# Cory Lindholm

Cory Lindholm writes about paid search and Google Ads, especially AI-driven bidding and advertising for complex e-commerce catalogs. Their posts focus on measuring performance against business margins and keeping human oversight over automated campaigns.

- Profile: https://b2binfluencer.marketing/creators/cory-lindholm/
- LinkedIn: https://www.linkedin.com/in/corylindholm
- LinkedIn followers: 5,432
- YouTube: https://www.youtube.com/channel/UC3-hERfSMvVVYqIw5OE9kDA
- Topics: artificial-intelligence, paid-ads, marketing-measurement
- Based on 11 dated posts, latest 2026-09-25

## Summary

Most useful for paid search and Google Ads analysis on AI bidding, margins, and e-commerce catalog oversight.

- Best for: B2B e-commerce software brands planning a co-marketing webinar on AI-era PPC strategy.
- Not for: Direct-to-consumer lifestyle brands seeking short-form video and lifestyle-led content on TikTok and Instagram with reach expectations.
- Check first: Confirm availability, rates, deliverables, approval, exclusivity, disclosure, and audience breakdown before booking.

### Takeaways

- Smart Bidding Exploration flexible ROAS may find orders but can spend on low-margin products.
- Feed errors and unchecked automated decisions can waste spend across a complex catalog.
- AI search visibility connects to product-feed and schema quality, though paid ad effects vary.

## Main ideas

- **AI in Google's ad stack makes human strategy more valuable, not obsolete**: Reacting to Google Marketing Live 2025, Cory Lindholm argues that new AI in Ads, GA4 and Merchant Center makes experts and custom analytics more—not less—valuable for large, complex e-commerce accounts. The post gives three reasons. Google's AI optimizes within Google's ecosystem, tuning platform metrics rather than a client's profit margins and goals. Treating AI output as a black box leads to mediocre results; the value lies in interrogating recommendations and understanding why they were made. And if every agency uses the same tools the same way, the edge must come from elsewhere: unique data interpretation, custom automation logic and statistical methods layered on top. The conclusion: a shift from campaign operator to what the post calls an AI Orchestrator and Business Strategist.
- **Judge automated bidding against your own margin math, not the platform's ROAS**: On Google's Smart Bidding Exploration — described by Google as its biggest bidding update in a decade, letting AI flex Target ROAS in real time — Cory Lindholm focuses on the trade-offs, not the announcement. Flexible targets cut both ways: ROAS can slide as the system chases incremental orders, which is acceptable only when contribution-margin math is solid. Exploration happens out of sight: the algorithm tests queries the interface never surfaces. On a large, messy catalog, that can direct budget to low-lifetime-value products unless clean product-level signals are in place. Attribution still lags the new placements, widening the gap between platform-reported value and real value. The recommendation: let the model scout beyond the existing keyword list, then measure the gains against the brand's own margin benchmarks before scaling.
- **Feed and schema quality is now part of paid search, because AI answers draw on it**: Cory Lindholm makes the case for a zero-click shift: Google rolled AI Overviews out to every U.S. searcher, overall click-through rates fell roughly 30 percent according to BrightEdge, and Search Engine Land counts 58 percent of searches as zero-click. Seer Interactive data shows retail paid-ad CTR falling from 21 percent to 10 percent when the AI answer block appears. The post also repeats the source's caveat: AI Overviews appeared in only 7 percent of its paid dataset and 2.2 percent of impressions, so ads and AI summaries are rarely paired, but the damage is severe when they are. AI summaries draw from product feeds, schema and E-E-A-T snippets—areas many paid teams never touch—so a competitor's page or a hallucination can supply the answer a brand should own. The proposed fixes: audit feed and schema quality as you would Quality Score, track 'answer-share' alongside impression share, and cross-train on generative and answer engine optimization.
- **Automated spend needs a named owner: AI decides at SKU speed, while the P&L reacts at human speed**: On the agentic capabilities announced at Google Marketing Live 2025, Cory Lindholm highlights a speed mismatch: AI makes decisions at SKU speed while a profit-and-loss statement bleeds at human speed. The post cites McKinsey Digital's finding that executives underestimate internal generative-AI adoption by a factor of three, so guardrails may be missing without anyone knowing until after the damage. Complex catalogs amplify the risk: data gaps or price swings can send models off course, turning today's feed error into tomorrow's out-of-stock ad drain. The operational question: who guards the guardrails when spend spikes?
- **Holiday results are decided weeks before the traffic arrives, and no template fits every catalog**: Cory Lindholm argues that Q4 paid search is a project, not a light switch to flip the week before Thanksgiving. Cyber Monday cleared 13.3 billion dollars online last year, per Adobe Analytics, and reliable November performance depends on early creative pressure-testing, feed tuning and time for Smart Bidding to learn. If the first creative variant fails when CPMs spike, there is no time for a redo. No single split test or campaign template works across every catalog: product mix, margins and auction volatility shift faster than a flash sale. The edge comes from asking sharper questions sooner and acting while clicks are cheap.

## Work to read

- [GML 2025: AI in Ads, GA4, and Merchant Center does not make analysts obsolete](https://www.linkedin.com/posts/corylindholm_googlemarketinglive2025-ppc-paidsearch-activity-7331324229902123008-UxZW)
- [Smart Bidding Exploration: what Google's biggest bidding update in a decade asks of your margin math](https://www.linkedin.com/posts/corylindholm_gml2025-ppc-paidsearch-activity-7331662932319096832-tv1p)
- [AI Overviews and the zero-click problem: audit feeds and schema like Quality Score](https://www.linkedin.com/posts/corylindholm_ppc-paidsearch-googleads-activity-7333154205995614209-7zy7)
- [Guardrails for agentic AI: who supervises the model when spend spikes?](https://www.linkedin.com/posts/corylindholm_gml2025-ppc-paidsearch-activity-7333882741706395648-wymL)
- [Black Friday prep is a project, not a switch flipped the week before Thanksgiving](https://www.linkedin.com/posts/corylindholm_ppc-paidsearch-googleads-activity-7336416257350488064-D8Hj)

## Brand relationships

- **Google** (mention)

## Partnerships

Cory Lindholm’s posts analyze Google Ads, paid search, AI bidding, and e-commerce campaign risk. Two invite readers to direct-message them for a data-science or risk review, framed as an account or campaign conversation rather than a sponsorship offer. For a co-marketing webinar or paid-search thought-leadership campaign, ask directly about availability, rates, deliverables, approval requirements, exclusivity, and disclosure practices.

## Alternatives

- Josh Gregory: Choose Josh Gregory when the webinar should focus on scaling e-commerce ad spend profitably rather than platform AI announcements. The available posts identify failures that emerge once Meta or Google budgets pass roughly £25–30K a month: cold-audience funnel gaps, Google and Meta planned in isolation, and no lifetime-value or offer strategy. They also give structural fixes: weekly creative testing cycles, margin and customer-acquisition-cost checks before budgets increase, offers that work for first-time buyers, funnel mapping from awareness to purchase, and landing pages built for cold clicks. The second post addresses a symptom many audience members will recognise—spending more without sales moving—and attributes it to targeting widening and conversion rates falling when spend rises too fast. It offers three remedies: campaign structure that controls where budget goes, audience signals and exclusions to keep targeting sharp, and search terms, feeds, and creative aligned to intent. The post closes with an offer of free Google Ads audits to e-commerce brands, giving a co-marketing partner a natural joint offer to promote. For a software product focused on account structure, creative testing, or cross-channel planning, this is a closer fit than a session on bidding features.
- Muhammad Salman Arshad: Choose Muhammad Salman Arshad when the webinar audience is early-stage e-commerce founders rather than paid-search practitioners. The first post diagnoses why e-commerce startups fail: margins that were never modelled, illustrated with a Rs. 3,000 sale against Rs. 1,500 acquisition and Rs. 500 operations and delivery; logistics and third-party delivery failures that damage the customer experience; creative fatigue from running one good ad into the ground; founder burnout from one person covering product, marketing, logistics, service, content, and hiring; cash-flow timing against gateways, returns, and supplier payments; and no repeat customers. It ends with three self-check questions on profitability after returns and shipping, control over fulfilment, and surviving 60 days without sales—ready-made workshop material. The second post covers the shift from Fiverr seller to agency owner and credits LinkedIn visibility, paid and organic content, and higher-value long-term clients with the change. It suits a session for agencies or consultants on client acquisition and positioning.
- Rupert Morris: Choose Rupert Morris when the campaign extends beyond paid search and targets B2B marketers generally rather than e-commerce account operators. The first post is a practical AI-adoption briefing. It describes changes brought by two new model releases, including working inside email, docs, and CRM without copy-paste; following house templates so decks and documents come out in the right format; and Claude becoming the default model inside Slack and arriving in Salesforce. It then recommends a concrete first step: take the team's most repetitive task, use the real template and real data, see how close the model gets, and reinvest the recovered hours in client and team work. The second post tackles zero-click search for B2B marketers: over 68% of searches in the first four months of the year ending without a click; buyers discovering, comparing, and forming opinions of a brand without visiting its site; attribution showing only a late interaction in a much longer journey; and the case for measuring something other than a linear path. This is the same question Cory Lindholm's AI Overviews post raises, applied to a services audience rather than a retail one, so the two could share a panel on measurement without repeating each other.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/