At DataVal Analytics we have been a part of solving many business problems across different domains. In our interaction with them, we learn that there are many business problems that are allowed to fester because our clients feel that there is no conceivable solution or that they feel that the solution is too expensive and hence not worth the effort. We also know that there are many proprietary solutions in the market that claim to solve such problems. Through their experience we have learnt that a one size fits all approach does not work. It may provide a partial cure but is definitely not a panacea for all business problems. Even despite investing money and resources in proprietary analytics products the problem remains unsolved. And that is where DataVal Analytics makes a difference.

What differentiates us from other competitors in the market is our "unique approach to problem solving". We carry out a deep dive into the problem space, leverage our experience and provide anĀ optimal solution that far exceeds the requirement of our customers. Our approach has left behind a string of success stories that has helped us gain confidence in our methodology and skill set. Given below are some of the success stories that speak about the capability of the DataVal team.


This document is a case study of customer analytics carried out for a particular client of DataVal Analytics. Understanding the customer is crucial for any business to grow and hence analytics plays a significant role in building a complete picture of the customer base. Analytics carried outĀ in this space includes segmentation analysis, churn prediction, geo-spatial analysis & engagement metric analysis with a customer centric focus.

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The document is a case study of a project implemented at DataVal Analytics involving yield prediction of different crop types across a given geographical area. The prediction pipeline custom built for this purpose, integrated data from multiple remote sensing and weather forecasting sources to estimate the type and health of a crop. The multiple data points measured across sample locations on a daily basis were used to predict the crop type, acreage and yield across a region.

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