What do you do if you need to uncover trends and patterns in customer feedback data?

Data is indispensable for comprehending customer trends and proactively managing risks. Tips to leverage data listed below 1. Make sure you have right data from various external systems in your analytics or CS tools. This involves data from surveys, CSM sentiment checks, or stakeholder alignment. This helps your understanding of customer needs and facilitates informed decision-making 2. After inputting the data into the analytics tool, create trend reports and actionable insights for both end-users and executives. 3. Send actionable alerts to users through email notifications or other available integrations to notify the teams promptly. 4.Make sure you have visualization reports in place to monitor whether the loop has been closed or not

Analyzing customer feedback data to uncover trends and patterns is essential for understanding customer preferences, identifying areas for improvement, and making data-driven decisions. Here's a step-by-step guide on how to do it effectively: Collect Data: Gather customer feedback data from various sources such as surveys, online reviews, social media, emails, and customer support interactions. Clean and Prepare Data: Preprocess the data to ensure its quality and consistency. This may involve removing duplicates, correcting errors, standardizing formats, and categorizing feedback by attributes such as product/service, sentiment, and topic.

Gather customer feedback from various sources, such as surveys, reviews, social media, emails, and customer support interactions. Ensure the data is structured and organized for easier analysis, using tools like CRM systems, survey platforms, or data analytics software.

Upon acquiring a refined set of customer feedback data, I shall first deploy an advanced CRM system for comprehensive analytics. Following this, I shall employ cutting-edge Machine Learning algorithms for EDA, enabling the text mining, pattern identification, sentiment analysis, and the development of predictive models to forecast forthcoming trends. These insights thensteer improvements in customer service, brand consolidation, refinement of user experiences, and the formulation of distinctive market differentiators.

You will most likely have a single Support CRM, where data flows into. Here you should ensure that the data is meaningful, truthful and has consistency. You can use the reporting/analysis capabilities of that CRM, say Zendesk, to do some analysis. For more advanced analysis including customer data from other platforms, such as a sales CRM, you will need to ask the help of the data analysis team.

It is important to have a clear understanding of what you want to achieve with your analysis. This could include identifying common issues, measuring customer satisfaction, or tracking product performance. To achieve your objectives, it's important to identify key metrics and KPIs (Key Performance Indicators) that are relevant to your goals. Examples of such metrics could be Net Promoter Score, customer satisfaction score, or sentiment analysis.

The categorization depends on what you want to achieve with the data. For example, if it's to determine what type of tickets are created by a certain customer segment and their tenure, then you have to make sure that these data points are present and accurate to derive meaning.

When dealing with quantitative feedback such as ratings or scores, it's important to use statistical methods to identify patterns. You can calculate averages, percentages, and trends over time to gain insight into shifts in customer satisfaction and identify specific areas that may be affecting your overall score. It's essential to consider the numbers within the context of real customer experiences to fully understand what they represent.

Context is everything, which is why the most valuable information in customer feedback is the unstructured answer. Quantifying unstructured feedback is possible with AI now. You can train an LLM to categorize feedback based on sentiment, or use a tool like SupportLogic. In combination with the already quantified data such as CES/CSAT/NPS score the data will tell a more actionable story.

Sentiment analysis on written customer communication is widely used by Enterprises now and through the implementation of AI tools this will expand to verbal communication. In my experience its incredibly powerful and more useful than surveys as it's predictive.

Data analysis is run to tell a story or to create a narrative, especially in a transactional environment like Customer Support. The visualisation becomes critical when needing to convey action (more budget, or "look at the savings"), especially to executive team. If you just have the raw data there are so many tools that can help you visualize it such as MS Copilot in Excel, or PowerBI.