You need to equip your market research team with the best tools. What should you look for?

This is the step most teams do not spend enough time on - understanding the research problem they are trying to solve which precedes the research objective formulation phase. Is the research problem a why or how problem and what are hypotheses that need to be tested and where do we already have great intel from earlier research so we can hone in on the most pressing issues. I have found doing issue trees (see Minto) really helpful here and asking the why question 3 times to get at the possible root causes so that you are developing a strong set of research objectives.

To empower your market research team, start by clarifying goals. Define what you want from the research – whether it's understanding customer satisfaction for a tech company or gauging regional preferences for a fashion retailer. Imagine this as creating a tailored shopping list before diving into tool selection. This clarity not only streamlines the process but ensures the tools chosen align precisely with your unique research needs. So, before you shop for tools, set your objectives – it's the compass guiding your team in the right direction.

I think someone else mentioned it, but soft skills. I'm going back 20+ years at this point, but it was the Corporate Research Board that called out the shift from researcher to consultant. I don't think things have really changed that much. Market researchers need to be consultants to marketing and business teams. The answer to what tools to equip them with is whatever the business needs. The ability to interpret data, present it in a concise format and with recommendations that factor in business constraints will always be of value. The key is to bring the voice of the consumer to the organization. The consumer is always be judge, jury and executioner so you may as well find out whether they like your idea or not before launch.

Unexpected insights may be missed if we have rigid goals. As the dynamics of the market change, so too should our research methodology. It's evident from historical business failures, such as Kodak's collapse as a result of rigidity, that responding to unexpected data is just as important as achieving original goals. Keep in mind that sometimes the insights you find are the most valuable ones you weren't looking for.

To equip your market research team effectively, prioritize tools with advanced analytics capabilities for data interpretation, real-time tracking, and predictive modeling. Choose platforms that facilitate seamless collaboration, automate routine tasks, and integrate with popular data sources. Look for user-friendly interfaces to ensure accessibility, and prioritize tools that offer customizable dashboards and reporting features. Additionally, consider solutions with robust security measures to protect sensitive information. Regularly update and upgrade tools to stay abreast of evolving market research trends.

When equipping a market research team from a data sources perspective, diversity and credibility are key. Look for sources that offer a comprehensive range of information, including industry reports, market surveys, consumer behavior data, and economic forecasts. Prioritize sources known for accuracy and reliability, like established market research firms, government databases and reputable news outlets. Accessibility to real-time data is crucial for staying abreast of market changes. Additionally, integrating sources that provide insights into emerging trends can offer a competitive edge. Ensuring a mix of qualitative and quantitative data sources will enrich the team's research capabilities.

Diversify your data sources strategically. Utilize surveys, interviews, and focus groups, but beware of their inherent biases. Incorporate social media and online reviews for real-time insights, but handle sentiment analysis with caution due to its nuances. The controversy lies in balancing quantity with quality, avoiding skewed perspectives, and adapting tools to the specific demands of each data source.

Depending on the type of data you will use (primary or secondary) you should pay attention to the following: For primary data collection, you need to ensure that your research design is done correctly, taking into account reliability and sample representativeness for quantitative research and eliminating any potential biases. For secondary data, similarly, you need to ensure that data will come from credible sources, be collected with reliable tools, and ensure that all relevant data points you need are covered when possible. You should always address any potential limitations coming from your data when reporting the findings.

For survey sample, engaging multiple sources increases randomness and improves consistency over time. A sample marketplace solution like PureSpectrum provides easy access to a wide range of panels and sample sources.

Data Collection and Survey Tools: Versatility: Look for tools that can handle various types of surveys (online, phone, face-to-face). Customization: The ability to customize surveys to fit different research needs. Integration: Tools should easily integrate with existing databases or CRM systems.

I’m going to give you my old school way to figure out the themes and then storyline. In my Opinion this should happen before mining at ML tools. I hard copy print all the data tables (just overall and maybe age or consumer segment or whatever seems most critical). I read them once and underline things that look interesting. Then second read (now that I have in my head what some of the key datapoints - not insights yet - could be). In this second reading I jot down on a blank sheet #1 surprising results #2 confirmatory results. Next I sort these into themes. A theme could be that people struggle to convey points in conference calls (maybe 2-3 data points per theme). Last I draw a visual outline of the story.

The controversy in data analysis methods centers on the nuanced selection of tools. Descriptive, inferential, predictive, or prescriptive analytics—the dilemma lies in navigating this labyrinth. Each method, a distinct landscape with charts, statistical tests, or machine learning algorithms. The real debate isn't about analysis but about the art of choosing. Do tools shape the analysis, or does the analysis dictate the tools? In this paradox, the controversy unfolds in the balance between methodical precision and interpretative finesse. It's not just about crunching numbers; it's about wielding tools with the wisdom to extract meaningful narratives from the intricate tapestry of data.

To equip your market research team effectively, prioritize your data analysis methods. After collecting data, analyze it using various techniques such as descriptive, inferential, predictive, or prescriptive analytics. Utilize tools like charts, graphs, tables, dashboards, statistical tests, or machine learning algorithms based on your analysis needs. Choose tools that facilitate meaningful and understandable presentation of results.

Descriptive Analytics involves analyzing historical data to understand what has happened in the past. Inferential Analytics involves using statistical models to make inferences about a population based on a sample of data from that population. Predictive Analytics involves using statistical algorithms and machine learning techniques to predict future outcomes based on historical data. Prescriptive Analytics involves using optimization and simulation algorithms to advise on possible outcomes.

The data analysis tools depends completely on the the kind of analysis and the insights we want from the data. There can be different methods in which data can be analyzed using different softwares like SPSS, regression models, Tableau, Power BI to represent the data and gain meaningful insights from the same

Your data visualization strategy would depend on your target audience. In some cases you can go with more complex visualization, in other - you should try to simplify it as much as possible. Data visualization is both an art and a science; in my experience, in some cases, data analysts might not be the best at creating visualizations, as we sometimes can presume that something that is understandable for us is also understandable for the audience, which might not be the case. It is always useful to have someone with specific skills for data visualization especially if we need to present findings in an easily understandable and graphically attractive way.

I used to create charts first and then adjust the layout to meet my needs. Now I think about the end in mind (e.g., C-suite audience vs general audience) before even creating a chart. It helps to make sure I know what story I want to tell.

The controversy in data visualization lies not in its importance but the labyrinth of options. Graphs, charts, maps—a visual cacophony vying for attention. The real debate isn't in the process but the essence—does visualization enhance understanding or risk oversimplification? The battleground? Navigating tools to weave a narrative, not drown in aesthetics. It's not about options; it's about the delicate balance between clarity and complexity. In this visual landscape, the real challenge is wielding tools with precision, ensuring that the data's story isn't lost in the spectacle, and that the visualization resonates authentically with the message.

Each visualization can be used in different scenarios depending on the nature of your data and the message you want to convey. Uncover the patterns, correlations, and insights that may not be apparent in raw data.

At the moment, I would say that spreadsheet software (Excel for many), statistics software (R, SAS. SPSS, Stata, among others), presentation software (Powerpoint for many), and a data visualization tool (Tableau or similar) should be adequate for most Market Research teams. If you specialize in an industry or technique, and it requires unique analytics, then clearly that’s on the list. We don’t yet know the definitive list for AI tools, but it’s coming and clearly something to experiment with in a controlled manner.

A market researcher needs to flexible when it comes to budget parameters. Costs can be prohibitive at times so it is important to know your options from free software to more complex support.

When considering what tools to invest in when doing market research, here are a few things to keep in mind: 1. Performance - Look for tools that have a track record of delivering accurate and insightful data. 2. User-friendly - Your team needs tools that are easy to use and understand to maximize their efficiency. 3. Integration - Opt for tools that can seamlessly integrate with other systems and software your team uses. 4. Cost-effectiveness - Consider your budget and resources. Look for tools that offer the best value for what you can afford. Don't forget to consider the long-term costs and benefits of the tools you choose. It's important to find the right balance between cost and quality.

I would look for the most efficient and effective tool and that which is within the budget. Efficiency in terms of time taken and effective in terms of output. I would also consider user friendly parameter.

Controversy brews in the budget-resource nexus of market research tools. It's not merely about affordability but the perilous trade-off between cost and efficiency. The real debate isn't in comparing features; it's in the potential bottleneck of learning curves. How much time spent on mastery risks diverting energy from actual research? It's not just about finding value; it's about deciphering whether the tools amplify insights or overshadow them. In this fiscal terrain, the controversy is not about tools alone but the strategic dance between equipping a team and the risk of drowning them in the intricacies of tool sophistication.

Might be off piste for this article but in my opinion, soft skills are even more important for insight teams development than tools. Consider storytelling, presenting (eg UK Bodytalk), Commercial Acumen (e.g. CAST Commercial Acumen), negotiation/ selling (e.g. Andy Bounds) and take advantage of all the great training provided by industry organisations like Data IQ, AURA, IMA etc

I would want them to have the power to question everything. I think the thing we’ve been missing are questioning and curious minds. Critical thinking requires the space and freedom to exercise that thinking.

I strongly believe that as AI will increasingly remove the need for researchers to 'do' research, it is not about 'tools' at all. The skills your team needs are in business consultancy, storytelling, curation and design.

It's important to recognize that tools are used to serve a job. Find tools that are easy to use and create the efficiency or quality outcomes you're looking for. For instance, an analysis platform with AI is only useful if the AI is useful for you. A lot of shiny objects out there, watch out.

If your data sets will include any personal identifiers, make sure part of your tools assessment is focused on data security and privacy. Where you store your data, how it is protected, and whether/how any identified information can be purged are all considerations to ensure your tools help you accomplish you research goals without introducing unnecessary privacy law/regulatory risks.