This page is updated every two months with current best practices for Google Ads keyword research and planning. Even in an automated, broad-match world, the keywords and themes you feed Google still decide which searches you compete for and what you pay. Each update draws on our own experience plus authoritative industry sources and verified real-time research. Bookmark this page and check back for the latest keyword research best practices. Each update includes worked examples with the arithmetic shown.
Last updated: 6 August 2026
In This Guide
- Executive Summary
- Benchmarks & Numbers at a Glance
- Role of Keyword Research
- Tools & Data Sources
- Building & Expanding Seeds
- Intent & Grouping
- Choosing Match Types
- Negative Keyword Research
- Broad Match & Search Themes
- Feeding PMax & Demand Gen
- Forecasting & Mapping
- Common Mistakes to Avoid
- What Changed Recently
- References
1. Executive Summary
Google Ads keyword research in 2026 is no longer a one-time setup task. It is a continuous discipline that shapes campaign structure, automation inputs, budget allocation, and landing page strategy. Five principles define best practice at this moment:
- Intent drives everything. Classify every keyword by user intent — informational, navigational, commercial, or transactional — before assigning it to a campaign. Conversion-focused campaigns should contain only commercial and transactional terms; informational queries belong in SEO, content, or remarketing, not prospecting campaigns.[2][7][9]
- Match types are traffic controls, not structural pillars. Use exact and phrase match for your highest-value terms and for new or low-data campaigns. Reserve broad match for campaigns where Smart Bidding has sufficient conversion volume, and always pair it with active negative keyword management.[7][8][10]
- Negative keywords are as important as positive keywords. A disciplined negative keyword workflow — drawing from real search term data reviewed at least weekly — is now one of the primary levers for protecting budget efficiency, particularly as broad match and automation expand reach.[3][7][18]
- Keyword research feeds automation, not just manual campaigns. The themes, intents, and phrases you identify through research should flow directly into Performance Max asset group structure, search themes, and audience signals. Keywords are inputs to the machine, not just targets for manual bidding.[3][15][16]
- Forecasting before launch is non-negotiable. Use Keyword Planner’s forecast path to validate that your chosen keyword set is affordable and realistic before building ad groups. Planner forecasts are refreshed daily and reflect the last 7–10 days of market data, making them a reliable short-horizon planning input.[5][18]
2. Benchmarks and Numbers at a Glance
| Metric | Typical range or threshold | Applies when | Source |
|---|---|---|---|
| Cross-industry average search CPC (Q1 2026) | USD $2.96 per click (approx. AUD $4.60) | All industries combined, Search Network; vendor study, sample size not stated | [75] |
| Cross-industry average display CPC (Q1 2026) | USD $0.44 per click (approx. AUD $0.68) | All industries combined, Display Network; vendor study, sample size not stated | [75] |
| B2B Google Ads average search CTR | 1.30% | B2B advertisers on Search Network; vendor study, sample size not stated | [68] |
| B2B Google Ads average search CPC | USD $6.29 per click (approx. AUD $9.75) | B2B advertisers on Search Network; vendor study, sample size not stated | [68] |
| B2B Google Ads average search conversion rate | 0.31% | B2B advertisers on Search Network; vendor study, sample size not stated | [68] |
| B2B Google Ads average cost per conversion | USD $606 per conversion (approx. AUD $939) | B2B advertisers on Search Network; vendor study, sample size not stated | [68] |
| Exact match cost per MQL (B2B) | USD $1,200 per MQL (approx. AUD $1,860) | B2B lead generation; vendor claim, sample size not stated — use as directional reference only | [68] |
| Phrase match cost per MQL (B2B) | USD $2,800 per MQL (approx. AUD $4,340) — approximately 2× exact match cost | B2B lead generation; vendor claim, sample size not stated — use as directional reference only | [68] |
| Cross-industry average cost per lead | USD $66.69 per lead (approx. AUD $103) | All industries, Google Ads Search; vendor claim from WordStream, sample size not stated | [65] |
| Minimum recommended keywords per negative list | 5–10 keywords per list at launch | New campaigns; vendor recommendation, not an empirical study | [4] |
| Minimum monthly search volume to justify a keyword in most niches | 1,000+ monthly searches | Standard commercial niches; vendor recommendation — may be lower for high-CPC B2B niches | [4] |
| Search volume threshold for broad-level keyword scouting | 50,000+ monthly searches | Discovery and scale phase with broad match; vendor recommendation only | [4] |
| Recommended seed keyword count to start Keyword Planner research | 20–30 seed terms | Initial research phase for any account; vendor recommendation | [3] |
| Typical total keyword count for a mature account | 200–500 keywords total | Most small-to-medium business accounts; vendor recommendation — enterprise accounts may exceed this | [3] |
| Keyword Planner forecast refresh cadence | Daily refresh; bid-range statistics based on last 30 days; forecast horizon based on last 7–10 days of market data | All Keyword Planner users; documented by Google | [5][18] |
Note: USD figures have been converted to AUD at an illustrative rate of AUD $1.55 per USD $1.00 for planning purposes. Always verify the current exchange rate before using these figures in client-facing documents. Where figures are vendor claims rather than independent studies, treat them as directional benchmarks only.
3. The Role of Keyword Research
Keyword research in 2026 serves a broader strategic function than it did in previous years. It is no longer simply about populating ad groups with terms. It is the mechanism by which a senior marketer translates customer language, commercial intent, and competitive landscape into a structured input for both manual Search campaigns and AI-driven campaign types.[7][15]
The core job of keyword research is to answer three questions before a single dollar is committed to media spend:
- What are customers actually searching for? This means understanding the precise language people use when they are close to a purchase decision, not the language your client uses internally.[12][20]
- Which of those searches are commercially valuable? Search volume is a demand signal, but it must be weighed against intent, CPC, and likelihood of conversion. A keyword with 500 monthly searches and strong transactional intent will almost always outperform a keyword with 50,000 monthly searches and informational intent.[3][14]
- Which searches should we actively exclude? Identifying what not to target is equally important, especially as automated bidding and broad match expand reach beyond what a human would manually select.[7][8]
Keyword research also informs campaign architecture. The themes you identify determine how many campaigns and ad groups you need, what landing pages you require, and which audience signals to build for Performance Max. A rigorous keyword research process at the planning stage prevents expensive structural rework later.[7][15][16]
Importantly, keyword research is not a one-time deliverable. The Search terms report continuously surfaces new data about what real users are searching, which means your keyword list and negative list should evolve on at least a weekly cadence in active accounts.[8][10]
Worked example
Establishing the commercial value of a keyword before launch
- Setup: A Brisbane commercial refrigeration supplier is evaluating two candidate keywords: “commercial fridge repair Brisbane” (800 monthly searches, estimated CPC AUD $12.40) and “how to fix a commercial fridge” (6,500 monthly searches, estimated CPC AUD $1.10).
- Numbers: At a 3% CTR, the first keyword delivers approximately 24 clicks per month at a cost of AUD $297.60. At a 2% conversion rate, that yields approximately 0.48 conversions. The second keyword at 3% CTR delivers 195 clicks at AUD $214.50 but, being informational, converts at 0.2%, yielding approximately 0.39 conversions — at a higher absolute spend with lower intent visitors.
- Decision: Add “commercial fridge repair Brisbane” as an exact match keyword in the transactional ad group. Exclude “how to fix a commercial fridge” as a phrase match negative at campaign level.
- Why: The benchmark guidance is to prioritise commercial and transactional intent over volume, because volume alone does not predict conversion value.[3][14]
4. Keyword Research Tools and Data Sources
A robust keyword research process in 2026 draws from multiple data sources rather than relying on any single tool. Each source reveals a different facet of search behaviour and commercial opportunity.[3][12]
Google Keyword Planner
Keyword Planner remains the primary starting point because it provides search volume, competition data, and CPC forecasts directly from Google’s own auction data.[12][5] The tool offers two main paths: “Discover new keywords” for expansion, and “Get search volume and forecasts” for validation and budget planning. Best practice is to use both paths — discovery first, then forecasting to pressure-test your shortlist.[5][18]
A critical limitation to understand: Keyword Planner reports volume in ranges (for example, 1,000–10,000) unless your account has sufficient spend history, at which point it shows more granular estimates. Bid-range statistics in forecasts are based on the last 30 days of auction data, and forecast estimates are refreshed daily using the last 7–10 days of market movement.[5][18] This means Keyword Planner is a reliable short-horizon planning tool but should not be treated as a guarantee of actual delivery.
The Search Terms Report
The Search Terms report inside Google Ads is arguably more valuable than Keyword Planner for ongoing research because it shows the actual queries that triggered your ads, not modelled estimates.[7][10] This report should be reviewed at least weekly in active accounts. It serves two functions simultaneously: identifying new negative keywords for waste reduction, and surfacing high-intent queries that deserve promotion to standalone keywords or new ad groups.[8][9]
Competitor Research Tools
Third-party tools such as SEMrush, Ahrefs, and SpyFu allow you to analyse competitor domains and identify the queries they are targeting in paid search.[3][14] A practical workflow is to paste competitor URLs directly into Keyword Planner’s “Discover new keywords” path, which will suggest keywords based on the content of that page. This quickly surfaces gaps where competitors are investing and you are not.[1][13]
AI-Assisted Keyword Expansion
In 2026, AI writing tools and search query assistants — including Google’s own AI features within the Ads interface — can help generate synonym sets and intent variations that a manual brainstorm might miss.[24] However, AI-generated keyword lists should always be validated against Keyword Planner volume data and filtered for commercial intent before use. Treat AI output as a brainstorming accelerant, not a final list.[3]
Worked example
Combining Keyword Planner with competitor URL research to fill a gap
- Setup: A Melbourne-based HR software company is building its initial keyword list. Its Keyword Planner seed list of 25 terms around “HR software Australia” returns 180 keyword ideas. The account manager then pastes the URL of the company’s two nearest competitors into Keyword Planner’s “Discover new keywords” path.
- Numbers: The competitor URL analysis surfaces an additional 94 keyword ideas not in the original 180. Of those 94, 22 have monthly search volumes above 1,000 and estimated CPCs between AUD $8.50 and AUD $24.00. After filtering for transactional and commercial intent, 11 keywords are added to the candidate list — expanding the total qualified list from 38 to 49 terms, a 29% increase in qualified coverage.
- Decision: Add all 11 competitor-derived keywords to the candidate list, assign each a match type (7 as phrase match, 4 as exact match based on specificity), and flag them for a separate “competitor intent” ad group with a dedicated landing page.
- Why: Competitor URL seeding in Keyword Planner is a documented best-practice method for uncovering gaps in keyword coverage that seed-term brainstorming alone misses.[1][13]
5. Building Seed Keywords and Expanding
The seed keyword phase is the foundation of everything that follows. A poorly constructed seed list produces a poorly targeted expanded list, regardless of how sophisticated the subsequent filtering process is.[12][3]
What makes a strong seed keyword?
Seed keywords should reflect the language your customers use — not your internal product nomenclature or industry jargon — at different stages of the buying journey. Google’s own guidance recommends thinking like your customer, being specific but not over-precise, using multiple phrases, and including location terms and well-known brand names where relevant.[12]
A practical seed list should cover five source categories:
- Products and services: The specific things you sell, including common synonyms and abbreviations.
- Problems and pain points: The problems customers are trying to solve, phrased the way they would phrase them.
- Solutions and outcomes: The results customers want to achieve.
- Competitor and brand names: Your own brand, competitor brands, and category-defining product names.
- Location modifiers: City, suburb, region, and “near me” variations where geographic targeting matters.[3][12]
Best practice is to build an initial seed list of 20–30 terms before entering Keyword Planner.[3] This gives the tool enough context to generate relevant expansions rather than generic high-volume suggestions.
Expanding the seed list in Keyword Planner
Once seed terms are ready, use Keyword Planner’s “Discover new keywords” path and enter seeds in batches of five to ten terms at a time rather than all at once. This produces more targeted idea sets that are easier to filter for relevance.[5]
After expansion, filter the resulting list by:
- Relevance to your offer — remove anything clearly off-topic.
- Intent — separate informational from commercial and transactional terms.
- Minimum search volume — in most commercial niches, a threshold of 1,000+ monthly searches is a reasonable starting filter, though high-CPC B2B niches may justify much lower volumes.[4]
- CPC against budget — check that the estimated CPC is affordable given your campaign budget before shortlisting.
For broad keyword scouting and discovery of new themes, a monthly search volume of 50,000+ is suggested as the threshold for terms worth examining as broad match signals.[4] For exact and phrase match selection, prioritise specificity and intent over volume.
Long-tail versus head terms
Head terms (one to two words, high volume, broad intent) are typically more expensive and convert less efficiently than long-tail terms (three or more words, lower volume, specific intent). A strong keyword list includes both: head terms for brand visibility and long-tail terms for conversion efficiency. However, head terms should generally be assigned tighter match types precisely because their intent is ambiguous.[7][8]
Worked example
Building and filtering a seed expansion for a trade services account
- Setup: A Sydney plumbing account spending AUD $6,000 per month builds a seed list of 24 terms covering emergency plumbing, blocked drains, hot water systems, and leak repairs in the Greater Sydney area. These 24 seeds are entered into Keyword Planner in four batches of six.
- Numbers: Keyword Planner returns 312 keyword ideas in total. After filtering for a minimum of 1,000 monthly searches, the list reduces to 87 terms. After filtering for commercial or transactional intent (removing “how to fix,” “DIY,” and “free” variants), the list reduces to 52 terms. After removing terms with estimated CPCs above AUD $35.00 (the account’s maximum CPC threshold based on a target CPA of AUD $120 and an assumed 4% conversion rate), 38 terms remain as qualified candidates.[4]
- Decision: Proceed with all 38 qualified keywords. Assign 12 high-specificity terms (e.g. “emergency plumber Sydney CBD”) as exact match, 19 terms as phrase match, and flag 7 broader terms (e.g. “plumber Sydney”) for broad match only once the campaign has accumulated 50+ conversions.
- Why: The filtering sequence (volume → intent → CPC affordability) ensures the final list reflects both demand and commercial viability before any budget is committed.[3][5]
6. Search Intent and Keyword Grouping
Intent classification is the most consequential decision in keyword research because it determines which campaign, which ad group, which ad copy, and which landing page each keyword is assigned to. Getting this wrong means paying for clicks that are structurally unable to convert.[2][7][9]
The four-intent framework
The most practical classification system for paid search in 2026 separates searches into four types:[4][6][9][20]
- Informational: The user is learning. Signals include “how to,” “what is,” “guide,” “tips,” “explained.” These queries rarely convert on first exposure and are better served by SEO or remarketing.
- Navigational: The user is looking for a specific website or brand. Signals include brand names, “login,” “website,” “official site.” These should sit in brand campaigns, not non-brand prospecting.
- Commercial investigation: The user is comparing options. Signals include “best,” “vs,” “review,” “alternative,” “top,” “compare.” These can convert but typically require more persuasive copy and a comparison-focused landing page.
- Transactional: The user is ready to act. Signals include “buy,” “price,” “quote,” “book,” “hire,” “near me,” “emergency.” These are the highest-priority terms for conversion-focused campaigns and should receive the tightest match types and the highest bids.[2][3][8]
Grouping keywords into themes
Current best practice favours theme-based ad groups over single-keyword ad groups (SKAGs). An ad group should contain multiple variants of the same intent that share a single landing page destination.[7][8][16] A practical rule of thumb is five to twenty keywords per ad group, provided they all answer the same underlying user need.[7]
The organising principle for campaigns is intent tier, not keyword count. Structure campaigns to separate:
- Brand versus non-brand (always separate — different conversion rates, different budget logic).[3][11]
- Transactional versus commercial investigation (different landing pages, different bid strategies).
- Geographic tiers where location influences offer or CPC materially.[7][8]
Worked example
Splitting a mixed keyword list into intent-based ad groups
- Setup: A Perth accounting firm has 41 approved keywords covering tax returns, BAS lodgement, SMSF advice, and general bookkeeping. Initial grouping placed all 41 in two ad groups. A senior specialist reviews the list and applies four-intent classification.
- Numbers: Classification produces: 9 transactional keywords (e.g. “hire tax accountant Perth,” “BAS lodgement Perth price”) with estimated CPCs of AUD $14–$28; 11 commercial investigation keywords (e.g. “best accountant for SMSF Perth,” “accounting firm review”) with CPCs of AUD $9–$18; 14 informational keywords (e.g. “how to lodge BAS,” “what is SMSF”) with CPCs of AUD $3–$8; 7 navigational brand-related keywords with CPCs of AUD $1–$4. The 14 informational keywords are removed from paid search entirely and flagged for the firm’s SEO content calendar. The 7 navigational keywords are moved to a separate brand campaign.
- Decision: Build three non-brand ad groups (transactional, commercial investigation, and a bookkeeping services theme from the remaining 11 terms), each with its own landing page. Set target CPA at AUD $95 for the transactional group and AUD $160 for commercial investigation, reflecting the longer decision cycle.
- Why: Grouping by shared intent and landing page ensures ad relevance, Quality Score consistency, and bid strategy alignment — mismatched intent within an ad group dilutes all three.[2][7][8]
7. Choosing Match Types
Match type selection in 2026 is a traffic-control decision, not a structural one. The wrong match type on the right keyword wastes budget; the right match type on the wrong keyword still wastes budget. Both dimensions require active management.[7][10][12]
Match type comparison
| Match type | Reach | Control | Best used when | Key risk |
|---|---|---|---|---|
| Exact match | Lowest | Highest | Highest-converting terms; new accounts with limited conversion data; tightest intent requirements | May miss valuable close variants; limits discovery |
| Phrase match | Medium | Medium-high | Core intent covered with controlled variation; balanced reach and relevance | Can still trigger adjacent queries; requires regular negative review |
| Broad match | Highest | Lowest | Mature campaigns with Smart Bidding, 50+ conversions per month, and active negative management | Significant irrelevant traffic without negatives and strong conversion signals |
The recommended sequencing approach
The most defensible practice across 2026 sources is to start conservatively and expand deliberately:[1][3][7][8]
- Launch new campaigns with exact and phrase match for all core keywords.
- Review the Search terms report weekly. Promote high-performing search terms to exact match keywords in dedicated ad groups.
- Introduce broad match only in separate campaigns or ad groups once the parent campaign has at least 50 conversions per month and Smart Bidding is active and stable.
- Never run broad match without an active negative keyword list. The risk of intent drift is substantially higher without negatives, particularly in the early weeks of a campaign.[7][8][10]
Where sources suggest a “broad-first” approach, the more conservative and broadly supported position is broad with automation and controls, not broad as a standalone default.[3][6]
On cost efficiency, vendor benchmark data suggests exact match generates B2B MQLs at approximately USD $1,200 (AUD $1,860) compared to USD $2,800 (AUD $4,340) for phrase match — a roughly 2× cost differential.[68] While this is a vendor claim without a disclosed sample size, it is consistent with the directional principle that tighter match types deliver higher-intent traffic at lower acquisition cost, particularly in B2B contexts.
Worked example
Deciding whether to introduce broad match in a Search campaign
- Setup: A Melbourne legal firm’s Google Ads Search campaign has been running for 14 weeks with exact and phrase match keywords. The account manager is evaluating whether to introduce broad match to scale lead volume.
- Numbers: Over the last 30 days, the campaign has recorded 38 conversions at an average CPA of AUD $210. Smart Bidding (Target CPA) has been active for 8 weeks. The 50-conversion-per-month threshold for broad match confidence has not been reached: 38 conversions is 24% below the 50-conversion threshold.[7][8] Monthly spend is AUD $7,980. Current Search Impression Share is 61%.
- Decision: Do not introduce broad match this month. Instead, expand phrase match coverage by adding 8 new phrase match keywords identified from the Search terms report, and increase bids on the 5 highest-converting exact match keywords by 12% to capture more impression share from those proven terms.
- Why: Best practice requires at least 50 conversions per month before broad match is introduced alongside Smart Bidding, to ensure the bidding algorithm has sufficient data to control for intent drift.[7][8][10]
8. Negative Keyword Research
Negative keywords are a primary budget protection mechanism in 2026, not a supplementary tidying exercise. As match type behaviour has broadened and automation has expanded the set of queries that can trigger ads, a disciplined negative keyword programme is essential for maintaining campaign efficiency.[7][10][18]
Negative keyword architecture
Maintain negatives at three levels to prevent both waste and inter-campaign cannibalisation:[3][7][18]
- Account-level negative keyword lists: Terms that are universally irrelevant to your business — e.g., “free,” “DIY,” “jobs,” “salary,” “course,” “Wikipedia.” These should be built at launch and maintained as a shared list applied to all campaigns.
- Campaign-level negatives: Terms that are relevant to the account but wrong for a specific campaign — e.g., brand terms excluded from non-brand campaigns; informational terms excluded from transactional campaigns.
- Ad group-level negatives: Terms that would be fine in another ad group but create intent overlap within this one — used to prevent the same query triggering in two competing ad groups.
Building negatives from Search terms data
The Search terms report is the primary source for negative keyword additions. The recommended process is:[8][9]
- Review the Search terms report at least weekly for active campaigns. In high-spend accounts (above AUD $10,000 per month), review two to three times per week.
- Bucket search terms into categories: high-intent (potential keyword to add), mid-intent (monitor), informational (negative), irrelevant (negative), competitor (evaluate separately).[3]
- Add negatives for patterns, not just single instances. A query that appears once and spends AUD $4 with no conversion may not warrant a negative. A pattern that has appeared eight times across three weeks with AUD $96 spent and zero conversions should be negated immediately.
- Use n-gram analysis — examining recurring one-, two-, and three-word patterns across all search terms — to identify systemic waste rather than individual bad queries.[7][18]
Worked example
Using n-gram analysis to find systemic negative keyword gaps
- Setup: A Gold Coast dental clinic account spending AUD $4,500 per month reviews its Search terms report for the 4-week period from 1 September 2026 to 28 September 2026. The account manager exports 340 search terms and runs a two-word n-gram frequency analysis.
- Numbers: The n-gram analysis reveals that the word “free” appears in 28 of 340 search terms (8.2% of all terms), collectively generating 74 clicks at an average CPC of AUD $6.80, totalling AUD $503.20 in spend. Zero of those 74 clicks converted. The word “course” appears in 14 terms, generating 31 clicks at AUD $6.20 each (AUD $192.20), again with zero conversions. Together, these two patterns account for AUD $695.40 — 15.5% of the AUD $4,500 monthly budget — with no return.
- Decision: Add “free” and “course” as broad match negatives at campaign level immediately. Set a recurring monthly n-gram review on the first Monday of each month to catch emerging patterns before they compound.
- Why: N-gram analysis identifies structural waste patterns that single-term negative review misses — the threshold for action is any pattern generating more than 2% of campaign spend with a 0% conversion rate over 28 days.[7][18]
9. Keyword Research for Broad Match and Search Themes
The role of keyword research changes character when broad match and search themes are the primary targeting mechanisms. Rather than producing a list of individually approved queries, research in this context defines the intent boundaries within which automation is permitted to operate.[3][15][16]
Broad match with Smart Bidding
Broad match in 2026 is best understood as an intent-expansion tool rather than a coverage tool. When paired with Smart Bidding and sufficient conversion data, it allows Google’s systems to identify queries with similar underlying intent that you have not explicitly listed — including long-tail variants and semantic neighbours.[3][6][15]
Keyword research supports broad match in two specific ways:
- Defining the intent anchor: The broad match keyword itself signals the core topic and intent. Choosing “commercial refrigeration Sydney” as a broad match anchor is more targeted than “refrigeration,” even though both are broad match. The research process should identify the most specific commercially viable anchor terms, not the broadest possible ones.[3][5]
- Building the negative boundary: Without a strong negative keyword system, broad match will expand into informational, navigational, and competitor queries. Keyword research — particularly intent classification — informs exactly which themes to exclude as negatives before broad match is switched on.[7][8]
Search themes in Performance Max
Search themes are the Performance Max equivalent of keywords — they tell Google the topics and intents you care about, rather than specifying exact queries.[15][16] Keyword research directly informs what search themes to include: the best themes come from your highest-performing Search campaign keywords, your most common converting search terms, and your intent classification framework.[3][10]
A practical approach is to translate your top 10–15 converting exact or phrase match keywords from Search campaigns directly into search themes in Performance Max asset groups. This gives the automation a proven commercial intent signal rather than a speculative one.[15][16]
Worked example
Translating Search campaign keywords into Performance Max search themes
- Setup: An Adelaide conveyancing firm runs a Google Ads Search campaign that has accumulated 112 conversions over the 16-week period from 1 June 2026 to 21 September 2026. The account manager is launching a Performance Max campaign alongside Search and needs to populate search themes.
- Numbers: The Search campaign’s top 12 converting keywords (exact and phrase match) generated 78 of the 112 conversions (69.6% of all conversions) at an average CPA of AUD $87. These 12 keywords span three intent clusters: property purchase conveyancing (5 keywords, 41 conversions), property sale conveyancing (4 keywords, 24 conversions), and first home buyer conveyancing (3 keywords, 13 conversions).
- Decision: Create three Performance Max asset groups — one per intent cluster — and enter the corresponding 4–5 keywords from each cluster as search themes. Do not enter informational or navigational terms as search themes. Apply the existing Search campaign negative keyword list to the Performance Max campaign from day one.
- Why: Search themes built from proven converting keywords give the automation a high-quality intent signal grounded in actual conversion data, rather than speculative topic selections.[3][15][16]
10. Feeding Performance Max and Demand Gen
Keyword research does not stop at the boundary of the Search Network. The intent signals, customer language, and thematic structure identified through keyword research should flow into every automated campaign type, including Performance Max and Demand Gen.[3][6][10][15]
Performance Max
For Performance Max, keyword research contributes to three areas:[15][16]
- Asset group structure: Each asset group should correspond to a distinct intent cluster or product/service theme identified during keyword research. The research determines how many asset groups you need and what promise each makes.
- Search themes: As described in section 9, the strongest search themes come directly from your proven Search campaign keywords. Populate search themes from your top-converting terms, not from broad category guesses.
- Audience signals: Keyword research reveals the language and themes that resonate with high-intent users. This language should inform custom intent audiences (built from search queries your customers use) and in-market audience selections within Performance Max.[3][15]
Keyword research also informs what to exclude from Performance Max. Because PMax has limited keyword-level negative controls, identifying the query themes you want to block — particularly informational, job-seeker, and competitor-brand queries — and applying them as campaign exclusions is a critical protective step.[15][16]
Demand Gen
Demand Gen does not use keyword targeting directly. However, keyword research informs Demand Gen in two indirect but important ways:[15][16]
- Message and creative angles: The pain points, solution language, and outcome-focused phrasing you identify through keyword research should be reflected in Demand Gen ad creative. If “reduce staff turnover” is a high-intent cluster in your Search campaigns, that exact language belongs in your Demand Gen video and display creative.
- Audience construction: Search intent data reveals what users are actively researching. This translates into custom intent audiences (built from search query lists) that can be targeted in Demand Gen to reach users while they are in the decision-making phase, even before they arrive at Search.[15]
Worked example
Using Search keyword data to build a Demand Gen custom intent audience
- Setup: A Sydney-based B2B cybersecurity software company has a Search campaign with 90 days of data (1 July 2026 to 28 September 2026). It wants to launch a Demand Gen campaign targeting users who are actively researching cybersecurity solutions but have not yet searched for the company’s brand.
- Numbers: The Search campaign’s Search terms report for the period contains 1,240 unique search terms. After filtering for commercial and transactional intent (removing “free,” “how to,” “what is,” and “jobs” variants), 187 unique terms remain. Of those, the top 60 terms by conversion volume are extracted. Average CPC for this set is AUD $31.50 (USD $20.32[68]). These 60 terms become the query list for a custom intent audience in Demand Gen.
- Decision: Build a custom intent audience in Demand Gen using all 60 Search terms. Set Demand Gen campaign budget at AUD $3,000 per month, targeting the custom intent audience plus in-market “Business Software” and “Cybersecurity” segments. Apply frequency cap of 3 impressions per user per week.
- Why: Search term data from a live campaign is the highest-quality input for custom intent audiences because it reflects actual purchase-intent language from real users in your category, not assumed interests.[3][15][16]
11. Forecasting, Prioritisation and Mapping to Landing Pages
Before committing budget to any keyword set, a senior specialist should run a forecast to validate that the chosen keywords can deliver meaningful traffic within the available budget, and that the expected CPC is consistent with the account’s target CPA.[5][18]
Using Keyword Planner forecasts
The “Get search volume and forecasts” path in Keyword Planner accepts a keyword list and returns estimated impressions, clicks, cost, and conversions based on a bid you set. Key points to understand about the forecast model:[5][18]
- Forecasts are refreshed daily and reflect the last 7–10 days of market data, meaning they respond to recent demand shifts and seasonal movements.[18]
- Bid-range statistics shown in forecasts are based on the last 30 days of auction data.[5]
- Forecast outputs are estimates, not guarantees. Use them to establish a plausible range and to compare keyword sets against each other, not to project precise revenue outcomes.[5]
Prioritising keywords for launch
Not all qualified keywords should launch simultaneously. Prioritise based on a three-factor scoring approach:
- Intent score: Transactional intent first, commercial investigation second, everything else deferred.
- Volume-to-CPC ratio: Keywords with sufficient volume to generate learning data within budget should launch before niche, high-CPC terms that may take months to accumulate conversion data.
- Landing page readiness: Only launch keywords for which a relevant, conversion-optimised landing page already exists. Sending traffic to a generic homepage from a specific keyword intent is a structural conversion rate problem, not a bidding problem.[7][8][16]
Mapping keywords to landing pages
Each ad group’s keyword theme should map to a single, dedicated landing page that fulfils the same user intent. This is not just a Quality Score consideration — it is the primary determinant of whether a click converts.[7][8][16] A keyword about “emergency plumber Sydney” mapped to a homepage with general service information will consistently underperform the same keyword mapped to a landing page specifically addressing emergency plumbing in Sydney with a click-to-call button above the fold.
Worked example
Using Keyword Planner forecasts to validate a launch budget
- Setup: A Canberra HR consultancy has a shortlist of 32 approved keywords and a proposed monthly Search budget of AUD $5,000. Before building ad groups, the account manager runs a forecast in Keyword Planner to validate whether AUD $5,000 can generate sufficient clicks and conversions to feed Smart Bidding within 30 days.
- Numbers: Keyword Planner forecast (run on 1 October 2026, reflecting market data from 21–30 September 2026) returns: estimated monthly clicks = 218; estimated average CPC = AUD $22.90; estimated monthly cost = AUD $4,992.20 (within the AUD $5,000 budget). At the account’s historical conversion rate of 4.2% from Search, 218 clicks × 4.2% = 9.2 conversions per month. Smart Bidding generally requires 30–50 conversions per month for stable Target CPA optimisation — 9.2 conversions is significantly below that threshold.
- Decision: Increase the proposed budget to AUD $12,000 per month (forecast: 523 clicks × 4.2% = 21.97 conversions) or reduce the keyword list to the 14 highest-intent terms and accept a slower ramp to Smart Bidding readiness. Present both options to the client with a recommendation to start at AUD $8,000 (forecast: 349 clicks × 4.2% = 14.7 conversions) and target Smart Bidding readiness by week 10.
- Why: Keyword Planner forecasts allow budget feasibility to be tested before launch, preventing the common error of under-funding a campaign to the point where Smart Bidding never accumulates sufficient data to optimise.[5][18]
Worked example
Mapping a keyword theme to a dedicated landing page to resolve a conversion rate problem
- Setup: A national Australian IT managed services provider has a Search ad group targeting “managed IT support small business” and related phrase match variants. The ad group receives 140 clicks per month at AUD $18.50 average CPC (AUD $2,590 per month) but converts at 0.7%, yielding fewer than 1 lead per month. All clicks land on the company’s generic homepage.
- Numbers: At 0.7% conversion rate: 140 clicks × 0.007 = 0.98 leads per month. Cost per lead = AUD $2,590 ÷ 0.98 = AUD $2,643. Industry cross-industry average cost per lead is USD $66.69 (AUD $103)[65], confirming this result is approximately 25.7× the cross-industry benchmark — a strong indicator of a landing page mismatch rather than a keyword or bid problem.
- Decision: Build a dedicated landing page for “managed IT support for small business” featuring a specific offer (free 30-minute IT audit), social proof from small business clients, and a single call-to-action form. Redirect the ad group to this page and re-evaluate conversion rate after 200 clicks (approximately 6 weeks at current volume) on 1 November 2026.
- Why: When cost per lead is more than 10× the industry average with normal click volume, the landing page is almost always the root cause — keyword-to-page intent mismatch is the most common structural conversion rate failure in Search campaigns.[7][8][16]
12. Common Mistakes to Avoid
The following errors appear consistently in underperforming Google Ads accounts and are directly addressable through disciplined keyword research and planning practice.[3][7][8][9]
Treating keyword research as a one-time task
Keyword research that happens only at account launch quickly becomes stale. Auction dynamics, seasonal demand, and customer language all shift continuously. Accounts that do not mine the Search terms report weekly and update their keyword and negative lists accordingly will gradually fund irrelevant traffic without realising it.[8][10][18]
Using broad match without Smart Bidding or negatives
Broad match without Smart Bidding and an active negative keyword programme is one of the fastest ways to spend budget on irrelevant queries. The two controls — automated bidding with conversion data and a maintained negative list — are not optional additions to broad match; they are prerequisites.[3][6][7][8]
Optimising for volume instead of intent
Adding high-volume keywords because they look impressive in Keyword Planner, without filtering for commercial intent, consistently produces high impression share and low conversion rates. A keyword with 1,000 monthly searches and transactional intent will almost always outperform a keyword with 100,000 monthly searches and informational intent in a conversion-focused campaign.[3][14][20]
Mixing intent tiers within an ad group
Combining “emergency plumber Sydney” (transactional, high urgency) and “how to fix a leaking tap” (informational, DIY intent) in the same ad group makes it impossible to write ad copy that serves both, and guarantees that one intent will always receive a suboptimal message. Intent-based ad group structure is not optional — it is the foundation of relevance.[7][8][16]
Neglecting brand versus non-brand separation
Running brand and non-brand keywords in the same campaign makes it impossible to set appropriate budgets for each, measure true non-brand performance, or protect brand quality scores from non-brand competition effects. Always separate brand into its own campaign with its own budget and negative keyword controls.[3][11]
Ignoring match type interaction effects
When multiple match types cover the same query, Google determines which keyword to enter in the auction based on a ranking system that considers match type and Quality Score. Without ad group-level negatives to manage this, two ad groups can bid against each other for the same query, inflating CPCs and fragmenting conversion data.[7][8]
Setting and forgetting Keyword Planner forecasts
Because Keyword Planner forecasts are refreshed daily and reflect the last 7–10 days of market data, a forecast run in January 2026 is not valid planning input for a campaign launching in August 2026. Always re-run forecasts within seven days of a planned launch or budget change.[5][18]
Worked example
Diagnosing budget waste from broad match without proper controls
- Setup: An e-commerce account selling Australian-made furniture has AUD $9,000 per month in Search spend. A monthly review on 1 August 2026 reveals that 34% of spend has gone to broad match keywords with no accompanying negative keyword list — a configuration that has been in place since the campaign launched in March 2026.
- Numbers: 34% of AUD $9,000 = AUD $3,060 per month attributed to broad match. Exporting the last 30 days of Search terms for those broad match keywords reveals 412 unique queries. Of those, 148 queries (35.9%) contain terms classified as informational (“furniture history,” “how to stain timber”) or irrelevant (“IKEA Australia,” “furniture removal”) — collectively responsible for AUD $847 in spend over 30 days with zero conversions. That is 9.4% of total monthly budget generating no return.
- Decision: Immediately apply the existing Search campaign negative keyword list to all broad match ad groups. Add 38 new negative keywords identified from the 148 irrelevant query patterns. Set a reminder to review Search terms for the broad match ad groups every Monday morning. Project AUD $7,200 in annualised savings from the negatives added.
- Why: Broad match without negatives is documented to expand rapidly into adjacent and irrelevant intent — the threshold for corrective action is any match type group spending more than 5% of budget with a 0% conversion rate over 30 days.[3][7][8]
13. What Changed Recently (Last 30 Days)
The following section summarises documented changes and confirmed best-practice updates relevant to Google Ads keyword research and planning as of August 2026. Where changes reflect Google’s own documented guidance, they are noted as such. Where no verifiable platform change has been confirmed, that is stated explicitly rather than implied.
Keyword Planner forecast model: daily refresh confirmed
Google’s current documentation confirms that Keyword Planner forecasts are refreshed daily and are built from the last 7–10 days of market data, while bid-range statistics shown within forecasts are drawn from the last 30 days of auction data.[5][18] This is a meaningful operational detail: a forecast run on a Monday reflects a different market snapshot than one run the following Friday, particularly during periods of rapid demand change such as end-of-financial-year or major retail events.
The practical implication is that for seasonal campaigns — for example, campaigns planned for the October 2026 to January 2027 peak retail period — forecasts should be re-run within seven days of launch, not weeks in advance, to capture the most current CPC and volume signals.[5]
Search themes: no new platform changes confirmed in the last 30 days
Based on available Google Ads documentation and published guidance, there are no confirmed changes to the search themes feature within Performance Max in the 30 days to August 2026.[15][16] Search themes remain a Performance Max concept and are not directly integrated into Keyword Planner. Best practice for search themes — deriving them from proven Search campaign keywords and high-converting search term data — remains unchanged.[3][15][16]
Match type policy: no new changes confirmed
Google’s documented keyword planning workflow continues to assign match type at the point of moving a planned keyword from Keyword Planner into a campaign — not as a Keyword Planner-level setting.[5] There are no confirmed match type policy changes in the 30 days to August 2026. The current best-practice hierarchy — exact for highest-value terms, phrase for controlled scale, broad only with Smart Bidding and negatives — remains the recommended approach.[7][8][10]
Account change history: 30-day review window
Google confirms that most keyword-level changes made in the last 30 days can be reviewed and undone via Change history in the Google Ads interface.[5] For accounts undergoing keyword restructures — for example, reclassifying match types or reorganising ad groups — this window provides a safety net for reversing changes that produce unexpected performance drops. Use it proactively in the first two weeks after any significant keyword restructure.
Continuing best-practice direction: intent architecture over keyword lists
The most significant ongoing shift in keyword strategy — not a single 30-day change but a confirmed directional trend across 2026 guidance — is the move from keyword-list management to intent architecture: using keyword research to define themes, inform automation inputs, control brand, and continuously prune waste, rather than manually managing individual queries.[3][15][16] Accounts that have not yet adapted their keyword research process to serve this broader architectural role — feeding Performance Max, informing Demand Gen audiences, and structuring search themes — are operating with an outdated workflow.
Worked example
Re-running a Keyword Planner forecast ahead of a peak seasonal campaign
- Setup: A national Australian online homewares retailer is planning a Google Ads Search campaign for the November 2026 Click Frenzy and Black Friday period (19–29 November 2026). The account manager ran an initial Keyword Planner forecast in August 2026 showing an estimated CPC of AUD $1.85 for “homewares sale Australia” and related terms, with a projected 2,400 clicks on a AUD $4,440 budget.
- Numbers: On 10 November 2026, the account manager re-runs the forecast. The refreshed forecast (reflecting the last 7–10 days of market data as at 10 November 2026) shows estimated CPC has risen to AUD $3.20 — a 72.9% increase from the August estimate — due to increased auction competition ahead of the peak retail period. The same AUD $4,440 budget now forecasts only 1,387 clicks, a reduction of 1,013 clicks (42.2%) from the August projection.
- Decision: Increase the campaign budget to AUD $7,680 (2,400 clicks × AUD $3.20) to maintain the originally planned click volume, or reduce keyword coverage to the 8 highest-converting exact match terms and accept 1,387 clicks. Present both options to the client by 12 November 2026 with a recommendation to increase budget for the peak window.
- Why: Keyword Planner forecasts are refreshed daily using the last 7–10 days of market data — an August forecast cannot predict November auction dynamics, making a reforecast within 7 days of launch mandatory for any seasonal campaign.[5][18]
References
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This page is maintained by Sean Cooney at Omologist.com. Content is refreshed every two months using real-time research from authoritative Google Ads sources. Worked examples are illustrative scenarios calculated from published benchmarks, not client results.

