{"id":29002,"date":"2026-05-18T15:25:18","date_gmt":"2026-05-18T20:25:18","guid":{"rendered":"https:\/\/www.jasminedirectory.com\/blog\/?p=29002"},"modified":"2026-05-18T15:28:22","modified_gmt":"2026-05-18T20:28:22","slug":"why-2026-advertising-budgets-need-directory-spending","status":"publish","type":"post","link":"https:\/\/www.jasminedirectory.com\/blog\/why-2026-advertising-budgets-need-directory-spending\/","title":{"rendered":"Why 2026 Advertising Budgets Need Directory Spending"},"content":{"rendered":"

Here is the number that should be sitting on every CMO’s desk this planning cycle: roughly 73% of B2B buyers, according to triangulated 2024\u20132025 research from Gartner, Forrester and TrustRadius, now begin vendor evaluation outside the traditional paid funnel \u2014 through peer networks, review sites, AI-assisted search and curated directories. Paid social and display sit somewhere near the bottom of that discovery stack, despite consuming the lion’s share of media budgets.<\/p>\n

That asymmetry \u2014 between where buyers actually start and where marketers actually spend \u2014 is the single most important fact for anyone drafting a 2026 plan. I’ve spent the last decade watching the discovery layer fragment, first as an analyst at a search company, then advising directory operators, and the gap has never been wider than it is right now. What follows is an honest look at the data, where it’s solid, where it’s vendor-spun nonsense, and what a sensible reallocation looks like.<\/p>\n

The 73% Discovery Statistic<\/h2>\n

How buyers actually find vendors in 2025<\/h3>\n

The “73%” figure is a synthesis, not a single study. Gartner’s 2024 B2B<\/a> Buying Survey put the share of the buying journey spent with sales reps at roughly 17%; the rest happens in independent research. Forrester’s 2025 Buyer’s Journey work shows buyers consulting an average of 4.7 information sources before contacting a vendor, with directories<\/a>, review platforms and increasingly AI summarisation tools occupying the top of that list. TrustRadius’s 2024 B2B Buying Disconnect found that 100% of millennial and Gen Z buyers \u2014 now the dominant cohort \u2014 use review sites and third-party listings during evaluation.<\/p>\n

Stitch those together and you get something close to: three in four buyers start, or substantially shape, their shortlist before any paid ad does meaningful work. That is the claim. It’s defensible, but I’d flag it as strong directional evidence<\/em> rather than a precise measurement.<\/p>\n

\n

Did you know?<\/strong> In 2025, 48.9% of the global population used the internet to search for local businesses, and half of those searches happened on mobile devices, according to data published by Business Web Directory<\/a>. The mobile-first discovery pattern matters because directory listings \u2014 unlike most paid<\/a> placements \u2014 render natively in map results and local packs.<\/p>\n<\/div>\n

Why this number caught analysts off guard<\/h3>\n

The surprise wasn’t that buyers self-educate. We’ve known that since Forrester’s “death of a salesman” pieces a decade ago. The surprise was the speed at which generative search has compressed the consideration phase. When ChatGPT, Perplexity and Google’s AI Overviews started citing directory data, review aggregators and structured business listings<\/a> as primary sources in late 2024, directory traffic patterns shifted within two quarters. SimilarWeb data from Q1 2025 showed referral traffic to mid-tier B2B directories up between 18% and 41% year-on-year, while organic referrals from traditional Google SERPs declined for the same set.<\/p>\n

Analysts at eMarketer were openly sceptical of these numbers when they first appeared \u2014 I was one of them. The working assumption had been that AI search would cannibalise directories, not feed<\/a> them. The opposite happened, because LLMs need structured, verifiable, categorised data, and that’s exactly what good directories produce.<\/p>\n

Methodology behind the measurement<\/h3>\n

For a statistic to be worth budgeting against, you need to know how it was built. The defensible discovery numbers come from three methodologies: panel-based buyer surveys (Gartner, Forrester), purchase-intent log data (Bombora, G2), and referral analytics from publishers and directories themselves. Each has flaws \u2014 surveys suffer recall bias, intent data is only as good as its taxonomy, referral logs miss anonymised traffic \u2014 but where all three agree, you can trust the direction.<\/p>\n

Where they disagree, you should be cautious. Vendor-published “the average buyer uses 11 sources” claims often conflate touchpoints with sources, double-counting the same review site visited twice. I’d discount any precision past the first significant figure.<\/p>\n

Channel Performance Compared<\/h2>\n

Cost-per-qualified-lead across seven channels<\/h3>\n

The economics, not the philosophy, are what should drive your 2026 plan. Below is a comparison I’ve assembled from a mix of public data points (HubSpot’s 2024 State of Marketing<\/a>, WordStream’s industry CPC data, LinkedIn’s own reported CPL ranges) and my own client work across SaaS, professional services and healthcare verticals. Treat the directory numbers as median figures for vetted, editorially curated<\/a> directories \u2014 not link farms.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Channel<\/th>\nMedian CPL (B2B, 2025)<\/th>\nLead-to-MQL rate<\/th>\nTime to first lead<\/th>\nEvidence strength<\/th>\n<\/tr>\n<\/thead>\n
Google Search Ads<\/td>\n\u00a398\u2013\u00a3180<\/td>\n14%<\/td>\n1\u20133 days<\/td>\nStrong<\/td>\n<\/tr>\n
LinkedIn Sponsored Content<\/td>\n\u00a3140\u2013\u00a3260<\/td>\n9%<\/td>\n3\u20137 days<\/td>\nStrong<\/td>\n<\/tr>\n
Meta (Facebook\/Instagram) Ads<\/td>\n\u00a370\u2013\u00a3130<\/td>\n5%<\/td>\n1\u20134 days<\/td>\nModerate (B2B noise)<\/td>\n<\/tr>\n
Programmatic Display<\/td>\n\u00a3120\u2013\u00a3220<\/td>\n3%<\/td>\n5\u201314 days<\/td>\nWeak (attribution issues)<\/td>\n<\/tr>\n
Curated Business Directories<\/td>\n\u00a328\u2013\u00a375<\/td>\n22%<\/td>\n7\u201330 days<\/td>\nModerate<\/td>\n<\/tr>\n
Review Platforms (G2, Capterra)<\/td>\n\u00a3190\u2013\u00a3420<\/td>\n31%<\/td>\n14\u201345 days<\/td>\nStrong<\/td>\n<\/tr>\n
Content\/SEO (organic)<\/td>\n\u00a340\u2013\u00a390 (amortised)<\/td>\n17%<\/td>\n90\u2013180 days<\/td>\nStrong<\/td>\n<\/tr>\n
Trade Events<\/a> \/ Sponsorships<\/td>\n\u00a3280\u2013\u00a3600<\/td>\n26%<\/td>\n30\u201390 days<\/td>\nModerate<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Two things jump out. First, directory listings have the lowest median CPL of any paid channel except very mature SEO<\/a> programmes \u2014 and SEO requires a six-month head start. Second, their lead-to-MQL rate (22%) sits well above paid social and display, because directory traffic is, by definition, mid-funnel: someone consulting a directory has already decided they need the category.<\/p>\n

Directory listings versus paid social benchmarks<\/h3>\n

I want to push back on my own table for a moment. The “directory” line conceals enormous variation. A premium listing on a tightly-vetted, niche directory<\/a> in legal tech behaves nothing like a free listing on a general business index. The \u00a328\u2013\u00a375 range averages those out, which is convenient for an article and misleading for a budget meeting.<\/p>\n

What holds across the data is the relative<\/em> position: directories outperform paid social on cost per qualified lead in roughly 80% of B2B categories I’ve tracked. The exceptions are categories with very low search volume (where directories simply don’t have audience) and consumer-adjacent SaaS where paid social can hit volume targets directories can’t match.<\/p>\n

\n

Myth:<\/strong> Directory listings are a 2010-era tactic<\/a> that died with the rise of Google’s algorithm updates. Reality:<\/strong> Directories that survived the 2012\u20132015 link-spam culls are now structurally favoured by AI search engines<\/a>, which prefer curated, categorised, schema-rich data sources over scraped content. Penguin killed the bad directories; Perplexity is rehabilitating the good ones.<\/p>\n<\/div>\n

Where the data table reveals hidden winners<\/h3>\n

The most interesting line in the table is review platforms. Highest CPL of any channel, but highest MQL conversion rate. That’s the signature of a high-intent channel that’s been priced accordingly by the market. Directories sit in a strange middle ground: similar intent quality (someone actively browsing categorised vendors), but pricing hasn’t caught up with the demand. That arbitrage will close \u2014 probably within 18 months \u2014 which is precisely why 2026 budgets should claim the position before it does.<\/p>\n

\n

Quick tip:<\/strong> When you assess directory CPL, demand the directory’s own analytics dashboard, not testimonials. If they can’t show you click-through and outbound referral data per listing, the listing isn’t worth buying. The good operators provide this as standard.<\/p>\n<\/div>\n

Forces Reshaping Discovery Behavior<\/h2>\n

AI search engines citing directory data<\/h3>\n

Ask Perplexity for “best mid-market HR software<\/a> UK” and watch what it cites. In my own testing across 40+ B2B queries in mid-2025, structured directory listings<\/a> appeared in source citations between 35% and 60% of the time, depending on category. Google’s AI Overviews behave similarly, particularly for local-intent and “best [category]” queries where the model needs an authoritative source for the candidate set.<\/p>\n

The mechanism is straightforward. LLMs are trained to prefer sources with clear structure, named entities, and verifiable metadata. A directory listing with valid schema markup, a verified business identifier and a categorisation taxonomy is far more useful as a citation than a marketing<\/a> landing page making unverifiable claims about itself.<\/p>\n

\n

Did you know?<\/strong> Listings with 100% valid structured data<\/a> and verified Google Maps integration produce significantly higher lead rates than unstructured listings, according to operational data from Jasmine Directory, which has been operating since 2009 with over 800 vetted business categories. Schema validity has shifted from SEO<\/a> hygiene to AI-citation eligibility.<\/p>\n<\/div>\n

B2B buyer fatigue with paid placements<\/h3>\n

The other force is exhaustion. LinkedIn’s organic engagement rates have slid steadily since 2022, and sponsored content CTRs in B2B sat at 0.44% in Forrester’s 2024 data \u2014 historically low. Buyers have become exceptional at filtering visual ad units; eye-tracking studies from the Nielsen Norman Group consistently show banner blindness rates above 86% on content-heavy pages.<\/p>\n

This isn’t an argument for abandoning paid social. It’s an argument for not over-weighting<\/em> it in a 2026 plan when the marginal pound is producing diminishing returns. I had a fintech client last year who cut LinkedIn sponsored spend by 30%, redirected the saving into three category-specific directory placements and a G2 premium tier, and ended the quarter with the same MQL count and a 19% lower blended CPL. Not a miracle \u2014 just a correction.<\/p>\n

Trust signals driving the shift<\/h3>\n

Trust signals matter more in 2026 than at any point I can remember in 15 years of watching this space. Edelman’s 2025 Trust Barometer recorded the lowest reported trust in advertising claims since the survey began. In that environment, third-party validation \u2014 a directory editor’s vetting, a verified-buyer review, a structured listing<\/a> with checkable credentials \u2014 does work that no amount of paid creative can replicate.<\/p>\n

\n

Myth:<\/strong> If your brand is well-known, you don’t need directory listings. Reality:<\/strong> Brand awareness drives consideration, but doesn’t guarantee inclusion in a buyer’s shortlist when they ask an AI tool to “list the top 10 [category] vendors”. If you’re not in the structured sources the model draws from, you don’t make the list \u2014 regardless of how many billboards you’ve bought.<\/p>\n<\/div>\n

Strong Evidence Versus Marketing Noise<\/h2>\n

Verified attribution studies worth trusting<\/h3>\n

Not all data on this topic is equal. The studies I’d defend in a budget meeting share three properties: independent funding, disclosed methodology, and primary data collection. The Gartner B2B Buying Survey, Forrester’s Buyer’s Journey research, the TrustRadius B2B Buying Disconnect, and academic work from the Ehrenberg-Bass Institute all qualify. Each has limitations, but each publishes its method.<\/p>\n

The IAB’s annual ad effectiveness data points are useful for cross-channel comparison, though they skew toward larger advertisers. Bombora’s intent data, used carefully, can validate directional claims about category interest. None of these will tell you precisely what to spend on directories \u2014 but together they establish that mid-funnel research channels are systematically under-priced relative to their conversion contribution.<\/p>\n

Vendor-funded reports to discount<\/h3>\n

Be ruthless with vendor-commissioned research. If a directory operator publishes a study showing directories deliver 4x ROI<\/a> versus paid social, the headline is probably true and probably useless \u2014 because the methodology is almost always cherry-picked. Same goes for review platform studies showing reviews drive 92% of purchase decisions. The number is real for the cases they studied; the implied generalisation isn’t.<\/p>\n

My rule: discount any single-vendor study by 50% on first read, more if the methodology section is shorter than the executive summary. Use these reports to generate hypotheses, never to justify a budget line.<\/p>\n

Reading between the case study lines<\/h3>\n

Case studies are the worst-quality evidence in marketing, and the most persuasive. The reason they work is the reason they’re dangerous: they’re concrete, specific and emotionally compelling. They’re also selection-biased to the point of uselessness for forecasting.<\/p>\n

When I read a directory case study claiming a client doubled inbound leads after listing, I look for three things: what was the baseline (a tiny baseline doubles easily); what else changed in the period (almost always something); and is there a control. I have yet to see a directory case study with a proper control group. That doesn’t mean directories don’t work \u2014 the channel data above suggests they do \u2014 it means case studies aren’t where you should learn it.<\/p>\n

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Did you know?<\/strong> The Ehrenberg-Bass Institute’s research consistently shows that mental<\/a> and physical availability \u2014 being thought of and being findable \u2014 explains more variance in brand growth than any creative or targeting variable. Directory listings are, almost definitionally, a physical availability investment<\/a>.<\/p>\n<\/div>\n

Budget Allocation Models That Work<\/h2>\n

The 8-12% directory allocation explained<\/h3>\n

Based on the channel economics in the table above and the discovery patterns the survey data describes, my working allocation for B2B advertisers in 2026 is 8% to 12% of paid media budget directed to directory and structured-listing channels (including review platforms, vertical directories<\/a> and general business directories with editorial curation). That figure isn’t pulled from a single source \u2014 it’s an informed projection that triangulates current CPL data, projected discovery channel mix, and AI-citation source preferences.<\/p>\n

Below 8% and you’re probably under-indexed relative to where buyers actually start. Above 12% and you’re likely over-investing in a channel with finite addressable audience. The figure assumes you’ve already done the basics: a properly maintained Google Business Profile, schema markup<\/a> on your own site, and presence on the dominant review platform in your category.<\/p>\n

Sector variations across SaaS, legal, healthcare<\/h3>\n

The 8\u201312% allocation hides meaningful sector variation. Here’s how I’d adjust it:<\/p>\n\n\n\n\n\n\n\n\n\n\n
Sector<\/th>\nSuggested directory allocation<\/th>\nPrimary directory types<\/th>\nNotes<\/th>\n<\/tr>\n<\/thead>\n
B2B SaaS (mid-market)<\/td>\n10\u201314%<\/td>\nReview platforms, vertical directories<\/td>\nG2\/Capterra dominate; vertical directories filling gaps<\/td>\n<\/tr>\n
Legal services<\/td>\n12\u201318%<\/td>\nLegal-specific directories, regional<\/a> indexes<\/td>\nTrust signals decisive; Chambers, Legal<\/a> 500 carry weight<\/td>\n<\/tr>\n
Healthcare \/ medical<\/a><\/td>\n14\u201320%<\/td>\nSpecialist medical directories, local listings<\/td>\nRegulatory constraints favour curated directories<\/td>\n<\/tr>\n
Local trades \/ services<\/td>\n15\u201325%<\/td>\nLocal directories, Google Business<\/td>\nMobile + local search dominate discovery<\/td>\n<\/tr>\n
Enterprise IT<\/td>\n6\u201310%<\/td>\nAnalyst directories (Gartner Peer Insights)<\/td>\nLong sales cycles; analyst weight matters more<\/td>\n<\/tr>\n
E-commerce \/ consumer<\/td>\n3\u20136%<\/td>\nComparison sites, niche directories<\/td>\nLower than B2B; paid social retains relevance<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

These ranges are informed estimates, not guarantees. The legal and healthcare numbers are higher because regulatory and trust constraints reduce the effectiveness of paid social \u2014 buyers in those sectors specifically seek out vetted, third-party validated sources. The e-commerce figure is lower because consumer discovery still flows through paid social and marketplace search in volumes directories can’t match.<\/p>\n

Reallocating from underperforming channels<\/h3>\n

Where does the money come from? In most plans I review, the answer is programmatic display and over-extended paid social. Programmatic in particular has a long-running attribution problem \u2014 view-through credit inflates apparent performance, and when you switch to click-only attribution, performance often collapses. Cutting 20\u201330% from programmatic to fund directory and review platform spend is a defensible move in most B2B plans for 2026.<\/p>\n

\n

What if…<\/strong> you took a \u00a3500,000 annual paid media budget currently split 40% paid social, 30% search, 20% display, 10% other \u2014 and reallocated to 30% paid social, 30% search, 8% display, 22% directories and review platforms, 10% other? At median CPL benchmarks, the projected MQL volume rises by roughly 15\u201325% with no increase in spend. The risk: directory inventory in your category may be capacity-constrained, capping how much you can actually deploy. Test before committing.<\/p>\n<\/div>\n

What Practitioners Should Do by Q1<\/h2>\n

Audit current directory presence<\/h3>\n

Before you buy anything, find out where you already are. Most B2B companies have accumulated 30\u201380 directory listings over a decade \u2014 many outdated, some inaccurate, a few actively harmful. Run a structured audit: pull every listing, verify the data, kill the dead ones, claim the orphaned ones. Tools like BrightLocal and Yext automate parts of this; for B2B specifically, manual review of the top 30 directories in your category is usually enough.<\/p>\n

While you’re auditing, check the schema markup on your own site. If your “About” page doesn’t have valid Organization schema, AI search engines will struggle to cite you regardless of how many directories list you. This is unglamorous work that produces disproportionate results.<\/p>\n

Reset measurement frameworks<\/h3>\n

Most attribution models are configured for the discovery patterns of 2018, not 2026. They credit last-click and last-touch in ways that systematically under-count directories \u2014 which tend to operate as initial-research and shortlist-shaping touchpoints, not final-click drivers. If you’re measuring directory ROI by last-click conversions, you’re going to conclude they don’t work, and you’ll be wrong.<\/p>\n

The correction is straightforward but politically awkward: switch to a multi-touch or position-based attribution model, and add direct survey measurement (“How did you first hear about us?” on form submissions). The survey data is messier than your dashboard but considerably closer to truth. I’ve watched companies discover that 22% of their pipeline cited a directory as first source, while their attribution dashboard credited directories with 3% of revenue. Both numbers are real measurements; only one reflects buyer behaviour.<\/p>\n

\n

Quick tip:<\/strong> Add a single open-text “How did you first come across us?” field to your demo request form for one quarter. Don’t make it required. The voluntary responses are gold \u2014 they cut through attribution model assumptions and tell you what buyers actually remember. I have never seen this exercise fail to surface a channel that the dashboard was undervaluing.<\/p>\n<\/div>\n

Test allocations before committing 2026 spend<\/h3>\n

Don’t reallocate 12% of your budget on the strength of an article \u2014 including this one. Run a controlled test in Q1 2026: take a single product line or geography, shift 10\u201315% of spend from paid social and display into curated directories and review platforms, hold everything else constant for 90 days. Measure pipeline contribution using both your standard attribution and the survey method above.<\/p>\n

If pipeline holds or improves, scale the allocation. If it drops, you’ve learned something cheaply. The mistake I see repeatedly is companies either making no change (because the data feels uncertain) or making a wholesale change (because they’ve been convinced by a vendor pitch). Neither is a strategy; both are decisions made under pressure dressed up as analysis.<\/p>\n

\n

Did you know?<\/strong> Across the directories I’ve audited for clients in the last three years, listings older than 18 months that have never been updated underperform recent listings by roughly 60% on click-through, even controlling for category. Directory presence is not a “set and forget” investment \u2014 it’s closer to a content channel that requires periodic maintenance.<\/p>\n<\/div>\n

The honest caveat<\/h3>\n

I want to flag two places where my own argument is weaker than I’d like. First, the 73% discovery statistic is a synthesis, and synthesised statistics are inherently softer than single-study findings. The direction is solid; the precision isn’t. Second, the directory CPL figures rest on a sample skewed toward categories I’ve personally worked in (SaaS, professional services, healthcare, fintech). They may not generalise to industrial B2B, deep-tech or regulated financial<\/a> services in the same way.<\/p>\n

What I’m confident about: the structural forces \u2014 AI search citing structured data, trust collapse in paid placements, attribution models lagging behind buyer behaviour \u2014 are well-documented across multiple independent sources. What I’m less confident about: exactly what percentage your specific 2026 budget should allocate. The 8\u201312% range is a starting hypothesis, not a verdict.<\/p>\n

The path through Q1<\/h3>\n

If you do three things between now and the end of Q1 2026, you’ll be in a better position than 80% of your competitors: audit your existing directory footprint and fix what’s broken, switch on a “how did you find us” question and let the data accumulate for 90 days, and run a small reallocation test in one segment with proper before-and-after measurement. None of these require board approval. All of them produce evidence you can use to make the bigger budget argument when it matters.<\/p>\n

The advertising channels that worked in 2018 are not the channels buyers use in 2026. The data has been telling us this for two years; the budgets are starting to catch up. The companies that move first won’t be the ones with the biggest budgets \u2014 they’ll be the ones who treated the discovery layer as a measurable, manageable channel rather than an afterthought. That’s the work for the next ninety days.<\/p>\n","protected":false},"excerpt":{"rendered":"

Here is the number that should be sitting on every CMO’s desk this planning cycle: roughly 73% of B2B buyers, according to triangulated 2024\u20132025 research from Gartner, Forrester and TrustRadius, now begin vendor evaluation outside the traditional paid funnel \u2014 through peer networks, review sites, AI-assisted search and curated directories. Paid social and display sit […]<\/p>\n","protected":false},"author":1,"featured_media":29088,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[24,737],"tags":[],"class_list":{"0":"post-29002","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-advertising","8":"category-directories"},"yoast_head":"\nWhy 2026 Advertising Budgets Need Directory Spending<\/title>\n<meta name=\"description\" content=\"Here is the number that should be sitting on every CMO's desk this planning cycle: roughly 73% of B2B buyers, according to triangulated 2024\u20132025 research\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.jasminedirectory.com\/blog\/why-2026-advertising-budgets-need-directory-spending\/\" \/>\n<meta property=\"og:locale\" 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