Setting up a Deep Insights query can provide valuable insights into audience conversations and trends.

However, there might be instances when your query doesn't yield the desired results. Here's a guide on what to do when your Deep Insights query fails:

1. Review Your Query:

Double-check the query you've entered. Ensure that your keywords, sources, and other parameters are accurate and relevant to your analysis.

2. Refine Your Query:

If your initial query doesn't provide the expected results, consider refining it. Modify keywords, adjust sources, or apply additional filters to narrow down your query and target the specific conversations you're interested in.

3. Check Time Range:

The time range you've selected for your query might affect the results. Make sure the time period you've chosen aligns with the conversations you're trying to analyze.

4. Consider Filters:

Filters such as location, language, sentiment, and more can influence the outcome of your query. Experiment with different filter combinations to see if they affect the results.

5. Topic vs. Keyword Queries:

• If you're using existing topics as reference queries, ensure that these topics accurately represent the conversations you want to analyze.

• If you're using keyword queries, make sure your keywords are comprehensive and well-defined.

6. Conversation Input Size:

Keep in mind that the size of your conversation input can impact the processing time. Larger inputs might take longer to generate a report. Be patient if your query involves a substantial amount of data.

7. Reach Out for Support:

If your query continues to fail despite your best efforts, don't hesitate to reach out to the Product Support team. They can provide assistance, troubleshoot issues, and offer suggestions to optimize your query.

8. Modify Expectations:

Deep Insights queries are based on available data, and there might be instances where the data pool doesn't align perfectly with your query. Be prepared to adjust your expectations and insights based on the data you receive.

9. Experiment and Learn:

The process of refining queries is iterative. Experiment with different combinations of keywords, filters, and sources to understand how they impact the results. Learning from failed queries can improve your query-building skills over time.

10. Consider Query Complexity:

Complex queries involving multiple filters and sources might require more processing time. Simplify your query to test its basic components before adding complexity.

In conclusion, encountering a failed Deep Insights query is a common part of data analysis. By reviewing and refining your query, adjusting parameters, and seeking support when needed, you can overcome challenges and derive valuable insights from your data.