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Travel Expense Forecasting

BUSINESS CHALLENGE: A CPG manufacturer wanted to leverage their Travel expense reporting system to forecast travel expenses based on historical and known events as well as understand key drivers of change in travel expenses quarter on quarter.

APPROACH: Integrated internal data (expenses by type/unit, etc.) and external data (economic indicators) and developed robust forecasting models at business unit level

BUSINESS IMPACT: Improved budgeting process by providing visibility into forecasted unit level travel expenses during each quarter and gaining insights into key drivers of these expenses

Employee Satisfaction Analysis

BUSINESS CHALLENGE: A technology company was seeking answers to the following questions – What is my overall employee satisfaction score? Which departments are more satisfied and what are the significant drivers of satisfaction? Is there a correlation between staff satisfaction and sales?

: Developed econometric models using data from employee satisfaction surveys to identify factors that drive satisfaction and the correlation with sales

BUSINESS IMPACT: Client enhanced their employee satisfaction by focusing on critical factors that drive employee satisfaction and this resulted in higher retention and productivity

Customer Retention

BUSINESS CHALLENGE: A retailer wanted to identify shoppers who are most likely to switch to competitive retailer and also understand the triggers of switch so that they can pro-actively retain the high risk customers through marketing communications APPROACH: Developed customer value segments from purchase data and then developed a propensity model on POS and loyalty card data to identify the customers who are most likely to migrate from higher value segments to the lower value segments, and, therefore have the highest the risk of attrition BUSINESS IMPACT: This analysis enabled the retailer to identify customers with the highest probability of attrition and reduced targeting campaig...

Customer Growth

BUSINESS CHALLENGE: A retailer wanted to develop a scoring algorithm to identify customers who have a higher propensity to buy from among dormant accounts for effective targeting

APPROACH: Developed algorithm using propensity modeling, scored loyalty members on propensity to buy and selected those who have the highest propensity to become frequent shoppers

BUSINESS IMPACT: Reduced direct mail campaign costs by targeting customers more likely to become frequent shoppers rather than mailing all customers

Customer Acquisition

BUSINESS CHALLENGE: A retailer wanted to use POS data to determine which items have a higher propensity to attract shoppers to the store and also identify which households have the highest propensity to convert into frequent shoppers.

APPROACH: Integrated POS data with variables such as loyalty index and store ratio and then developed propensity models for customer acquisition

BUSINESS IMPACT: Client leveraged insights to design cost effective and efficient campaigns for customer acquisition..

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