Marketing & Social Media
Explore how NaturalText A.I. allows companies and marketing agencies glean deeper CX insights from customer feedback.
MARKETING & SOCIAL MEDIA USE CASE
Customer Experience (CX) & Sentiment Analysis
Understanding customer feedback—including knowing what customers like, what they want, and what their pain points are—is paramount to increasing sales. When a flood of messages, reviews, tweets, chats, and emails flow into various channels, however, it is a challenge to get a comprehensive overview of customer expectations and opportunities for improvement.
Customer feedback comes in a variety of file types, complicating efforts to have an efficient and streamlined feedback review and extraction process.
Marketing and social media analytics produce massive datasets, with millions of data points. Drawing meaningful insights at a business-viable cost from these datasets can seem nearly impossible.
Today, customers can get simple snapshots of customer feedback through programs that classify product reviews as either "good" or "bad." These programs do not deliver the full picture customers provide through their feedback, though, nor do they filter spam and unsolicited messages. These basic programs can form the basis for complex A.I. models that give a more complete overview of feedback, but creating these models requires great time and effort to select messages for training, and then to train and tune them.
Feedback from social media and messaging channels often use quirky language—including abbreviations, slang, and misspellings—that can trip up basic sentiment analysis models.
When it comes to customer experience, context is critical. Two pieces of feedback may appear to use the same word, but the writers may intend two completely different meanings. Unfortunately, sentiment analysis tools that can assess context are more complicated, and require extensive (and expensive!) preparation.
Complex machine learning models require training of data, and tuning and retraining over time. This method incurs very high upfront costs, and each model is prepared for only one set of data.
NaturalText A.I. goes beyond simple binary classification of reviews as either "good" or "bad"—rather, it can group and rank tens of millions of customer reviews, based on contextual similarity. This means you can see which topics your customers discuss the most, and understand what they really think.
Reviews can be classified in multiple languages, so companies can get a truly global perspective. Different formats of messages, such as emails and tweets, can be merged to create a unified view of customer satisfaction. Lastly, spam and junk messages can be flagged based on content, and removed to avoid skewing results.
Simplifies web and social media management to improve responsiveness and make dramatic increases in customer satisfaction
Saves time and money spent on identifying and resolving customer issues
Turns customer reviews and other text data into quantifiable, actionable insights to increase sales revenue
Links with other analysis tools to give a more robust picture of customer experience
Uncovers trends in customer feedback and sentiment through secure analysis of both public and private channels