2026 B2B CPC, Lead-Action CPA, and MQL Benchmarks for Agriculture, Forestry, Aquaculture & Fisheries

Who this report is for

This report is for agricultural-input and equipment suppliers, forestry and wood-products vendors, aquaculture technology providers, commercial-fisheries suppliers, exporters, and B2B revenue teams that need a transparent planning reference before they have enough mature first-party data. It is most useful when the team can preserve producer, dealer, processor, cooperative, and resource-operator cohorts rather than blending them.

Contents

1 Benchmark snapshot 2 What the data says 3 Industry demand context 4 Why CPC behaves this way 5 Why CPA and MQL diverge 6 Seasonality x producer/dealer/processor adjustment grid 7 Illustrative planning scenario 8 How to compare your data 9 What this does not prove 10 FAQ, sources, related knowledge, and visual brief

Benchmark snapshot

Metric and unit 2026 value Evidence label Denominator Interpretation Advertising CPC, USD per paid click USD 4.10 pricing_system_value Paid clicks Modeled traffic-cost reference Lead-action CPA, USD per captured action USD 61 sellersgpt_modeled Valid captured lead actions Upstream action cost, before MQL qualification Implied click-to-action rate, % of clicks 6.72% derived_calculation Paid clicks Consistency diagnostic from CPC / CPA Modeled lead-to-MQL rate, % of lead actions 33.8% sellersgpt_modeled Captured lead actions Fit-plus-engagement planning assumption Modeled cost per MQL, USD per MQL USD 180 sellersgpt_modeled Modeled MQLs Rounded acquisition-cost estimate Planning band, USD per MQL USD 140 to USD 220 sellersgpt_modeled Modeled MQLs Scenario range, not a statistical interval Confidence Medium-High sellersgpt_modeled Model mapping Strong adjacent evidence, but a composite category The SGT-01 CPC is 80.4 on an index where the 30-industry portfolio mean of USD 5.10 equals 100. That comparison is descriptive. It does not prove that any specific campaign will buy clicks at USD 4.10.

What the data says

The model separates three successive units. CPC measures advertising spend divided by paid clicks. Google Ads documents average CPC as total click cost divided by total clicks. Google Ads Help, "Average cost-per-click (Avg. CPC): Definition" Lead-action CPA measures cost divided by the conversion action selected for the benchmark. Google's CPA definition likewise depends on the counted conversion; it does not decide whether that conversion is commercially useful. Google Ads Help, "Average CPA: Definition" The MQL layer is narrower. The model applies a 33.8% fit-plus-engagement rate to captured lead actions. The resulting USD 180 is an acquisition-cost estimate for the model's MQL definition, not a SellersGPT Qualified Lead price. An actual SellersGPT product price remains null in this research record and must come from the effective pricing system. Public search benchmarks are useful anchors but are not a census of agricultural B2B advertising. The 2026 WordStream/LocaliQ program covers thousands of U.S. search campaigns across broad categories and includes Google and Microsoft Ads. It is used as one source family, not as a direct measurement of every producer, dealer, forestry buyer, or fishery operator. WordStream by LocaliQ, "Google Ads Benchmarks 2026: Competitive Data & Insights for Every Industry"

Industry demand context

SGT-01 combines at least four materially different buying environments. First, farm and ranch purchases can depend on crop or livestock specialization, cash flow, weather, input prices, planting or harvest windows, and replacement urgency. USDA's farm-finance program publishes performance indicators by resource region and commodity specialization, demonstrating why one annual national label cannot represent every operating context. USDA Economic Research Service, "Farm Sector Income Forecast" Second, forestry includes land management, harvesting, primary wood products, engineered materials, equipment, and downstream manufacturing. The U.S. Forest Service describes forest products as a substantial manufacturing system, not a single buyer population. U.S. Forest Service Research and Development, "Forest Products" A sawmill looking for process equipment, a landowner reading stewardship guidance, and a distributor sourcing consumables should not share one conversion denominator. Third, commercial fisheries and aquaculture involve vessel operators, farms, processors, cold-chain partners, ports, and regulators. NOAA Fisheries reports commercial landings, revenue, prices, employment, sales, income, and value-added impacts separately from recreational activity. NOAA Fisheries, "Fisheries Economics of the United States" That distinction matters for traffic quality: recreational research and commercial procurement are different demand populations. Finally, the SellersGPT taxonomy is a commercial reporting layer. NAICS is the official U.S. classification system used for statistical reporting, but it does not supply CPC or lead economics. U.S. Census Bureau, "North American Industry Classification System" A campaign should preserve its own subsegment and buyer type instead of assuming SGT-01 is one official statistical industry.

Why CPC behaves this way

The USD 4.10 CPC can be a sensible planning anchor because many searches are regional, product-specific, or operational rather than crowded with broad consumer-finance or enterprise-software advertisers. That is a SellersGPT inference, not a causal finding. Auction price still depends on country, language, keyword specificity, device, match type, quality, season, and competitors. Three mixtures can move the actual CPC away from the benchmark: 1 Urgent operational demand. Searches for repair, disease control, replacement parts, feed, water systems, or harvest equipment may be narrow and commercially valuable. 2 Informational and public-program demand. Grants, regulations, weather, commodity prices, recreational fishing, and academic research can generate inexpensive clicks with limited sales relevance. 3 Dealer and producer overlap. A manufacturer may target dealers, large operators, processors, or cooperatives. Each audience uses different language and has different click economics. The correct response is not to force every click into one average. Separate campaigns by buyer role and application, then compare each cohort with the benchmark.

Why CPA and MQL diverge

The implied click-to-action rate is USD 4.10 / USD 61 = 6.72%. It is a consistency diagnostic derived from two point estimates, not an independently observed industry conversion rate. A captured action can still be a hobbyist question, grant enquiry, job seeker, small consumer purchase, unsupported geography, or dealer enquiry outside the desired route. The 33.8% modeled lead-to-MQL rate reflects a fit-plus-engagement planning definition. Public lead-to-MQL research provides useful industry and channel proxies, but its agency cohort and MQL definition cannot be transferred unchanged. First Page Sage, "Lead-to-MQL Conversion Rate Benchmarks by Industry & Channel" For SGT-01, qualification should test organization type, operating scale, application, geography, purchase role, commercial timing, and the evidence needed for the offer.

Seasonality x producer/dealer/processor adjustment grid

Use this tool before replacing the public benchmark with a campaign plan. Dimension Low-cost / higher-yield case Base case High-cost / lower-yield case Timing Active buying or replacement window Mixed in-season and planning traffic Off-season research or delayed budget Buyer route Named dealer, processor, or commercial operator Mixed direct and channel buyers Consumer, hobby, public-program, or unclear role Operating fit Scale, species/crop, application, and geography confirmed Some applicable fields Unknown Core production or service requirement fails Offer Specific configuration, parts, service, or supply solution General category solution Broad information with no commercial next step Planning factor 0.80 to 0.95 x benchmark 0.95 to 1.10 x benchmark 1.10 to 1.30 x benchmark Apply factors only to an explicit scenario. They are editorial planning assumptions, not new market observations. A hard fit failure should be excluded, not rescued with a favorable factor.

Illustrative planning scenario

This fictional example tests arithmetic, not performance.

• Planned paid clicks: 1,000

• Benchmark CPC: USD 4.10 per click

• Implied click-to-action rate: 6.72%

• Modeled lead-to-MQL rate: 33.8% Media spend = 1,000 x USD 4.10 = USD 4,100 Modeled lead actions = 1,000 x 6.72% = 67.2 Modeled MQLs = 67.2 x 33.8% = 22.71 Implied cost per MQL = USD 4,100 / 22.71 = approximately USD 180.54 The small difference from the canonical unrounded USD 180.47 comes from using the displayed 6.72% diagnostic instead of full-precision intermediate values. The published point estimate is USD 180 after the nearest-USD-5 rule. The scenario excludes creative, labor, data, agency, sales, and follow-up costs. It predicts no meeting or revenue.

How to compare your data

Compare like with like. Use one reporting period and preserve campaign, country, currency, network, conversion action, buyer route, and qualification version. Remove spam and duplicates consistently, but do not silently delete valid poor-fit actions. Measure both action volume and the share passing your documented MQL criteria. Wait for cohort maturity. A farm-equipment request received before planting may convert quickly; a forestry-capital project can remain open for months. Report immature cohorts separately. Replace the public benchmark only after your sample is large and stable enough to explain by subsegment.

What this does not prove

This report does not prove a universal bid, click price, lead volume, MQL rate, meeting rate, opportunity rate, revenue, CAC, ROAS, or ROI. It does not state an actual SellersGPT Qualified Lead unit price. Medium-High confidence describes the quality of the proxy mapping, not a statistical probability that results fall inside the band.

FAQ

Is USD 4.10 the price of an agricultural lead? No. It is USD per paid advertising click. The modeled lead-action CPA is USD 61, and the modeled cost per MQL is USD 180. Should dealers and producers use the same benchmark? Use the same public row only as a starting reference. Separate the cohorts because their search language, application evidence, order size, and route-to-market logic differ. Does a seasonal enquiry count as an MQL? Only if it passes the defined fit-plus-engagement criteria. Timing can be Unknown without being a pass or failure, but a consumer or noncommercial enquiry should not be counted as B2B demand. Why is confidence not High? SGT-01 combines several sectors and has no direct public lead-to-MQL series covering all of them. Strong official context and adjacent advertising evidence support the model, but a composite remains necessary.

Sources and methodology

1 DEF-01. Google Ads Help, "Average cost-per-click (Avg. CPC): Definition". Accessed August 25, 2026. Defines CPC only; supplies no SGT-01 benchmark. 2 DEF-03. Google Ads Help, "Average CPA: Definition". Accessed August 25, 2026. Defines CPA; does not define the counted action. 3 BENCH-03. WordStream by LocaliQ, "Google Ads Benchmarks 2026: Competitive Data & Insights for Every Industry". Accessed August 25, 2026. Search-cost anchor; not a global pure-B2B agriculture sample. 4 BENCH-05. First Page Sage, "Lead-to-MQL Conversion Rate Benchmarks by Industry & Channel". Accessed August 25, 2026. Stage-rate proxy; agency cohort and definition limitations apply. 5 GOV-01. U.S. Census Bureau, "North American Industry Classification System". Accessed August 25,

2026. Taxonomy control; no advertising cost data. 6 SGT01-USDA-ERS. USDA Economic Research Service, "Farm Sector Income Forecast". Accessed August 25, 2026. Farm-finance and specialization context; no lead benchmark. 7 SGT01-NOAA-FEUS. NOAA Fisheries, "Fisheries Economics of the United States". Accessed August 25,

2026. Commercial and recreational fisheries context; no CPC or MQL data. 8 SGT01-USFS-FP. U.S. Forest Service Research and Development, "Forest Products". Accessed August 25, 2026. Forest-products sector context; no campaign economics. How this was modeled The canonical CPC is imported from the frozen pricing-system benchmark registry. The lead-action CPA, modeled rate, cost per MQL, band, and confidence come from the SellersGPT 2026 source-consistency model. The model weights a normalized industry proxy at 70% and a 31% channel baseline at 30%, caps the raw proxy between 24% and 38%, and rounds cost per MQL to the nearest USD 5. External sources are assigned roles rather than averaged as equivalent observations.