Google Ads: new and returning customers via shop data

On October 22, 2026, Google announced an expansion of the customer lifecycle goals in Google Ads: high-value new customers are set to become targetable in Shopping campaigns as well, retention goals are coming to Performance Max, Search and Shopping, and percentage uplifts on the value of a new customer are leaving beta (Google). This lets you steer new and returning customers separately in Google Ads. The data is what matters: Google recognizes who is new, who has lapsed and who is high-value only as precisely as the customer lists from your shop define it. This article shows how to derive the segments from the order history, transfer them to Google with consent and set the new customer parameter in the conversion tag before Black Week brings in new first-time buyers. If you wish, our Google Ads integration takes care of the technical side.

What Google announced on October 22

The announcement appeared on Google's US site for advertisers and sums up three changes (Google). First, the mode for high-value new customers, previously available for Search and Performance Max, is being extended to Shopping. Second, after beta tests Google is making the retention goals more broadly available: re-engagement for customers who have not ordered in a while, a mode for members of a loyalty program and one that focuses campaigns exclusively on defined segments of existing customers. Third, percentage multipliers are leaving beta: the uplift for a new customer then grows with the basket instead of being booked as a fixed amount.

Customer Retention Goals are rolling out this month for Performance Max, Search and Shopping campaigns.

Google, Three updates to customer lifecycle goals to help you win the holiday season

For shops, retention is the main novelty, because until now the lifecycle goals were mostly about acquisition. The overview compares the German help page with the announcement.

Why the new modes depend on your customer lists

For customer acquisition, Google can detect existing customers on its own: if conversion tracking records purchases, Google automatically creates an audience list based on campaign activity and tracked purchases of the last 540 days (Google Ads Help). Google itself calls this the easiest but less comprehensive method, because users can remove cookies and opt out. For the new modes, that is not enough. For high-value new customers, for re-engagement and for high-value re-engagement, Google accepts only customer lists from connectors in Google Ads Data Manager, from the Google Ads API or from a direct upload; autodetection is not compatible with these modes (Google Ads Help).

You define what constitutes a "lapsed customer" based on your unique business criteria, whether it's a period of inactivity or a specific lack of engagement.

Google Ads Help, About customer lifecycle goals

So the definition is up to you, and it is created where the orders are. In addition, the modes for high-value new customers and for high-value re-engagement use the same list of high-value existing customers (Google Ads Help). In one mode it serves as a model for similar new customers, in the other it is combined with the list of lapsed customers. A vague definition therefore has an effect in two places.

Black Week: winning first-time buyers and keeping them

The expansion comes a good five weeks before Black Friday, which falls on November 27 in 2026. According to a representative Bitkom survey, about half of the respondents (52 percent) wanted to use the deals around Black Friday and Cyber Week 2025 (Bitkom). Of these shoppers, 69 percent planned to do so online (Bitkom). So the promotion days can also bring orders from people who did not know the shop before.

Some of these buyers follow mainly the price. In 2025, 22 percent of Black Week shoppers described themselves as deal hunters who actively look for bargains, regardless of whether they need the products (Bitkom). Then there are the platforms: 92 percent of the online shoppers surveyed regularly buy on large online marketplaces (Bitkom). A first-time buyer in November is therefore not yet a regular customer. This is exactly where the lifecycle goals come in: win new customers in November, and after the holidays bring back the buyers who do not return with re-engagement.

How to split the budget for these days between Search, search partners and the Display Network is covered in our article on channel prioritization in Google Ads. If you are also planning sale prices in the feed, you will find the details in Black Week in Merchant Center. This article is about the question that comes first: how does Google know who is new and who is an existing customer?

Deriving segments from the order history

Google does not prescribe the segments. The configuration guide states explicitly that the definitions are business specific and that you organize your own data into the segments according to your own definitions (Google Ads Help). For a shop, four fields per customer are usually enough: the date of the last paid order, the number of paid orders within the window, the value after returns and membership in the loyalty program. Four lists result from this.

Google's own example helps with the term high-value: customers who buy twice as many products as others; Google leaves the threshold to your observations (Google Ads Help). Revenue alone is a weak yardstick for this: someone who orders a lot and returns a lot looks high-value but is not. Deduct returns and cancellations and, where the data allows, calculate with the contribution margin. How to calculate customer value over time is covered in our article on customer lifetime value.

The following query shows the principle using a simplified data model. The window, the inactivity limit and the threshold for high-value customers are variables at the top; the values are examples and should be adapted to your product range. For consumables, the usual purchase interval is shorter, for durable products considerably longer.

SET @window_days = 540;
SET @lapsed_days = 180;
SET @high_value_threshold = 500;

WITH customer_stats AS (
  SELECT o.customer_id,
         MAX(o.paid_at) AS last_paid_order,
         COUNT(*) AS paid_orders,
         SUM(o.net_revenue - o.refunded_amount) AS net_value
  FROM orders o
  WHERE o.payment_status = 'paid'
    AND o.paid_at >= CURRENT_DATE - INTERVAL @window_days DAY
  GROUP BY o.customer_id
)
SELECT s.customer_id,
       CASE
         WHEN s.last_paid_order < CURRENT_DATE - INTERVAL @lapsed_days DAY
           THEN 'lapsed'
         ELSE 'existing'
       END AS lifecycle_segment,
       s.net_value >= @high_value_threshold AS high_value
FROM customer_stats s;

One subtlety lies in the window. Anyone who last bought more than 540 days ago counts as a new customer again under Google's default setting (Google Ads Help). The list of lapsed customers should therefore only contain customers whose last purchase falls within this window. Otherwise the same customer is on the list of lapsed customers and is reported as a new customer at the same time on their next purchase.

Guest orders and the loyalty program

Many buyers order without a customer account: 27 percent of the online shoppers surveyed say they only use features that do not require an account (Bitkom). So assign guest orders to the same customer via the normalized email address, otherwise a returning customer appears as a new customer. Where the assignment remains open, Google explicitly provides for the case "not specified" for the new customer parameter, for example with guest checkout (Google Ads Help).

In the loyalty program, membership is unambiguous because it is tied to a customer account. Google needs a member list from Customer Match and the program benefits in the Merchant Center feed (Google Ads Help). Which types of program work well in a shop is covered in our article on customer loyalty programs.

Minimum sizes, durations and processing

Before you slice segments more finely, it is worth looking at the lower limits. A customer list needs at least 100 active members in at least one network to be used in lifecycle goals (Google Ads Help). With fewer than 1,000 members, Google points out that the campaign's ability to acquire new customers may be impacted (Google Ads Help). What counts are active members, that is matched users who are active on the respective network, not the uploaded rows; the match rate after the upload shows how much of the list can be matched (Google Ads Help).

For smaller shops, this means: two robust lists are better than four that are too small. The high-value segment is usually the smallest, so count there first. Further limits that matter for planning:

• Duration: Customer Match memberships last a maximum of 540 days (Google Customer Match policy). To keep a list usable, at least 100 members must have been added or updated within the last 540 days (Google Customer Match policy).
• Processing: An upload can take up to 48 hours (Google Ads Help). Lists for Black Week therefore do not belong on the day before.
• Number: Up to 100 audience lists can be selected to define existing customers (Google Ads Help).
• Access level: Any policy-compliant account can use lists for observation and exclusions. For "Targeting" and manual bid adjustments, Google requires 90 days of account history and more than USD 50,000 in total lifetime spend (Google Customer Match policy).

Consent and Customer Match: what the export must carry

How Customer Match works in principle and why it does without third-party cookies is covered in our article Retargeting without third-party cookies. For the lifecycle goals, what matters is what the export must carry for each customer. The Customer Match policy only allows data you collected yourself in a first-party context, such as a purchase in the shop. Your privacy policy must disclose that you share customer data with third parties who perform services on your behalf. Where the law or Google policies require it, you need consent for this (Google Customer Match policy).

For users in the European Economic Area, Google has anchored this technically since March 2024: customer lists can only be used there if both consent fields are set to GRANTED during the upload, ad_user_data for sending user data for advertising purposes and ad_personalization for ad personalization (Google Ads Help). If the information is missing or set to UNSPECIFIED, Google treats it as missing consent. The status applies to every record in an upload job; customers with different statuses therefore belong in separate jobs.

For the data model, this means: consent is a field on the customer, with a timestamp and a source. There is a willingness to share: 56 percent of the online shoppers surveyed provide data in exchange for discounts (Bitkom), and 62 percent trust retailers not to misuse their data (Bitkom). This trust only lasts if a withdrawal takes effect. Under Art. 7(3) GDPR, consent can be withdrawn at any time (GDPR), and after an objection to direct marketing, the data is no longer processed for such purposes under Art. 21(3) GDPR (GDPR). Google names two ways to handle a withdrawal: remove the customer from the lists or replace the list without them (Google Ads Help).

The automatic list export from the shop

A one-time upload goes out of date quickly: new customers become existing customers, existing customers become lapsed over time, and withdrawals have to arrive promptly. A daily run that transfers only the changes makes sense. For new integrations, Google recommends Data Manager or the Data Manager API and advises against building new Customer Match integrations on the Google Ads API (Google Ads Help). The export in six steps:

1. Calculate segments: only paid orders, returns deducted, guest orders assigned.
2. Check consent: only customers with valid consent, separated by status.
3. Normalize: email addresses in lowercase, phone numbers in E.164 format, for Gmail addresses without periods in the part before the @ (Google Ads Help).
4. Hash: SHA-256 as a hex value (Google Ads Help), so that no address leaves the shop in plain text.
5. Transfer: additions and removals per list, withdrawals as removals.
6. Log: number of rows, match rate and errors per run, so that a shrinking list is noticed.

Normalization and hashing decide the match rate: a trailing space or a capital letter results in a different hash, and the customer is not found. The example shows the steps for an email address:

import hashlib

def normalize_email(email: str) -> str:
    email = email.strip().lower()
    local, _, domain = email.partition("@")
    if domain.removesuffix(".com") in ("gmail", "googlemail"):
        local = local.replace(".", "")
    return f"{local}@{domain}"

def sha256_hex(value: str) -> str:
    return hashlib.sha256(value.encode("utf-8")).hexdigest()

print(sha256_hex(normalize_email(" Kunde.Beispiel@example.com ")))

If the customer data is in a CRM, the segment logic belongs where the complete customer base is; notes on the architecture can be found in our article on CRM integration in the online shop.

The new customer parameter in the conversion tag

The lists tell Google who belongs to the existing customer base. The conversion tag tells Google with every purchase whether it came from a new customer. For this, Google provides the parameter new_customer: true for a customer who has not purchased within the chosen period, false for a returning customer and no value if it is unclear (Google Ads Help). As the period, Google recommends 540 days and sets it as the default, but does not require it (Google Ads Help). If you track purchases via Google Analytics, set customer_type with the values new and returning instead.

gtag('event', 'purchase', {
  send_to: 'AW-CONVERSION_ID/CONVERSION_LABEL',
  transaction_id: '{{ order.number }}',
  value: {{ order.net_value }},
  currency: 'EUR',
  new_customer: {{ order.is_new_customer }}
});

The shop template fills the placeholders in double curly braces. The value for new_customer is calculated on the server at checkout, using the same rule as the list of existing customers; otherwise the tag and the list contradict each other. If the assignment is open, for example for a guest order with a new address, leave the parameter out instead of guessing. If tracking runs through your own server, the calculation belongs there; more on this in our article on server-side tracking for Google Ads.

Setting values and multipliers

Most lifecycle goals work with value-based bidding. For each segment, you store an additional value that Google adds to the purchase value if the buyer falls into the segment. The higher this value, the more the campaign optimizes toward the segment (Google Ads Help). As a starting point, Google gives the following guidance (Google Ads Help):

• New customers: twice the value of a regular customer
• High-value new customers: higher than the new customer value
• Lapsed customers: lower than the new customer value, higher than existing customers
• High-value lapsed customers: higher than lapsed customers, lower than new customers
• Loyalty program members: 1.25 to 1.75 times a regular customer

Google stresses that these values only give a direction. For a shop, the yardstick that counts is the contribution margin of a second purchase after returns and shipping: it shows how much a new customer may additionally cost to acquire.

With percentage multipliers, the form of the uplift changes. A fixed amount has a strong effect on small baskets and hardly any on large ones: an uplift of 10 euros is 20 percent on a basket of 50 euros, but only 2 percent on 500 euros. A multiplier, on the other hand, grows with the basket.

Google's example is an uplift of 20 percent, that is 1.2 times, with which a jewelry shop can bid just as aggressively for a USD 500 basket as for a USD 50 basket (Google). This has a consequence for reporting: the "Conversion value" column includes both the original purchase value and the additional value from the lifecycle goals (Google Ads Help). If you read revenue per advertising euro from it, you see more than actually reaches the till. So compare it with the revenue in the shop.

Timeline up to Black Week and beyond

Between the publication of this article and Black Friday on November 27, there are just under three weeks. A realistic sequence:

1. By mid-November: check in the account which modes are enabled; define segment rules and count the list sizes in the order history.
2. By November 20: set up the export, transfer the first lists, activate the new customer parameter and check it with test orders.
3. Black Week: keep New Customer Value or high-value new customers active and do not change values in the middle of the promotion days.
4. December: mark Black Week first-time buyers as a separate group in the shop.
5. From January: add first-time buyers without a second purchase to the list of lapsed customers. For them, the rule may be shorter than for the rest of the customer base, because the first purchase was tied to a promotion.

At the same time, Google has further changes in the pipeline, such as the switch from dynamic search ads to AI Max, which our article on migrating to AI Max describes. For the promotion page itself, a category that fills itself from the reduced items is worthwhile; how this works is shown in the article on the promotion category with dynamic product groups.

How we support you with customer lists and lifecycle goals

The lifecycle goals are quickly activated in Google Ads. The work lies in the data before that, and that data is in the shop. We connect both sides:

For Shopware shops, we implement this as an extension in the shop, for other systems via a dedicated integration. More about how we work can be found on the page of our Shopware agency. If you want to know before Black Week which lists your customer base can support, get in touch with us.