The order analytics page enables you to monitor and analyze returns in relation to their source - the orders from which they came.
Gain valuable insights into how orders impact return patterns and customer behavior, helping you make better decisions about pricing, products, the customer experience, and more.
Understanding your return data in relation to order data and analyzing your store’s performance can help you optimize your returns process to improve profitability and efficiency.
What is Order Analytics?
Order analytics are a way of tracking patterns and trends in your returns in comparison to your orders, and identifying opportunities for further improvement.
All your orders and returns data is displayed in a clear, easy-to-understand way on your ReturnGO analytics dashboard, using pie charts, graphs, and numbers to highlight key information.
Accessing Your Order Analytics
You can find your order analytics on your ReturnGO dashboard under Analytics > Orders.
To analyze your order data, select a timeframe to filter by. The analytics on this page will be based on orders placed during the selected timeframe.
Please note that this is different from the return analytics page, where the data is based on returns made during the selected timeframe.
What Can You Learn From this Page?
The page has three types of data in it that can help you identify order patterns and specific products that lead to returns.
Return Rates
At the top of the page, you can see an overview of your return rate, number of ordered items, number of returned items, and information about the monetary value that was ordered, returned, and generated.
This gives you an idea of your overall numbers.
You can use the return rate chart to track your return rate trends over time. This can help you identify any seasonal trends or other factors that may be affecting your return rate.
On this page, you can track return rate trends and answer questions such as:
- Is the return rate in line with your expectations?
- Which products have a higher return rate than average?
- Review the return rate trend - is it stable, growing, or are there sudden spikes?
- Identify problematic sales and dates with higher return rates.
You can also use filters to identify peaks of higher return requests and discover which order types are more associated with resolutions, enabling you to fine-tune your return policy effectively.
Order Types
Track the return rates for different order types, such as full-price orders, discounted orders, and orders with returns. This information can help you understand how different order types affect your return rate.
For example, you may find that you have a higher return rate for discounted orders. This could be because customers are more likely to return items that they didn't pay full price for.
You can also see the impact that ReturnGO-related orders (exchanged orders, and store credit and gift card redemptions) bring to the table in terms of customer retention and new revenue.
Take a look at things like:
- Do you have a different return rate for discounted products compared to full-price purchases?
- Do the order price and items in the order impact the chance of a return? Should you customize your return policy accordingly to reduce returns?
- How much new revenue do you get from exchanges and store credit redemption?
- What products do customers buy most when using return-related store credit or gift cards?
Products
View the return rates for individual products. This information can help you identify products that are more likely to be returned, so you can take steps to improve the quality of those products or to provide better customer support.
You can also use the return reason distribution chart to see the most common reasons for returns for each product. This information can help you identify problems with product batches, sales, and campaigns.
Analyze patterns like:
- Do you see a trend of a certain return reason with a specific product? Might it be a problematic batch? Maybe it should be recalled?
- Are there recurring delivery issues? Are there some days with a peak of “arrived late” or “not delivered” return reasons?