BIP developed an actionable framework able to activate a Customer Experience measurement through the investigation and analysis of User Stories, identifying measurable KPIs to be converted into qualitative indicators and specific enhancement actions. Traditional measurement methods (e.g. surveys, questionnaires, …) are typically partial, biased or unable to determine the key drivers that impact the Customer Satisfaction. BIP framework bypasses all this, as the analysis is completely based on the tracking and monitoring of Customer interactions with the company’s touchpoints, so it is fully data-driven. BIP framework enables to effectively verify the actual measurement of the Customer Experience, to define Customer Personas automatically from customers interactions data, to identify the root-causes that impact the Customer Experience and to support the Organization to devise actions to correct pain points. Furthermore, by leveraging on proprietary Machine Learning Clustering algorithms, BIP framework hides the complexity of the analysis turning a highly complex problem into a simple yet valuable opportunity to catch.
This is where BIP and Celonis combine their strengths:
Define new KPIs for CX measurement and monitoring, which can be Customer Personas-specific or “generic” for the whole Customer Base
Represent customer process diagrams starting from data, and autonomously inspect the Customer Experience Maps
Leverage on Machine Learning to cluster together similar “customer behaviors" and automatically support the definition of client-specific Customer Personas starting from interactions data
Perform root-cause analysis on events of interest, to understand the origination of a “bad” CX and find insights to define corrective and preventive actions
Identify, implement and integrate clear data-driven automatic or manual actions (and monitoring metrics) to enhance the Customer Experience, generically or personas based
A snapshot on the main characteristics of the monitored Customer Base, exploring their summary statistics and providing an overview on the Customer Personas’ high-level KPIs
Allows the exploration of the main interaction pattern KPIs (length and duration) and to analyze the preferred interaction channels for each activity, for each selected Customer Persona
A detailed dashboard presenting the main KPIs describing each data-driven Customer Persona discovered with the behavioral clustering algorithm
Allows to perform side-by-side comparison between different customer personas, both in terms of journey maps and other socio-demographic characteristic distribution.
Allows the exploration of unique customer Journey Maps (variants) for each selected Customer Persona
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