What can you do with sentiment analysis?
Sentiment analysis results can be made available on Omningage IQ reports and Wallboards in near real-time, and as historical data.
Use cases for near real-time sentiment analysis
When the negative sentiment, expressed by many customers, suddenly increases, it is a sign of trouble. Near real-time alerting can prompt supervisors to investigate and take prompt action to limit damage.
Alerts can indicate agent sentiment. This gives supervisors an early warning of extreme stress, fatigue or burnout.
Queues, teams or agents can be displayed on a ranking report showing customer sentiment. Managers can see who has the highest and lowest average sentiment and be aware of any changes from the normal order of things. (In a private hospital, for example, one would expect billing to have a lower average sentiment score than appointment booking).
When these charts deviate from the normal order or when the value of the lowest average sentiment score becomes much lower than usual, officials investigate and react quickly.
Use cases for sentiment analysis of historical data
Queues, teams or agents can be displayed on a ranking report showing customer sentiment. Using data from the past day, week, or month, it gives you insight into how customers are affected by interacting with specific queues, teams, or agents.
When a specific queue, team, or agent scores significantly lower or more negatively than others, supervisors can investigate and take action to improve it.
Time series reports:
A time series report shows how sentiment changes over time. Contact Lens generates such reports showing how sentiment changes during a call. This brings valuable insight to sales calls, to see where the sales conversation is most at risk of failing. This helps sales coaches focus on those tricky parts of the call.
These reports can also be used on a larger scale to see how customer attitudes toward products or services change over time. Marketing can use this data to track the lifecycle of marketing campaigns or product launches.
For individual agents, customer and agent sentiment time series reports can provide early warning of agent burnout. Supervisors and coaches can take steps to help the agent resolve these issues.
Correlation with other KPIs:
This shows how management decisions affect customers.
If volumes are high, supervisors can tell agents to end calls quickly. Agents may not be as polite or build rapport as they normally do. Sentiment scores indicate how customers react to this.
If not following elaborate onboarding scripts or building relationships makes no difference to sentiment scores, this could be an opportunity to reduce average handle time and gain operational efficiency.
Sentiment analysis is a very useful “consistency check” for the quality team. One would expect a positive correlation between quality scores and sentiment scores. If not, it may be appropriate to review the definitions of corporate “quality”.
Correlation with key phrases:
Sentiment can be used to target root cause analysis. Keyphrases that occur where sentiment is low can point to the root cause of customer dissatisfaction. It could reveal broken processes, product defects, or the influence of current events, such as price inflation, on customer sentiment. Company management can use this information to take corrective action.