Survey Data Analysis Solution Helps a Sales Management Consulting Firm Reduce Manual Effort by 47%
The client provides data-driven insights, actionable recommendations and results to its customers who need better sales ROI and improved revenue. The client collects relevant data from their customers and performs analysis on the collected data.
Extensively lengthy manual processes were used to conduct analysis of the collected survey data from different sources.
The client needed an integrated business intelligence solution that would help them gather, store, and analyze the data provided by their customers. They wanted to develop a survey data analysis solution to help them perform sales benchmarking, with powerful yet intuitive survey data analysis.
Solution and Approach
Indusa built a data warehouse system, based on the client’s specified requirements, to allow their employees and clients to display, analyze and export survey reports from the client’s survey data warehouse.
The data structure was designed and developed based on the functional requirements from the client, after evaluating the survey results of various customers. The data from Excel spreadsheets was transformed and inserted into tables to generate a common data warehouse.
A robust database security structure was also provided in the system that allows restricted access to the data warehouse by the admin and IT maintenance departments.
The system uses Microsoft’s SQL Server Integration Services (SSIS) component that has the ability to perform extract, transform, and load (ETL) the data.
SQL Server Integration Services (SSIS) enables:
- Transforming data from Microsoft Excel into SQL Server using ETL packages
- Enhanced management for multiple services, including sales cost benchmarking, sales time benchmarking, and sales quote performance and benchmarking
- Facilitating functionalities like error logging and assessment of duplicate records
New Survey Upload
The survey data analysis system helped the client effectively solve the complex problem of pulling data out of transactional systems and converting that data into actionable information, for instance, processing of large and complex queries in a highly-efficient manner.
Since the users of the systems can quickly access critical data from a number of sources, all in one place, they can discover deeper insights, make predictions, and generate recommendations. This has reduced the manual efforts by 47%, and helped the client achieve 33% savings in time and cost.
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