We use Data Science to improve business efficiency and create additional value by means of data, developing predictive analyses to generate insights and maximize customers’ business, mitigate risks, increase self-service channels’ retention, and minimize callbacks and complaints. Our value proposition focuses on performance (propensity models, people analysis), reduced cost per interaction, and machine learning to enable the AI platform.
Use of the Stress-o-meter, a statistical model without parallel in the market that identifies and categorizes complaints filed over all kinds of channels.
Digital Voice tool for mass analysis of data collected from interactions on voice channels and to spot opportunities for improved indicators and processes.
Predictive analyses to generate insights and maximize customers’ business.
Improved business performance through predictive models and people analysis.
B2B sales-oriented operation available 24/7, which combines digital and non-digital strategies with analytics to increase sales conversion, together with highly qualified agents to establish a long-term relationship with customers.View more
Intelligent interactions applied over the customer lifecycle, focusing on reversing wishes or requests to cancel services or products, as well as complaints monitoring to predict possible cancellation requests. High skilled anti-attrition profile agents prepared to reverse the cancel request or to make an alternative offer.View more
Collection management for debtor companies, seeking to reduce bad debts and maintain active clients. We assertively locate financial managers of the companies and use negotiators with a different profile who, associated with the use of innovative technologies, guarantee the best delivery in credit recoveryView more
Combines digital and traditional sales with Data Science for more efficient sales and maximized conversion. Optimizes investment and reduces costs based on an end-to-end view of the customer journey.View more
Applies semantics technology and Data Science to predictive analysis of the best course of action, with agile customer responses, focusing on engagement and efficient troubleshooting. Plus content curating and service systems integration, in addition to human contact as needed.View more
The most efficient end-to-end collections solution: it operates on every phase and channel to gather the various features and tracking KPIs on a single platform. Simplifies management and adds intelligence and speed to the process.View more
Combination of channels and technology to offer additional products to end consumers in an innovative way.View more
Driven by Predictive Analytics that triggers proactive interactions with customers, using integrated multichannel to provide better customer experience, which focuses on to avoid the churn risk and keep a long-term relationship.View more
A resolutive and automated social media engagement solution that offers excellence in service, monitoring, moderation and management, through social networks, supported by semantic technology.View more
Managing the late stage collection process focusing on credit recovery. It has an analytics layer to improve data in order to ensure the location of the final consumer, generate models of payment propensity, scoring and intelligent segmentation. Offering efficient channels, attraction and re-marketing campaigns for negotiation with reach and recovery.View more
See how this solution works in practiceContact us
LUI – Language User Interface
The challenge was to develop a humanized IVR for a payments company, focusing on the main journeys and best possible user experience.
Digital Agent Sales
Data Science at the service of propensity and behavior analysis, in addition to multidisciplinary teams tracking results in real time, helped increase conversion-process agility.
This project earned public recognition (Cliente S/A 2019 award, Smart Customer 2019 award), improved service, and built the loyalty of the power utility’s customers. The use of virtual services was extended and service costs were down 30%.