Conversational analytics leverages artificial intelligence and natural language processing (NLP) to analyze customer conversations across various social media platforms, providing valuable brand insights. Here’s how it can enhance your marketing strategy:
1. **Discover Brand Insights**: By analyzing social chatter, you can gain insights to grow your social presence and improve brand perception. This includes identifying the right social media influencers and building successful co-branding partnerships.
2. **Gain Competitive Intelligence**: Conversational analytics helps in deriving competitive intelligence by filtering customer discussions about your competitors and your brand across platforms like Reddit, Google My Business, and Instagram. AI detects competitor brand names and sentiment analysis provides contextual understanding of these conversations, aiding in setting competitive benchmarks.
3. **Process the Data**: To filter out noise from tons of customer conversations, conversational analytics tools use AI tasks like NLP, named entity recognition (NER), and sentiment analysis. This helps identify important topics and themes, measure customer experience, and maintain positive brand health. Tools like Sprout’s Query Builder and AI-powered Queries by AI Assist refine data analysis by suggesting relevant keywords and filtering out spam.
4. **Harness Social Data**: Powerful social media listening tools like Sprout’s AI capabilities enable brands to have actionable insights. These insights can enhance customer service, maintain positive sentiment, and grow market share holistically. For instance, Indiana University used Sprout’s tools to understand audience feedback across their social channels, proactively identifying and tracking issues, and developing an effective social response strategy.
5. **Implementation & Action**: The actionable insights from this processing enable brands to build multi-layer strategies. AI tools like Sentiment Analysis quickly find and address negative comments or complaints, ensuring the brand’s reputation remains unscathed. Additionally, tools like Message Intent and Recommended Response help teams respond in real-time, focusing on the most important interactions to improve the customer experience.
### **Key Points:**
– **Data Collection**: Gathering a comprehensive dataset from social media posts, customer reviews, surveys, customer care logs, NPS scores, and emails.
– **Data Processing**: Using AI and ML algorithms to interpret and analyze text or numerical data. Text data is processed using NLP and text analysis to understand language nuances and sentiment, while numerical data is processed using statistical and ML models.
– **Data Analysis & Visualization**: Analyzing the processed data by identifying important parts or patterns, then visualizing the data into graphs and charts to provide a clear understanding of trends and outliers.
By leveraging conversational analytics, brands can gain a deeper understanding of their audience’s sentiments and behaviors, leading to more informed marketing strategies. For more details, visit [Sprout Social](https://sproutsocial.com/insights/conversational-ai/).