Optimize the Readability and Relevance of Your Revealers on Gravite
Segment Your Data
Creating and Segmenting Your Data
Launching a revealer on Gravite is a simple yet essential step to obtain relevant and actionable analyses. To create a revealer, you must first collect and organize your data. Proper segmentation of your data is key to improving the quality of your analysis.
- Data Collection: Import data from various sources such as support tickets, call recordings, emails, etc.
- Data Organization: Categorize your data based on relevant criteria such as feedback type, product involved, or communication channel.
For more details on the data creation and segmentation process, we recommend reading this detailed article that explains each step in depth.
Data segmentation is crucial because it allows you to precisely target your analyses. For instance, by specifically segmenting feedback from users of your payment module, you will obtain much more precise and actionable insights. If the data is not properly segmented, the quality of the results provided by Gravite can decrease by approximately 30%.
Use Cases for Segmentation
- Customer Pain Points Analysis: To analyze the issues faced by your customers, it is essential not to include data from prospects. Problems encountered with your service or product should be separate from inquiries about the value proposition. For example, negative feedback on the payment feature should not be mixed with questions from prospects about pricing.
- Example: A SaaS company segments negative feedback on the payment process and discovers that many users find the process too long. By separating this data from other feedback, the company can focus on improving this specific point.
- Product-Based Segmentation: If your company offers multiple products, it is useful to segment feedback by product to identify friction points specific to each product. This allows you to quickly identify which products need priority improvements.
- Example: A tech company separately analyzes feedback from its mobile apps and website. It discovers that mobile app users face navigation issues, while website users complain about slow loading times.
- Support Channel Segmentation: By separating data from support tickets, phone calls, and emails, you can understand which channels are the most problematic and need specific improvements.
- Example: A customer service team identifies that support tickets about software bugs are more numerous via phone calls than email. This allows them to specifically train phone agents on quickly resolving these bugs.
- Geographical Segmentation: Analyze user feedback based on their location to detect region-specific issues. This can reveal local trends that require specific actions.
- Example: A delivery company discovers that complaints about delivery times are more frequent in a specific region. They can then investigate and resolve logistical issues in that region.
Focus Your Analyses on a Specific Time Period
To increase the relevance of the results, a revealer is configured by default to analyze data from the "Last 30 Days." This period helps avoid presenting outdated issues and provides an up-to-date view of current trends and problems.
- Today: For analyzing data collected today. Ideal for immediate feedback on a new feature or recent campaign.
- Yesterday: For examining yesterday's data. Useful for a daily review of performance and feedback.
- Last 7 Days: For a view of the past week. Helps track weekly trends and identify recurring patterns.
- Last 30 Days: The default period, offering a monthly analysis that includes enough data for significant insights without outdated information.
- All Time: For an analysis without a time limit. Useful for comprehensive historical performance reviews.
- Custom Date: To define a specific period according to your needs. This flexible option allows you to tailor analyses to relevant periods for specific projects or events.
By adjusting the time period, you can tailor your analyses to reflect current trends and the specific needs of your business. For example, after launching a new feature, you can adjust the filter to analyze feedback from the first few days to quickly detect potential issues and address them.
Conclusion
By optimizing data segmentation and adjusting time periods for your analyses, Gravite enables you to obtain precise and relevant insights. These practices help you improve the quality of your services, respond quickly to issues, and maximize customer satisfaction.
