Accenture Global Services Gmbh

Astrid Bohe

Matteo Maga

Paolo Canale

Gianluca Cervini

Gianluca Zobi

As a company expands its businesses to include digital products and services in addition to more traditional telecommunications and media offerings, digital consumption data may be used along with telecommunications consumption data and other customer-centric information in order to create a more comprehensive data model and make better predictions. A customer-centric data model identifies entities with data from diverse locations and product sectors associated with a single customer. Customer analytic records aggregate existing data into base variables and store it for each customer along with additional variables generated by performing calculations on exiting data. Both the data model and the customer analytic records can be used by the company to make more accurate business and marketing decisions.

Digital consumer data model and customer analytic record

A method and system that provide analytical tools for increasing average revenue per user (ARPU) allows users to design and execute marketing campaigns that target customers with a statistically significant likelihood of accepting a marketing campaign offer and generating the greatest increase in revenue. The method and system creates statistical models to determine an individual customer's propensity to respond positively to a campaign and propensity to generate increased revenue. The method and system scores the customers according to the customers' propensities, and uses the scoring results to select an optimal mix of customers to contact during the marking campaign.

Identifying target customers for campaigns to increase average revenue per user

A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to churn from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets.

Churn prediction and management system

Systems and methods are provided for analyzing a business's profitability. A system for analyzing customer profitability includes an interface for receiving customer, revenue and cost data from external business operating systems. Customer, revenue, and cost data are stored on a database. A data transformation module operates on the customer data, allocating revenue and costs to individual customers. A customer profitability engine calculates the profitability of customers based on the allocated revenue and cost data. A report module generates reports illustrating the profitability results and other key performance indicators. Optionally, an average customer lifetime value may be calculated based on the customer profitability results. A method for analyzing customer profitability includes identifying sources of revenue, identifying sources of costs, and establishing rules for allocating revenue and costs to individual customers. The method further includes receiving actual customer, revenue and cost data and allocating revenue and costs to individual customers according to the allocation rules. Customer profitability and KPI values are calculated for individual customers and reports are generated illustrating the profitability results and various KPIs for individual customers or groups of customers sharing a common characteristic.

Customer profitability and value analysis system

An enhanced business reporting methodology and system architecture provide a way to receive, store, and manipulate customer and revenue data to increase average revenue per user for a business. The system and method analyze a business's current revenue streams and historical revenue trends to identify those revenue streams that may benefit from stimulation, assess the effectiveness of past marketing campaigns, select a revenue stream and strategy to increase the revenue for the selected revenue stream, select a previously successful marketing campaign consistent with the selected strategy. The system and method analyze the customer base to identify characteristics of customers who have responded positively to previous marketing campaigns and whose revenue has increased as a result, in order to create statistical models to identify customers with similar traits, and apply the statistical models to score and select customers to contact during future campaigns.

Growing community of inventors

Kronberg, Germany

Astrid Bohe