Navigating the Jungle: Amazon Data Remains a Hurdle for Consumer Brands

Understanding multichannel performance and profitability remains an existential struggle for consumer and CPG brands, and organizations often fail to develop reliable and automated analytics, especially for those expanding their Amazon.com presence.

Amazon has provided some, but quite limited, help to this end.

Although Amazon’s updated API (the SP-API) — released toward the end of 2021 — offered brands greater data availability over its previous API, sellers on Amazon struggle to pull and analyze their Amazon Seller Central data.

In order to grow more profitably and scale successfully, brands must make a point of investing in their data. They must understand their Amazon data in isolation and as a part of a unified data whole.

How Retailers Try to Handle Their Amazonian Challenges

Challenges around Amazon data impact those in both nontechnical and technical roles, though the particulars may vary depending on the organization.

On the nontechnical side, brands may have a dedicated "Amaz-analyst" who downloads, reformats and combines dozens of Amazon reports, often from 10 or more selling regions, every day.

The result?

Besides arthritic downloading fingers and a substantial Advil budget, the subsequent computer-crashing spreadsheets make Amazon reporting slow and inefficient. Analysts may ultimately spend over half their time trying to format data rather than analyzing and taking tactical action based on Amazon performance.

On the technical side, for brands with data teams, significant struggles remain with automating custom-built ELT via in-house data pipeline builds.

Amazon MWS (Amazon’s previous API) was notorious for stingy rate limiting and presented engineers with dozens of quirks and irregularities depending on the particular endpoints they called.

Although SP-API is an improvement, offering teams more endpoints and reports than MWS, rate limiting remains tricky, and transforming Amazon data into a usable (and useful) model remains trickier.

A Unified Data Picture at Scale, to Scale

To continue to grow, invest and test at a healthy clip, brands must have a thorough understanding of their multichannel profitability and performance trends. To do so, they must invest in an automated and reliable ELT pipeline, specifically one with a unified order schema to facilitate automated reporting and useful analytics.

Especially as Amazon sales volume grows and brands expand into more regions, Amazon data cannot remain a manual process. Analysts must be available to run ad hoc analyses and uncover insights from data, not relegated to mashing spreadsheets together by hand.

Amazon remains a fundamental customer acquisition and overall growth opportunity for both scaling and enterprise consumer brands. Therefore, investing in Amazon data and letting analysts do what they do best will pay dividends over time.

Dan LeBlanc is the CEO and co-founder of Daasity, a modular data platform.