The efficient path to an integrated customer experience

Unify and utilize data about customer touchpoints, sales, marketing and service more effectively than ever before

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What is the customer touchpoint analytics?

Customer touchpoint analytics (CDA) is a data management and analysis solution that uses big data technologies to integrate diverse types of data from various sources in an extremely efficient way. The technology is able to access a broad range of data sources and enables data analysis on a level of flexibility not possible with traditional methods. This creates to a cost-efficient unified view on customers, customer touchpoints and channels.


Customer touchpoint analytics is optimized for processing and analyzing any kind of customer-touchpoint data and data related to sales, marketing and service. It’s built on proven, open data management platforms but adds specific extensions for sales, marketing and service use cases.






Data management technologies like Neo4J or Spark open up new possibilities for scalable data processing, extensive data ingestion and modern data science.

But the underlying technologies are complex, require special knowledge and raise many architectural questions.

At the end of the day, the goal is to generate business value as quickly as possible. This is why we combined best practices and the best components to put together a package that ensures fast time-to-market for data projects in sales, marketing and service.

Benefits

Rapid Time-to-Value

Fast business value through focus on uses cases in sales, marketing and service with a functional solution

No Lock-In

Long term extensibility, maintainability and integrability through open standards and architectures

Start Small & Expand

Enables faster and more agile implementation of use cases than traditional analytics technologies

CUSTOMER EXPERIENCE MANAGEMENT

Deep knowledge about customers with actionable recommendations

Customer touchpoint analytics is a solution specifically for sales, marketing, service and customer analysis. An AI engine constantly scans and links the data and detects customer risks, revenue potentials and leading indicators for specific customer behavior. Based on this metadata, the AI proactively generates recommendations that improve daily operations. For instance, the customer journey can be steered proactively with next best actions.

DATA SCIENCE

Predictive insights into revenue potentials and hidden risks

Customer touchpoint analytics enables faster and deeper analysis of a wide variety of data structures and types (e.g. tables, text, images, IoT). A key component in this area is machine learning and advanced analytics. These technologies make critical patterns, early warning indicators and untapped revenue potentials with customers or processes transparent. The way data is stored and managed make it particularly flexible. Solutions can be built incrementally in shorter iterations. Data scientists can realize truly agile data science projects.

DATA MANAGEMENT PLATFORM

A foundation built on open standards

Customer touchpoint analytics encompasses knowhow and best practices that save valuable time during projects and lower risks significantly. Because the underlying technologies are de facto industry standards, there is no vendor lock-in and dependence. The platform can be extended and customized freely. For instance, it can be used as a staging area to feed third party business intelligence systems.