25 questions to ask your CallOptix data.

What customer complaints came up most often in the last 30 days? Group them by theme and show supporting calls.

Look for recurring experiences rather than treating one complaint as a trend.

Find calls from the last 30 days where customers mention a problem after booking. What issues do they describe?

Use conversation evidence to distinguish service issues from pre-purchase questions.

Show negative-sentiment calls longer than five minutes from the last seven days. Summarize the concerns raised.

Narrow the review queue by sentiment, duration, and date.

What product or service requests appear in customer conversations from the last 30 days? Include exact customer wording from supporting transcripts.

Separate explicit requests from inferred needs; ask what records were reviewed.

Compare customer feedback themes in the last seven days with the previous seven days. Which themes appear more often in the available data?

Check comparable time windows and ask about data coverage before drawing conclusions.

Which objections increased in the last seven days compared with the previous seven days? Show the calls behind the change.

Review counts alongside call volume to understand whether the mix changed.

Find calls from the last 30 days with price objections. What are customers comparing the price against?

Use transcripts to distinguish affordability concerns from questions about value.

Which competitors were mentioned most often in the last 30 days, and what did customers say about them?

Pair mention patterns with source quotes instead of assuming every mention is a loss.

Show how agents responded to price objections in the last seven days. Include transcript examples of the different approaches.

Compare responses without assuming an approach caused a deal outcome.

For lost deals with linked calls in the last 30 days, what objections appear in those calls? Separate stated reasons from possible explanations.

Connect deal outcomes to conversation evidence and make uncertainty explicit.

Summarize our current pipeline by configured deal stage. Include deal values where available and highlight missing values.

Start with the workspace's actual stages and recorded deal data.

Review calls linked to open deals from the last seven days. Which customers express buying intent, and what exactly did they say?

Treat expressed intent as a signal to investigate, not a guaranteed sale.

Which open deals have linked calls with negative sentiment or objections in the last 30 days? Summarize the concerns with source evidence.

Build a review list without claiming an automatic risk prediction.

Compare recorded revenue by agent for the last 30 days with the previous 30 days. Explain which dates and revenue measure were used.

Check the reporting basis before comparing performance across agents.

Review deals recorded as lost in the last seven days and their linked calls. What reasons did customers state, and which losses lack conversation evidence?

Keep explicit customer reasons separate from hypotheses about lost revenue.

Summarize team performance for the last seven days using available dashboard analytics. What changed from the previous seven days?

Start with the overview and ask the assistant to identify gaps in the comparison.

Compare available agent scores with our scoring benchmarks for the last 30 days. Which scoring areas deserve a closer look?

Use the workspace's benchmarks rather than an invented standard.

Compare call volume by agent over the last seven days. Flag small samples before comparing performance.

Understand workload and sample size alongside outcomes.

How does sentiment differ by conversation type in the last 30 days? Show examples from the most negative group.

Account for conversation context when interpreting sentiment.

Find calls from the last seven days with low analysis scores. Explain which scored criteria need attention and show relevant transcript passages.

Move from a score to the behavior recorded in an individual call.

Based on available scores, coaching suggestions, and recent calls, which agents may benefit from coaching on price objections? Show the supporting evidence.

Use this as preparation for a manager's review, not an automatic personnel decision.

Summarize the saved coaching moments available in our workspace. Group them by the skill each moment illustrates.

Turn the existing coaching library into a focused review list.

What AI coaching suggestions are available for our agents? Group related suggestions and include supporting call references where available.

Review the platform's existing suggestions with their source context.

Which coaching assignments are outstanding, and what skills or agents do they relate to?

Inspect assignment status without implying that the assistant can change it.

Using saved coaching moments and available call transcripts, draft a coaching discussion about handling customer objections. Cite the examples and identify any missing evidence.

Ask the assistant to synthesize retrieved information into a discussion you can review.