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Consline AG’s AI-supported Big Data Platform CIMS 6.0 Leads to Quantum Leap

Many companies put considerable effort into receiving customer feedback from different channels: Dealers, warranty cases, call centers, discussion boards and social media, market research etc. These information offer a huge potential for the improvement of products and services. However, these data have very different structures and formats, i.e., a comprehensive and integrated use is only possible with considerable expenditure of time and requires the creation of new documents with the compiled findings. This is why these information have only been used selectively so far.

Based on its long-time experience with the analysis of unstructured quality data, Consline AG has extended its Customer Insights Management System (CIMS) in the now released version 6.0 to a platform for the integrated use of all customer feedback channels (see fig. 1). The content of the different input sources can be comprehensively analyzed both quantitatively and qualitatively. The basis for this is the 4C methodology developed by Consline, which makes all information and data comparable, categorized, condensed and countable (see fig. 2). Via configurable APIs, all languages and formats (text, image, audio, video) can be imported – as a full service offer or as a basis software as a service version (SaaS).

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From the users’ point of view, CIMS 6.0 offers more than 40 new features and functionalities – besides the new user interface with optimized usability and considerably extended functions for complex search patterns, the analysis area was completely redeveloped: Individual dashboards with 29 different diagram types, comparative charts and drill-in from any data point to the original customer statements and data behind them allow for the quick identification of errors, faults and patterns of quality perception.

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Consline AG is a service provider in the area of business intelligence for leading companies from sectors such as automotive, pharma, retail and energy.

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