Eric Coats shares practical advice for Amazon sellers, including how to analyze returns, repeat purchases, and inventory to improve business decisions.
Eric Coats is most useful for practical Amazon seller advice on returns, repeat purchases, and inventory decisions.
Best for
Amazon seller growth agencies or home-goods brands needing diagnostic help with returns and inventory.
Not for
Consumer brands or creator-platform companies seeking paid LinkedIn creator campaigns to reach marketing leaders.
Key takeaways
January returns can include purchases from earlier months, masking January order return rate.
Match return order IDs to purchase months and compare cohort rates with reported rates.
Use return reasons and reviews to find root causes, then change pages, packaging, manufacturing, or service.
Check first: Ask for client references, engagement terms, and measured business outcomes beyond illustrative figures.
Main ideas
Platform-reported numbers can hide what is actually happening · Reduce returns by finding root causes, not by watching the average · Repeat-purchase data should set how much you spend to acquire customers
Eric Coats shares practical advice for Amazon sellers on LinkedIn.
The posts show how to use seller reports to investigate returns, repeat purchases, inventory, and B2B orders.
They are especially useful to readers who want to understand what a metric does—and does not—show before deciding what to change.
Coats connects Amazon data to concrete operating decisions.
In one post, they explain how returns recorded in January can include purchases from earlier months, masking the actual return rate for January orders.
The post walks readers through matching order IDs to purchase months and gives contrasting reported and cohort rates from a client example.
Other posts turn platform reports into next steps: investigate return causes through return reasons and reviews, use repeat-purchase trends to inform acquisition spending, and check aging inventory by SKU.
These are practical, concise walkthroughs rather than broad theory.
One post also points readers to a newly visible business-name field in B2B orders, while a separate post about referring a friend for creator-industry roles shows that the posts are not exclusively about Amazon operations.
These examples make the posts worth following for sellers seeking specific ways to interpret Amazon data.
Eric Coats writes short, practical posts about Amazon seller operations, mostly under a “Boring Amazon Hack” label.
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The premise: mundane reports and dashboards sellers ignore are where money is won or lost.
Across the posts, the argument recurs in different forms: a platform's headline number is a starting question, not an answer.
A reported return rate mixes purchase months; an average hides the products driving it; a repeat-purchase percentage matters once translated into how much a brand should pay to acquire a customer; an inventory figure represents a capital cost, not just a stock count.
Coats's method is to pull the specific report, recut the data at the level that matches the decision — cohort, SKU, or product — then act on listings, packaging, service, pricing, or budget.
They also flag platform changes as they appear.
Platform-reported numbers can hide what is actually happening
Coats distrusts headline metrics until they have been rebuilt from order-level data.
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Amazon reports a monthly return rate as units returned divided by units sold that month, but January returns include holiday purchases from earlier months.
Sellers who accept the reported figure can mistake a healthy product for a problem, or miss a real one until later in the quarter.
Coats recommends matching each return to its purchase month, then calculating the rate for each purchase cohort separately.
For a client, the reported December return rate was 4.6%, but the December cohort was actually 6.6%. January looked alarming at 22% reported, yet the January cohort was only 2.6% — and some January returns traced back to purchases made as far back as September.
The same instinct shows up in a later post about inventory: Coats points readers to trailing metrics and SKU-level data, not a single summary figure.
Reduce returns by finding root causes, not by watching the average
Coats frames returns mitigation as both a profitability and customer-experience project: fewer returns mean better margins, happier customers, better reviews, and more sales.
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They recommend a four-step loop: check rates, identify products with excessive returns, determine root causes from return reasons and reviews, then address them. Coats also tells sellers to read neutral and positive reviews for complaints, even when overall sentiment looks fine.
Coats treats roughly 3–4% as a general prompt to investigate in many categories, noting that apparel runs higher. Possible responses include clearer product detail pages, manufacturing or packaging changes, and enabling Amazon's customer-service features so buyers can reach the seller.
Whether a brand retains 4% or 40% of customers should dramatically change what it is willing to pay to win a new one.
Coats also treats the trend as a diagnostic: a falling repeat rate may point to competition, quality, or negative reviews, while a rising one may reflect promotions, tentpole events, or a strong product.
Possible next steps include running a Subscribe & Save coupon to encourage a second purchase, correcting a manufacturing issue, or developing a version two of the product.
Aging inventory is a capital problem, not just a stock count
Coats points to Amazon's Inventory Analytics dashboard to see aging, aged, and total inventory against fulfillment and sell-through.
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They focus on the costs: what stagnant stock is costing the business, what the platform recommends, and how the data breaks down by SKU. This framing turns inventory from a warehousing question into a cash-flow and pricing question.
The dashboard pairs inventory levels with the cost of aging stock, platform recommendations (usually to lower the price), and per-SKU data.
Once you can identify a business buyer, support the relationship directly
Coats describes a platform change that matters mainly for business-to-business sellers: Amazon historically did not report the business name on B2B orders, making it hard to give business customers the support they need.
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A buyer-company-name column now appears in the All Orders report, allowing sellers to identify the purchasing business and handle post-purchase and technical support more directly. Coats sees this as a route to retaining high-value accounts, while drawing a clear line at Amazon's rules: customer support is allowed; soliciting Amazon customers off the platform is not.
Coats says they spent hours manually cross-referencing truncated addresses on B2B FBA orders to guess which business had bought. The new column removes that work.
The post describes a platform change Coats discovered the same day, showing how the tooling they rely on can shift.
Move beyond basic reporting and understand the statistical traps in major e-commerce platforms.
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This post offers a practical, step-by-step method for analyzing seller health.
Amazon’s standard monthly return rate can be distorted by seasonal sales: holiday returns from previous months may be processed in January.
Eric Coats explains that relying on the standard metric can mislead.
Cross-reference returns data with the original purchase month in a Cohort Returns Analysis to determine the true return rate for specific cohorts.
Using this method, a reported January return rate of 22% proved much lower when properly segmented.
For brands planning a campaign
A campaign focused on seller metrics or supply chain health could use this methodology to show how detailed analysis uncovers hidden inefficiencies and unexpected strengths.
Newcomers can use this actionable framework to tackle return rates, a critical financial metric for any e-commerce seller.
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Returns affect profitability and customer satisfaction. Eric Coats outlines four steps to mitigate them: 1) Monitor return rates using Business Reports; 2) identify products with excessive return rates; 3) analyze review data and return reasons to find the root cause; and 4) implement solutions, such as updating product descriptions (PDP), adjusting manufacturing or packaging, or improving customer service processes.
For brands planning a campaign
This guide can inform training modules for sellers or agencies on building resilient product lines and improving customer experience to drive long-term profitability.
Understand how platform-provided analytics can inform business strategy and help guide decisions about ad spend and marketing focus.
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Brand Analytics provides data on repeat purchase behavior, essential for calculating Customer Lifetime Value (LTV). The percentage of repeat customers informs marketing investment strategy: when repeat purchases are low, focus on acquisition; when they are high, the higher LTV justifies investing more heavily in new customer acquisition.
For brands planning a campaign
This data-driven insight gives marketing briefs quantitative metrics to justify budget allocation between customer acquisition and retention.
Sellers focused on the B2B sector should read this to learn about an update that transforms relationship management on the platform.
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Business names were often hidden in B2B transactions on Amazon, making high-touch relationship management difficult.
A recent update to the 'All Orders' report adds a column displaying the 'buyer-company-name' for B2B purchases.
Sellers can now contact purchasing businesses directly for post-purchase support and relationship building, which is vital for retaining high-value B2B accounts.
For brands planning a campaign
For agencies targeting B2B markets, this feature opens opportunities for personalized outreach and retention programs using newly available buyer data.
Coats explains why a calendar-month return rate can mislead: January returns may come from holiday purchases, masking the rate for January orders.
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They show how to match return records to order dates and compare rates by purchase cohort. In their example, December’s cohort return rate was 6.6% against a reported 4.6%, while January’s was 2.6% against a reported 22%.
Coats names the Amazon reports to collect, explains how to match order IDs with a VLOOKUP, and uses client figures to show how the analysis changes the interpretation.
For brands planning a campaign
A returns-analysis engagement could use this approach to separate delayed seasonal returns from problems affecting recent orders and identify when a product or process needs attention.
Coats frames returns reduction as both a profitability and customer-experience issue.
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They recommend checking return rates, investigating products with unusually high rates, and reviewing return reasons and reviews—including positive or neutral reviews that mention problems. Possible responses include clearer product detail pages, manufacturing or packaging changes, and better customer service.
The post lays out four steps: measure, investigate, identify causes, and respond. It gives 3–4% as a general investigation prompt for many categories, while noting that apparel can differ.
For brands planning a campaign
This is relevant to brands seeking practical returns mitigation, especially those able to provide product-level return data and make changes to listings, packaging, or support.
Use repeat-purchase data to guide acquisition spending
Coats points to Amazon Brand Analytics reports on repeat-purchase behavior across weekly, monthly, quarterly, and yearly views.
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They argue that repeat purchasing and lifetime value should inform how much a business is willing to spend to acquire customers. They also suggest investigating changes in repeat rates for possible links to competition, product quality, promotions, or seasonal events.
Coats pairs a platform report with diagnostic questions and possible next steps, such as a Subscribe & Save coupon, fixing a manufacturing issue, or developing a product version two.
For brands planning a campaign
A growth engagement could connect repeat-purchase trends to acquisition budgets and follow-up actions, rather than treating customer acquisition spend as separate from retention.
Coats directs sellers to Amazon’s Inventory Analytics dashboard to review aging, aged, and total inventory alongside fulfillment and sell-through.
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They note that it also shows aging-inventory costs, recommendations, and SKU-level data.
The post offers a short navigation tip rather than a worked analysis: it points readers to a dashboard and summarizes the metrics available there.
For brands planning a campaign
This provides a starting point for an inventory-optimization engagement: identify aging products, compare stock with sell-through, and assess costs and possible actions at the SKU level.
Use business-buyer information for post-purchase support
Coats describes a newly visible buyer-company-name column in Amazon’s All Orders report for B2B orders.
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They say identifying the purchasing business can help sellers understand support needs and maintain relationships with higher-value business customers. They distinguish permitted post-purchase or technical support from soliciting Amazon customers outside the platform.
Coats explains where to find the report, the change they noticed, and how it relates to a previous manual effort to identify business buyers.
For brands planning a campaign
This may suit brands with meaningful B2B sales seeking to improve post-purchase support and retention. For evidence of returns or inventory analysis, ask for a relevant example.
Amazon is the marketplace whose seller tools the posts examine, but their relationship to Amazon is unstated.
Kapoq is a returns-monitoring tool named once; the post explicitly notes it is not sponsored.
The posts also refer to client work and to a laid-off friend in the creator-partnerships space, but name no business.
Ask about any relevant relationships with Amazon or the unnamed businesses.
Amazon
Mention
Amazon is the subject of five of the six posts, which walk through seller reports and dashboards such as All Orders, Brand Analytics, Inventory Analytics, and Voice of the Customer.
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The posts treat Amazon as the platform being analyzed.
In a post on returns mitigation, Eric Coats names Kapoq as an alternative to Amazon's Business Reports and Voice of the Customer for checking return rates.
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The post carries an explicit hashtag stating it is not sponsored; no affiliate or partnership terms are described.
Eric Coats publishes short, numbered Amazon seller-operations posts on LinkedIn, most of them labelled "Boring Amazon Hack," and the clearest way into the work is to read those by subject rather than in sequence.
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The recurring subject is a platform report that sellers underuse: return rates by purchase cohort, the returns-mitigation loop, repeat-purchase behaviour and lifetime value, inventory aging and sell-through, and the buyer-company-name column that appeared for B2B orders.
Posts are practical rather than personal — they name the report path, describe the steps, and use client or worked figures to show why the headline number misleads.
Readers who want the reasoning behind the tips should start with the cohort returns analysis and the repeat-purchase post; readers who want a shorter operational prompt can start with inventory analytics or returns mitigation.
The material reviewed carries no publication dates, so it supports the ideas but not a timeline of how Coats's positions changed.
Both write about ecommerce retention and lifetime value by unpacking a headline metric.
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Kitchen's audit of a US outdoor brand shows 12-month customer value rising while the repurchase rate falls. A post on two brands above $80 million in revenue shows first-purchase bundling hurting long-term value — the same instinct Coats applies to reported return rates and repeat-purchase percentages.
Kitchen works from Shopify and direct-to-consumer data, building arguments around retention levers such as subscriptions, direct mail and complementary products. Coats stays within Amazon's seller reports, mapping each point to a specific report path sellers can open.
Both explain a numerical idea through a short worked example.
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Mahajan's posts pair a friend's jewellery store and a cousin's cafe with cohort analysis and price-change experiments. That narrative approach echoes Coats's client example, which shows a December cohort return rate of 6.6% against a reported 4.6%.
Mahajan's examples are small, owner-run businesses and focus on price sensitivity and demand. Coats's examples come from Amazon marketplace operations, with named reports and seller-side actions such as listing changes, packaging and customer-service settings.
Both translate Amazon platform changes into what sellers should do next.
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Fishman's Accelerate posts walk through new tools — drone delivery, the Quick assistant, Seller Flex, profit analytics and scenario modelling — and end with an action. Coats follows the same structure: they report the new buyer-company-name column and explain how to use it for B2B support.
Fishman covers announcements and news across the Amazon seller platform, framing them around profit and SKU-level decision-making. Coats writes step-by-step hacks focused on a single report or metric and its arithmetic.
Eric Coats’s posts suit campaigns focused on Amazon returns analysis and inventory optimization, with examples covering return diagnosis, return rates by purchase cohort, and inventory metrics.
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Before deciding on placement, request recent results for comparable Amazon-operations content and documented audience data on geography, age, gender, employer, occupation, seniority, and seller and buyer roles.
Eric Coats specializes in practical strategic advice for Amazon sellers, covering business operations, inventory analytics, true return rates, and repeat purchase behavior.
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Brands interested in creator partnerships or corporate sponsorships should ask about available formats and how to inquire.
Good fit: an Amazon seller growth agency wants a short diagnostic engagement to explain high January returns and tied-up inventory for a home-goods brand.
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Decision: choose Coats.
Their posts show a concrete cohort returns method: pull the All Orders report and returns reports, match return order IDs to the month of purchase, and compare each cohort's return rate with Amazon's monthly figure.
In the worked example, December's cohort return rate was 6.6% against a reported 4.6%, while January's was 2.6% against a reported 22%.
They also give a four-step returns mitigation sequence: measure rates, flag products above roughly 3–4% for investigation, determine root cause from return reasons and reviews, then change product detail pages, packaging, manufacturing, or customer service.
For inventory, their posts point to Amazon's Inventory Analytics dashboard for aging, aged, and total stock, sell-through, aging-inventory cost, and SKU-level recommendations.
This is directly useful to an agency assessing a diagnostic engagement for a home-goods brand.
Before contracting, ask for client references, engagement terms, and measured business outcomes beyond the illustrative figures in one post.
When to choose a specialist
Poor fit: a consumer brand or a creator-platform company wants a paid LinkedIn creator campaign to reach marketing leaders, with audience guarantees and sponsorship deliverables.
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Decision: reject based on what Coats's posts say.
They do not describe sponsorship formats, rates, or an inquiry process, and provide no measured audience geography, age, gender, employer, occupation, seniority, reach, or engagement data.
Without that information, a buyer cannot judge delivery or compare the offer against another creator.
The job-referral post shows they will connect someone in the creator and content-strategy space, but that is a personal favor, not a commercial format.
Before reconsidering a paid LinkedIn campaign aimed at marketing leaders, request documented sponsorship examples, platform or survey audience analytics, and a rate card.
A worked campaign example
Worked campaign angle: a four-week Amazon returns and inventory diagnostic.
Week 1, map the problem: pull monthly return rates and the All Orders and returns reports; run the cohort analysis so the brand sees which January returns came from November and December purchases.
Week 2, find root causes: rank products and SKUs by return rate, then review return reasons and negative reviews, including neutral or positive reviews that still mention problems.
Week 3, inventory review: open Inventory Analytics to compare aging, aged, and total inventory with sell-through, note aging-inventory cost, and list SKU-level actions.
Week 4, fix plan: update product detail pages to set expectations, flag manufacturing or packaging changes, add customer-service options, and connect repeat-purchase data to acquisition spending.
Deliverables: a cohort return-rate table, a prioritized root-cause list, an inventory-aging review, and a one-page action plan.
The angle uses what the posts demonstrate—report navigation, a step-by-step returns method, and a client example—rather than promising revenue lift.
If the brief includes B2B sales, add the All Orders buyer-company-name review for post-purchase support.
Choose Mahajan when the agency's brief shifts from Amazon report mechanics to why customers do or do not come back.
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The two posts work through cohort retention at a handmade jewelry store and a pricing experiment at a cafe: group customers by the month of first purchase, find that first purchases were strong and second purchases almost absent, then act through email, thank-you notes, and price changes.
Mahajan's pattern is to ask a different question of the numbers, find the leak, and change the customer experience.
For a brand that is not primarily a marketplace seller, or a brief about repeat-purchase demand and pricing rather than returns root cause, that maps directly onto the diagnosis the agency has to present.
Mahajan's posts describe retail and DTC situations and the retention and pricing lenses applied to them; they do not show Amazon's own reports, the FBA and FBM returns reports, or a cohort return-rate method. For a brief requiring a January returns explanation built inside Seller Central, Coats's report paths and returns sequence offer more usable evidence.
Choose Kitchen when the engagement is about lifetime value and repeat purchase across a portfolio rather than a single returns problem.
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One post breaks customer value into four levers—acquisition, first-order spend, repurchase frequency, and repeat spend—and shows a US outdoor brand where 12-month value rose each year while the month-one repeat rate fell from 10.2% to 8.9%, with growth coming from higher first orders.
The second reports two brands above $80 million in revenue where bundling on the first purchase hurt 180- and 365-day LTV.
This is the right evidence when a client wants repurchase economics tested before spending more on acquisition.
Kitchen's figures come from Shopify and DTC brand audits, so the levers are email, text, direct mail, subscription, and bundling—not Amazon returns reporting or Inventory Analytics. Kitchen says outcomes vary by business and must be tested over time; for a returns root-cause engagement, Coats names the reports and four-step sequence the agency can use inside Seller Central.
Choose Fishman when the campaign needs forward-looking Amazon channel strategy or SKU-level profit work.
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The two posts cover Accelerate announcements—Prime Air drone delivery, Amazon Quick pulling Seller Central data into AI, Seller Flex, Profit Analytics, Scenario Modeling, better inventory visibility, and AI supply-chain planning—and Fishman's stated approach of starting Amazon audits at the SKU-level profit and loss, so a brand can see which products are losing money to ad spend, fees, fulfillment, or returns. For a client weighing which Amazon features to adopt or where margin leaks by product, this frames the decision.
Fishman's posts report announcements and a preferred audit starting point, but do not show a returns cohort method or step-by-step use of the returns and All Orders reports. For a deliverable explaining which December purchases came back in January and what to change, Coats's posts give the report path and worked numbers; Fishman's offer direction and a profit-and-loss lens.
Six LinkedIn posts from Eric Coats cover Amazon seller operations (cohort returns analysis, returns mitigation, repeat-purchase behavior, inventory analytics, and B2B buyer identification) and refer a friend for senior marketing, creator-partnership, and content-strategy roles.
The posts do not give publication dates, so recurring subjects and methods are described rather than a timeline of how Coats's views changed.
The posts do not say that Coats has a book, show, business, or earlier career work.
Brand relationships rest on the posts themselves: Amazon appears as the marketplace whose seller reports the posts examine, and one returns-monitoring tool is named once with a note that the post is not sponsored.
Neither establishes a sponsorship, employment, or client relationship.
Comparisons with similar creators draw on published posts from three other creators on ecommerce retention, Amazon platform changes, and small-business pricing.
The posts do not provide audience demographics, per-post reach, engagement data, sponsorship formats, rates, or client references, so nothing here should be read as campaign-performance evidence.
