A screenshot from Sprout's platform that demonstrates message spike detection.

[fomayof928@mowline]

In the screenshot, you can see the Smart Inbox and luxembourg b2b leads a message alert that reads: We started detected a spike 5 minutes ago.
Scalability
AI analytics helps alleviate a company’s growth pains by efficiently handling large datasets from disparate sources to provide real-time insights—something not possible with traditional data analysis methods.

When integrated with a company’s customer relationship management (CRM) and business intelligence software, such tools exponentially increase the output of targeted insights by cross-referencing data points and identifying patterns in customer profiles and purchase patterns. Thus, giving a more holistic view of your current customers and prospects.

For example, Sprout’s Salesforce integration enables you to deliver exceptional customer experiences at every touchpoint by leveraging social data throughout the CRM. Create personalized touch points throughout the customer journey and increase the impact of your initiatives while implementing efficient workflows across the teams.

Accuracy
Results from AI data analysis are more accurate than manual data analysis methods. This is because there’s less scope for human error, biases or limitations such as excluding data subjectively, or while calculating complex metrics, especially from multiple sources.

Business applications of AI analytics
Companies use AI analytics to derive a stronger return on investment (ROI) from their social and digital data, while championing productivity and growth through intelligent automation. Let’s dig deeper.

Sentiment analysis
Sentiment analysis algorithms scan feedback data to prioritize key themes and topics, giving brands an in-depth view of common complaints or recurring product or service issues. In this way, brands can develop proactive strategies to ensure better customer experience to boost brand loyalty.