Advanced Catalog Analysis
Advanced Catalog Analysis deep dives into the catalog characteristics which can help troubleshoot the inventory, sales and product category related issues. Adyogi’s Advanced Catalog Analysis feature is available only for Shopify Clients as of now.
It broadly covers two aspects: Category level Analysis and Inventory Level Analysis.
Category Analysis
A. Category Sales Split
This Page gives us the Detailed view of Month and Category level Sales insights. It is helpful in comparing current month vs last 2 month’s category level sales split.
It answers the question “ Which of my categories contributed to the most revenue this month vs last month?”
From the above screenshot we are able to understand that Coord sets are contributing the most revenue consecutively, so it would make sense to promote the best sellers from the said category seperately via custom ads in both Facebook and Google.
B. Product Age Analysis
This Page gives us the Detailed view of Revenue contributed by Different Age groups of Products. It helps answer questions like “ Are my new arrivals working better or worse than the products which have been in inventory for more than 3 months ?”
From the screenshot above we can infer that the newer set of inventory is generating more revenue in January as compared to older ones. We can try giving/increasing the discounting on those products so that the revenue generated from older inventory also increases.
C. Best Seller Analysis
This Page gives us the Best Seller for Selected time Range with their Corresponding Current Inventory. It helps identify the best seller SKUs and whether they are at risk of getting out of stock or have good inventory.
The products where Days of Inventory is highlighted in color Red are the products which are at risk of getting out of stock. We can stop promoting those via custom ads if replenishing the inventory is not feasible. Otherwise it is always recommended to keep the Best sellers in stock.
D. Discount Analysis
This Page shows us the Revenue and Units Sold under Different Discount Slab, Month on Month. It also shows the daily discounting trend which can be very helpful in troubleshooting performance. It helps answer questions like “ Was the drop in performance on a certain day due to the drastic fall in Average Discounting ?”
We can infer from the above screenshot that (11-20) percent discount slab is contributing the most to revenue and is working out the best for the brand. So giving the same discounting on non performing categories may work out for us.
Inventory Analysis
A. Product Activeness Analysis
This page gives us detailed analysis about product like active, OSS, Full size, Cut Size product count. It gives us chart and table view which shows us the Active product trend for last 30 days and analysis on Active vs Full size vs Cut size. It answers the question “ what is your Active percentage of products and Cut size percentage?”
B. Variant Activeness Analysis
This page gives us detailed analysis about variant intense ratio, and activeness count. Variant Intense ratio is calculated by dividing the Active variant by Active product. This view help us to analyze the Active variant trend and Variant intense ratio for last 30 days. It will also shows age wise Variant availability for today.
C. Catalog Discount Analysis
This page gives us total product count, total discounted product count and average discount at category level. It shows us the Average discounting trend and discount percentage for overall variants available in the catalog. It also shows us the overall count of products, discounted products and the % discounted as of today's data.
D. Sell Through Analysis
This page gives us the total inventory, total inventory value and sell through rate at category level. Sell through rate is calculated by dividing the actual units sold by total units available. It will also shows us the value of the inventory available in hand.
