Guru Hariharan writes about retail media, e-commerce, and AI for consumer packaged goods brands, with a focus on product data, advertising returns, and the risks of deploying AI.
Guru Hariharan is most useful for advisory commentary on CPG retail media, product data, and AI deployment risks.
Best for
CPG retail-media and product-data campaigns weighing Amazon spend against Kroger, Walmart, and Instacart returns.
Not for
DTC skincare or fashion launches needing short-form video, creator demos, affiliate codes, or whitelisting.
Key takeaways
Set retail media budgets by measured return, not last year's split.
AI-powered Sponsored Prompts reportedly convert 48% higher when product attributes, bullets, and reviews are useful.
The practical AI risk is poor deployment, not science-fiction takeover.
Check first: Confirm audience demographics, sponsored-post results, and commercial terms before contacting them.
Main ideas
Set retail media budgets by measured return, not last year’s split · Product content is part of the advertising system when AI uses it to recommend products · Retail-event results depend on conversion and stock, not traffic alone
Guru Hariharan writes about retail media, e-commerce, and AI in the context of consumer packaged goods.
Across these posts, they connect platform changes to decisions brands can make: whether product information is detailed enough for AI-powered shopping, how to compare advertising returns across retailers, and whether inventory can meet demand.
They also write about the costs of deploying AI without adequate governance, measurement, or safeguards.
Follow their work for arguments that link retail trends to practical questions about product data, media budgets, and operational risk.
Hariharan’s distinctive approach is to make broad technology shifts concrete through business decisions and specific examples.
In one post, they connect Amazon’s reported results for AI-powered Sponsored Prompts to the quality of product attributes, descriptions, and reviews.
In another, they challenge a budget split based on last year’s spending by comparing reported retail-media returns and the role of loyalty data.
Their Prime Day recap sets higher conversion alongside rising stockout losses, rather than treating advertising performance in isolation.
They also argue that AI adoption can produce real costs, from false outputs to unmanaged spending and data exposure.
Together, these posts help readers weigh the opportunity and operational risks of AI-enabled commerce.
Across four posts, Guru Hariharan argues that CPG brands should manage retail media and AI as connected business systems: allocate spend by return rather than habit, make product data useful to shopping AI, and plan for inventory and deployment risks.
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The posts offer specific examples of these positions.
Set retail media budgets by measured return, not last year’s split
Hariharan challenges the habit of carrying forward a platform allocation simply because it was used before.
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The post argues that brands should compare profit return and incremental sales across retailers; Amazon’s scale does not by itself make it the most efficient choice. Loyalty-linked purchase data can give grocery retailers clearer measurement.
In a Q3 planning example, a CPG brand put 58% of its budget on Amazon and 12% on Kroger because that resembled the prior year’s mix. Hariharan cites figures from Keen Decision Systems showing profit ROI of $2.35 at Kroger and $1.85 at Instacart, both higher than Amazon, Walmart, and Target in that comparison.
Product content is part of the advertising system when AI uses it to recommend products
Hariharan connects product-data quality to the performance of AI-powered retail advertising.
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AI shopping assistants draw on attributes, product descriptions, and reviews; thin or stale information gives them less to work with. That makes coordination between content and media teams a practical performance issue.
Hariharan reports that shoppers interacting with Amazon’s AI-powered Sponsored Prompts converted 48% higher and spent 21% more per transaction. The post uses those figures to make the case for auditing product data before scaling AI-driven ad formats.
Retail-event results depend on conversion and stock, not traffic alone
Hariharan reads event performance through traffic, conversion, revenue, advertising mix, category returns, and inventory.
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A smaller audience can still convert more efficiently, while stockouts can erase sales when demand arrives. The post also argues that brands need to prepare for AI-assisted product discovery, not only traditional search and browsing.
In a Prime Day 2026 recap, Hariharan reports traffic down 10% year over year, unit conversion up 17%, and revenue down 1.7%.
The post also reports stockout-driven revenue loss up 173% and says results varied sharply by category: Beauty was nearly flat against its baseline, while Tools & Home Improvement fell 38%.
Hariharan links the conversion shift to Alexa for Shopping and agentic recommendations, but does not establish that they caused it.
The practical AI risk is poor deployment, not science-fiction takeover
Hariharan emphasizes operational failures: unmanaged use, false outputs, biased decisions, unclear business value, and inadequate training.
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The argument is to assign ownership and safeguards before deployment, and to connect AI use to measurable results rather than treating adoption itself as success. The post also flags liability coverage as an emerging gap.
Hariharan describes cases including an AI ordering system that added items customers had not ordered, a court-policy hallucination, and confidential data leakage. The post also cites untracked token costs at a healthcare company and argues that firms should track AI spending and outcomes.
Answers a practical question: why would a CPG brand treat product content and retail media as one system rather than two teams?
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Hariharan starts with Amazon’s $19.8 billion quarterly advertising revenue, then moves to the more useful number: shoppers who interact with AI-powered Sponsored Prompts convert 48% higher and spend 21% more per transaction.
The argument is specific.
When a shopper asks Alexa for Shopping about a product, the AI reads product data, attributes, bullet points, and reviews to generate a conversational ad.
Rich, structured data gives the system something to work with; thin or stale data gives the lift to another brand.
The post’s force comes from connecting a platform change to an operational failure inside the brand.
For brands planning a campaign
Use this as the case for auditing product content before increasing Amazon spend. The audit should test whether attributes, bullets, and review themes are specific enough for an AI assistant to select and explain the product.
Answers whether Amazon’s scale automatically gives a CPG brand the best retail media return, and shows how to question a budget set by habit.
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The post opens with a CPG brand allocating 58% to Amazon, 22% to Walmart, 12% to Kroger, and 8% elsewhere because that is roughly what it did last year.
Hariharan contrasts that habit with Keen Decision Systems data: Kroger’s profit ROI is $2.35 and Instacart’s is $1.85, both above Amazon, Walmart, and Target on return.
Kroger retail media investment grew 54.2% year over year, the steepest rise of any network.
The explanation is closed-loop purchase data tied to loyalty programs reaching more than 60 million households, which lets a brand see who bought and whether spend created incremental volume.
For brands planning a campaign
Use this to argue for a portfolio review rather than a default Amazon-heavy split. The review should compare profit ROI and incrementality by platform, not just impressions or reach.
Answers what changed in shopper behavior and ad concentration during a major retail event, and why inventory discipline matters more when AI assistants shape the funnel.
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Hariharan summarizes a Prime Day 2026 recap: traffic fell 10% year over year, unit conversion jumped 17%, and revenue landed 1.7% below last year.
Stockout-driven revenue loss rose 173%, the sharpest swing in the event.
Brands put 88% of ad spend into Sponsored Products, cut Sponsored Display 70% and Sponsored Brand 25%, and saw ROAS diverge by category: Beauty held nearly flat at -0.5% versus baseline while Tools & Home Improvement compressed 38%.
They read the conversion increase as evidence that Alexa for Shopping and agentic recommendations are shortening the funnel.
For brands planning a campaign
Use this to pressure-test a Q4 plan on two fronts: whether the brand is visible when an AI assistant recommends a product, and whether inventory can support the demand that visibility creates.
Answers what can go wrong when a company deploys AI badly, using named failure modes rather than abstract warnings.
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Hariharan writes that they ran an AI company and have seen AI deliver 30% sales lifts for CPG brands and 2x campaign performance improvements, as well as hallucinate court cases, leak confidential source code, and rack up $6 million in untracked token costs at a single healthcare company.
The post names five perils: a governance vacuum from unmanaged personal accounts, a hallucination tax shown by McDonald’s AI ordering system, a bias blind spot illustrated by a Workday lawsuit, an ROI mirage where only one-third of enterprises can link AI outputs to business results, and a people problem where most organizations have not redesigned jobs around AI.
It closes on the insurance gap for AI-specific liability.
For brands planning a campaign
Use this as a risk checklist before scaling AI-assisted content or media. The checklist should assign ownership for governance, test outputs for false claims, and define how AI spend and results are tracked.
Treat retail media allocation as a portfolio, not a repeat of last year
In a post about Q3 planning, Hariharan describes a CPG brand splitting retail media spend 58% Amazon, 22% Walmart, 12% Kroger, and 8% across everyone else, roughly matching the prior year’s split.
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They contrast that habit with Keen Decision Systems figures showing $2.35 profit ROI at Kroger and $1.85 at Instacart, both above Amazon, Walmart, and Target.
The post points to Kroger’s closed-loop loyalty data across more than 60 million households as a measurement advantage and notes Kroger retail media investment growing 54.2% year over year.
The approach brands can evaluate: allocate by platform profit ROI and incremental volume, not by default budget share.
For brands planning a campaign
For a CPG allocation review, ask for profit ROI and incrementality by platform, then test shifting marginal spend rather than resetting the whole budget at once. Use the current Amazon share as a baseline and require measured support for any platform allocation above or below it.
Audit product content before scaling AI-driven ad formats
Hariharan’s Amazon advertising post links AI-powered Sponsored Prompts to a reported 48% higher conversion rate and 21% larger basket among shoppers who interact with them.
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The post says the AI reads product attributes, bullet points, and reviews, so rich, specific product data gives the system useful material while thin or stale data produces weaker responses. They also say content and media teams feed the same AI engine, making disconnected teams a performance problem, not just an organizational one.
For brands planning a campaign
Start a product-data audit with the fields an AI shopping assistant would use: attributes, bullets, reviews, and category detail. Fix gaps before increasing spend, and bring content and media owners together for the same product-level performance review.
Plan retail media events around conversion and stock availability
Hariharan’s Prime Day 2026 recap reports traffic down 10% year over year, unit conversion up 17%, and revenue 1.7% below the prior year.
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The recap also reports stockout-driven revenue loss up 173%, 88% of ad spend consolidated into Sponsored Products, and sharp category differences in ROAS, with Beauty nearly flat and Tools & Home Improvement down 38%. They attribute part of the conversion shift to Alexa for Shopping and agentic recommendations shortening the funnel, and argue that inventory discipline mattered as much as ad spend.
For brands planning a campaign
For an event plan, set separate targets for traffic, conversion, and in-stock rate, and review category-level ROAS before shifting spend. Treat stock availability as a media decision, not just an operations issue.
Put AI governance and measurement ahead of AI adoption claims
Hariharan’s enterprise AI post lists five operational risks: unmanaged personal accounts, hallucination costs, biased screening tools, weak links between AI output and business results, and insufficient worker training or job redesign.
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The post cites McDonald’s AI ordering shutdown, a Workday screening lawsuit, Air Canada’s hallucinated policy, and Samsung’s data leak, and says most companies lack AI-specific liability coverage. They argue the practical risk is bad deployment, not a sudden takeover.
For brands planning a campaign
Before expanding AI-assisted retail media or content work, assign clear ownership, document guardrails, and set a business-result measure for each use case. Review training, escalation paths, and liability coverage so campaigns do not depend on unmanaged AI use.
The posts do not disclose a sponsorship, client, affiliate, or investment relationship with a named brand.
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They contain sustained analysis of Amazon, Walmart, Kroger, Instacart, and Target, showing which platforms Hariharan tracks for CPG retail media work. One post says they ran an AI company but does not name it; ask about any business relationship with that company.
Amazon
Mention
Hariharan analyzes Amazon's advertising business, citing $19.8 billion in quarterly ad revenue and AI-powered Sponsored Prompts.
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They argue that CPG brands need structured product data to benefit from the system.
In a post on retail media allocation, Hariharan cites Walmart's 22% share of a CPG brand's spend and 23.1% growth, contrasting its return profile with Kroger and Instacart.
Hariharan cites Kroger's $2.35 profit ROI, 54.2% year-over-year retail media investment growth, and loyalty data reaching more than 60 million households to explain why the network can outperform larger platforms on return.
Guru Hariharan's posts offer a working feed for CPG teams trying to make retail media and AI-driven product discovery pay off.
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Start with the retail media allocation post, which contrasts a brand's carry-over Amazon-heavy split with reported profit ROI at Kroger and Instacart.
Then read the Amazon Sponsored Prompts post on how product attributes and reviews feed AI advertising, the Prime Day 2026 recap on conversion, stock, and category-level ROAS, and the enterprise AI post on deployment risk.
In that order, the posts move from budget choices to the content and inventory conditions behind them, then to the governance questions raised by the same technology.
The posts are argument-led and draw on platform and third-party figures they cite.
Brett also works from public retailer datasets, turning them into numbered implications for CPG teams.
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His posts separate overall retail growth from digital growth using Q1 2026 comparable-sales and digital figures for Walmart, Kroger, Costco, and Home Depot, then offer a short list of actions.
Hariharan does the same with retail media profit ROI and platform allocation.
Both treat a data point as the start of a decision, not the conclusion.
Brett's posts cover ecommerce growth across retailers and direct readers to marketplace execution, content, media, and inventory as one commercial plan for his own firm's services.
Hariharan goes deeper into a single channel's mechanics: ad formats, product attributes that feed AI recommendations, stockout losses during an event, and category-by-category ROAS.
Readers seeking the broad investment case should start with Brett; those looking for platform-level retail media detail should start with Hariharan.
Brownstein writes measured, citation-heavy posts on where advertising money moves and how it should be judged.
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The pharma and TV post strings together iSpot, VAB, Kroger, Instacart, Amazon, and Evercore figures before spelling out what would need to be true for a grocery-pharmacy ad product to work.
The summit post asks what a CMO would show a CFO to justify AI visibility spending.
That pairing of reported numbers with unresolved measurement questions echoes Hariharan's approach to retail media ROI.
Brownstein focuses on pharmaceutical advertising, linear TV and streaming, and conference-level debates about AI visibility and attribution.
Hariharan covers CPG retail media, Amazon ad formats, and product data readiness.
Brownstein is the better follow for pharma and TV ad economics and open questions about funding AI experiments; Hariharan is the better follow for CPG platform allocation and catalog work.
Sinclair covers the same shift Hariharan describes, from shelf and search visibility to AI-mediated recommendations.
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His post on Walmart's tightened 90-character title limit and structured Brand + Product Line + Features + Size + Count format argues that clean product data is what an AI shopping agent can read—the same connection Hariharan draws between product attributes and Amazon's Sponsored Prompts. The OpenClaw post extends the idea to agents that reorder household staples on their own.
Sinclair writes fast news alerts on agentic commerce and marketplace rule changes for brands that need to react quickly to catalog requirements.
Readers tracking AI shopping announcements and catalog changes should follow Sinclair; those weighing how to manage retail media spend and product data should follow Hariharan.
Guru Hariharan's posts speak directly to CPG brand teams working on retail media, Amazon advertising, product data, and AI deployment, making the account topically relevant to a campaign aimed at CPG retail-media decision-makers.
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The LinkedIn account shows 30,002 followers as of 8 October 2026, but the posts provide no measured audience breakdown by job function, seniority, employer, geography, or CPG category. Before placing spend, ask for LinkedIn follower demographics, an audience survey covering job function, seniority, and CPG category, and recent sponsored-post results for a comparable retail-media or product-data campaign.
Guru Hariharan's four posts offer no details about partnerships or sponsorships.
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They are commentary on retail media, CPG e-commerce, and AI risk; none describes a sponsored-content format, advertising package, rates, disclosure practice, or process for brands to commission work.
The posts cover Amazon advertising and AI-driven product data, retail media budget allocation across Amazon, Walmart, and Kroger, Prime Day performance, and enterprise AI governance and liability, giving buyers a basis to judge topical relevance.
Ask directly about available formats, deliverables, review steps, pricing, and disclosure practices.
A strong fit for a CPG retail-media and product-data campaign weighing whether to keep defaulting most spend to Amazon or compare profit return and incrementality across Amazon, Walmart, Kroger, and Instacart.
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Hariharan’s posts address that decision directly.
The strongest evidence is an allocation post contrasting a habitual 58% Amazon / 22% Walmart / 12% Kroger / 8% other split with reported profit ROI of $2.35 at Kroger and $1.85 at Instacart, both above Amazon, Walmart, and Target.
An Amazon advertising post supplies the other half of the campaign: AI-powered Sponsored Prompts reportedly convert 48% higher and increase basket size 21%, but only when product attributes, bullets, and reviews give the system useful material.
That connects a product-content audit with a platform rebalance in one campaign.
A Prime Day post adds a useful operating check on conversion, category-level ROAS, and stockout loss.
Choose Hariharan for an advisory or expert-commentary engagement built around those decisions.
Agree commercial terms and request stronger audience data before proceeding.
When to choose a specialist
A poor fit for a DTC skincare or fashion launch requiring short-form video, creator-led product demonstrations, affiliate codes, and paid whitelisting.
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Hariharan’s posts offer text commentary on retail media, Amazon advertising, CPG product data, Prime Day performance, and enterprise AI risk, not evidence of visual product storytelling, short-form video production, lifestyle content, affiliate promotion, or consumer-facing launch work.
The audience information is too thin for a conversion-led consumer campaign: 30,002 LinkedIn followers as of 8 October 2026 is a reach signal, not a measured audience of likely skincare or apparel buyers.
A brand needing creator-produced assets, rapid iteration on visual hooks, and tracked affiliate sales would be buying against unproven capabilities.
Ask for examples of visual consumer campaigns and audience-composition data before considering this work.
A worked campaign example
Campaign angle: “Fix what the AI reads, then move the marginal dollar.”
Run a six-week product-data and retail-media review rather than resetting the whole budget at once.
Week 1 establishes the baseline: current spend by platform, profit ROI, incrementality where available, and the existing split.
Treat a split such as 58% Amazon, 22% Walmart, 12% Kroger, and 8% other as the starting map, not the target.
In weeks 2 and 3, audit the top-selling SKUs against the questions an AI shopping assistant would answer.
For a laundry detergent brief, check whether attributes, bullet points, and review themes clearly address sensitive skin, scent, load size, and format.
Record every thin or stale field and fix the highest-traffic products first.
Week 4 sets the measurement rule: compare profit ROI and incremental volume by platform, not impressions or reach alone.
In weeks 5 and 6, run a controlled test that holds total budget steady and moves 10-15% of marginal Amazon spend into Kroger or Instacart test cells, where closed-loop purchase data can show whether the spend created new volume or only pulled forward existing demand.
Add an event-readiness check before any Q4 push: confirm stock cover on promoted SKUs, set category-level ROAS thresholds, and review whether the brand is visible when an AI assistant recommends a product.
Deliverables: a one-page content-gap audit, a revised allocation with a control group, a measurement dashboard, and written decision rules for scaling or reversing the shift.
Hariharan’s published arguments can anchor a LinkedIn commentary series or working session around the plan.
Agree scope, deliverables, disclosure, and terms directly; no sponsorship format or rate card is documented.
Choose Brett when the brand needs to make the internal case for treating eCommerce as its own investment line, not just decide which ad platform gets the next dollar.
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Brett’s October 2026 posts use a dataset of 52 public U.S. retailers and roughly 3,300 points traced to filings, separating retailer growth from digital growth across individual retailers: Walmart at +2.6% comparable sales against +24% digital, Kroger at +1.0% against +19%, and Costco at +6.8% against +21%.
The Amazon grocery post adds demand-side evidence for a brief that includes Amazon expansion: perishables shoppers spend $5,121 a year against $3,771 for the average Amazon shopper, shop Amazon 105 times a year against 76, and represent only about a quarter of shoppers.
If leadership still budgets eCommerce as a smaller copy of the store business, Brett addresses that objection more directly than Hariharan, whose posts assume the reader already accepts reallocation.
Brett’s posts draw on Pattern’s services and NielsenIQ data, and each closes with a Pattern, podcast, community and newsletter sign-off.
The result is more vendor positioning and fewer neutral decision rules.
The material does not compare profit returns across Kroger, Instacart, Amazon, Walmart and Target, or cover AI product-data readiness and how a shopping assistant reads attributes, bullets and reviews.
A brand that still needs the marginal-dollar test and content-gap audit can take the framing from Brett and the audit mechanics from Hariharan.
Choose Brownstein when the retail media plan runs into health-category rules or the key question is what a finance team would fund again.
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One post examines pharma’s position in U.S. linear TV—13% of national linear spend in Q3 2025, a $5.96 billion full-year Rx figure, and a 5% H1 2026 decline led by a 71% drop in diabetes and blood-disorder advertising, including GLP-1s—then connects it to Kroger and Instacart’s August 2026 combined grocery and prescription delivery across more than 2,200 pharmacies.
It names the constraints a brand would face before spending: HIPAA authorization for prescription records, Meta and Google health-targeting limits, and Instacart’s ban on Rx marketing.
The second post, from the EMARKETER Future of Digital Summit in September 2026, frames AI investment as an approval problem: what a CMO would show a CFO to call paid AI visibility successful, and what would move the spend from an experimental line to a recurring one.
For a plan touching pharmacy refills, loyalty frequency or AI budget justification, that framing is more useful than a retail-media ROI comparison.
These posts cover pharma advertising and pharmacy delivery economics, not CPG product content.
The EMARKETER post poses open questions rather than recommending a method: it helps a team structure the funding conversation but offers no allocation rule or audit checklist.
Much of the detail is attributed to iSpot, VAB, Evercore and Crossix.
The author also states where terms could not be confirmed with Amazon or where Kroger policy could not be found, so check the figures before reusing them in a client plan.
Choose Sinclair when the immediate blocker is whether an AI shopping agent can read the catalog at all.
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One post covers Walmart tightening title limits from 150 to 90 characters and adopting a fixed Brand + Product Line + Key Features + Size + Count structure.
It ties the change to statements about Walmart’s Sparky agent: weekly active users up over 100% quarter on quarter, units purchased through Sparky up more than 4x, and Sparky customers carrying a 35% higher average order value.
The other post covers agentic commerce infrastructure: OpenClaw’s growth, Peter Steinberger joining OpenAI, and OpenAI’s Instant Checkout work with retailers including Walmart and Etsy.
It provides the execution layer for the “rich, specific, and structured” product data Hariharan describes, while spelling out what marketplaces now require.
A brand that needs to reformat listings this quarter before an agent stops recommending it gets a faster starting point here.
Both posts are short alerts with “like and comment” calls pointing to gated breakdowns.
They give the headline and a partial argument, not the full method.
Neither covers profit ROI, incrementality or budget allocation across platforms, leaving out the spending side of the campaign-fit task.
Claims about Walmart’s rejection pattern, Sparky’s metrics and the OpenAI hire appear without a linked primary source in the post text; use them to flag a catalog change for verification, not as settled policy.
This profile is based on the full text of four LinkedIn posts by Guru Hariharan published in 2026.
They cover retail media allocation across Amazon, Walmart, Kroger and Instacart; Amazon AI-powered Sponsored Prompts and product data; Prime Day 2026 results; and enterprise AI risk.
Three similar-creator comparisons draw on two posts each.
Figures and examples are reported as the posts present them and were not independently verified.
The posts here do not include older posts, transcripts, other channels, audience demographics or commercial terms, so career history, audience composition and sponsorship options remain open questions.
