ChatGPT ads won’t dent holiday sales this year

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• Why many advertisers are treating ChatGPT ads as an experiment
• Which companies are investing in OpenAI ad inventory
  • Who’s spending and why
• Key obstacles: pricing, targeting, and user trust
  • 1. Cost pressure
  • 2. Limited audience control
  • 3. Credibility and relevance
• What marketers are learning from early tests
• Voices from the industry: cautious optimism and skepticism
• How some teams structure experiments on AI platforms
• Where ChatGPT ads fit into a broader media plan
• Data and partnerships shaping advertiser decisions

As retailers prepare for the busiest weeks of the year, many marketers are watching ChatGPT ads with cautious curiosity. The ad product from OpenAI has attracted attention fast, but most brands say it won’t be the cornerstone of holiday campaigns. Advertisers describe the channel as experimental, expensive, and still earning user trust.

Why many advertisers are treating ChatGPT ads as an experiment

Since ad placements rolled out earlier this year, marketers have rushed to test how AI-driven answers can surface products. Yet the rush hasn’t translated into confidence. Several digital ad leaders tell a similar story: ChatGPT delivers discovery moments, but not dependable purchase lifts.

• Timing matters: Fourth-quarter budgets are precious. Marketers hesitate to divert funds to unproven channels ahead of peak shopping days.
• Measurement gaps: Many teams lack validated methods to prove that ChatGPT ads drive incremental sales.
• High cost per click: Early campaigns show ad rates well above search and social benchmarks.

Which companies are investing in OpenAI ad inventory

Data from industry trackers shows adoption is real, but concentrated. Big brands with larger media budgets dominate the early spend on the platform.

Who’s spending and why

• Financial services, travel, and infrastructure brands have appeared among the top spenders.
• Large advertisers can afford to allocate test budgets and accept uneven early returns.
• Smaller direct-to-consumer brands often wait until the format and pricing stabilize.

Sensor Tower reported a steady rise in unique advertisers month to month since the launch. That growth reflects curiosity more than wholesale migration from established channels.

Key obstacles: pricing, targeting, and user trust

Marketers point to three core barriers constraining scale and performance.

1. Cost pressure

Campaigns currently show a higher click cost compared with legacy search and social platforms. One industry analyst estimated ChatGPT CPMs and CPCs to be several times higher.

2. Limited audience control

Unlike Google or Meta, advertisers cannot yet define audiences the same way. Sponsored items appear within AI answers, not inside a demographic or intent bucket selected by the brand.

3. Credibility and relevance

For AI assistants to convert reliably, they must earn user trust as recommendation engines. Early placements risk feeling intrusive or off-target during a conversational flow.

What marketers are learning from early tests

Brands that have run pilots share similar takeaways. Results are uneven. Clicks may be plentiful, but conversions often lag. In many cases, ad exposure reaches users who already planned to buy, producing low incremental value.

• Discovery first, conversion later: AI assistants appear to work well for product discovery and awareness.
• Non-incremental sales are common: Many purchases simply shift channel attribution rather than increase total demand.
• Walled garden limitations: Attribution and optimization tools are not yet mature within AI platforms.

Voices from the industry: cautious optimism and skepticism

Executives from measurement and creative firms describe a balance of risk and opportunity. They encourage testing but warn against reallocating core budgets yet.

• One measurement leader advised that brands seeking efficient, immediate ROI should favor channels with proven performance.
• Another ad-tech founder noted that many clients treat AI slots as learning labs to understand how product mentions appear inside answers.
• Early adoption can provide a competitive edge if a brand is prepared to invest a small learning budget.

How some teams structure experiments on AI platforms

Advertisers that press forward are crafting controlled, low-risk trials. Their approaches are practical and focused on data.

1. Set a modest “learning budget” separate from holiday spend.
2. Run short creative and placement tests to see how products surface in recommendations.
3. Compare results against matched-control groups on search and social to assess incrementality.
4. Monitor cost-per-click and conversion rates closely, and cap spend if ROAS falls below thresholds.

Where ChatGPT ads fit into a broader media plan

For now, marketers say AI-assisted ads are best viewed as a discovery channel that complements search and social. Brands that rely on Meta and Google for direct response are not shifting significant budgets.

• Use AI placements to spotlight new products or to test messaging in conversational contexts.
• Keep core acquisition funnels on platforms with established attribution models.
• Reserve experimentation for brands or categories that benefit from curated suggestions.

Data and partnerships shaping advertiser decisions

Partnerships between measurement firms and AI platforms aim to improve transparency. Marketers hope these integrations will reduce friction and answer crucial ROI questions.

In the meantime, many advertisers will continue a wait-and-see stance. They want clearer benchmarks and lower costs before committing larger shares of their seasonal budgets.