Othot provides several tools to help you explore the data in the Analyze tab of the Insights section.
As you analyze your data, you'll find that there are many variables, and that some may or may not be useful to you as part of your analysis. With just a few clicks, you can choose which columns appear in your View.
Note: you can quickly remove a column by clicking the X icon in any header.
Use filters to narrow and display the data you want to analyze. Once you've filtered the data, you can view updated charts and perform more targeted What-if and Sensitivity Analyses.
It can be useful to quickly view summary statistics of quantitative variables. For instance, you may want to know the predicted amount of financial aid dollars to be disbursed without needing to export your data to Excel. For columns with numeric values, measures like sum, average, and count can be easily populated as rows at the bottom of the grid.
Rather than repeatedly applying the same filters or adding the same columns, you can save yourself time by saving view configurations.
While the majority of your analysis can be completed within the platform, you may choose to export data for use within Excel or other applications.
You can view summary statistics by arranging variables and values from your data in a customized table.
Many marketing activities are dependent on having a way to segment large populations into meaningful classifications. This feature takes a data science approach by clustering individuals together into similar Groups based on the variables most important to an outcome of enrollment or retention. These Groups can then be targeted in ways that are meaningful to who they are and their interests.
Groups are created using only historical data where the enrollment or retention outcome is known. Because of this, the criteria that determine a Group will not change unless the underlying model is rebuilt. Once Groups are built, all prior and current students are assigned to the Groups in the grid for analysis and to assist in your decision-making.
Students are first assigned a Groups label by Life Cycle Phase, then by a double-digit numerical value. This value is in ascending order of highest average Likelihood Score by Group. For example, students in the “Inquiry-00” Group will be more likely to enroll than students in the “Inquiry-03” Group on average.
A student’s Group value will update as they progress through the Life Cycle. Their Group value may also update if there is something that’s significant to their current Group value that changes in their data.
You can better understand the similarities of individuals in each Group by referencing the Groups Breakdown visualization. Groups are organized by Life Cycle Phase, which are then sorted by the Phases with the most records to the fewest that are currently present in the grid. Click the arrows next to Prediction Overview until the Groups Breakdown chart is visible.
When viewing the breakdown, you can:
After understanding the similarities of historical students that make up each group, you can see which current students meet the same criteria. This will enable you to make more targeted decisions, such as who to market to and with what message.