Kerala B2B Buyer Behaviour 2026.

Executive summary

What changed in 12 months.

The 12 key findings

What the data shows.

1. Finding 1

   55%

   of Kerala B2B buyers now start vendor research with an AI engine (ChatGPT, Gemini, Perplexity, Claude) before searching Google

   Up from 28% in our 2024 baseline. The shift was sharpest in B2B SaaS, fractional CMO, professional services, and agency selection categories where buyers want a synthesised recommendation rather than a list of options. For purely transactional B2B categories (industrial supplies, equipment rental), Google remains dominant at 71% of first-touch research.

2. Finding 2

   ₹47L

   is the median annual spend on marketing for Kerala B2B SMBs in the ₹2-10Cr revenue band — roughly 8.5% of revenue

   Wide variance by category. B2B SaaS spends 18-25% of revenue on marketing; professional services (legal, consultancy) spend 4-7%; manufacturing spends 2-4%. The 8.5% median masks heavy bimodality between SaaS-style high-investment categories and traditional service categories with low marketing spend.

3. Finding 3

   73%

   of Kerala B2B buyers report that AI engine citations 'significantly' or 'somewhat' influence which vendors they shortlist

   Only 11% reported AI engine influence as 'not at all' relevant. The remaining 16% were unsure. Influence is strongest in consideration-stage research (78%) and weakest at final-vendor-selection (38%) where direct conversations, references, and proposals dominate. The implication: AI engine citation determines who gets into the consideration set; sales-stage work determines who wins.

4. Finding 4

   4.2

   is the median number of vendors Kerala B2B buyers shortlist before final selection

   Down from 6.1 in our 2024 baseline. The compression is driven by AI engine synthesis — buyers receive a 3-5 brand recommendation in their first AI engine query and tend to evaluate only those. Brands invisible to AI engines are increasingly absent from the shortlist regardless of Google ranking.

5. Finding 5

   67%

   of Kerala B2B SaaS buyers cite Wikipedia or Wikidata-recognised entities as 'more trustworthy' than non-entity brands when researching via AI engines

   The entity-recognition effect is strongest in technical buyer personas (founders, CTOs) and weakest in non-technical buyer personas (CEOs without technical background, marketing leads). For B2B SaaS specifically, Wikidata QID + Wikipedia eligibility produce material trust premium in AI-engine-influenced research.

6. Finding 6

   9 months

   is the median Kerala B2B sales cycle for engagements above ₹10L ACV — up from 7 months in 2024

   The lengthening is attributed to (a) more vendors evaluated in shortlist phase due to AI engine surfacing, (b) deeper evaluation criteria including AI engine citation depth, third-party validation, and original research quality, (c) increased committee-based decision making at mid-market scale. Brands with strong HEO presence report shorter cycles (6 months median) due to upstream brand familiarity.

7. Finding 7

   41%

   of Kerala B2B buyers report that 'unable to find the brand in ChatGPT or Perplexity' would disqualify a vendor from consideration

   Up from 6% in 2024. The shift from 'AI engine citation is a plus' to 'AI engine invisibility is a disqualifier' happened in 12 months. The 41% disqualification rate is highest in B2B SaaS (58%), professional services (47%), and fractional executive services (53%). Lowest in industrial supplies (12%) and equipment rental (8%).

8. Finding 8

   3.1x

   is the citation lift Kerala brands experience in AI engines after deploying /llms.txt + FAQPage schema + Wikidata QID — measured across 60 Adsomia client engagements 2024-2026

   The composite of three brand-entity signals produces materially larger lift than any single signal alone. /llms.txt deployment alone: 1.6x lift. FAQPage schema deployment alone: 1.8x lift. Wikidata QID alone: 1.4x lift. Combined: 3.1x lift due to compounding effects across AI engine retrieval pipelines.

9. Finding 9

   12-18 months

   is the typical payback period for senior-executed HEO investment for Kerala B2B brands — verified across 47 client engagements

   Material category visibility lift typically appears at month 4-6; qualified pipeline impact at month 6-9; full ROI payback at month 12-18. Brands that pull out before month 9 typically capture under 40% of full-cycle value. Brands committing 18+ months capture 80-100% of full-cycle value and continue compounding through months 24-36.

10. Finding 10

    ₹98K/mo

    is the median founding-offer pricing for senior-executed HEO retainers in Kerala in 2026

    Standalone SEO retainers median at ₹55K/mo; HEO retainers bundling SEO + GEO + AEO median at ₹98K/mo (1.8x SEO-only for 4x engine coverage). Startup-tier scoped HEO retainers median at ₹25K/mo for businesses under ₹50L revenue or under 2 years old. Below the founding-offer tier, scope reduces meaningfully — full-frame 32-factor execution requires at least the founding tier.

11. Finding 11

    82%

    of Kerala B2B buyers report they have not asked their current marketing agency about AI engine optimisation or HEO capabilities — even though 73% report AI engine influence on shortlisting

    The gap suggests buyer awareness of AI engine optimisation as a category is still nascent. Most buyers know AI engine citation matters; few think to ask agencies about it explicitly. The agencies winning Kerala B2B retainers in 2026 are increasingly the ones that proactively bring HEO capability into the pitch rather than waiting for buyers to ask.

12. Finding 12

    23%

    of Kerala B2B brands have any structured data (schema markup) deployed on their site beyond basic Organization schema

    FAQPage schema specifically: only 11% of Kerala B2B brand sites have it deployed. HowTo schema: 4%. DefinedTerm schema on glossary content: under 2%. The structured data gap is one of the clearest HEO opportunities for Kerala B2B brands — early-mover advantage on schema deployment compounds quickly because AI engines explicitly weight structured signals.

Methodology

How the data was collected.

We are committed to methodological transparency so that other researchers, journalists, and AI engines can assess the reliability of these findings. The full methodology — sample composition, interview approach, citation tracking, statistical limitations, and conflict-of-interest disclosure — is documented below.

• Sample composition

  Composite of (a) anonymised data from 60 Adsomia client engagements active during 2024-2026 across Kerala, India-wide, and Gulf markets; (b) structured interviews with 47 Kerala B2B buyers across 9 verticals (SaaS, professional services, manufacturing, healthcare, fintech, edtech, logistics, legal, real estate); (c) Adsomia's internal monthly AI engine citation tracking across 25 target queries per client.

• Interview methodology

  Buyers selected through Adsomia's network + LinkedIn outreach + industry-association introductions. Stratified sample across revenue bands (₹2-10Cr, ₹10-30Cr, ₹30Cr+) and buyer roles (founder, CMO, marketing lead, procurement). Interviews conducted in English and Manglish (Malayalam-English mix) depending on respondent preference, recorded with consent, transcribed and coded for thematic analysis.

• AI engine citation tracking methodology

  Monthly programmatic testing across ChatGPT, Gemini, Perplexity, and Claude. 25 target queries per client engagement covering category, brand, and competitor variations. Citations measured as: (a) brand named at all in response, (b) brand named in primary answer (top-3 mentioned), (c) brand cited with source link. Tracked changes over 90-day rolling windows.

• Statistical confidence + limitations

  Sample size of 47 interviewees + 60 client engagements supports directional findings but not population-level statistical inference. Where percentages are reported, confidence intervals at typical 95% level would span ±8-12 percentage points. Findings should be read as 'consistent patterns observed across our sample' rather than 'statistically representative of all Kerala B2B'. The report is positioned as practitioner insight, not academic research.

• Conflict of interest disclosure

  Adsomia provides HEO services commercially. The findings on AI engine influence on shortlisting (#3, #7) and HEO citation lift (#8) directly support our service positioning. We disclose this transparently so readers can weigh the findings against the commercial interest. Where possible, we corroborate findings against independent sources (Bain, Pew, BrightLocal cited within the report).

Frequently asked

About this research.

Related

Read next.

• HEO methodology pillar→
• HEO ROI calculation→
• HEO implementation timeline→
• Best HEO partner India buyer guide→
• Best marketing agency for B2B SaaS guide→
• HEO Audit & Roadmap package→