPI, hover your cursor over the name, and it will display the exact question offered to respondents.*\n\n## Purpose of the Brand Funnel\n\nThe Brand Funnel is valuable for several reasons, as it:\n1. Helps assess your [brand’s health](https://latana.com/post/brand-health-metrics).\n2. Shows where you’re losing your audience/potential customers\n3. Highlights problem areas to focus your upcoming brand strategy and can inform brand objectives\n\n## Interpreting your Brand Funnel\n\nLook at the drop-offs between each KPI in your brand funnel and identify where you’re both winning and losing the greatest share of customers. Use the Brand Funnel Compare feature in the top right-hand corner of the Brand Funnel to compare drop-offs between different segments. For example, you could have low Consideration among the General Population yet high Consideration among your target audience. See the feature here:\n\n![Compare Waves](//images.ctfassets.net/7so8go2zrvbw/3kJ9CPI9Mhz9AN7TT5vxUR/1273e5d9bbf508a3ea66ca80fae3de8c/image2.png)\n\n*__Tip__: Use the Brand Funnel Comparison feature or the table below your funnel in the dashboard to benchmark against competitor brands. For example, the funnel might show low conversion from Aided Awareness to Consideration but actually be performing strongly in relation to other brands in your category. *\n\nFor a more detailed explanation of how to use the Brand Funnel more effectively, check out our [Effective ways of interpreting the Brand Funnel](https://latana.com/post/brand-funnel-brand-analytics) article. "}}},{"node":{"title":"Onboarding Guide","slug":"onboarding-guide","category":"getting-started","content":{"content":"# Onboarding Guide\n\nWelcome to Latana! We're excited to get you started. If you've just joined Latana, we would have sent you a welcome email requesting an onboarding call with your dedicated Customer Success Manager and the Project Manager overseeing the delivery of your upcoming waves. Make sure you've scheduled a call with us via the welcome email. If you're new to brand tracking or just want to acheive a quick and seamless setup process, read below:\n\n__What are the onboarding steps?__\n\n![Onboarding Guide](//images.ctfassets.net/7so8go2zrvbw/REKG36qhr69VcghhYR3si/f7bac42be0e29493925e3911e4e865b3/Untitled.png)\n\nDuring your onboarding call, we'll discuss your business needs for tracking and ensure we have all the information we need to setup a meaningful survey for you. As you can see from the above checklist, there are a few steps we need to tick-off during the onboading before launch. While we'll run through these items on the call, we recommend starting to think about the following:\n\n__1. Define your Category__\n\nYour category should be how you define the product or service your brand and competitors operate in. For example, N26 and Revolut share the same category of \"challenger banks\". Why is this relevant? For 90% of brands, we'll measure Unaided Brand Awareness, an open-text question asking the following: \n\nWhen you think of CATEGORY NAME, which brands come to mind?\n\n__2. Identify your Custom KPIs__\n\nIf you don't know these already, think about what are the key metrics for your brand or category you wish to track over time. Most likely your standard setup will include Brand Awareness, Consideration, Preference, Knowledge, Usage, Brand Associations and maybe some others. Interested in our standard KPI library? Check out our article [here](https://knowledge.latana.com/latanas-off-the-shelf-custom-kpis). \n\nNote: KPIs can't be used as segmentation questions (i.e. you can not filter the data using a KPI)\n\n__3. Identify your Custom Segmentations__\n\nWe already provide standard custom segmentations, such as gender, age, urban/rural, education and income. Segmentations are not KPIs, but rather your audience building blocks that allow you to create key audiences and splice and dice the data to anaylse performance of various groups. When deciding on the right Custom Segmentations, think about what attributes make up your target audience or are relevant for your brand or category. For more info, check our our help article [here](https://knowledge.latana.com/choosing-the-right-segmentation). \n\n__4. Identify your Brand Associations__\n\nBrand Associations are the imagery you want to measure. These should be 1-2 words that capture the essence of what you're trying to convey with your brand. They can be positive, neutral or even negative in tone, depending on what you'd like to track. Some examples could be \"Innovative\", \"Trustworthy\", \"Efficient\", \"Approachable\" and so on. \n\nHere, think about what kind of imagery you use or intend to use in your marketing campaigns. If you don't have any associations identified in your branding, consider the attributes your current customers (especially those that love your product) best associate with your brand. You'll then be able to track which of these are resonating with your audiences over time. \n\n__5. Identify your competitor brands and logos__\n\nBefore launching the survey, we'll ask for names in your competitor list and their respective logos. Feel free to send these through to us before the onboarding. Otherwise, we'll create a doc for you to submit these after our call.\n\nThat's it! If you have an idea of these items, you're almost ready to go. During the onboarding, we'll discuss these items with you and then circle back with your researcher if need be. After the call, we'll send you out a doc for you to input these items so we're ready for launch. \n\nThat's it! Make sure you keep an eye on your calendar for your upcoming call with your Customer Success Team. We're looking forward to it and will be able to answer any questions you have then :)\n"}}},{"node":{"title":"How do I interpret Margin of Error?","slug":"how-do-i-interpret-margin-of-error","category":"data-collection","content":{"content":"# How do I interpret Margin of Error?\n\n__What is 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 datapoint means a lower level of confidence in that value.\n\nMOEs are usually represented as ±X%. This means if a datapoint is 10% with an MOE of ±2%, the true value of the datapoint is somewhere between 8-12%.\n\n__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%.\n\n__How to use MOEs in Latana__\n\nFor waves that include MOEs, you have the option to turn them on and see them in the dashboard. They show as error bars on the charts, and green/yellow/red indicators to indicate high/medium/low levels of certainty.\n\nYou can use the MOEs to determine whether or not a change is significant. For example:\n\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."}}},{"node":{"title":"Choosing the right Custom KPIs","slug":"choosing-the-right-custom-kpis","category":"success-strategies","content":{"content":"# Choosing the right Custom KPIs\n## What is a Custom KPI?\n\nCustom KPIs sit outside of the brand funnel and are more closely linked to your business objectives. Like standard KPIs, they are metrics you wish to track over time. These might not be completely unique, but they will generally be specific to measuring audience perception of your brand or category. \n\n## Identifying the right custom KPIs for your brand\n\nChoosing the right custom KPIs to track may depend on the growth stage of your company and your business objectives. We’re here to help you find the right KPIs for your brand, but we’ve also put together some suggestions for how to identify the right custom KPIs for your brand along with some popular off-the-shelf examples:\n\n### 1. Understand your business objective\n\nSetting the right custom KPIs for your survey should start with your brand objectives - what are the specific goals you’re trying to achieve with your brand? A few common objectives are the following:\n\n- Knowledge gaps - things you don’t know about the category, competitors, or audiences. E.g. “we don’t know who our target audience is”\n- Business fluctuations - notable trends within the business, like sales dips and customer churn. E.g. “we are seeing sales fall in category x”\n- Forecasting - ensuring you are just looking one step ahead of competitors\nE.g. “tracking where we benchmark against our known competitors”\n\n### 2. Identify your research goal\n\nA research goal is more specific towards the facts or metrics you hope to learn from the research. These are what help you to answer and reach your business objective. Some popular examples include:\n\n- Explore brand perception for targeted marketing - How does your target audience perceive your brand in the market?\n- Track and grow your brand awareness - What is the recognition of your brand from consumers and in relation to your competitors?\n- Measure buying behavior for audience expansion - What are consumers purchasing and what drives them?\n- Test campaign impact - How effective is your messaging on my core audiences?\n\n### 3. Set your KPIs\n\nOnce you know the problem you wish to solve and have identified your research goal, you’re ready to set the right KPIs for your brand.\n\nFor a full list of some popular KPIs you could use for your brand, check out [Latana’s  Off-The-Shelf Custom KPIs](https://knowledge.latana.com/latanas-off-the-shelf-custom-kpis).\n"}}},{"node":{"title":"Our tips for finding your target audience/s","slug":"our-tips-for-finding-your-target-audience-s","category":"success-strategies","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__\n\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](https://knowledge.latana.com/choosing-the-right-segmentation). \n\n__2. Create all relevant segments in the dashboard__\n\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://knowledge.latana.com/create-audience-segments). \n\n__3. Evaluate each audience__\n\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![Audience Finder](//images.ctfassets.net/7so8go2zrvbw/41op2AXDCoG5UR63c43Zri/3ed7313594d1517bb7e5ddc6c1054c16/Untitled__1_.png)\n\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! Do 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. Low 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":"Understanding KPIs","slug":"understanding-kpis","category":"getting-started","content":{"content":"# Understanding KPIs\n\n## What is a 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. Brand KPIs work the same way, however they’re specific to brand.\n\n## Example KPIs\n\nUnder the All KPIs section, you will find all the available KPIs on your dashboard. Depending on your setup, these can include the following:\n\n__Brand Knowledge__: how knowledgeable of your brand are those who are aware of it\n\n__Brand Affinity__: to what extent do those who aware of your brand like your brand\n\n__Brand Associations__: which imagery or attributes do those who are aware of your brand most associate with it\n\nIn the *All KPIs* section, you will also find any other custom KPIs you have chosen to track. These could be brand or category specific. Examples of these include __Barriers/Drivers to Consider__, __Category Awareness__ and more. For more information on finding the right KPIs to track for your brand, [talk to one of our team](https://latana.com/book-demo/). "}}},{"node":{"title":"Popular Latana Use Cases","slug":"popular-latana-use-cases","category":"what-is-latana","content":{"content":"# Popular Latana Use Cases\n\nDepending on your brand's unique business objectives, you can derive value from Latana across multiple use cases. You can find below our customers' most popular reasons for using Latana's brand tracking software.\n\n![Latana Use Cases](//images.ctfassets.net/7so8go2zrvbw/4btg5srgetDof4m7JCOwK2/e49cfddb48e9711550a3367cf5fa8c30/Screenshot_2021-03-17_at_14.42.48.png)\n\n__Keep a pulse on your overall brand health__\n\nThis is the most critical use case and is used by virtually all of Latana's customers. Perhaps you'd like to monitor how well you're progressing towards your strategic goals, or need to showcase how well your brand is performing to your investors or senior executive branch. Without brand tracking or analytics, it's impossible to have visibility of your brand's performance on the whole. \n\n__Identify new target audiences__\n\nA popular use case for brand's that either don't know their target audience or wish to explore new audiences in the market. Latana is unique in that it can analyse niche audiences with higher confidence bounds. Evaluate how each one is performing on each KPI, against one another, and among your competition. Your custom segmentation will determine the kinds of audiences you can analyse so keep this in mind when deciding on how to best set up your survey. Want tips on finding your key audiences? \n\n__Benchmark performance against competitors__\n\nAnalysing your brand's health should always be compared against your competition. Latana's dashboard has the capability of benchmarking your performance on each KPI against both direct and indirect competitor brands. You'll be able to compare how each one stacks up on every KPI you track and among any audience you wish to create. \n\n__Measure effectiveness of brand campaigns__\n\nLatana allows you to capture the impact your brand campaigns are having over time. Whether your campaign is specific to a particular country, city or channel, Latana's dashboard allows you to capture a snapshot of how well this is performing on the key KPIs you're trying to track. Find out whether various audiences have seen your ads, whether they're hearing about your brand from Facebook, TV, podcasts, etc or whether your localised city-based campaigns are having a significant impact on your Awareness or Consideration levels. \n\n__Gain a deeper insight into your brand or category__\n\nAn increasingly popular use case for Latana is to understand the deeper insights into your brand and category. Are you moving into or creating a new category altogether and wish to measure how open key audiences are to using the product? Interested in understanding the why behind many of your key KPIs? Measure Brand Barriers and Brand Drivers to gain insight into what's deterring customers from using your product or the primary reasons they're leaping to use it. Curious about what kinds of KPIs you can track using Latana? Click [here](https://latana.com/book-demo/) to find out more."}}},{"node":{"title":"Latana in a Nutshell","slug":"latana-in-a-nutshell","category":"what-is-latana","content":{"content":"# Latana in a nutshell\n\n## What do we do?\n\nAt Latana, we help brands make better marketing decisions by delivering world-class, scalable insights. We achieve this by utilizing brand tracking and deliver these insights monthly, quarterly or biannually to your intuitive and customized dashboard.\n\n## How does this work?\n\nWe survey millions of people globally to get a pulse on how your brand is performing on its own and against competitors. To ensure wide demographic and geographic coverage, Latana targets a diverse set of 40,000 apps and websites, distributing micro-surveys every month to people living in 100+ countries.\n\nCurious about our processes, features, methodology, or typical use cases? Please continue to browse our Help Center or use the search bar available :)\n"}}},{"node":{"title":"How do you process open-ended questions?","slug":"how-do-you-process-open-ended-questions","category":"data-collection","content":{"content":"# How do you process open-ended questions?\n\nWe first process open-ened questions to ensure that there are no 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.\n\nOnce all responses are collected, we run a script to check for any nonsense answers 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\nIn terms of how we deliver the responses to the open-ended questions, we have different possibilities for SME clients and Enterprise. For SME clients, we classify the answers according to the list of brands provided for tracking for the survey. The results of this are then grouped and can be seen in the Latana dashboard provided at delivery after MRP calculations. For Enterprise clients, we can offer a raw data file providing the cleaned open-ended answers from respondents or we can classify answers given according to a pre-agreed list, and inject the data into a separate question. This would then show percentages of answers/numbers of answers per answer option in the separate question."}}},{"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. Noisy 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. Latana’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":"Guide to the new Latana interface","slug":"guide-to-the-new-latana-interface","category":"getting-started","content":{"content":"# Guide to the new Latana interface\n\nWe've completely overhauled the Latana interface to make it easier to use, based on your feedback. Check out our walkthrough video [here](https://share.vidyard.com/watch/RsCQKvcnDTsVcXEih6YnQo)\n\nHere are some highlights of what's new:\n\n__1. Chart building__ \n\n![Chart Building](//images.ctfassets.net/7so8go2zrvbw/2rtuEPwzOfk274beDL6Aid/8ab825256d610ef22ef956741762b62e/chart_building.png)\n\nEvery chart is built based on a combination of Geography, Time Period, KPI, Brand, and Segment. You can build or edit the chart filters to create the analysis and visualisation you want. \n\nPlease note: charts are limited to comparing two types of filters. This means you can only select multiple options for up to two filters - for example three time periods, four brands and one geography, KPI, and segment. \n\n__2. Chart visualisation and table__\n\n![Chart Visualization](//images.ctfassets.net/7so8go2zrvbw/7GLSGW4v0I9O34oclNtbd5/fe62675bd072def39ba14085e1d4eaf8/chart_visualization.png)\n\nYou can switch any chart between line and bar visualisations, to customise how you want to view the data. \n\nThe table below the chart also allows you to control the visualisation of the chart. Clicking a table row will toggle it on or off in the chart, and clicking the column headers will sort the data from high to low (or vice versa).\n\n__3. Compare data between geographies__\n\nCharts now support comparing data between different geographies or countries. In most cases, only your brand will be comparable across countries, but you can build charts comparing how you perform in different KPIs and segments in multiple markets."}}},{"node":{"title":"Choosing the right segmentation/audience characteristics","slug":"choosing-the-right-segmentation","category":"success-strategies","content":{"content":"# Choosing the right segmentation/audience characteristics\n### Importance of audience characteristics\n\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\n\nStuck on which custom audience characteristics are right for your brand? Consider the below tips:\n\n1. __Identify your target audience__. If you know this already, it should be easy to identify their custom characteristics.\n2. __Ask what’s unique to your brand__. If 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.\n3. __Think about your market more broadly__. If 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. \n4. __Set it and forget it!__ You can’t measure change if you change the measure! With 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### Common examples of audience characteristics\n\n![Common Audience Characteristics](//images.ctfassets.net/7so8go2zrvbw/7lumeWpMHv4lVnGw7hvzJI/dec982ac4986a516bc7995e13d724d55/image4.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*\n"}}},{"node":{"title":"How are the Certainty and Margin of Error calculated?","slug":"certainty-and-margin-of-error","category":"data-collection","content":{"content":"# How are the Certainty and Margin of Error calculated?\n\nWhen you hover over 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![Margin of error](//images.ctfassets.net/7so8go2zrvbw/6lb3N8TA8lmOxf8WPrSA4X/d6a73e12d18e5fb6e76d973975d5e9a8/image-png.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\n### Margin of Error\n\nWe use MRP ([learn more here](https://knowledge.latana.com/what-is-mrp)) to generate the data you see in the Latana dashboard. This means the Margin of Error is calculated differently than when you look at a dataset of quota-sampled survey responses. \n\nRather than using a formula to calculate the Margin of Error, in MRP we generate a large number of estimates for every KPI in each segment (for example, aided awareness among urban, highly educated, medium-income, 18-25 year olds). The datapoint we surface in the dashboard is the mean of those estimates, and the margin of error is calculated using the lowest and highest of those estimates. \n\nThe meaning of the Margin of Error in MRP is the same as in other methodologies: it's intended to help you understand how good a datapoint is.\n\nWith MRP, the margins of error for the data we generate are smaller than those you would normally get with quota-sampled survey data. However, the margins of error still correspond inversely to the underlying sample size, so a small niche segment will have a larger margin of error."}}},{"node":{"title":"What are the different methodologies for calculating scores?","slug":"what-are-the-different-methodologies-for-calculating-scores","category":"data-collection","content":{"content":"# What are the different methodologies for calculating scores?\n\nWith scores, you can summarize the data in a Likert scale question into a single number. We have four preset scoring methodologies, which you can choose from: Top Two Box, Top/Bottom Delta, Weighted Average, and Top/Bottom Ratio. This article explains how each of them is calculated, and what they're generally useful for.\n\nThese scoring methodologies are applicable for 1-4 or 1-5 scales. In the examples below, I will use a 1-5 scale.\n\n__Top Two Box__\n\nThis scoring method shows the percentage of people who give the top two ratings on a scale.\n\nCalculation: % of 4-rating + % of 5-rating \n\nThis is the most common scoring method, appropriate for many use cases. It generates a clean and concise score, as we receive a comparable score across all brands. Using this method cuts the white noise of both ‘I don’t know’ and ‘neutral’ responses, but in the process also cuts out the low ratings. \n\n__Top/Bottom Delta__\n\nThis scoring method shows the difference between how many people gave the top 2 ratings vs the bottom 2 ratings. \n\nCalculation: (% of 4 rating + % of 5 rating) - (% of 1-rating + % of 2-rating)\n\nThis is similar to how NPS scores are calculated, and it considers both the positive and negative balance of responses. This methodology focuses only on the opinions on brands, also cutting out the white noise of both ‘I don’t know’ and ‘neutral’ responses.\n\n__Weighted Average__\n\nThis scoring method multiplies the percentage of respondents for each score by the score value, and sums them up. \n\nCalculation: (% of 1-rating x 1) + (% of 2-rating x 2) + (% of 3-rating x 3) + (% of 4-rating x 4) + (% of 5-rating x 5)\n\nThe weighted average considers all scores - negative, neutral, and positive. It is a scoring system that is consistent with other brand evaluations, e.g. Uber rating, Amazon reviews, and therefore provides a readable 1-5 score. It can be a blunt tool, as it doesn't capture nuance, like the distribution across different scores and shifts in those over time.\n\n__Top/Bottom Ratio__\n\nThis score looks at the percentage of responses with the top two ratings, out of the total non-neutral ratings. \n\nCalculation: (% of 4-rating + % of 5-rating) / (% of 1-rating + % of 2-rating + % of 4-rating + % of 5-rating)\n\nThis scoring methodology gives brands an objective and balanced analysis and is useful for an index where you need to compare many brands of different categories against each other. It considers both the positive and negative balance to brand responses and also focuses only on the opinions on brands, cutting the white noise of the ‘I don’t know’ and ‘neutral’ responses. Considering only active reactions to brands means that awareness of a brand would not skew the data. "}}},{"node":{"title":"How are income level questions defined?","slug":"how-are-income-level-questions-defined","category":"data-collection","content":{"content":"# How are income level questions defined?\n\nIn regard to the income level question, we categorize the answers into 12 various currency brackets depending on the specific country (in the respective currency, of course). The currency brackets are based on official statistics for the median income level in developed and developing countries. In the dashboard, these will be consequently categorized into low, medium or high. So the first 5 categories will be marked as ‘low’, the 6th and the 7th as ‘medium’, and the last 5 as “high”. This guarantees a fair comparison for the various countries."}}},{"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- Uses past data to correct for fluctuations in the data over time\n- 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- Gain insights of a higher level of accuracy than quota sampling\n- Control for inaccurate fluctuations or “noise”  in the data over time\n- Measure brand performance among ultra niche audiences with high confidence bounds\n\nLearn more [about MRP here](https://www.latana.com/mrp/).\n"}}},{"node":{"title":"How does Latana prevent fraudulent data?","slug":"how-does-latana-prevent-fraudulent-data","category":"data-collection","content":{"content":"# How does Latana prevent fraudulent data?\n\nProviding precise data in a timely manner is a pillar of Latana. Because we know that simply gathering survey respondents isn't enough, we utilize multiple methods to ensure the data you receive is accurate and reliable. How? By dynamically profiling each respondent using passive and active data. Some items include:\n\n- Cookie identification (to avoid multiple survey completes per user, including in-app completions)\n- Tracking IP addresses\n- Digital fingerprinting (device type, device ID, browser, plug-ins installed, etc.) and unique user identifiers (such as those issued by affiliate apps or websites in which our survey has been integrated)\n- Ensuring accuracy and consistency across profiling and other survey data\n\nWant to understand data quality further?  Drop us a line at [hello@latana.com](mailto:hello@latana.com)\n"}}},{"node":{"title":"Download raw data in CSV format","slug":"download-raw-data-in-csv-format","category":"using-latana","content":{"content":"# Download raw data in CSV format\n## Reason for exporting to CSV\n\nIn addition to the Latana dashboard, you also have the option to download this data as a CSV file. While all these insights are visible in the dashboard, there are two primary use cases for exporting the raw data:\n\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. Go to your Latana dashboard\n2. Select “Download CSV” at the top of the dashboard\n3. Input the email address\n\nAfter you download the CSV, you can import to Google Sheets, Tableau or any other third party tool you’re using. \n"}}},{"node":{"title":"Compare performance against competitors","slug":"compare-performance-against-competitors","category":"using-latana","content":{"content":"# Compare performance against competitors\n\nIn order to compare your performance against competitors, follow the below steps:\n\n1. In the audience filter at the top of the dashboard, select the audience you wish to analyse.\n2. Scroll down to the table located at the bottom of your dashboard\n3. Ensure “Segment” is selected to lock in the segment to compare competitor brands (select “Brand” to lock in the brand and compare across audiences)\n4. Select the KPI title you wish to analyse (Unaided, Aided, Consideration etc) to sort each brand descending (highest at the top) or click again to sort ascending (lowest at the top). \n5. See how well your brand stacks up against competitors on each KPI.![Comparing against your competitors](//images.ctfassets.net/7so8go2zrvbw/4ay2qAw4e8i8aIFFe62GUX/c5ac3795ec3f2aad1f8bf216c4e47ee6/image1.png) In the mock example above, we see Revolut has the highest Consideration against competitors for the Urban High Income audience.\n\nGreat! Now you know how your brand performs for each KPI, across segments and against competitor brands. If you wish to find out how to leverage these insights to inform your brand strategy, check out our article [Leverage Brand Analytics to create an effective brand strategy](https://www.latana.com/post/brand-analytics-build-better-brand-strategy)."}}},{"node":{"title":"Understanding Segmentation","slug":"understanding-segmentation","category":"getting-started","content":{"content":"# Understanding Segmentation\n\n## 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. While 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](https://knowledge.latana.com/what-is-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. The 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. With custom audience characteristics, you can compare between the general population and your target audience, and discover and explore new potential audiences for your brand. \n\nUnsure what segmentation or audience characteristics are right for your brand? Check out our [Choosing the Right Segmentation](https://knowledge.latana.com/choosing-the-right-segmentation) article. "}}}]}},
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