This article explains the purpose of datasets and how they can support different analytical needs. Below, we've grouped key dataset reports by their use cases to help you determine which reports to use independently or in combination.
1. Product and category performance
Assess brand performance at product and category levels.
Financial summary of returns: Track financial performance linked to returns.
Return request details with current tracking status: Analyse product and category return rates.
Return rate by market: Evaluate return rates across major markets.
Partial and full return statistics: View historical order vs. return data.
2. Financial Impact Analysis
Gauge revenue loss due to returns with the help of following return statistics:
Financial summary of returns: The value of the orders returned, inspected (not refunded) and not refunded by the return request date to help you forecast your refund
Refund transaction report: Get an overview of lost revenue due to refund and store credit per order ID, processed via yayloh
Return shipping label details: Use this to track labels generated in yayloh and cross check with your 3PL shipping label generation invoices.
Collection level report: Review revenue vs. losses per collection.
3. Marketing campaigns
Enhance customer engagement and run personalised campaigns.
Customer comments: A list of all comments made by each customer for each SKU. Utilise customer emails and comments from the 'Customer comments' dataset to run personalised campaigns.
4. Returns forecast
Sometimes you want to know your incoming returned product to plan your inventory and adjust customer expectations for exchanges. For example, an exchange for a product that is out-of-stock in the warehouse is requested. With this dataset, you can see if it is coming back in stock and will be available for exchange.
Incoming returned products by tracking status: Track returns to optimise stock levels.
Return request status: Monitor ongoing and pending return to utilize your headcount efficiently on daily basis.
Possible return based on double sizing: Predict future returns for specific products.
5. Root Cause Analysis
Do you want to know what went wrong? Use the following reports to know:
Customer comments Use the information to know what your customers think about the product and find the most common reason a product is being returned. Update size guides and support your design teams by analysing the most frequent return reasons and comments
Product level report: Use this report to analyze the most returned product variants. know your products return rate to zero in the issues with the products.
Top reason by product: See the top reasons and number of return count.
Top 100 returned products: Find the root cause for top 100 returned products.
6. Custom analysis
If you are an analytics geek and want to create your own reports, access to:
Daily google export: Access additional raw data.
Return request details with current tracking status: This dataset is also rich source of data, which you can use to build your own reports.
7. Efficiency/Productivity
To assess the efficiency of your internal teams, see for below reports:
Inspection and refund speed statistics: Monitor your team’s efficiency in processing and handling returns.
Inspection status: Use this to see the efficiency of your Warehouse teams in performing inspections.
Return request status: Track daily return request handling to optimise team performance.