Buy Back Comparison Analysis

Modified on Tue, 2 Jul at 10:52 AM

The Buy Back Comparison Analysis dashboard, different from the Buy Back Activity dashboard, is intended to give you a tool to compare time periods by store & product category. You will want to look at your data in longer time periods to capture patterns and trends. Start by selecting two time periods for the comparison, and specific stores or product categories if your focus is targeted to a sub-set of data. If you are interested in specific buy back grades, select from that filter as well - note that because sales are not tracked by the buy back grades, the sales & profit metrics and graphics on the dashboard will not respond to that filter. Depending on your historical buy back data, you may have some grades that refer to reversals and store transfers - at the bottom of the dashboard you will see two tables with the counts and reference notes for any that are in your data.

At the top of the dashboard you will see a few KPI metrics to give you a high level idea of your average profits. Next you'll see a couple of scatter charts that show where your average profit dollars intersect with profit %, both by store and product category. You can use these scatter charts as filters for the dashboard by clicking on a store or product category that is of interest. Also, you can toggle the points in these charts on and off by clicking on the legend for the chart - if a single outlier dominates the chart, you may want to toggle it off to see the rest of the data come into sharper focus.

Be aware of volumes when looking at your average profit margins. You may, for example, see a very high profit margin for a product category and find that the volume of sales is very low. In contrast, your best sellers volume-wise might have a lower margin yet be a significant contributor - challenge any assumptions you have when looking at this data.

The pivot tables allow you to compare your buy back metrics between the date ranges you selected in the filters. By right clicking in the pivot table you are able to add and remove fields to expand or contract the granularity of the pivot table. For example, you may want to see your metrics only at the store level, or you may want to expand that to see categories within stores or stores within categories. You can add the buy back grade (ex. A, B, C, D) as either a row or a column on the pivot table. Remember that you are able to filter the overall data in the report using the filters to the right of the dashboard. Note that when you filter to grade D, damaged buy backs, the quantity will be associated with a generic damaged item & category.

A second pivot table in each section allows you to see the buy back quantity determined to be damaged, by store, category & date. Note here that the quantity in the second pivot can include items from both 'buy back' transactions and 'declined buy back' (and other) transactions, so when you filter to Buy Back Grade D, the total will not always be 1:1 with the 'Buy Back Used Quantity' in the first pivot. The quantity counted in the second pivot is form a separate transaction that is created for damaged scans, including a relevant item description.

Several line graphics are below the pivot tables to help you visualize your two year trends - note these date ranges are preset. The graphic on the left of each section is the overall two year trend and to the right is a year over year view by month. Again, you can use the filters to narrow what appears in the trends. Look for patterns in the YoY graphic, especially among the different metrics. For example, where your buy back quantity increases, how did your profit margins look? That will no doubt relate to your buy back dollars - using the filters or the scatter charts, you can find how your stores or product categories are contributing to those margins.

You can export the data in these graphics and tables, to work with it outside of the dashboard. Do this by clicking on the 3 dots at the top right corner of the widget, choose Download and export an image or file. The recommended file type for a spreadsheet is CSV.