Applications of Data Mining to Reward points collection service 

Continuation of use cases:  Some use cases that stand out from the others for reward points collection service include the following.  Classification algorithms are useful for the categorization of reward point assignments based on source attributes. The primary use case is to see clusters of appreciation patterns that match based on attributes. By translating to a vector space and assessing the quality of a cluster with a sum of squares of errors, it is easy to analyze a large number of grants as belonging to specific clusters which can provide insight to management for group dynamics. Reward points grant for a user demonstrate elongated scatter plots in specific categories. Even when the grants come from varying contexts, the time to next appreciation can be plotted along the timeline. One of the best advantages of linear regression is the prediction of time as an independent variable. When the data point has many factors contributing to their occurrence, a linear regression gives an immediate ability to predict where the next occurrence may happen. This is far easier to do than come with up a model that behaves like a good fit for all the data points. Customer segmentation based on reward points is a very common application of this algorithm. It helps prioritize the response to certain customers. Association data mining allows the management of an organization to see helpful patterns such as “employees who appreciated this user also appreciated this other user”. Sequence clustering can be used for patterns of appreciation from the same user. With these examples, it is possible for organizations to understand and appreciate what may be missing from mere performance evaluation from the organizational hierarchy. 

Outliers can also be detected by data mining algorithms where the choices for similarity measures between rows could include distance functions such as Euclidean distance, Manhattan distance, graph-distance, and L2 metrics. The choices for aggregate dissimilarity measures are the distance of K nearest neighbors, the density of neighborhood outside the expected range, and the attribute differences with nearby neighbors. Outliers are important to discover new strategies to encompass them. If there are numerous outliers, they will significantly increase organizational costs. If they were not, then the patterns help identify efficiencies. A Decision Tree algorithm uses the attributes of the service requests to make a prediction such as the relief time on a case resolution. The ease of visualization of split at each level helps throw light on the importance of those attributes.  This information becomes useful to prune the tree and to draw the tree. Logistic regression helps with the determination of user appreciations based on demographics and it can be used for finding repetitions in requests. Neural networks can be used with a softmax classifier to classify appreciation terms in chat text on channels. 

Conclusion: The rewards point service is an investment for the organization where the costs and benefits are both improved by promoting organizational health, satisfaction, and productivity. The use of data mining algorithms on this data also empowers the management with better knowledge of group dynamics.