The typical B2B SaaS Google Ads account is keyword-only. Keywords control which searches trigger which ads. Smart Bidding adjusts bids based on auction signals. Audiences — if present at all — are added as an afterthought, often in targeting mode on Display campaigns that struggle to spend. This is a significant underuse of the targeting stack available in Google Ads, and it means Smart Bidding is making bid decisions without the buyer-identity signals that would most improve its accuracy.
Audience targeting in Google Ads does not replace keywords. It augments them. Customer Match tells the algorithm when a searcher is an existing customer or a known high-value prospect. In-market audiences tell it when a searcher's recent behavior signals active software evaluation. RLSA tells it when a searcher has already been to your site. Combined with strong keyword intent signals and accurate conversion tracking, these audience layers give Smart Bidding the information it needs to allocate bids precisely — not just by query, but by the identity and history of the person behind the query.
Why audience signals matter in keyword campaigns
Smart Bidding operates on auction-time signals: the search query, device, location, time of day, and a range of contextual factors it has learned from conversion history. Audience lists are one of those contextual factors. When you add a Customer Match list, an RLSA list, or an in-market audience in observation mode, you give Smart Bidding access to a buyer-identity signal it could not infer from the keyword alone.
Consider the same keyword — "project management software for engineering teams" — searched by three different users. The first is an anonymous visitor who has never encountered your brand. The second is a visitor who came to your site from a blog post last month, looked at pricing, and left without converting. The third is a current trial user looking for documentation. Without audience signals, Smart Bidding bids the same on all three. With Customer Match and RLSA properly configured, it can bid more aggressively on the high-intent past visitor, exclude the existing trial user from the paid click entirely, and treat the anonymous visitor normally. The same keyword, three different bid decisions — all made at auction time with no manual intervention.
This matters more for B2B SaaS than most verticals because B2B SaaS sales cycles are long and the same buyer may search category keywords multiple times across weeks or months before converting. RLSA and Customer Match let you increase pressure on buyers who are progressing through the funnel without changing your keyword strategy.
Customer Match: using CRM data in Google Ads
Customer Match is the most precise audience type available in Google Ads for B2B SaaS because it is based on your own first-party data rather than Google's inferred behavioral signals. You upload email addresses from your CRM, Google matches them to signed-in Google accounts, and the resulting list can be used for bid adjustments, audience targeting, or exclusions across Search, Display, YouTube, and Gmail.
The most immediately valuable Customer Match application for B2B SaaS is exclusion. Any acquisition campaign — trial signup, demo request, free account creation — should exclude your existing paying customers. Without this exclusion, you pay for clicks from customers who already converted, which inflates your cost per acquisition and misattributes spend. Upload your current customer list, add it to all acquisition campaigns as an exclusion, and refresh the list quarterly as your customer base grows. This alone is worth the setup time.
Beyond exclusions, Customer Match is valuable for bid escalation. Upload a list of prospects in active pipeline — contacts who have had a discovery call or demo but have not converted. Add this list to your branded and competitor keyword campaigns with a positive bid modifier (typically 25–50% increase). These are buyers who have already evaluated your product and are comparison-shopping; being more visible in their branded searches accelerates the close. Combine this with the competitor keyword strategy used for conquest campaigns: you can exclude your own pipeline from competitor campaigns (they already know you) and include them in branded campaigns with elevated bids. Match rates on B2B email lists typically run 40–60%, so upload every contact in the relevant CRM segment rather than filtering aggressively before upload.
In-market audiences for B2B SaaS
Google's in-market audiences identify users whose recent browsing behavior suggests active purchase intent. Unlike Customer Match, which uses your data, in-market audiences use Google's behavioral modeling across the web. For B2B SaaS, the most relevant in-market segments are in the Business and Industrial category: Business Software and Applications, and specific subsegments matching your vertical (CRM Software, Cybersecurity Software, HR Software, Project Management Software, and similar).
The right way to use in-market audiences in B2B SaaS Search campaigns is observation mode first. Add the relevant in-market audience segments without restricting reach — observation mode shows you how matched users perform versus unmatched users in your campaigns without changing who sees your ads. After 4–6 weeks of data, pull the audience insights report and check whether users in the business software in-market segment convert at a different rate, or at a different downstream pipeline quality, than the rest of your traffic. If they do, add a positive bid modifier or switch to targeting mode. If they do not, the segment may be too broad to provide useful signal for your specific vertical — common for segments like "Business Services" that mix widely different buyer intent.
In-market audiences are most defensible in Display campaigns, where they help Google's automatic placement system identify relevant audiences without requiring managed placement lists. Combined with the placement exclusion strategy that removes low-quality inventory, an in-market audience layer on Display gives the algorithm two filters — behavioral relevance and placement quality — rather than one. Neither alone is sufficient; together they produce a significantly cleaner traffic mix.
RLSA: remarketing lists in Search campaigns
Remarketing Lists for Search Ads (RLSA) apply audience membership to Search campaign bidding. When a user on your RLSA list searches for a keyword you are bidding on, you can increase or decrease the bid — or restrict the campaign to show only to list members in targeting mode. For B2B SaaS, RLSA is most powerful as a bid escalation layer rather than a restriction layer: you want to be more visible to past visitors when they are actively searching category or competitor keywords, not limit your reach to them exclusively.
Build your RLSA segments based on the buyer journey pages that indicate purchase intent. Pricing page visitors represent higher intent than homepage visitors; demo request page visitors (who did not convert) are higher intent still. Set up these audience segments in Google Analytics (linked to Google Ads) and apply bid modifiers accordingly: pricing page visitors typically warrant a 30–50% bid increase, demo page non-converters 50–75%. The specifics depend on your account's conversion rate data — check whether these segments actually convert at above-average rates in your account before committing to the modifier magnitude.
One underused RLSA application for B2B SaaS: applying remarketing lists to competitor keyword campaigns. A past visitor who searches a competitor's brand name is comparison-shopping — they already know you exist and are evaluating alternatives. That is a materially different searcher than an anonymous person searching the same competitor term, and a significantly higher bid can be justified. This integrates directly with the dynamic remarketing strategy for keeping past visitors engaged across the evaluation cycle.
Audience exclusions: who to filter out
Audience exclusions operate in parallel with keyword and contextual targeting — they remove specific users from your ad delivery regardless of what they search. For B2B SaaS acquisition campaigns, the highest-value exclusion is your existing customer list (applied via Customer Match). Secondary exclusions worth considering: people who visited your careers or jobs pages (recruiters and job applicants, not buyers), people who submitted a lead form more than 180 days ago without progressing in CRM (cold leads who did not respond to nurture), and for Display campaigns, people who have only ever visited single blog posts without any product page engagement.
Apply exclusions at the campaign level or ad group level in Google Ads under Audiences > Exclusions. For Customer Match exclusions, upload the list and apply it directly to the relevant campaign or ad group. RLSA-based exclusions work the same way — create the remarketing audience in GA4, import to Google Ads, and apply as an exclusion where relevant. The combined effect of a well-configured exclusion stack is measurable in cost-per-acquisition: you eliminate spend on users who would not convert regardless of how relevant the ad is, which concentrates budget on the portions of your keyword traffic most likely to produce pipeline-quality leads rather than vanity form fills.
Layering audiences with Smart Bidding
Smart Bidding does not make audience targeting irrelevant — it uses audience list membership as one of the many signals it weighs at auction time. The more complete your audience signal library, the more precise the bid can be for each impression. The workflow: add all relevant audience types (Customer Match, RLSA, in-market) to your campaigns in observation mode first. Let Smart Bidding learn from the data for 4–6 weeks. Then review performance by audience segment in the Insights tab or the Audiences report to see which segments convert at above- or below-average rates.
The segments that convert materially better are the candidates for explicit bid adjustments or targeting-mode restrictions. The segments that convert at or below baseline are still providing useful negative signal to Smart Bidding in observation mode — they do not need manual intervention. What you want to avoid is applying aggressive targeting restrictions (include mode with small audiences) before letting the algorithm learn from observation data. Restricting too early limits the training set and slows Smart Bidding's ability to optimize for the signals that actually matter in your account. For a full view of how bidding strategy interacts with campaign setup for B2B SaaS, see the bidding strategies guide.