Identifying Your Best and Worst Metrics Fast

When analyzing the data, it is often important to see how specific answers rank not only for one specific product or for the total market, but also to see how the ranking looks across multiple elements. Have you ever wanted to see what the top 5 most important reasons for purchasing a specific product are? Or what are the 3 biggest brands in each of your markets separately? For these use cases and a variety of other similar problems, Halo offers dedicated solutions that let you cut through the noise in huge data tables and get straight to the meaningful insights fast.

If your preferred reporting output is charts, the authoring mode in Reports offers a built-in Frequency Analysis visualization that automatically transforms a cross-table with two questions into a visually appealing ranked chart.

The visualization offers a dedicated set of settings that will allow you to tune the chart according to your needs. If you’re looking into the most important purchase reasons across the brands on the market, you may either generate a separate ranked list for each brand or focus on your main brand of interest and see how other brands compare to the results of this benchmark.

If you’re more comfortable with tabular outputs, worry not, the Top/Bottom N analysis is easily accessible for any categorical question within our standard Crosstab and Pivot tables. For any query output you have, you are just a few clicks away from a ranked overview. Flexible query building options make it possible to integrate Top/Bottom N even for more complex scenarios.

This feature can also be used to catch the best and worst numeric metrics across a given dimension. In a quite common scenario, your data can include a variety of satisfaction variables, focusing on different aspects of your products. It can be quite tedious to go through all the results for a multitude of such questions to get to the valuable insights manually. With the Top/Bottom N option, you can easily rank the numeric question in the same way you would do with the answer within a categorical question. As a result, you can get a table that exposes, for example, the 3 best and 3 worst satisfaction metrics for each of your products.

Such a table can include a lot of information, potentially requiring a more thorough examination to identify the insights needed. In many cases, this can be simplified by using Color Palettes to enhance the readability of the table. Color coding will allow users to visually follow specific elements across the whole table, immediately identifying more common options.

Or you can track your target profile statements across various models, to quickly catch where it is part of the top, and where it did not reach the expected levels.