FAQ
Frequently Asked Questions
What are audience signals in Google Ads?
Audience signals are hints you provide to Performance Max and AI Max campaigns to help Google's AI understand what your ideal customer looks like. Unlike traditional audience targeting (where you force your ads to only show to a specific audience), signals are non-binding — they tell the AI where to start looking, but the AI can extend beyond your signals if it identifies other high-value audiences.
How does Customer Match work in Google Ads?
Customer Match allows you to upload hashed email addresses, phone numbers, or physical addresses from your CRM. Google matches these to logged-in Google accounts (match rates typically 40–70%) and creates an audience of your actual customers. You can use this for: exclusion from acquisition campaigns, bidding multipliers on remarketing campaigns, and as lookalike seed audiences for prospecting.
What is Enhanced Conversions and why does it matter?
Enhanced Conversions improves Google Ads measurement accuracy in cookieless environments by sending hashed first-party customer data (email, phone) at the point of conversion. Google matches this against logged-in Google account data to attribute conversions that would otherwise go unmeasured. In markets where cookie consent rates are declining, Enhanced Conversions recovers 15–35% of previously lost attribution.
How large does my Customer Match list need to be?
Minimum 100 contacts for basic audience creation. For meaningful performance impact, aim for 1,000+ contacts. For Similar Audience (lookalike) creation, you need 1,000+ contacts. For statistical significance in bid adjustments, 5,000+ contacts is recommended. If your list is smaller than 1,000, focus on growing your email database before expecting Customer Match to meaningfully impact campaign performance.
What first-party data should I prioritise collecting in 2026?
Priority order: (1) Email addresses with opt-in consent — the most versatile signal for Customer Match and Enhanced Conversions; (2) Phone numbers — increasingly used in Malaysian consumer behaviour via WhatsApp integration; (3) Purchase history with transaction values — enables predictive LTV bidding in Smart Bidding; (4) CRM lead quality scores — enables offline conversion value-tiering to train Smart Bidding on quality not just volume.