Manage Social media Presence by automated analysis and detection of adverse content

With the rise of social media and customers’ increasing reliance on these platforms, it has become crucial for organizations to monitor customer feedback on social media. According to a survey by the American Bankers Association, 89% of banks believe social media is important for their operations, and 88% are active on their social media accounts.

This indicates that banks recognize the value of social media for addressing customer concerns and improving customer service.

Many customers today express their frustrations on social platforms after failing to receive solutions through official channels and also on external government complaints agencies like CFPB. These public complaints can attract the attention of other customers and negatively impact brand positioning and image.

Therefore, implementing a social media listening solution is an essential step for modern organizations.

This advanced text mining tool analyzes social media data to provide:

1. Sentiment scores that classify conversations into positive and negative based on text transcripts.
2. The ability to capture the intensity of sentiment, from strong positive to strong negative.
3. Identification of topics that consistently generate positive or negative sentiment.

This solution is for organizations that:

1. Have a strong social media presence.
2. Are looking to develop a social media strategy and want to leverage data analytics.
3. Want to understand customer sentiment on social media.

This solution offers the following benefits:

1. NPS/VOC/CSAT & Loyalty: For brands with a social media presence, identifying customer sentiment and pain points is essential. This can improve the brand image on social media platforms.

2. Indirect revenue impact: Positive interactions on social media help attract customers and instill confidence in prospects. Leveraging social media effectively can boost future revenue.

This analysis necessitates text data from social platform conversations.

“1. Final outputs could include a list of activities across social media channels, detailing their topics and sentiment summaries.
2. PowerPoint summaries or automated Excel files.
3. Interactive dashboards.”
Lexicon-based methods are used to classify words.

To classify conversations, deep learning methods or support vector machines are applied.

N-gram graphs are used to visualize the results for business insights and to validate the findings

1. On prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. Final outputs could include a list of activities across social media channels, detailing their topics and sentiment summaries.
2. PowerPoint summaries or automated Excel files.
3. Interactive dashboards.