Five months ago, feels like five years, back in late 2019, I wrote, "CPG companies' online channel is rapidly growing … with sales expected to double in five years." My, how things have changed. In less than one month, there were 35% more e-commerce CPG buyers, Amazon could no longer meet its 2-day prime shipping promise, and I am still waiting for the desk that I ordered two months ago.
Regardless of where things were, the Consumer Goods industry must rapidly adapt now to e-commerce whose rules are being set by Amazon and other major players. Concerning the Amazon-channel, poor forecast accuracy remains one of the largest challenges as it can significantly affect CPG's Amazon sales. But even before the pandemic, leading players operated at less than 50% accuracy. You can only begin to speculate what it is now.
But Amazon's operational practices complicate the matter. While I went into this extensively in another post, a refresh is warranted.
For starters, Amazon must determine which seller gets the "buy box." For those of you unfamiliar with the buy box, it is the buy button for an item. Since Amazon is an online marketplace, there can be multiple sellers for an item, including Amazon, but there can only be one seller attached to the buy box.
Amazon uses an undisclosed algorithm for determining who gets the buy box for a customer visit. Yet, there are three influential factors:
- The item's price (offered by the seller)
- The seller's stock availability
- The seller's reputation and competency: sellers that sell items as “Fulfilled By Amazon” (FBA) are preferred to the “Merchant Fulfilled Network” (MFN).
As seen above, the better the forecast, the better item availability, the better chance of landing the buy box. But Amazon's operational processes make forecasting difficult for many CPG companies. For example, despite their massive warehouses, Amazon doesn't keep large amounts of stock on hand for individual products. As a result, the reordering interval is short. For fast-moving items, Amazon can submit a purchase order as frequently as twice a week.
However, Amazon does provide some unique data sets that can be used to quantify the demand drivers and constraints on historical sales. Some of the data shared includes:
- Glance views: How many times each item's page has been viewed in a week
- Unique visitors: The number of unique visitors who viewed each item's page in a week
- Total number of customer reviews per item, updated weekly
- The average customer review ranking per item, updated weekly
- The ranking of the item's page unit sale and sales amount compared with other items within the same Amazon category or subcategory
- LBB (Lost Buy Box): The number of times that Amazon has lost the buy box to a third-party seller because of the pricing
- Rep OOS: The Number of times that Amazon has lost the buy box because a replenishable item has been out-of-stock
- Sellable and unsellable on-hand units: Sellable and unsellable stock per item per week
It is important to remember that Amazon delivers this item information by the Amazon Standard Identification Numbers (ASIN), not specifically to the individual seller's item, as there are multiple sellers for the same item. However, the information is still very valuable. The number of glance views can reflect the sponsoring of some items during paid promotion programs to bring the item to the top of search lists. Changes in sales ranking can reveal dynamics between the sellers' promotion strategies and other competing products. Another rather unique element to Amazon measurement is LBB (Lost Buy Box), which can be used to estimate third-party pricing strategies as a valuable factor in promotion planning.
These are some unique factors that many CPG companies do not have to consider for traditional offline retailers, thus explaining some of the difficulty. Yet if you know how to use this rich data that Amazon provides, you can obtain a much higher forecast accuracy, improve service level, improve your chances to appear in the buy box, and dramatically increase sales.