Discover how companies and entrepreneurs use deep learning to sell more and increase customer retention and happiness.
An airline wants to prioritize customers who are having problems with their flight.
To detect those customers who have problems, the airline trains a deeper with common phrases of customers when they miss a connection or have delayed flights. It then uses the API to connect its helpdesk to the Deep Talk trained Deeper to evaluate customer messages in real time and trigger alerts that lead customers directly to a particular customer service agent who can help them.
A retail company needs to analyze thousands of surveys from different countries.
Using the Deep Talk API, the retail company sends all its surveys to Deep Talk. Deep Talk then segments the texts, predicts the sentiment, and finds all recurring topics that the clients mention. Based on these analyses, Deep Talk can automatically add tags to each survey, directly informing the retail company about their client's main pain points. With automated survey analysis, the retail company can easily monitor and track their client's satisfaction over time.
A mental health organization serves adolescents and adults through psychological care and wants to prioritize cases of people who might commit suicide.
A group of psychologists trains a model on the deepers dashboard with phrases that show "urgency" of attention to the person. Then, based on their experience in psychological care, they search and label the thousands of conversations with young people uploaded to Deep Talk with worrying phrases that may show a person's suicidal intent.
It then connects its care system with Deep Talk to prioritize and not keep waiting for care for those people that the Deeper detects might be in trouble.
A bank searches in social networks and conversations with customers for those who have an intention to buy a car, house, or another requirement that requires a possible bank loan.
Using our "topic detection" feature, phrases are identified inside the "Loan Topic," showing a product's purchase intent.
With dozens of product purchase intent phrases, a "Buyer Intent" Deeper is trained by detecting those customers who might want to purchase a specific product in the Bank's social networks and chat systems.
Those customers and their phrases detected are then written in a Google Sheet accessible by a special Bank sales team that reviews it daily to contact those customers.
A supermarket chain seeks to detect problems with its products in thousands of daily surveys.
Using techniques like sentiment analysis, entity detection, clustering, and segmentation of the surveys. Deep Talk detects those products that customers say there are quality problems or supermarket services that are failing.