Analyze customer sentiment on Social Intents with AI
Swiftask connects your Social Intents chats to AI models to detect, classify, and analyze customer sentiment in real time.
Result:
Instantly identify dissatisfied customers and improve your service quality with actionable data.
AI Agents
social intents
Connector social intents · Secure OAuth 2.0
Your Social Intents conversations are untapped goldmines
Every day, your agents handle dozens of chats on Social Intents. But without systematic analysis, you miss weak signals: customer frustration, product trends, or sales opportunities. Data remains buried in chat history.
Main negative impacts:
Delayed crisis detection
Unaddressed customer dissatisfaction can escalate quickly. Without proactive analysis, you often react too late.
Underutilized customer data
The overall sentiment of your customer base is invisible. You are navigating without qualitative performance indicators.
Manual analysis costs
Rereading hundreds of transcripts to assess satisfaction is impossible at scale for a support team.
Swiftask automates sentiment analysis on every message received via Social Intents. You get a clear score and automatic tags for every interaction.
BEFORE / AFTER
What changes with Swiftask
Without automated analysis
Your agents finish chats without knowing if the customer is truly satisfied. Managers must manually sample conversations to assess quality, which is time-consuming and subjective.
With Swiftask + Social Intents
Every chat is analyzed in real time by AI. If sentiment turns negative, an alert is sent to a supervisor. You have a global dashboard of customer satisfaction.
Enable Social Intents sentiment analysis in 4 steps
1
STEP 1 : Connect Social Intents to Swiftask
Authenticate your Social Intents account in Swiftask to enable chat stream reading.
2
STEP 2 : Configure the AI analysis engine
Choose the AI model and define sentiment scales (positive, neutral, negative) tailored to your business.
3
STEP 3 : Define automated actions
Create rules: for example, send a Teams notification if the sentiment is 'very negative'.
4
STEP 4 : Visualize your insights
Track the evolution of customer sentiment in real time via automatically generated reports.
Advanced features for your chats
AI analyzes tone, word choice, urgency, and the emotional context of every customer message.
- Target connector: The agent performs the right actions in social intents based on event context.
- Automated actions: Automatic chat classification (positive/negative). Emotional urgency detection. Frustration keyword extraction. Real-time alerts on critical chats.
- Native governance: All data is aggregated to provide a macroscopic view of your customer experience.
Each action is contextualized and executed automatically at the right time.
Each Swiftask agent uses a dedicated identity (e.g. agent-social-intents@swiftask.ai ). You keep full visibility on every action and every sent message.
Key takeaway: The agent automates repetitive decisions and leaves high-value actions to your teams.
Why analyze sentiment with Swiftask
Increased customer reactivity
Intervene immediately on conversations that are going poorly.
Continuous support improvement
Identify recurring friction points in your response processes.
Easier agent coaching
Use sentiment data to train your teams on complex cases.
Automated reporting
Stop wasting time creating satisfaction reports manually.
Seamless integration
Works in the background without disrupting your agents or customers.
Data security and privacy
Swiftask applies enterprise-grade security standards for your social intents automations.
- Guaranteed privacy: Your chat data is processed according to the strictest GDPR standards.
- Environment isolation: Each workspace is completely isolated to ensure the security of your insights.
- Enterprise compliance: Swiftask meets the security standards required for businesses of all sizes.
To learn more about compliance, visit the Swiftask governance page for detailed security architecture information.
RESULTS
Measure the impact on your CX
| Metric | Before | After |
|---|---|---|
| Dissatisfaction detection time | Several days (or never) | Instantaneous |
| Customer data quality | Subjective and fragmented | Objective and exhaustive |
| Analysis capacity | Limited sampling | 100% of conversations |
Take action with social intents
Instantly identify dissatisfied customers and improve your service quality with actionable data.