You're analyzing market research data. What are some common mistakes to avoid?

Un error común es la “interpretación” de los datos, cuando hay mucho sesgo en la investigación. Por eso es muy importante delimitar las rúbricas de análisis y lograr conclusiones objetivas, así como recomendaciones que sean verdaderamente utilitarias.

Take care to interpret data accurately and avoid drawing conclusions beyond what the data supports. Be mindful of potential confounding variables and alternative explanations for your findings.

Ignoring outliers: Ensure outliers are investigated for data accuracy Overlooking sample bias: Verify sample representativeness Not validating data: Cross-check data sources for consistency Confusing correlation with causation: Establish causation before concluding Failing to consider context: Understand the broader context of the data Ignoring qualitative data: Incorporate qualitative insights for a holistic view Overcomplicating analysis: Keep the analysis simple and focused Not involving stakeholders: Collaborate with stakeholders for meaningful insights Relying too heavily on one source: Validate findings with multiple sources Not updating analysis: Regularly update analysis to reflect current trends

When analyzing market research data, steer clear of these common mistakes: Sampling Bias: Ensure your sample is representative of your target population. Sampling bias, where certain groups are over- or under-represented, can lead to skewed and unreliable results. Leading Questions: Craft questions carefully to avoid bias or leading respondents towards a particular answer. Biased questions can influence responses and compromise the integrity of the data. Misinterpreting Correlation and Causation: Be cautious when inferring causation from correlation. Establishing a correlation between variables doesn't necessarily mean one causes the other.

It’s crucial to match the problem you’re solving with the right tool for the job. For example, you wouldn’t use a hammer to fix a watch. Similarly, choosing the wrong statistical method can lead to incorrect conclusions. Using an advanced technique like regression analysis without first checking if your data fits certain conditions is like trying to read a book in the dark — you won’t get the full picture.

Biased interpretation is someone people have early on in their career. It should fade over time with learning with more exposure to more research projects. As time goes on, if bias remains one must take a step back and share project work with others. Objectivity is crucial in market research otherwise your client is paying for inaccurate data.

Biased interpretation occurs when individuals unconsciously interpret data in a way that aligns with their preconceived beliefs or notions. This can lead to skewed results and inaccurate conclusions. For example, if you are conducting a study to test a new product and you have a strong belief that the product will be successful, you may be more inclined to focus on the data that supports your hypothesis. Cherry-picking is a common form of biased interpretation. This occurs when individuals selectively choose certain data to highlight while downplaying other data that may not support their agenda.

Existem diferes formas de pensamento que nos orientam para uma leitura analítica: - Pensamento dedutivo, indutivo e dialético. Ao Passo que existem diversas estruturas de pensamento dentro da lógica que precisam ser evitadas: - Falácias e sofismas.

Na minha opini?o, interpreta??o de dados vai além da pesquisa. O responsável pelas análises necessita ter uma vis?o ampla e imparcial, deixando de lado o seu ego e objetivo pessoal. é crucial que a interpreta??o parta de uma pessoa com vis?o ampla do negócio e que sua característica seja senso de dono.

Please, please, please be specific when mentioning sources! The source should allow anyone to retrieve your numbers should they need to (and if your charts have any value at all, there's a strong chance someone will). If you don't, your colleagues using the numbers will struggle to answer any questions. Whenever possible, include the specific study (not just the vendor), the specific dates (not just the year), the base on which your percentages were calculated (overall population or a subgroup) and the sample size for that specific view, not the overall study. I case the above data is too sensitive (for instance using confidential data), then include the detailed source in a place where your colleagues can find it but your viewers can't.

One mistake to avoid is comparing data from different sources for benchmarking purposes and not ensuring similar terms, timeframes, and samples. The best benchmarks are when you work with a range of organizations and companies, and create the benchmarks, or you develop a national survey with questions that mirror those of a client.

Tapping into external data sources is fairly common in market research, but it should not be the only source of data. Furthermore, it should not be compared and contrasted as if the data were part of one study.

A contextualiza??o dos dados é fundamental para aumentar a significancia da informa??o que está sendo trabalhada. No caso de marketing temos bons exemplos: Se eu digo, minha capta??o de receita foi de R$2MM em Fevereiro este dado por si só n?o traz grandes acionáveis. Contudo, ao contextualizarmos tempo, espa?o e perspectivas comparativas teremos ótimas informa??es. 1) O ROI de Janeiro foi de 1MM 2) O ROI de Fevereiro/23 foi 3MM 3) O ROI do competidor foi de 2MM no mesmo mês. Com esses contextos podemos inferir que: 1) Numa perspectiva MoM, meu resultado melhorou em 1MM 2) em YoY meu resultado foi pior 3) Em rela??o ao competidor, geramos o mesmo resultado

Por favor!!! é imprescindível desenvolver uma vis?o generalista para o responsável da pesquisa de mercado e análise dados!! Contextualizar o negócio n?o pode ter uma vis?o de apenas um pilar da empresa, mesmo essa vis?o tenha profundidade de análise. O mercado envolve muitas coisas e n?o apenas um pilar. Independente da análise (complexa ou n?o), eu gosto de ter no mínimo estes pilares para contextualiar: 1. Vis?o mercado macro e micro econ?mico, 2. Vis?o do segmento em si, 3. Vis?o dos Produtos, concorrentes e seus respectivos posicionamentos, 4. Vis?o de canais, clientes e território, 5. Comportamento consumidor / shopper

The days of handing over a 50+ powerpoint slide with 100 data points per slide are dead. Clients don't understand them and are lost by slide 5. Visually appealing and succinct visuals can tell a story in a rapid manner. Think of the limited time a CEO has - then develop content directed at that audience. Then develop a deeper presentation for marketing, sales, etc - as they want and need more details for their jobs.

This one is the most glaring one - using the wrong type of chart. For example, using a pie chart to represent data that is not easily divided into categories can lead to confusion among viewers. Similarly, using a line graph to show data that does not have a clear trend over time can misrepresent the information and create a false perception of the data. Another common mistake is using overly complex visualizations overwhelming the audience. While it can be tempting to include as much data as possible in a single visualization, it can make life difficult for viewers to identify the key takeaways. Instead, it is important to focus on presenting the most relevant information in a clear and concise manner thus avoiding unnecessary clutter.

My go-to source for data visualization best practices is Steve Wexler's The Big Picture. This book is readable by all audiences and gives you a very useful overview of the DOs and DON'Ts for each type of data and chart. It also provides a common vocabulary, which is very valuable when it comes to discussing things as a team because it literally gets everyone on the same page.

Independente da análise (complexa ou n?o), eu gosto de ter no mínimo estes pilares para contextualizar: 1. Vis?o mercado macro e micro econ?mico, 2. Vis?o do segmento em si, 3. Vis?o dos Produtos, concorrentes e seus respectivos posicionamentos, 4. Vis?o de canais, clientes e território, 5. Comportamento consumidor / shopper

In preparation for the study, spend quality time with the business and process owners engaged in the activity you are researching. Learn their processes. Understand where the internal process touches the customer. Ask questions to understand the environment in which the research learning will be applied. Build a research project team that includes associates who will be on point to implement the new knowledge from the research. Partner with finance leaders to make sure you are bringing forward ideas that the business can afford to implement. By engaging the people who will act on the recommendations in the entire research process you increase the likelihood that the business’s executives will embrace the plan.

A Good Survey of the Market can result in a fruitful result. Winning over the Competition can only be done with following the right practices and right strategy in the Market.

N?o se toma decis?o sem analisar uma pesquisa!!! Uma boa interpreta??o de pesquisa, além de construir estratégias sólidas, traz resultados rápidos e certeiros. Todos que analisam pesquisas já deveriam direcionar o plano tático e todos que gostam de plano tático deviam agu?ar sua aptid?o para interpreta??o de dados