Don Brett shares practical analysis of eCommerce and retail for consumer-goods businesses, including marketplace strategy, digital growth, social commerce, and retail media. Brett also identifies with Pattern, The CPG View podcast, and the Retail eCommerce Club.
Don Brett is most useful for turning eCommerce and retail data into practical decisions for consumer-goods businesses.
Best for
A marketplace services firm, retail media network, or eCommerce consultancy wanting analysis for retail and CPG practitioners.
Not for
Consumer product launches needing creator-led demand, product demos, live selling, affiliate codes, or paid shopper placements.
Key takeaways
Third-party Amazon sales grew faster across five categories, but food unit growth ran the other way.
Track digital growth separately from a retailer's total growth before setting investment.
Use social commerce to test demand and build a path to retail.
Check first: Confirm audience demographics and whether a sponsorship policy, rate card, or media kit exists.
Main ideas
Track digital growth separately from a retailer’s total growth · Choose an Amazon operating model by category, not by slogan · Judge Amazon grocery by the behavior it can create beyond grocery
Audience
48055 LinkedIn followers · #7; 93.9/100 LinkedIn score Favikon U.S. eCommerce and Retail LinkedIn ranking
Don “eCommerce” Brett is known for LinkedIn commentary on eCommerce and retail, especially what growth data means for consumer-goods businesses.
Their posts connect marketplace and digital-commerce trends to practical business questions: where growth is coming from, how Amazon’s retail and marketplace models differ, and how social commerce can help emerging brands earn wider retail distribution.
They also identify with Pattern, The CPG View podcast, and the Retail eCommerce Club.
Their posts describe a goal of educating, encouraging, and empowering readers, sharing lessons and a point of view rather than publishing for volume.
Don Brett’s distinctive contribution is turning industry data into decisions without pretending one headline settles the issue.
In a post comparing Amazon first-party and third-party growth, they note that third-party sales grew faster across five categories, while food’s unit growth ran the other way.
They advise comparing category economics, volume, price and mix, margins, inventory, and control before choosing a model.
In another post, they separate retailers’ total growth from digital growth, then argue that investment should reflect each retailer’s own pattern rather than treating eCommerce as a smaller version of store business.
They also look beyond platform sales: the TikTok Shop analysis follows emerging brands from creator-led demand to retail distribution, while the Unilever Prestige example frames AI around organizational learning and decisions rather than tool count.
That mix of specific numbers, exceptions, and practical next questions makes the posts useful to readers seeking a concise way to interpret commerce trends.
Don Brett’s recurring argument is that CPG companies should follow how commerce is changing, but make decisions by retailer, category and business outcome—not by a single growth headline.
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They turn outside data and company examples into practical steps, while often connecting digital strategy to the wider commercial plan.
Track digital growth separately from a retailer’s total growth
Brett argues that a retailer’s overall sales can hide much faster growth online.
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They don’t frame this as stores becoming irrelevant; instead, they say brands should compare where a retailer is growing with where they invest people, media and capabilities, then plan for each retailer individually.
In a post about Q1 2026, Brett contrasts median comparable-sales growth of 2.9% with median digital growth of 12.5% among the companies reporting it. They highlight Walmart at 2.6% comparable growth versus 24% digital growth, and say eCommerce should be part of the broader commercial plan, not a side business.
Choose an Amazon operating model by category, not by slogan
Brett treats Amazon’s first-party retail and third-party marketplace as options with different economics, not as a one-size-fits-all choice.
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Faster sales growth alone doesn’t show which model is more profitable or whether customers are buying more units. They recommend examining category, volume, price and mix alongside margin, inventory and control.
For the 52 weeks ending May 23, 2026, the post reports faster third-party sales growth in five categories, but notes that food units grew faster through first-party retail: roughly 42% versus 30% for third-party. They use that exception to argue for a category-level decision rather than a blanket recommendation.
Judge Amazon grocery by the behavior it can create beyond grocery
Brett argues that grocery can matter to Amazon and its brand partners because frequent purchases may deepen engagement, generate customer insight and create opportunities in other categories—not just because grocery sales are rising.
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They suggest asking what a change in one category could mean for the wider shopping relationship.
The post says Amazon shoppers who buy perishables spend about $5,121 annually and shop 105 times a year, compared with $3,771 and 76 trips for the average Amazon shopper. Brett points to those differences and the fact that only 25% of Amazon shoppers currently buy perishables when discussing possible opportunities in categories such as beauty, household, health and pet.
Use social commerce to test demand and build a path to retail
Brett presents TikTok Shop as more than a sales channel: brands can use creators to explain products, make buying easy and learn quickly, then carry signs of demand into retail conversations.
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They recommend a distinct TikTok approach rather than simply reusing another channel’s campaign.
In a post citing analysis from Jared Parker and Watt, Brett connects TikTok Shop examples to retail expansion: Cymbiotika was reported to have reached roughly 2,000 Target stores, and Waterboy to have expanded into Target, Sprouts and Walmart. They recommend testing products and messages, then using what works across retail, marketplaces and the broader plan.
Build AI advantage through faster organizational learning
Brett argues that AI adoption is about improving decisions and sharing lessons, not accumulating tools.
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They emphasize applying what works across a portfolio, treating unsuccessful pilots as useful information and linking experiments to commerce execution.
Brett describes an AI Acceleration Day at Unilever Prestige involving eight beauty brands and more than 150 colleagues and partners. They credit the Unilever Prestige team’s focus on sharing what worked, what did not and what to try next; the learning-speed argument and four recommendations are their interpretation.
Make executive publishing useful, not just frequent
Brett says a point of view and shared learning matter more than posting for its own sake or chasing follower numbers.
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They describe the purpose of publishing as educating, encouraging and empowering, with community and career enrichment among its stated values.
In a post about a Favikon ranking that placed Brett seventh among U.S. eCommerce and retail LinkedIn executive influencers, they say the ranking is less important than helping someone learn, make a better decision, advance a career or make a valuable connection.
Answers the first question a CPG planner usually has: is a retail partner’s growth coming from stores or digital?
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This is the clearest single post for seeing how Don Brett separates the two and turns the gap into an investment decision.
Don Brett works through a dataset assembled by Jason Goldberg, covering 52 public U.S. retailers and roughly 3,300 data points traced to company filings and earnings releases.
For early 2026, 85% of tracked retailers reported positive comparable sales.
Median comparable growth was about 2.9% across 47 retailers, while median digital growth was about 12.5% across the 22 companies reporting it.
Retailer-by-retailer gaps carry the argument: Walmart at about 2.6% comparable versus 24% digital, Kroger at 1.0% versus 19%, Costco at 6.8% versus 21%, and Home Depot at 1.3% versus 11%.
They explicitly say this does not mean stores are disappearing.
The recommendation has four steps: report retailer growth and digital growth separately; compare investment in people, media and capability with where the retailer is actually growing; treat each retailer’s digital opportunity as its own strategy; and fold digital into the wider commercial plan rather than running it as a side business.
For brands planning a campaign
A useful benchmark for a retail-media or marketplace pitch: the argument rewards proposals that break a partner’s total growth into store and digital components, then tie spend to the faster-growing line rather than treating eCommerce as a separate, smaller budget.
Addresses the operating-model decision underlying most Amazon strategy conversations and shows how Don Brett handles a dataset that points in different directions.
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A newcomer sees their method: read the numbers by category, then question what drives the growth.
Using NielsenIQ Omnisales data highlighted by MarketMaze, Don Brett reports that over the 52 weeks ending 23 May 2026, third-party marketplace sales grew faster than Amazon’s own first-party retail sales in all five measured categories: food 18% versus 23%, health and beauty 15% versus 22%, household care 13% versus 25%, pet care 4% versus 18%, and baby care minus 6% versus 17%.
Unit sales complicate the story: third-party also grew units faster in four categories, but food was the exception, with first-party units growing roughly 42% versus 30% for third-party.
They conclude that faster dollar growth alone does not establish which model produces stronger demand or better profit.
The advice: decide by category, separate volume from price and mix, and compare first-party, third-party and hybrid options on margin, inventory requirements and control.
For brands planning a campaign
Strong reference material for a marketplace-strategy engagement: it demonstrates category-level rigour and rejects a blanket recommendation. A consultancy can point to it when arguing that the operating-model choice requires per-brand economics, not a portfolio-wide rule.
Answers why a company outside food should care about Amazon’s fresh business.
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The post shows how to read a growth statistic for its second-order effects, not just its headline size.
Don Brett uses NielsenIQ data credited to Jack O’Leary to show rapid growth in Amazon’s fresh categories: seafood up 888%, deli up 686%, dairy up 675%, meat up 642% and produce up 622%.
The number they single out is behavioural rather than category-specific: Amazon perishables shoppers spend about $5,121 a year, against $3,771 for the average Amazon shopper, and shop on Amazon about 105 times a year versus 76 times.
Only around 25% of Amazon shoppers currently buy perishables, leaving room to grow.
Their reading is that grocery matters less for its own sales than for the shopping frequency and customer data it creates across the rest of Amazon, with knock-on opportunity in beauty, household, health and pet.
For brands planning a campaign
A useful argument for brands in adjacent categories weighing whether to follow Amazon’s grocery shoppers into complementary aisles. It supports proposals built around frequency and basket adjacency, not grocery sales alone.
Explains the link that matters most to a brand evaluating social commerce: prove demand on a platform, then use that proof to win retail distribution.
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Read this for the emerging-brand playbook Don Brett describes.
TikTok Shop US generated $11.8 billion in the first half of 2026, nearly double the same period a year earlier, according to analysis Don Brett cites from Jared Parker and Watt.
The examples use different measures, as they note: O Positiv Health at $35.4 million in TikTok Shop sales, E.L.F.
Beauty at 3.3 million units sold on the platform, and Cymbiotika at $150 million in 2025 revenue and reportedly tracking above $200 million in 2026.
The retail connection is the point: Cymbiotika reportedly reached roughly 2,000 Target stores, while Waterboy expanded into Target, Sprouts and Walmart.
Their recommended approach is to study emerging brands, not just traditional competitors; give TikTok its own playbook around creator demonstrations, consumer questions and easy purchasing; test products and messages quickly; and carry what works into marketplaces and the broader plan.
For brands planning a campaign
Directly relevant to a creator-led commerce proposal: the sequence Don Brett describes—build demand with creators, prove consumers will buy, then use those results to earn distribution—supports social-commerce testing budgets aimed at retail listings.
Answers what separates companies that get value from AI and gives a concrete example of an organisation trying to do it.
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Newcomers see how Don Brett uses a company case to make an operating argument, not a technology one.
Don Brett describes an AI Acceleration Day run by Unilever Prestige, framing it as decision intelligence rather than a tool showcase.
The event brought together eight prestige beauty brands, with more than 150 colleagues and partners participating globally.
Teams spanning eCommerce, marketing, finance, supply chain, digital product and consumer insights worked to one principle: share what worked, what did not, and what to try next.
Their argument is that a portfolio’s advantage is not scale alone, but the ability to move a lesson from one brand to the rest quickly.
The four recommendations: build AI around decisions rather than tools, create a portfolio learning system, treat pilots that miss as data rather than failure, and connect AI to commerce execution.
They name Narayan P. and the wider Unilever Prestige team as the people putting this into practice.
For brands planning a campaign
Useful for an advisory conversation about organisational adoption rather than model selection: it supports engagements focused on decision speed, cross-brand learning and measuring adoption instead of counting tools deployed.
Answers what Don Brett is trying to do with their publishing, in their own words.
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It is the quickest way for newcomers to understand the intent behind the rest of this section and see the executives they are ranked alongside.
Don Brett shares a Favikon ranking of U.S. eCommerce and retail LinkedIn executive influencers, placing seventh with roughly 48,000 followers and a score of 93.9 out of 100.
The list includes the chief executives of Gap, McDonald’s, Dick’s Sporting Goods, PepsiCo North America and Amazon’s worldwide stores.
More revealing is their stated intent: share what you are learning and hold a point of view rather than publish for volume; stay close to the people doing the work; and build around a mission larger than follower count.
That mission is educate, encourage, empower, underpinned by four values: collaboration, career enrichment, community and connectivity.
They say the ranking is enjoyable but impact matters more, and credit the 48,000 people who chose to follow along.
For brands planning a campaign
Sets expectations for a partner: the tone is educational and peer-facing rather than promotional. A proposal that teaches the audience something concrete fits the stated mission better than one built around reach statistics.
Recurring format: executive data briefing with action steps6
Amazon grocery as a frequency and data engine1
Amazon 1P versus 3P marketplace economics by category1
Retail health versus digital growth and CPG investment allocation1
TikTok Shop and creator-led demand as a route to retail distribution1
AI adoption as a portfolio learning system1
Industry thought leadership and community building1
Amazon grocery as a frequency and data engine
Don Brett uses NielsenIQ data to show Amazon's fresh categories growing quickly: perishables shoppers spend $5,121 a year versus $3,771 for the average Amazon shopper, and shop Amazon 105 times a year versus 76.
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Only 25% of Amazon shoppers currently buy perishables. Brett argues grocery matters because it changes shopping frequency, customer data and cross-category engagement, not just grocery sales.
Third-party data summary followed by a framed question, key numbers and four action steps, including working with NielsenIQ for visibility and Pattern for execution.
The underlying NielsenIQ data is cited through the post and was not independently verified here.
For brands planning a campaign
Useful for a CPG brand benchmarking Amazon grocery strategy, including content or advisory work on category-level share movement, basket size, frequency and adjacent-category opportunity.
Amazon 1P versus 3P marketplace economics by category
Over the 52 weeks ending May 23, 2026, Don Brett reports that third-party marketplace sales grew faster than Amazon's own retail sales across five categories: food 18% versus 23%, health and beauty 15% versus 22%, household care 13% versus 25%, pet care 4% versus 18%, and baby care negative 6% versus 17%.
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Food was the exception on units, with 1P units growing roughly 42% versus 30% for 3P.
Brett says faster dollar growth does not settle which model delivers better demand or profitability.
They recommend deciding by category, separating volume from price and mix, and keeping 1P, 3P and hybrid options open.
Presents growth comparisons, unit nuance and decision steps; does not provide a single recommended model.
Based on NielsenIQ Omnisales data highlighted by MarketMaze; margin and profitability are discussed as considerations, not measured in the post.
For brands planning a campaign
Supports content or advisory work comparing 1P, 3P and hybrid Amazon models by category, including tradeoffs around margin, inventory, control and marketplace execution.
Retail health versus digital growth and CPG investment allocation
Don Brett summarizes a Jason Goldberg dataset covering 52 public U.S. retailers and roughly 3,300 data points.
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In Q1 2026, 85% of tracked retailers reported positive comparable sales, median comparable sales growth was 2.9% across 47 retailers, and median digital growth was 12.5% across 22 companies reporting it.
The gaps include Walmart at 2.6% comparable sales versus 24% digital, Kroger at 1.0% versus 19%, Costco at 6.8% versus 21%, and Home Depot at 1.3% versus 11%.
Brett argues that brands should separate retailer growth from digital growth, follow growth when allocating resources, build retailer-specific strategies, and connect digital growth to the broader commercial plan.
Uses a third-party retailer dataset and a four-step action list to turn the data into allocation decisions.
The dataset is credited to Jason Goldberg and company filings. Ask for the full methodology before using it to set benchmarks.
For brands planning a campaign
For a CPG brand or eCommerce consultancy, this is directly relevant to retail media and eCommerce investment benchmarks, retailer-specific planning, and content about why digital should not be treated as a smaller version of the store business.
TikTok Shop and creator-led demand as a route to retail distribution
Don Brett reports that TikTok Shop US generated $11.8 billion in the first half of 2026, nearly double the same period last year.
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Examples include O Positiv Health at $35.4 million in TikTok Shop sales, e.l.f.
Beauty at 3.3 million units sold, and Cymbiotika at $150 million in reported 2025 revenue and reportedly tracking above $200 million in 2026.
The post also links social commerce to retail distribution, citing Cymbiotika reaching roughly 2,000 Target stores and Waterboy expanding into Target, Sprouts and Walmart.
Brett recommends studying emerging brands, giving TikTok its own playbook, using it to learn faster, and connecting execution through Pattern.
Summarizes platform and brand examples, then gives four actions for testing, learning and connecting social commerce to broader distribution.
Figures credited to Jared Parker/Watt measure different things; ask for their definitions before comparing them.
For brands planning a campaign
Useful for brands evaluating creator-led social commerce as a demand-generation and retail-distribution strategy, and for advisory content on creator demonstrations, product talkability and rapid testing.
Don Brett describes Unilever Prestige's AI Acceleration Day, which brought together eight prestige beauty brands and more than 150 colleagues and partners across eCommerce, marketing, finance, supply chain, digital product and consumer insights.
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The stated principle was to share what worked, what did not and what to try next. Brett argues that AI winners will not necessarily be the companies with the most AI but the ones that learn fastest, and recommends building AI around decisions rather than tools, creating a portfolio learning system, rewarding experimentation, and connecting AI to commerce execution.
Uses one company example and a four-step adoption framework: decisions before tools, portfolio learning, rewarded experimentation and commerce execution.
Draws on a single Unilever Prestige event. Ask for performance evidence before using it to assess impact.
For brands planning a campaign
Relevant for brands and consultancies evaluating AI adoption in commerce organizations, especially content or advisory work on moving from experimentation to operating change and linking AI to marketplace execution.
Industry thought leadership and community building
Don Brett reports ranking seventh among U.S. eCommerce and retail LinkedIn executive influencers, with 48,000 followers and a 93.9/100 LinkedIn score.
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They say the value lies not in posting for its own sake, but in sharing what they are learning and offering a point of view.
They describe a mission to educate, encourage and empower, with values around collaboration, career enrichment, community and connectivity.
The post also names Pattern, The CPG View, The Retail eCommerce Club and The Retail Media View.
Uses a third-party ranking and a self-described mission and values to explain audience growth and engagement approach.
The post presents the ranking, follower count and score; ask for engagement data to assess engagement quality.
For brands planning a campaign
Brands considering content collaboration, community sponsorship or executive thought leadership can assess Don Brett by topic alignment, audience size and stated community mission—not follower count alone.
Recurring format: executive data briefing with action steps
Across the posts, the format typically opens with a headline claim, poses a Big Question, presents By the Numbers from third-party research, lists What I'd Do actions and closes with a Final Thought.
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Posts repeatedly point to Pattern, The CPG View podcast, The Retail eCommerce Club community and The Retail Media View newsletter.
The structure is consistent: third-party data or a reported example, interpretation, practical steps and a cross-platform call to action.
Based on six 2026 LinkedIn posts; longer-term format changes and non-LinkedIn work were not reviewed.
For brands planning a campaign
Brands can use this format as a template for executive-facing content or assess Don Brett for short, data-led posts that turn marketplace and retail media developments into action lists.
Don Brett's posts tie them most clearly to Pattern, the global eCommerce accelerator they speak as part of and list first in the "Ways to Reach Us" block that closes every post.
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The same block names three other outlets — The CPG View podcast, the Retail eCommerce Club community and The Retail Media View newsletter — as ways to reach them.
Ask who owns or operates those outlets and what Brett's role is in each.
Brett also cites NielsenIQ data repeatedly and recommends working with it; ask about any commercial relationship.
Other companies in these posts, including Amazon, Walmart, Kroger, Costco, Target, TikTok Shop and Unilever Prestige, are subjects they analyse rather than partners named as such.
Pattern
Employment
Pattern is the business Don Brett speaks as part of.
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They write that connecting marketplace execution, content, media, inventory and conversion to a retailer's broader growth plan is "something we spend a lot of time thinking about at Pattern," and every post closes with "Ways to Reach Us: Pattern (global eCommerce accelerator)."
A rankings post they shared identifies them as "Don 'eCommerce' Brett, Pattern / The CPG View / The Retail eCommerce Club."
The posts describe Pattern as helping brands turn data into marketplace action across assortment, pricing, content, advertising, inventory and logistics, and as working with brands on Amazon and other marketplaces.
Brett repeatedly uses NielsenIQ data as the backbone of their retail analysis and recommends it to brands: "use NIQ to understand where the share is moving," and "this is where working with NielsenIQ becomes increasingly valuable."
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They credit a named NielsenIQ researcher for one dataset and reference a NielsenIQ report dated 2026-09-04. Ask about any commercial relationship with NielsenIQ.
Every post lists "The CPG View (podcast)" among the ways to reach Brett, alongside Pattern and its other outlets, and a third-party rankings post names it next to their name.
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Ask whether Brett owns, hosts or works for the podcast.
Listed in the closing block of every post as "The Retail eCommerce Club (community)," one of the outlets Brett presents as ways to reach them, and named beside their name in a rankings post they shared.
Don Brett publishes short, data-led LinkedIn briefings on Amazon, retail media, CPG eCommerce and AI adoption.
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The Amazon grocery post explores how fresh categories can change shopping frequency and basket size; the Amazon 1P/3P post covers category-level marketplace economics; the retail-vs-digital post examines investment allocation; the TikTok Shop post considers social commerce as a route to retail distribution; and the Unilever Prestige post presents AI as a learning system. For adjacent voices, Guru Hariharan covers AI risk and Amazon advertising mechanics; Vincenzo Toscano covers European Amazon Ads and seller events; Christopher Dorsey tracks retail media network economics and enterprise AI governance.
Covers Amazon advertising, CPG brands, retail media and AI's operational effect on commerce in short executive posts that turn platform changes into action.
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Places more emphasis on AI risk, governance and enterprise liability, and drills into Amazon ad mechanics such as AI-powered Sponsored Prompts. Don Brett more often starts from retailer or category growth data and CPG investment choices.
Covers Amazon Ads, marketplace expansion and Q4 planning for brands, often from an operator or event perspective, with practical takeaways for sellers.
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Focuses on European Amazon sellers, agency events and advertising execution; Don Brett focuses on U.S. retail and CPG eCommerce data and broader commercial planning across retailers.
Analyzes retail media networks, retailer economics and enterprise AI developments in short LinkedIn posts for brands and the vendor ecosystem.
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Focuses more on market structure, retail media network economics, AI governance and enterprise software sales. Don Brett concentrates on CPG eCommerce growth and category-level investment decisions.
Don Brett’s audience profile points to a professional eCommerce and retail following on LinkedIn, not a consumer audience.
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The profile snapshot shows 48,055 followers observed 8 October 2026, and a 7 October 2026 post cites a Favikon ranking of #7 among U.S. eCommerce and Retail LinkedIn executive influencers, with a 93.9/100 LinkedIn score.
For a CPG brand or eCommerce consultancy benchmarking retail media and marketplace strategy, that signals reach among people who follow retail and eCommerce leadership topics, but does not confirm they are decision-makers at a specific retailer or CPG company.
Ask for recent LinkedIn audience demographics or an audience survey covering job function, seniority, industry, and geography, matched to the intended campaign audience.
LinkedIn followers
48055
LinkedIn profile snapshot · 2026-10-08
Favikon U.S. eCommerce and Retail LinkedIn ranking
#7; 93.9/100 LinkedIn score
Favikon ranking cited in Don Brett’s LinkedIn post · 2026-10-07
Across six LinkedIn posts dated 5–8 October 2026, Don Brett does not publish a sponsorship policy, rate card, media kit, or stated partnership format.
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Each post points readers toward the same channels: Pattern (described as a global eCommerce accelerator), The CPG View podcast, the Retail eCommerce Club community, and The Retail Media View newsletter, followed by a newsletter sign-up link.
These are Brett’s own or employer-affiliated properties, not a documented creator-sponsorship offering.
The posts show no disclosed paid brand collaborations, sponsorship specifications, or dedicated partnership inquiry page.
Buyers would need to ask directly about commercial terms, deliverables, and operational requirements.
A good fit is a marketplace services firm, retail media network or eCommerce consultancy that wants a credible outside voice in front of retail and CPG practitioners, and needs analysis rather than entertainment.
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The six posts, all published 5–8 October 2026, follow the same working shape: a third-party dataset, a framed question, key numbers, then a short list of decisions the reader can act on.
A brand asking "is my retail partner growing in stores or online?" gets a concrete frame: comparable sales growth around 2.9% across 47 retailers against median digital growth around 12.5% across 22 reporting companies, with retailer-level gaps such as Walmart near 2.6% comparable versus 24% digital.
A brand weighing Amazon's first-party versus third-party model gets category-level numbers and a deliberately restrained conclusion: faster dollar growth does not settle which model is more profitable.
The post recommends deciding by category, splitting volume from price and mix, and comparing margin, inventory and control.
That is the content a consultancy can point a client to instead of writing the argument itself.
The second reason is format reliability.
Every post carries a repeatable structure, so a sponsored series can be planned without inventing a new editorial product: one dataset, one question, one action list, one closing view.
The topics also cluster tightly around buyer decisions: Amazon grocery frequency (perishables shoppers spending about $5,121 a year against $3,771 and shopping about 105 times a year against 76), TikTok Shop demand as a route to shelf space, and AI adoption framed as decision speed rather than tool count.
A LinkedIn executive-influencer ranking cited in one post, seventh among U.S. eCommerce and retail executives, with a 93.9/100 score, supports the reach claim for a professional audience, though it is a self-published citation and not an independent audit.
Audience composition is not published, so a buyer should request job titles, employers, seniority and geography before claiming the profile's 48,055 followers are decision-makers at a named retailer or CPG company.
The underlying data belongs to NielsenIQ, MarketMaze, Jason Goldberg, Favikon and Watt, and is not verified here.
No sponsorship formats, rate card or inquiry route are published, so commercial terms must be scoped directly rather than selected from a menu.
Fit is strongest when the deliverable is an executive-facing data briefing or advisory session, and weakest when the deliverable is consumer reach.
When to choose a specialist
A poor fit is a consumer product launch that needs creator-led demand on TikTok Shop or Instagram: product demonstration videos, live selling, affiliate codes and paid placements aimed at shoppers who will buy the item themselves.
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The six posts are written for people who run eCommerce businesses, not for people buying products.
They analyse third-party data, name the analysts behind it, and end with action steps for executives: study emerging brands, give TikTok its own playbook, test messages quickly.
The audience information points the same direction: a 48,055-follower LinkedIn following built around retail and eCommerce leadership topics, with no demographic breakdown published.
A follower base of category managers and eCommerce directors does not convert into consumer purchases at the rate a performance-led launch needs.
The second failure is operational.
Across all six posts, there is no sponsorship policy, rate card, media kit, disclosed paid brand collaboration or inquiry page.
Each post instead points readers to the same set of owned or employer-affiliated properties: a global eCommerce accelerator, a podcast, a community and a newsletter, plus a newsletter sign-up link.
That is channel promotion, not a documented creator offering.
A brand that needs published deliverables, fixed pricing and a compliance trail would be starting from zero on all three.
Ask directly whether sponsorship deals are offered and, if so, what formats and terms are available.
A consumer product launch would also be difficult to measure from the available information.
The posts do not include engagement data, video performance history, a transcript of any talk or broadcast, or long-form work.
A campaign that has to be justified on view-through, click-through and direct sales cannot be planned against any of those numbers.
Where this person fits is the decision-maker audience; where a consumer product launch needs to land is the shopper.
Those are different rooms.
A worked campaign example
Angle
Run a sponsored series called "The Digital Growth Gap," built on the argument already present in the posts: retail partners are healthy, but digital growth inside them is running about four times faster than the headline. The series takes one dataset per week and turns it into a decision the reader can act on, matching the structure the posts already use.
Brief
A marketplace services firm wants a North American retail media and marketplace practice to reach senior eCommerce and CPG leaders at large brands. The business goal is qualified conversations, not follower growth: brief downloads, roundtable attendees and inbound advisory requests. Budget is for a six-week sponsored thought-leadership push on LinkedIn, plus one live session.
Deliverables
Four sponsored LinkedIn briefings, one per week, each opening with a single number and closing with a three-step action list. Working topics from the posts: separating retailer growth from digital growth; deciding Amazon first-party versus third-party by category; reading Amazon grocery for frequency and basket size rather than grocery sales alone; treating AI adoption as learning speed rather than tool count.
One live 45-minute session for the firm's existing community or client list, structured as a working session: each attendee brings one retail partner's numbers, splits store growth from digital growth, and leaves with a one-page allocation memo.
A one-page decision framework as the lead magnet, extracted from the four briefings: three columns for retailer growth, digital growth and where people and media are currently invested.
A short co-branded episode or interview on the creator's own podcast channel, offered as a placement to be confirmed rather than assumed.
Repurposing the four briefings into a gated email sequence the firm owns and can rerun with new data each quarter.
What to confirm
Request a recent LinkedIn audience breakdown covering job function, seniority, industry and geography. Without it, the firm cannot promise its sales team that the reach is senior decision-makers.
Ask directly whether sponsored placements are offered, what a standard format looks like, and what disclosure practice applies. Treat the lack of information in the posts as unconfirmed rather than as a refusal.
Confirm whether the podcast, newsletter and community are available as placements and who controls each, since they are presented in the posts as affiliated channels.
Agree measurement up front: brief downloads, session attendance, qualified inbound conversations and named pipeline, rather than impressions or follower count.
Confirm the pre-publication review rights for any sponsored briefing, and agree how advertising disclosure will be worded in each post.
Why this works
The angle does not ask the creator to change what they already do. Every post takes a third-party dataset, frames a question, lists numbers and gives practical steps in an executive register. Sponsorship slots around that structure are additive rather than disruptive. The topics map onto real buying decisions — Amazon operating model, retail media allocation, social commerce as a route to distribution — so the series reads as useful to the audience even when a sponsor is attached. The lead magnet gives the firm something portable: a framework a client can use with their own retailer numbers, which is exactly the kind of artifact the firm's own sales team needs.
Two cautions belong in the plan. First, all four data points come from third-party research the firm did not commission, so the contract should state who is responsible for accuracy and whether the firm may re-cite the figures. Second, no sponsorship format is published, so define deliverables, usage rights for repurposing, disclosure wording and a named accountable author in the agreement rather than inheriting them.
Choose Guru Hariharan when the buyer task is AI readiness rather than retail-media allocation: deciding whether a brand's product data and content are strong enough for AI-driven ad formats, or assessing AI governance and liability exposure before scaling adoption.
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One post walks through Amazon's AI ad layer, where a shopper's conversational prompt is answered from product attributes, bullet points and reviews, and cites Amazon's disclosed figures of 48% higher conversion and 21% larger baskets for shoppers who interact with Sponsored Prompts.
Its practical argument is narrow and testable: if content is thin or stale, the conversion lift goes to a competitor.
The second post supports assessing AI governance and liability exposure before scaling adoption, listing adoption failures with numbers (85% of AI projects never reach production, 47% of enterprise AI users making major decisions on hallucinated content) and tying them to who pays when an algorithm errs at scale.
That combination fits a consultancy advising clients on content operations, AEO/AI visibility, or AI risk transfer better than a firm that needs retailer-level growth analysis.
The tradeoff is scope and register.
These posts do not analyse retailer comparable sales, Amazon first-party versus third-party economics, or how a brand should split budget across retail partners by category—the decisions the main profile's posts address.
The AI-risk post is an operator thesis, drawing on the writer's experience running an AI company and closing on an insurance gap; the Amazon post covers one platform's ad mechanics rather than a portfolio view.
Buyers seeking an executive briefing on where digital growth is coming from inside Walmart, Kroger or Costco should ask for retailer-level data.
Budget the second post as a liability and governance discussion, not a media plan.
Choose Vincenzo Toscano when the need is European marketplace expansion, peak-season advertising planning, or an in-person format with an agency team delivering the work.
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Both posts centre on live events in London: an Amazon EU Accelerate meetup attended by brands, vendors and operators, and a keynote at the Amazon Ads EU Business Accelerator.
The Q4 post gives four tactical messages clients can act on: stop treating Q4 advertising as extra spend; use Amazon Marketing Cloud and DSP to see who is buying rather than who is clicking; build audiences before peak so cost-per-click spikes do not eat margin; and keep retargeting into January because the sales cycle does not end on Black Friday.
The second post covers multi-country expansion across Europe and the US, AI moving into advertising and operations, fulfilment changes such as FBA Select and AWD, and lessons from smaller brands entering retail.
It also demonstrates event and speaking capability, including a sponsor role and a recorded podcast interview.
The material reads as event recap and channel promotion rather than standalone analysis a client can cite in a strategy document.
It offers no third-party dataset, retailer-level numbers, or summary-plus-action-list structure for a sponsored series.
The posts name co-speakers, sponsors and clients, and thank partners—a useful credibility signal in the European seller community, but limited editorial substance.
Coverage is Europe-first and Amazon-centric; the US retail media comparison, Walmart and Kroger advertising economics, and the first-party versus third-party operating-model question that anchors the main profile's posts are absent.
Buyers needing an executive-facing written briefing should ask for one; those seeking a live session and on-ground presence in European Amazon circles are a better match.
Choose Christopher Dorsey when the buyer task is the commercial argument underneath retail media: who actually pays for it, and what a brand should do about that in a retailer negotiation.
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The post on Costco Wholesale contrasts a $93.9 billion quarter and $5.9 billion in membership fees, collected from 84 million members, with Walmart's advertising growth of almost 50% and a CFO statement that ads plus membership are nearly a third of operating income.
It also cites Kroger's advertising arm growing profit 24% in a quarter when it cut sales guidance.
The practical point for a brand is that Costco's joint business plan has no media line—you pay in price, pack size and velocity—while other retailers want a slice of the marketing budget.
The second post covers Microsoft's Agent 365 release and argues that an IT governance dashboard turns unmanaged AI agents from a productivity win into a flagged risk, offering a sharp read on enterprise AI buying for anyone selling or briefing on AI tooling.
The two posts are argument-led and conversational, with the full piece behind a link in the comments.
They offer a point of view rather than a dataset, decision framework and numbered action list.
They also cover unrelated domains—retail media economics and enterprise AI governance—making a single themed sponsored series harder to plan than one built around the main profile's consistent eCommerce growth frame.
The Costco post is sharp market commentary, useful for positioning but less useful as neutral benchmark data: its claims rest on other outlets' reporting rather than a cited dataset.
For a multi-week briefing series, ask for a repeatable structure; this material is better suited to one strong opinion piece or a negotiation-facing argument.
Don Brett published six LinkedIn posts during 2026, and had about 48,000 followers on 8 October 2026.
Each post follows the same shape — one outside dataset, a framed question, the key numbers, then a short action list — covering Q1 2026 retailer results, Amazon grocery, Amazon first-party versus third-party growth, TikTok Shop, an Unilever Prestige AI event and a LinkedIn influencer ranking.
The posts credit their data to NielsenIQ, Jason Goldberg, MarketMaze, Favikon, Watt and a named NielsenIQ researcher; those underlying sources were not independently verified, and the numbers are reported here as the posts present them.
Podcast episodes, newsletters, long-form reports, talks and video transcripts are not covered here, so this page reflects short-form LinkedIn publishing rather than the full body of work.
The posts do not say how the audience is composed or provide commercial terms — sponsorship formats, rates and inquiry routes — leaving those as open questions for anyone making contact.
