How to best manage product feedback collection and classification from different global markets / clusters?

How to best manage product feedback collection and classification from different global markets / clusters?

ASK by Martina Barbati Inglessis :

Suggestion on how to best manage Market feedback collection and classification. I would like to ask some suggestions on a task I have asked to do which seems extremely manual and tedious.?In my company we have huge spreadsheet for Market feedback that our sales team collects including info like: Name of the client, type of client, the product we are trying to offer them, what competitor they use or mention in the call, and then the feedback in a very lengthy text format. Usually included overall, e.g. status, what they liked, what they disliked, I have asked to read through these feedback and there are over 150 rows. I need try to get a sense on whether we are missing some key signals from this potential clients feedback i.e., understand whether there are any clear buckets, new product request, negative feedback to consider, any other useful insights. How would you tackle this instead of doing this manually? Do you use any free tools (we cannot invest in one at the moment) to gather key insights?

ANSWER by Rituraj Patil :

Managing and analyzing market feedback can be a time-consuming task, but there are several strategies and free tools given below that can help you to streamline the process.

Data Preprocessing:

Clean the data: Ensure that the spreadsheet data is consistent and free from errors or duplicates.

Categorize the columns: Assign clear labels to each column (e.g., "Client Name," "Client Type," "Product," "Competitor," "Feedback," etc.).

Text Analysis:

Sentiment analysis: Use free text analysis tools, such as VADER (Valence Aware Dictionary and sEntiment Reasoner) or TextBlob, to determine the sentiment (positive, negative, neutral) of the feedback.

Keyword extraction: Utilize techniques like TF-IDF (Term Frequency-Inverse Document Frequency) or keyword extraction libraries like RAKE (Rapid Automatic Keyword Extraction) to identify important keywords or phrases within the feedback.

Classification:

Create predefined categories: Define a set of categories or buckets that align with your goals (e.g., "New Product Requests," "Negative Feedback," "Positive Feedback," "Competitor Mentions," "Use Cases," etc.).

Rule-based classification: Develop rules or patterns based on keywords, phrases, or sentiment scores to automatically assign feedback to the relevant categories. For example, if a feedback mentions a competitor, it can be classified under the "Competitor Mentions" category.

Manual validation: While rule-based classification can automate some categorization, it's important to manually review and validate the results to ensure accuracy.

Visualization and Reporting:

Generate visualizations: Use free data visualization tools like Tableau Public, Google Data Studio, or Python libraries (e.g., Matplotlib, Seaborn) to create charts, graphs, or word clouds to present the collected insights visually.

Summarize key findings: Write a summary or report highlighting the main trends, patterns, and insights discovered through the analysis.

Collaboration and Feedback Loop:

Share findings: Collaborate with relevant stakeholders, such as sales teams, product managers, or executives, to discuss the findings and gather additional insights or perspectives.

Iterative improvement: Use feedback from stakeholders to refine and enhance the categorization process, improving the accuracy and relevance of the analysis over time.

Remember that these suggestions provide a general framework and you can tailor them to your specific needs and available resources.