> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Audience Overlap with Email Programme, Google Ads > Re-acquiring buyers you already own via email = wasted spend. Exclude email subscribers from prospecting campaigns. How to read it, why it matters, and how... **Metrics type:** [Cross-Platform Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ad Platform](/nerve-centre/connectors#connectors-by-type) > Re-acquiring buyers you already own via email = wasted spend. Exclude email subscribers from prospecting campaigns. ## At a glance > Cross-platform card: percentage of Google Ads converters who are ALSO active subscribers in the email programme (Klaviyo, Dotdigital, Mailchimp). High overlap means you are paying Google Ads to reacquire customers you already own via email. The fix is to exclude email-subscribers from prospecting (cold-acquisition) campaigns, freeing budget for genuinely new customers. | | | | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **The formula** | Cross-connector join. Numerator: Google Ads converters in the window (matched to commerce-platform customer IDs via the conversion pixel's user-ID enhancement). Denominator: same converters intersected with the email-platform's active subscriber list. Result: `(matched ÷ all_gads_converters) × 100`. | | **GAQL resource + metric** | `FROM customer` selecting `metrics.conversions` aggregated to user-IDs. Cross-joined to email-platform subscriber list (Klaviyo's lists, Dotdigital's address books, etc.) on email hash or commerce-platform customer ID. | | **Account currency (single by design)** | Not currency-relevant (the card is a percentage). Spend implications can be derived in account currency by multiplying overlap percentage by total Google Ads spend. | | **Conversion attribution model (configurable)** | Numerator uses Google Ads' configured attribution model (DDA default for new accounts, Last click for older). The overlap is sensitive to this only insofar as the model affects which conversions enter the numerator. | | **View-through inclusion (excluded by default)** | Primary `conversions` only. View-through conversions don't typically have a clear user-ID match, so they would be excluded from the join anyway. | | **Bot / IVT filter** | Numerator pre-filtered by Google's Invalid Click Filter; bots don't enter the conversion list. The email-platform side is also human-verified (subscribers actively opted in or were imported as customers). | | **Micros conversion** | Not applicable to this card (the spend implication is derived, not surfaced). | | **Real-time vs ingestion lag** | Both sides have lag: Google Ads conversions take 1-4 hours to ingest; email-platform subscriber updates depend on the platform (Klaviyo: real-time; Dotdigital: 5-minute batch). The card refreshes every 4-6 hours. | | **MCC aggregation** | Per child account; the email programme is per-store typically, so each Google Ads account joins to one email platform. Multi-store setups need explicit configuration. | | **Time window** | `30D` (the same window as primary ROAS reporting, allows clean comparison). | | **Alert trigger** | `> 40%` overlap. At this level, more than four in ten Google Ads converters are already on the email list, money likely wasted on re-acquisition where email could have done the work. | | **Roles** | owner, marketing | ## Calculation Calculated automatically from your Google Ads data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading. ## Worked example A UK fashion brand. The 30-day window covers 14 Mar 26 to 12 Apr 26. | Bucket | Count | % of Google Ads converters | | ----------------------------------------------------------------- | ----- | -------------------------- | | Total Google Ads converters (30D) | 996 | 100% | | Of which: also active Klaviyo subscribers | 612 | 61% | | Of which: also active AND placed an email-attributed order in 30D | 218 | 22% | | Of which: NEW to brand (no prior order) | 384 | 39% | The card reads **61%** (the first overlap row). What this scenario tells the analyst: 1. **61% overlap is high.** At account-average \~£27.50 CPA, the brand spent \~£16,800 on Google Ads acquiring 612 customers already on the email list. Of those, Klaviyo flow-revenue suggests email would have driven \~40-50% of those sales anyway (£6,700-£8,400 of "self-cannibalised" Google Ads spend). Action: exclude active Klaviyo subscribers from non-branded prospecting campaigns (Search Display, PMax prospecting), keep them included for retargeting and Branded Search. 2. **The 22% who placed an email-attributed order in window are the highest-confidence overlap.** Those customers are actively engaging with email; paying Google Ads to bring them in is direct duplication. \~£6,000 of spend. 3. **The 39% NEW-to-brand are exactly who Google Ads SHOULD be reaching.** Budget reallocation should keep Google Ads focused on this cohort. The number to grow is "% of converters who are NEW", not the overlap percentage itself. 4. **Excluding email subscribers from prospecting saves budget AND improves attribution clarity.** When Google Ads only acquires NEW customers, ROAS is more meaningful and can be benchmarked against other paid channels' acquisition cost. With overlap blurring the picture, ROAS comparisons across channels are biased. 5. **Compare to industry benchmark.** Healthy DTC overlap is 25-40%; below 25% means email programme is small (build it); above 50% means re-acquisition through paid is wasteful. This brand at 61% is on the high side; a typical 6-12 month investment in email-programme growth + paid-audience exclusion brings it to 40-45%. Quick sanity tests: * Overlap > 60%: review paid prospecting strategy. Most overlap typically lives in PMax and Discovery, where audience signals are coarse. Add customer-list exclusions. * Overlap \< 25%: email programme is under-developed. Invest in Klaviyo automations and list growth. * Overlap rose +10pp month-on-month: paid channels shifted toward retargeting (more matches with subscribers); audit campaign mix. * Overlap dropped sharply: either the email list lost many subscribers (check email-platform health), or Google Ads is reaching genuinely new audiences (good news). * Multi-store / multi-region: each store's overlap is per-store; international stores may have different overlap norms. ## Sibling cards merchants should reference together | Card | Why pair it with Audience Overlap | | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | [Google Ads ROAS](/nerve-centre/kpi-cards/google-ads/roas) | The headline that overlap inflates. With 61% overlap, ROAS includes self-cannibalised customers; "true acquisition" ROAS (excluding overlap) is a cleaner read. | | [Google Ads ROAS by Campaign](/nerve-centre/kpi-cards/google-ads/roas-by-campaign) | Branded Search overlap is high by definition (subscribers know the brand); Display Prospecting overlap should be low. Use to identify which campaigns are most overlap-heavy. | | [Google Ads xc Revenue Share](/nerve-centre/kpi-cards/google-ads/google-ads-revenue-share) | Channel-concentration view; this card adds the within-paid quality dimension. | | [Klaviyo Total Revenue](/nerve-centre/kpi-cards/klaviyo/email-attributed-revenue) | The email-programme revenue contribution. High overlap with Google Ads suggests email could drive more of these conversions itself. | | [Klaviyo Subscribers](/nerve-centre/klaviyo/klv_subscribers) | The email-list size; a small list means low overlap regardless of paid quality. | | [Dotdigital Subscribers](/nerve-centre/dotdigital/dd_subscribers) | Same role for Dotdigital users. | | [Shopify Customers](/nerve-centre/shopify/customers) | Total customer base. Overlap between Google Ads converters and total customer base (returning vs new) is also informative. | | [GA4 New vs Returning](/nerve-centre/google_analytics/ga_new_vs_returning) | Independent measure: GA4's new-vs-returning user split. Should align directionally with this card's overlap. | ## Reconciling against the vendor's own dashboard **Where to look in Google Ads UI:** This is a cross-connector card with no native Google Ads UI equivalent. The closest reference points: [Google Ads > Audience manager > Customer match lists](https://ads.google.com/aw/audiences) lets you upload your email list as an audience, then either include (for retargeting) or exclude (for prospecting) it from campaigns. The overlap percentage shown there is Google's match rate (typically 60-80% of uploaded emails match Google accounts), not the conversion-overlap metric this card computes. [Google Ads > Insights > Audience insights](https://ads.google.com/aw/audiences/insights) shows broad demographic / interest segments of your converters; not directly comparable. [Klaviyo > Audience > Performance](https://www.klaviyo.com/) for the email-side counterpart: which Google Ads-acquired customers become engaged email subscribers. Other views that *look* like this number but aren't: * **Google Ads Customer Match audience size**: the number of your subscribers Google could find in their network; not a conversion-overlap metric. * **GA4 "New vs Returning" users**: directionally similar but uses Google's first-touch logic, not commerce-platform customer history. Differs by 10-20pp typically. * **Klaviyo's "Acquisition Source" tagging**: shows which channel acquired each subscriber; useful but answers a different question. * **Marketing-mix model output**: the econometric estimate of channel overlap; useful but slow. **Why our number may differ from vendor UIs (rare):** | Reason | Direction of divergence | | --------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | | User-ID matching depends on enhanced conversions or Customer Match data quality. Some Google Ads converters have no resolvable user ID. | Card may understate true overlap if pixel sends fewer user IDs than actual buyers. | | Email-platform subscriber freshness. A customer unsubscribed yesterday is not in today's match. | Marginal; rolling 30-day window absorbs most of this. | | Hashing differences. Google Ads uses SHA-256 of normalised email; some email platforms use different normalisation rules. | \<2% match-rate difference typically. | | Currency. Not relevant for this card. | None. | **Why the BUSINESS metric often differs (the IMPORTANT one):** The "wasted spend" implication of high overlap depends on what the email programme would have done in the absence of paid: * **A robust email programme with abandoned-cart, browse-abandonment, and back-in-stock automations** would have recovered most of the overlapping conversions independently. Paid spend on these customers is largely cannibalisation. Action: exclude email subscribers from prospecting. * **A weak email programme with only a monthly newsletter** wouldn't have driven those purchases. Paid spend on email subscribers is still acquisition (just from a known audience). Action: invest in email programme first, then exclude. * **High-intent, time-sensitive products (sale, drops)**: email can't always reach customers in real time. Paid surfaces the announcement. Overlap is OK if paid is genuinely catching customers in their high-intent moment. * **B2B / wholesale**: email lists may be sales-team contacts, not consumers. Overlap with Google Ads converters is meaningless if the email programme isn't sending to retail buyers. Cross-connector reconciliation: | Card | Expected relationship | What causes legitimate divergence | | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------ | | [`klaviyo.klv_total_revenue`](/nerve-centre/kpi-cards/klaviyo/email-attributed-revenue) | Email-attributed revenue. High overlap + high email revenue = email could absorb the paid spend. | None expected, complementary view. | | [`klaviyo.klv_subscribers`](/nerve-centre/klaviyo/klv_subscribers) | List size sets the ceiling on overlap. Small list means low overlap. | None expected. | | [`shopify.customers`](/nerve-centre/shopify/customers) (returning vs new split) | Returning-customer share of orders should align with overlap directionally. | Returning customer != email subscriber; some returning customers unsubscribed. | | [`google_analytics.ga_new_vs_returning`](/nerve-centre/google_analytics/ga_new_vs_returning) | GA4's new-user share inversely correlates with this card. | GA4 first-touch logic; different timing. | ## Known limitations / merchant FAQs **My overlap is 65%, what's my action?** Three steps. (1) Upload your email list to Google Ads as a Customer Match audience. (2) Exclude that audience from prospecting / cold-acquisition campaigns (Display Prospecting, PMax with broad audience signals, non-branded Search). (3) Keep the audience INCLUDED for retargeting and Branded Search. Within 30 days expect overlap to drop 10-20pp and net-new acquisition to rise. **Why does Branded Search typically have very high overlap?** Customers searching your exact brand name are typically already aware of you, often already subscribers. Branded overlap of 70-90% is normal. Don't try to lower this; instead, recognise that Branded Search is a defensive (intent-capture) channel, not a prospecting channel, and accept the overlap there. **Should I aim for 0% overlap?** No. Some overlap is healthy: customer-list audiences in Customer Match for retargeting drive efficient repeat purchases. Healthy overlap is 25-40%; below 25% means email programme is small or paid prospecting is exceptionally well-targeted; above 40% means cannibalisation. **My PMax keeps showing email subscribers in conversions even after I excluded them, why?** PMax's audience signals are advisory, not authoritative. Google's optimiser still surfaces ads to whoever it thinks is most likely to convert, which often includes your email list. The exclusion reduces but doesn't eliminate overlap. Better PMax exclusions: (1) tighten audience signals to lookalike-of-NEW-customers, (2) lower the bidding aggressiveness, (3) consider running prospecting in a separate non-PMax campaign. **Why don't I see this overlap in Google Ads itself?** Google Ads doesn't have direct visibility into your email-platform subscriber list (unless you've uploaded it via Customer Match). Even with Customer Match uploaded, Google shows the list size and basic match rate, not the conversion overlap. This card joins your two systems directly to surface what neither sees alone. **My Customer Match audience match rate is only 50%, not 80%, why?** Customer Match needs Google-account email addresses to match. Your subscriber emails may be (a) generic provider emails (Yahoo, ProtonMail) where Google has lower coverage, (b) work emails not associated with personal Google accounts, or (c) old subscribers no longer using the email. A 50-60% match rate is not unusual; it's not the same as the conversion-overlap metric here. **My email programme is small (5,000 subscribers), why is my overlap 60%?** A small list with high engagement can produce high overlap if the brand has a small repeat-customer base. Each subscriber represents a higher-percentage chunk of your buyer pool. Action: grow the list AND exclude from prospecting. The combined effect is a better overlap reading. **Multi-store: my UK store and US store have separate email lists; how does the card handle?** Per-store. Each store's Google Ads account is joined to its respective email-platform list. UK overlap is computed against UK Klaviyo list; US overlap against US Klaviyo list. **Privacy concerns: does the card actually share email addresses?** No. The join uses one-way SHA-256 hashes of normalised emails (Google Ads' standard Customer Match format). Vortex IQ can compute the overlap without seeing plaintext emails. The match is privacy-preserving by design. **Why is the threshold 40%?** 40% is a rule-of-thumb cannibalisation floor. Below 40%, overlap is mixed (some retargeting, some prospecting reaching subscribers naturally); manageable. Above 40%, you're meaningfully paying to reacquire owned audience; intervention pays off. Industry benchmark for healthy DTC is 30-40%. **Currency-wise, where does the spend implication come from?** The card itself shows a percentage; the £ implication is derived by multiplying overlap × Google Ads spend × estimated email-driven recovery rate (typically 40-50% of subscribers would have purchased without paid). This is an estimate, not a precise dollar value. Use it for decision-making, not for precise budget reconciliation. *** ### Tracked live in Vortex IQ Nerve Centre *Audience Overlap with Email Programme* is one of hundreds of KPI pulses Vortex IQ tracks across Google Ads and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.