Proof of Concept Projects
Plan before you build
Plan before you build
Prior to making a serious commitment to analytics, you might wish to check whether analytics indeed can generate value for you. In addition to verifying this for yourself, you need to demonstrate the usefulness of analytics for your colleagues. Recognizing the importance of this approach, Megaputer offers carrying out a proof-of-concept (POC) project based on a sample of your data. By creating a custom prototype solution we help the customer determine the real value of analytics for their specific task.
We need to learn your objectives, business processes, and data, so that we can prepare a proposal for carrying out a POC project for you.
To get the POC project started, give us a call to discuss your project. Depending on the scope of the project and the comprehensiveness of results you are seeking, the development of a POC project might take us from a few hours to a few weeks or even months. Let us discuss your project and we will compile for you a realistic plan for moving forward.
A major fast food company was striving to improve overall quality and customer satisfaction. It decided to track the performance of the chain at the national, regional, city, franchisee, and individual restaurant level by analyzing customer reviews including customer survey responses and complaints. With thousands of locations to manage, the company desired uniformity, transparency, and timeliness in their performance measurement process.
To excel in customer service, a large financial company wanted to monitor its associates’ performance. It used over 40 performance metrics to evaluate the online dialogues of 300+ associates in its call center for supporting customers executing complex financial transactions. With 1.3M+ free text dialogues recorded annually, automated analysis was the only viable option.
To identify potential subrogation opportunities, a large P&C insurance company was relying on a team of analysts who manually read claim notes. This process however resulted in a high percentage of missed subrogations. The company was seeking an automated solution for extracting relevant information from claim notes and using it to identify good subrogation opportunities across all claims accurately and sufficiently early.
Patient electronic medical records (EMRs) hold a wealth of information, including doctor and nurse notes as well as lab test results. Hospitals and physicians can use this information for improving patient care. However, a large portion of the EMR data is stored as unstructured text. The complexity of textual data analysis, combined with the complexity of the medical domain per se, implied that only specially trained medical professionals were able perform manual analysis of this data, making the process of knowledge discovery in EMRs very slow and expensive.
A US company specializing in the development of medical technologies and devices decided to implement a more efficient way to monitor market and technological trends, follow key activities of competitors, and identify potential acquisition targets. The company was planning to use the results of competitive intelligence to increase its market share and boost the annual revenue.
A major pharmaceutical company needed to analyze data streaming from its customers across the world: over 1.5M text communications received annually through multiple channels including call centers, conferences, online chat dialogs, and focus groups. It wanted to identify the most popular topics for existing drugs, and automatically detect key topics for recent launches.