Answer any data question with Dimensions

Identify specific product features requested or suggested by customers in their feedback.

Extract statements highlighting customer frustrations or challenges with the product.

Identify aspects of the product that generate customer satisfaction based on individual responses.

Highlight when customers compare our product to a competitor's product in their feedback.

Extract suggestions for improving specific features or products.

Extract comments related to customer perceptions of pricing fairness or value.

Extract statements pointing out perceived product quality concerns.

Analyze the tone and content of transcripts to determine if a conversation is likely to escalate.

Identify whether agents provided clear and actionable resolutions to customer issues.

Detect specific statements where agents express empathy during the interaction.

Note customer reactions or issues related to the length of the call.

Extract the emotional tone of the customer within the transcript.

Check whether agents are following approved scripts within the written transcripts.

Identify text markers indicating interruptions by the agent during the conversation.

Highlight instances where customer concerns remain unaddressed within the transcript.

Based on each individual's survey response, assign a predicted NPS score on a scale of 1-10

 Based on the individual's survey responses, predict which customer profile they belong to based on the following customer profiles: [INSERT INFORMATION ABOUT YOUR CUSTOMER PROFILES]

Extract the main specific driver of positive sentiment based on the individual's survey response

Extract the main specific driver of positive sentiment based on the individual's survey response

Measure the specific emotion conveyed in each individual's open-ended survey response.

Extract specific ideas about promotions or offers from respondents.

Based on the support interaction, assigned a Predicted CSAT score on a scale of 1-10 that the customer would have assigned for the interaction.

Evaluate whether agents followed Standard Operating Procedures for each support case.

Confirm if the customer's issue was resolved within the analyzed interaction.

Categorize the individual customer issue into predefined problem types.

Assess if agents responded to the customer promptly within the case timeline.

Verify if the solutions provided in individual cases were correct and actionable.

Identify instances where the agent adjusted their tone to suit the customer's demeanor.

Highlight cases where follow-up action was explicitly noted or required.

Identify specific product features requested or suggested by customers in their feedback.

Highlight suggestions from individual employees for workplace improvements.

Identify specific mentions of management or leadership in individual feedback.

Detect individual feedback that indicates potential burnout or high stress.

Highlight responses suggesting the employee might be at risk of leaving.

Extract feedback about specific skill development or training needs mentioned by employees.

Identify references to your brand in individual social media posts.

Highlight when customers compare your brand to competitors in individual posts.

Detect instances where customers actively promote your brand.

Analyze individual influencer posts to evaluate their effectiveness.

Identify posts with negative sentiment directed toward your brand.

Extract customer feedback about specific products from individual social media posts.

Assess whether the chatbot correctly identifies customer intents in individual interactions.

Evaluate if the chatbot's response is directly relevant to the customer's query.

Identify when the chatbot fails to respond appropriately and defaults to fallback mechanisms.

Determine whether the chatbot resolves the customer's query without escalation to a human agent.

Analyze customer sentiment as reflected in individual chatbot interactions.

Measure how quickly the chatbot responds to the customer within each interaction.

Highlight instances where customers express confusion or frustration with the chatbot's responses.

Identify scenarios where the chatbot could have taken action but failed to do so.

Assess the level of personalization provided by the chatbot during each conversation.

Detect and record when and why the chatbot escalates a query to a human agent.