The client used an established Customer Experience platform but needed specific features tailored to their business processes and customer’s needs.

Managing multiple global customers, each with their own customer journeys and interaction points, required a flexible multi-channel survey engine, data processing and custom reports.

The goal of the project was crystal clear from the start: a significant reduction in costs while improving the actionable insights.

We implemented a survey engine with maximum flexibility to define target groups, types of surveys, language, frequency and channel.

The engine is connected to their back-end CRM and is triggered by a variety of tailored events, including PoS transactions. Services communicate with public web applications synchronously or asynchronously – by request buffering (Azure Service Bus/Azure Event Hubs).

The outcome of the surveys is processed, enriched and presented in custom dashboard, each with their own data visualization and actionable insights.

We utilized machine learning to

Analysis and recommender system with integrated BI and reporting tool combined with Machine Learning and Natural Language Processing algorithms allow the client to understand their customers’ experiences in real time, along all points of their customer-specific journey.

Our client wanted a platform that acts as a virtual wallet and enables digital payment transactions.

Fiat-to-virtual-wallet transactions needed to be instant, validated and guarded against fraud through dedicated, self-learning detection algorithms. Information security principles needed to be followed including ISO 27001 and PCI/DSS compliance.

The application infrastructure needed to be designed in such a scalable way that it can process up to 30M transactions per month flawlessly.

We’ve selected a robust backend system, based on an Oracle database and a highly scalable data layer, to serve microservice based applications.

Transactions, validations and fraud detection and prevention are designed and delivered as dedicated standalone miroservices, ready to scale based on load. This approach ensures that the system remains stable during a DDoS or overload attack.

In order to comply with data security and privacy regulation, we’ve adopted a Privacy by Design (PbD) approach.

Our client, a global TV network company, owns multiple channels that are targeted at viewers from different ages, locations, areas of interest etc.

Measuring engagement is key but the large amount of source data originated from separate audience measurement systems with different reporting formats and different data usage complicated comparison and analysis.

The goal was to enable data-driven decision making and optimization across 30 countries, 120 channels and more than 5000 daily programs.

Our approach was to create a robust, error-immune extraction process, that works with any of the very different data sources in a reliable and stable way.

To ensure correct data comparison a format and structure were designed, and incoming data is transformed.

The transformed data is uploaded to Power BI, enabling end users to create various custom reports and dashboards that result in improved targeting and retention.