Prompt

What's the most cost-effective way to scale prospecting campaigns using an open auction network?

Advertising · Ad Networks / Ad networks1 observationLast seen Jul 25, 2026

Latest observation

Jul 25, 2026 · OpenAI APIWeb search: off

The most cost-effective way is usually to scale only the highest-intent audiences in the open auction, then optimize relentlessly toward marginal CPA/ROAS instead of raw reach.

A practical approach:

  1. Start with narrow, high-signal targeting

    • Use first-party data, lookalikes, site visitors, CRM lists, and in-market/contextual signals.
    • Avoid broad prospecting until you have strong conversion signals.
  2. Use value-based bidding or tCPA/tROAS where possible

    • Let the auction find cheaper converters, not just cheap clicks.
    • Feed it enough conversion volume for the algorithm to learn.
  3. Prioritize efficient inventory

    • Exclude low-performing placements/apps/sites.
    • Focus on placements with strong viewability, fraud controls, and historical conversion rates.
  4. Build layered audiences

    • High-intent segments first, then expand incrementally.
    • Scale from seed audiences to modeled audiences before broadening to general prospecting.
  5. Optimize creative before increasing spend

    • In open auction, creative often drives the biggest efficiency gains.
    • Rotate multiple variants and cut losers fast.
  6. Use frequency caps and recency controls

    • Prevent waste from overexposure.
    • Tighten caps as you scale.
  7. Scale budget gradually

    • Increase spend in small steps to avoid resetting learning or bidding too aggressively.
    • Watch CPA, conversion rate, and impression quality as budget rises.
  8. Measure incrementality

    • Don’t just chase cheapest attributed conversions.
    • Use holdouts or geo tests to ensure the open auction spend is adding real net-new business.

If you want, I can turn this into a step-by-step scaling playbook for DSPs like DV360, The Trade Desk, or Meta-style open auction buying.

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How did Obsurfable measure this prompt?

Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.

Which AI systems does Obsurfable collect answers from?

OpenAI, ChatGPT, Google, Gemini, Google AI Mode, Anthropic, Claude, Perplexity, Grok, DeepSeek, Mistral, Copilot, and Meta AI.