Was tun Sie, wenn Sie Schwierigkeiten haben, Marktforschungsdaten zu verstehen?

People LIE! They want to please, seem smart, give right answers + language is imprecise. This is even truer when the topic is new. Use the results to ask smarter questions and see areas where education is needed. Try to disprove your assumptions not affirm them lest you skew the answers (New Coke). Use an outsider to force another perspective. Your expertise is your blind spot. Focus on deliverables' impact and view the research from competitors' responses. Use as a vision expanding tool, not a determinative one. Use a mix of methods. See it as a story and embed it in a what-if scenario. Research draws on known pasts to predict an unknown future. Look for the touchstones that make change comfortable -emotions, not numbers

Once the data is collected (regardless of the method) the first peek at the data may be overwhelming. There might be so many different variables to consider, so much data that may be potentially interesting. A good start is to take a step back at this moment and think back to the questions that led to the research on the first place. What was the problem facing the company? What were the research objectives? Why did we collect this data? The answer to these questions should guide what the research wants to find in the data, and what are the variables that the researcher should focus on the data analysis.

Mi enfoque se basa en el liderazgo inclusivo y el reconocimiento de la diversidad de habilidades y perspectivas dentro del equipo. Esto implica asignar tareas de manera equitativa, fomentar un ambiente donde todos se sientan valorados y capacitar a cada miembro para alcanzar su máximo potencial. La inclusión de diversas voces en el proceso de toma de decisiones fortalece nuestra capacidad para abordar desafíos complejos y encontrar soluciones innovadoras. Además, la comunicación clara y el apoyo continuo son fundamentales para garantizar el éxito en la delegación de responsabilidades. Como director, estoy comprometido con crear un entorno donde cada miembro del equipo pueda prosperar y contribuir al éxito colectivo.

Before undertaking any market research activity, it is imperative for researchers to define the objectives and scope of their work. After defining the objectives, one can carefully examine the available data points and definitions for various variables to determine if they assist in achieving the defined objectives. Without defining objectives, it would be very difficult to develop solutions or uncover insights.

THe organizational structures of data should exist prior to gathering it. The first step is identifying factors of influence around a product category.. and then collect around those. If you do not have the hyper structures first ,, their is no way to weight or appraise the value of a insight or bit of data

Once the objectives are set, it is very important for researchers to organize their data. If someone is sourcing the data for the first time instead of leveraging existing data, it will be very helpful to use some dummy data to see if the gathered data supports the answering of their objectives. Organizing the data would include understanding variables, filling missing values (with either the mean, median, or mode), identifying outliers, or creating additional variables leveraging existing data points.

Organizing data is easier said than done. Simply because data is information and has the power to change the course of events. Market research analysis is a tool utilized by industry leaders— however its almost impossible to control the logistics of timing into factoring decisions if information comes in tiers. Due to running into this common issue while conducting research, it was time to come up with a method that allowed me to restructure analysis regardless of the chronological order of information discovery. I focus on my end result. Re-ask myself if the objective has the possibility to be flexible based on a tier system in organizing information. Instead of using a numbering system, I aim to tell the story as best of possible.

Trends Over Time: For instance, a retailer may notice increased sales of winter clothing during colder months, indicating seasonal purchasing patterns. Correlations Between Data Sets: For example, a restaurant chain might find that customers in urban areas are more likely to order plant-based menu items, revealing correlations between location and dietary preferences. Repetitive Occurrences: For instance, an e-commerce platform may observe a spike in sales every year during holiday seasons, prompting targeted marketing campaigns to capitalize on increased consumer spending.

In the realm of market research, effectively making sense of complex data can be significantly enhanced by utilizing advanced AI tools like Google AutoML and IBM Watson Studio. These platforms are designed to simplify the application of sophisticated machine learning models, including Natural Language Processing (NLP) techniques. NLP is particularly valuable for extracting actionable insights from unstructured data such as customer reviews, social media comments, and open-ended survey responses. By analyzing this data, businesses can gain a deeper understanding of consumer sentiments and emerging market trends.

Tools and programming languages such as SPSS, Power BI, Python (Pandas and Numpy functions etc), R etc., would really come handy when trying to unlock the insights from the data sets. Liner Regression, Logistic Regression, K-Means, KNN, etc.

An often overlooked aspect of market research is the initial alignment of the research strategy with the actual data collection methods. A discrepancy here can lead to significant challenges in making sense of the data. Before moving into data collection, it's critical to ensure that the strategy is not only well-planned but also clearly communicated to every member of the team. This strategic alignment helps mitigate risks of data misinterpretation and enhances the overall quality of insights derived from the research.