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Five types of field execution fraud costing Indian brands money right now

A complete taxonomy of how India's ₹80,000 crore physical economy is being silently drained. Five fraud types, twenty sub-categories, real campaign examples, and the detection capabilities that surface each one.

G
gOGig Editorial
··15 min read

₹15,000–20,000 Cr

Annual leak across India's physical economy from the five fraud types catalogued in this blog. Every rupee categorised, every mechanic mapped, every detection method documented.

₹80,000 CrIndia physical economy spend
13 millionFMCG retail outlets
3 millionFMCG field reps
4,00,000+FMCG distributors

A senior CFO at a top FMCG brand asks: "Which fraud is costing us the most this year?" The marketing team cannot answer. Not because the fraud is hidden, but because no one has classified it. This blog is the classification.

The five fraud types at a glance

Fraud typeEstimated industry-wide annual costDetection difficulty
1. Ghost retail coverage₹3,500–5,000 CrHigh (requires outlet-level verification)
2. Duplicate & fake retailer onboarding₹2,500–3,500 CrMedium (requires de-duplication logic)
3. Proof-of-performance fraud₹4,000–5,500 CrMedium (requires AI image verification)
4. Field force productivity leakage₹3,000–4,000 CrHigh (requires real-time visibility)
5. Manual reporting manipulation₹2,000–2,500 CrLow (requires platform-based capture)
Total industry-wide annual cost₹15,000–20,500 CrVaries by category

The five fraud types ranked by detectability

RankFraud typeTime to detect (legacy)Time to detect (FEI)
1 (easiest with FEI)Manual reporting manipulationMonths or neverAt submission
2Proof-of-performance fraudWeeksAt submission (AI)
3Duplicate retailer onboardingQuarterly auditAt onboarding (OTP + geo)
4Ghost retail coverageAnnual audit / neverContinuous (geo-verification)
5 (hardest)Field force productivity leakageYear-end performance reviewReal-time dashboards

What ghost retail coverage actually means

Ghost retail coverage is when outlets that have closed, never existed, or no longer stock the brand still appear as "active" in DMS systems, sales reports, and target sheets. The brand pays for distribution, sales effort, and trade promotions tied to outlets that are functionally invisible.

Sub-types of ghost retail coverage

Sub-typeMechanicIndustry impact
Closed outlets still activeOutlets that shut down still counted in distribution targets2,000+ shut stores still counted in single city audits (Ahmedabad example)
Phantom outletsOutlets that never existed, fabricated in DMS1–3% of any large FMCG database typically
Inactive outlets billed as activeOutlets that exist but no longer stock the brand5–8% of typical DMS records
Wrong address / location mismatchOutlet name correct, location wrong for billing2–4% of records typically
Multiple billing under one outletOne physical store split into 2–3 billing IDs1–2% of records typically

How ghost retail coverage drains money

Drain channelMechanismTypical % of trade spend lost
Inflated distribution targetsASMs hit targets by selling to ghost outlets3–5%
Trade scheme payoutsSchemes claimed for outlets that didn't stock2–3%
Promoter deploymentPromoters scheduled at outlets that don't exist1–2%
Visual merchandising spendPOSM material billed for ghost locations1–2%
Sales incentive payoutsReps earn incentives against ghost coverage2–3%
Stock allocationInventory pushed to outlets that don't sell1–2%
Total typical impact--10–17% of trade spend

The Ahmedabad ghost outlet pattern

IndicatorReportedVerified
Active outlets in city~20,000–25,000~18,000–22,000 (estimated)
Closed shops still in DMSHidden2,000+ reported by distributor body
Ghost rate (closed/total)0% reported8–12% estimated
Annual trade investment per outlet₹3,500–5,000--
Estimated ghost cost (city level)--₹70 lakh–1 Cr per brand per city

Industry-wide ghost retail exposure

IndustryGhost outlet rateAnnual industry impact
FMCG (food & beverage)8–12%₹1,200–1,800 Cr
FMCG (personal care)6–10%₹800–1,200 Cr
FMCG (home care)5–9%₹500–800 Cr
Telecom / consumer durables4–7%₹400–600 Cr
Auto parts & lubricants5–8%₹300–450 Cr
Pharma OTC4–6%₹250–400 Cr
Total estimated impact--₹3,500–5,000 Cr

Detection methods for ghost retail coverage

MethodEffectivenessImplementation cost
Manual annual audit (third-party)40–60% detection₹50–200 per outlet
OTP-verified outlet onboarding75–85% detection₹5–15 per outlet
Geo-locked outlet verification85–95% detection₹5–15 per outlet
AI deduplication + image fingerprinting90–98% detection₹2–8 per outlet
Continuous quarterly verification95%+ detection sustained₹20–40 per outlet annually

What duplicate retailer onboarding actually means

Duplicate retailer onboarding happens when the same outlet is registered multiple times in the DMS with slight variations (different spellings, addresses, owner names). Fake retailer onboarding happens when retailers that do not exist at all are added to inflate distribution numbers and trigger schemes.

Sub-types of duplicate & fake retailer onboarding

Sub-typeMechanicTypical detection signal
Spelling variantsSame retailer entered as "Shree Krishna", "Shri Krishna", "Sri Krishna"Fuzzy name matching
Address variantsSame physical location entered with shop number / pincode variantsGeo-coordinate clustering
Owner name variantsSame owner registered under different shop namesPhone number / GSTIN matching
Phone number variantsSame shop with different phone numbers each entryGeo + name combination matching
Phantom outletsOutlets fabricated with fake names and addressesOTP verification failure
Shell registrationReal outlet registered as a different categoryGSTIN + category match
Sub-stockist passthroughSub-stockist registered as direct retailerOrder pattern analysis

The 1.6M to 2.6M verification case

StageOutlets recordedStatus
Pre-audit DMS database~1.6 millionConsidered authoritative by brand
Duplicates identified~150,000–250,0009–15% duplication rate
Phantom outlets identified~30,000–60,0002–4% fake rate
Genuine outlets missed (under-registered)~1 millionDiscovered through OTP + geo expansion
Post-audit verified universe~2.6 million98%+ accuracy
Net database change+1 million netCombination of corrections and expansion

Cost impact of duplicate registrations

Cost dimensionPer-duplicate annual costBrand-level impact
Inflated distribution KPIs₹500–1,500Misallocation of growth investment
Trade scheme over-payment₹2,000–5,0002–3% of scheme budget
POSM material wastage₹300–8003–5% of merchandising spend
Sales rep time wastage₹1,000–2,5004–7% of field force productivity
Data analytics distortionDifficult to quantifyTrade strategy built on wrong data
Quarterly business review accuracy loss--Incorrect investment decisions next quarter

Industry-wide duplicate retailer exposure

IndustryTypical duplication rateIndustry annual impact
FMCG general trade8–15%₹1,000–1,500 Cr
FMCG modern trade1–3%₹100–200 Cr
Consumer durables5–10%₹400–600 Cr
Pharma OTC distribution6–12%₹300–500 Cr
Auto parts4–8%₹200–300 Cr
Telecom retail3–7%₹250–400 Cr
Total estimated impact--₹2,500–3,500 Cr

Verification techniques for duplicate detection

TechniqueWhat it catchesAccuracy
OTP verification at onboardingPhantom outlets, fake phone numbers80–90%
Geo-coordinate de-duplicationAddress variants of same location85–95%
GSTIN matchingSingle owner multiple registrations95%+
Phone number + name fuzzy matchSpelling variants85–92%
Image fingerprinting (shop photos)Same physical shop, different IDs90–95%
Combined multi-signal verificationAll sub-types98%+

What proof-of-performance fraud actually means

Proof-of-performance fraud happens at the moment of submission. Photos are recycled from previous campaigns, GPS coordinates are spoofed, hoardings are photographed once and submitted as five installations, attendance is faked for promoter deployment, and lead lists are fabricated.

Sub-types of proof-of-performance fraud

Sub-typeMechanicTime to execute
Recycled campaign photosPhotos from previous campaigns submitted as new30 seconds
GPS spoofing via mock-location appsFree Play Store apps spoof location coordinates3–5 minutes
Same hoarding photographed multiple anglesOne physical asset becomes 3–5 reported installs2 minutes
Pre-dated photo submissionPhotos taken weeks ago submitted as fresh2 minutes
Photo of competitor brand's setupWrong brand's installation submitted1 minute
Fabricated lead listsNames and phone numbers invented15–30 minutes
Recycled lead databasesOld leads submitted as new captures5 minutes
Fake promoter attendancePromoter signed in but not present1 minute
Setup-and-dismantle billing inflation4-hour deployment billed as 8 hoursEnd of shift
Wrong-location photo with right captionPhoto from accessible spot, claimed at contracted spot5 minutes

The OOH 1,200-hoarding audit

IndicatorReportedVerified
Hoardings contracted1,2001,200
Cities covered180180
Reported compliance~96%Photo-based agency report
Non-compliance found0%4.2% (50 sites)
Recycled proofs detectedHiddenIdentified across multiple sites
Media spend protected₹0₹18.6 lakh in single campaign cycle
Extrapolated annual impact (sector)--₹120–180 Cr nationally

Cost impact by submission type

Submission typeTypical fraud ratePer-submission costAnnual industry impact
OOH installations4–8%₹2,000–8,000₹500–700 Cr
Wall painting proofs10–15%₹1,500–4,000₹250–400 Cr
Pole board installations15–25%₹400–800₹350–500 Cr
Mobile van route proofs20–30%₹12,000–18,000₹300–500 Cr
Promoter attendance15–25%₹1,200–3,000 per day₹700–1,000 Cr
Lead generation submissions25–40%₹15–50 per lead₹600–900 Cr
Sampling drive proofs15–25%₹50–200 per outlet₹500–700 Cr
Visual merchandising audits10–18%₹800–2,500 per outlet₹400–600 Cr
Field sales visit proofs20–30%₹150–400 per visit₹400–700 Cr
Total estimated impact----₹4,000–5,500 Cr

AI detection capabilities by fraud sub-type

Sub-typeDetection methodAccuracy
Recycled photosImage hash fingerprinting95–98%
GPS spoofingMock-location flag + EXIF cross-check85–92%
Same hoarding multiple anglesImage similarity scoring + geo clustering88–95%
Pre-dated submissionsServer-side timestamp validation99%+
Wrong brand setup photosAI logo and brand element detection92–96%
Fabricated leadsOTP verification + first-call validation95–99%
Fake promoter attendanceGeo-fenced check-in + selfie verification90–95%
Setup-dismantle inflationTime-stamped activity proofs at intervals92–96%

What field force productivity leakage actually means

Field force productivity leakage is the invisible drain from sales reps, merchandisers, and promoters spending billable time on activities that don't deliver business outcomes. Shelf failures, stockouts, route deviation, and unverified merchandising activities all sit in this category.

Sub-types of field force productivity leakage

Sub-typeMechanicTypical share of field time lost
Route deviationReps skip contracted outlets, visit easier ones15–25% of route plan
Stockout invisibilityOOS condition unreported, no replenishment trigger1 in 10 planned purchases lost
Shelf failure (planogram drift)Planogram goes 10% out of compliance per week30–50% drift by end of quarter
Substitution rearrangementStore teams refill empty shelves with whatever's available5–15% of planogram breaks
Idle time on shiftPromoter present but inactive during deployment20–40% of deployment hours
Phantom outlet visitsVisits logged from one location for multiple outlets10–20% of daily call reports
Manual reporting overheadReps spend 2–3 hours daily on Excel/WhatsApp updates15–30% productivity loss
Skipped audit checkpointsVisual merchandising audits filled without entering store5–15% of audits
Promotional non-installationPOSM material billed but not installed10–25% of POSM spend

Productivity leakage in numbers

MetricIndustry benchmarkSource
Planogram out-of-compliance rate10% per weekNational Association of Retail Marketing
Planogram compliance in highly managed retail70–85%Industry research
Planogram compliance in informal retail40–60%Industry research
Trade promotion failure rate~60%RIS News
Planned purchases lost to stockouts1 in 10Retail execution studies
Sales loss per retailer from poor execution$1M–$30M (US)RIS News
FMCG scheme leakage (% of gross margin)2–3%Industry estimates
Field force time on manual reporting15–30%Retail execution studies
Smart real-time scheme ROI uplift20–25%DMS industry data

Productivity leakage by role

RoleTypical workforce size (FMCG major)Productivity loss %Annualised cost per role
Sales rep / ASM500–2,00015–25%₹1.5–3 Cr per 1,000 reps
Merchandiser1,000–5,00020–30%₹2–4 Cr per 1,000
Promoter5,000–15,00020–40%₹3–6 Cr per 1,000
Field auditor200–80010–20%₹0.5–1.5 Cr per 1,000
Regional sales manager (RSM)50–20010–15%₹1–2 Cr per 100
Distributor sales personnel10,000+25–40%₹4–7 Cr per 1,000

Industry-wide field force productivity leakage

IndustryField force size (India)Productivity leak %Annual impact
FMCG (food, personal care, home care)3 million+20–30%₹1,500–2,000 Cr
Pharma medical reps800K–1M15–25%₹500–800 Cr
Telecom retail200K+20–30%₹250–400 Cr
BFSI field sales500K+15–25%₹300–500 Cr
Consumer durables150K+20–30%₹200–300 Cr
Auto field sales100K+15–25%₹150–250 Cr
Total estimated impact----₹3,000–4,000 Cr

What manual reporting manipulation actually means

Manual reporting manipulation is the manipulation that becomes possible when verification depends on WhatsApp groups, Excel sheets, and supervisor sign-offs. Each tool was built for communication or modelling, not for fraud-resistant verification.

Sub-types of manual reporting manipulation

Sub-typeMechanicWhy it works
WhatsApp metadata strippingWhatsApp removes GPS and EXIF in standard modeCompression destroys forensic evidence
Excel cell editing without audit log"Execution %" updated without version controlNo history captured
Forwarded photos as fresh submissionOld photos forwarded into new campaign group"Forwarded" tag often invisible
End-of-day batch upload of fake workAll photos uploaded at 9 PM after the workdayBulk uploads escape scrutiny
Supervisor voice notes as "verification""All done sir" voice note treated as sign-offNo identity verification
PDF report compilation by interested partySame vendor writes the report on their own workConflict of interest structural
Selective photo sharing in campaign groupOnly flattering photos shared with brandBrand sees curated subset
Multi-group fragmentationDifferent photos to different stakeholder groupsNo single source of truth

WhatsApp standard mode metadata destruction

Metadata fieldOriginal photoAfter WhatsApp standard send
GPS coordinatesLatitude + longitude embeddedRemoved
Capture timestampOriginal time capturedReplaced with download time
Camera make / modelDevice identifierRemoved
File size4–8 MB typical~73% smaller after compression
Image dimensions3456 x 4608 pixels1599 x 1200 pixels
EXIF metadata blockFull block intactBlock effectively emptied
Hash fingerprintIdentifiableAltered by compression

Industry-wide impact of manual reporting manipulation

Industry% of brands still on WhatsApp/ExcelAnnual impact
FMCG (general trade)85–90%₹700–900 Cr
OOH media80–90%₹400–500 Cr
BTL agencies (mid-size brands)90%+₹300–450 Cr
Pharma (field force)60–75%₹200–300 Cr
Telecom (retail audits)70–85%₹150–250 Cr
BFSI (lead generation)75–85%₹150–250 Cr
Total estimated impact--₹2,000–2,500 Cr

Combined fraud exposure by industry

IndustryGhost retailDuplicate onboardingProof fraudProductivity leakManual reportingTotal annual
FMCG (food & beverage)₹1,200–1,800 Cr₹600–800 Cr₹800–1,000 Cr₹600–800 Cr₹300–400 Cr₹3,500–4,800 Cr
FMCG (personal & home care)₹800–1,200 Cr₹400–500 Cr₹500–700 Cr₹500–700 Cr₹200–300 Cr₹2,400–3,400 Cr
Pharma₹250–400 Cr₹300–500 Cr₹400–600 Cr₹500–800 Cr₹150–250 Cr₹1,600–2,550 Cr
Telecom & durables₹400–600 Cr₹250–400 Cr₹300–450 Cr₹250–400 Cr₹150–250 Cr₹1,350–2,100 Cr
Auto & lubricants₹300–450 Cr₹200–300 Cr₹250–400 Cr₹150–250 Cr₹100–150 Cr₹1,000–1,550 Cr
BFSI field operations₹100–200 Cr₹150–250 Cr₹300–450 Cr₹300–500 Cr₹150–250 Cr₹1,000–1,650 Cr
Real estate & OOH-heavy₹100–150 Cr₹50–100 Cr₹500–700 Cr₹100–200 Cr₹100–150 Cr₹850–1,300 Cr
QSR & multi-outlet retail₹150–250 Cr₹100–150 Cr₹200–300 Cr₹200–300 Cr₹100–150 Cr₹750–1,150 Cr

Combined fraud exposure by format

FormatMost common fraud typesTypical exposure %
Wall painting (rural)Proof fraud, productivity leak, manual reporting15–25%
Mobile van & roadshowsProof fraud (route), productivity leak20–30%
OOH hoardings & pole boardsProof fraud (recycled photos)15–25%
No-parking boardsProof fraud (duplicate boards)25–35%
Bus & cab brandingProof fraud (vehicle swap)15–25%
Auto rickshaw brandingProof fraud, productivity leak20–30%
Shop name boardsGhost retail, proof fraud10–20%
Visual merchandisingGhost retail, productivity leak15–25%
Sampling drivesProof fraud (stock diversion)20–35%
Promoter activationsProof fraud (attendance), productivity leak15–25%
Field sales visitsProductivity leak, manual reporting20–30%
Lead generationProof fraud (fabricated leads)30–50%
Trade scheme payoutsGhost retail, duplicate onboarding12–18%
RWA / society activationProof fraud, productivity leak15–25%
Technician verificationProof fraud (location)10–15%
Franchise compliance auditProductivity leak, manual reporting15–25%

Combined fraud exposure by geography

GeographySubmission count shareAnomaly rateDominant fraud type
Tier-1 metros (8 cities)45%14–15%Manual reporting manipulation
Tier-2 cities (40 cities)27%20–21%Proof of performance fraud
Tier-3 cities (200+ cities)17%27–28%Field force productivity leak
Rural BTL belt11%32–33%Ghost retail + proof fraud

City-level anomaly variance (tier-1)

CityAnomaly rate
Mumbai11.4%
Bangalore12.1%
Delhi NCR13.7%
Hyderabad14.3%
Pune15.2%
Chennai15.8%
Kolkata16.6%
Ahmedabad17.4%

Detection methods cross-reference

Detection methodGhost retailDuplicateProof fraudProductivity leakManual reporting
OTP verificationStrongStrongMediumWeakStrong
Geo-locked captureStrongStrongStrongStrongStrong
Time-locked submissionMediumWeakStrongStrongStrong
AI image fingerprintingMediumStrongStrongMediumStrong
EXIF integrity checkMediumMediumStrongMediumStrong
Mock-location detectionMediumStrongStrongStrongStrong
Accelerometer cross-checkWeakWeakStrongStrongMedium
Geo-fence violationsStrongStrongStrongStrongMedium
Real-time dashboardMediumMediumStrongStrongStrong
Cross-vendor deduplicationMediumStrongStrongWeakStrong

Run a free audit of your last campaign.

Pick one BTL or retail campaign you closed in the last 90 days. Run it through gOGig's verification engine. See how many of these five fraud types show up in your data.

Book a free campaign audit →

Fraud detection accuracy under FEI

Fraud typePre-FEI detection ratePost-FEI detection rateImprovement
Ghost retail coverage10–20% (audit-dependent)90–95%5–7x
Duplicate onboarding30–50% (quarterly checks)95–98%2–3x
Recycled photos5–15% (manual review)95–98%10–15x
GPS spoofing0–5%85–92%20x+
Pre-dated submissions0–5%99%+20x+
Fake attendance10–20%90–95%5–7x
Fabricated leads20–30% (post-call check)95–99%3–4x
Route deviation15–25%95–98%4–5x
Manual reporting fraud15–25%98–99%5x
Setup-dismantle inflation10–20%92–96%5–8x

How each fraud type compounds with the others

Compound combinationMultiplier effectExample
Ghost retail + duplicate onboarding2–3x costOne ghost outlet registered 3 ways
Proof fraud + manual reporting2x costRecycled photo submitted via WhatsApp where verification is impossible
Productivity leak + ghost retail2.5x costReps spend hours visiting ghost outlets and reporting it
Manual reporting + GPS spoofing3x costField rep spoofs location and reports via WhatsApp with no metadata
Duplicate onboarding + scheme leakage2–3x costSame outlet claims schemes under multiple IDs
Proof fraud + setup-dismantle inflation2x cost4-hour activation billed for 8 hours with recycled photos
Productivity leak + planogram drift2x costMerchandiser visit billed but planogram never refreshed
Fake attendance + lead fabrication3x costPromoter not present, fabricated lead list submitted

Pre-FEI vs FEI fraud exposure benchmark

Pre-FEI environment

5 fraud types active simultaneously. Detection rates 5–30% per type. Combined exposure 20–30% of physical economy spend. Time to surface fraud: weeks to never. Re-execution costs 30–60% of original spend. Compounding cost across quarters.

FEI environment

All 5 types detected at submission. Detection rates 85–99% per type. Combined exposure reduced to 5–10% of spend. Time to surface fraud: real-time. Re-execution avoided through mid-campaign correction. Quarterly improvement compounding.

Year-on-year reduction in fraud exposure under FEI

Year of FEI adoptionFraud exposure remainingCumulative savings
Pre-adoption baseline20–30%--
Year 1 of adoption8–12%60–70% of pre-baseline
Year 2 of adoption5–8%75–80% of pre-baseline
Year 3 of adoption3–5%85–90% of pre-baseline
Year 4–5 of adoption (steady state)2–3%90–95% of pre-baseline

The 7 warning signs your brand is exposed

Warning signWhat it indicates
Agency reports 90%+ execution consistentlySelf-reporting bias; verified rate likely 65–75%
No GPS or EXIF integrity check at submissionProof fraud detection effectively absent
WhatsApp groups are primary campaign toolManual reporting manipulation enabled
Trade scheme leakage in 12–18% rangeGhost + duplicate retailer fraud active
Field force time spent on Excel >20%Productivity leak compounding
OTP verification absent at outlet onboardingDuplicate / phantom onboarding active
Annual audit only, no real-time verificationAll five fraud types operating undetected

The 60-day fraud assessment roadmap

Days 1–15: Outlet universe verification

Run OTP + geo validation on existing retailer database. Identify ghost outlets, duplicates, and phantom records.

Days 16–30: Proof of performance audit

Sample 10% of last quarter's campaign submissions. Run AI image fingerprinting, EXIF check, GPS validation.

Days 31–45: Field force productivity diagnostic

Deploy real-time dashboards for 30 days. Measure baseline route compliance, reporting overhead, and planogram drift.

Days 46–60: Combined fraud exposure report

Quantify exposure across all 5 fraud types. Present to CFO and audit committee. Begin PBP rollout for top 3 fraud sources.

five types field execution fraud
FAQ

Frequently Asked Questions

Glossary
Ghost retail coverageOutlets that have closed, never existed, or no longer stock the brand still appearing as active in DMS systems. The first major fraud type.
Duplicate retailer onboardingThe same outlet registered multiple times in the DMS under slight variations. Phantom variant: outlets that do not exist at all.
Proof-of-performance fraudManipulation at the moment of submission. Recycled photos, GPS spoofing, fake attendance, fabricated leads.
Field force productivity leakageField workforce time spent on activities that do not deliver business outcomes. Route deviation, planogram drift, manual reporting overhead.
Manual reporting manipulationFraud enabled by WhatsApp groups and Excel sheets being used as verification tools. Metadata stripped, version control absent.
Field Execution IntelligenceThe category of platforms that detects all five fraud types at submission. WhatsApp-native capture + AI verification + real-time dashboards.
Blind TrustThe legacy operating standard where all five fraud types operate undetected. The structural enemy FEI was built to replace.
Ground TruthWhat actually happened on the ground, independently verified. The signature outcome of FEI across all five fraud types.
DMS (Distributor Management System)Software managing FMCG primary and secondary sales between brand, distributor, and retailer. Where ghost retail and duplicate onboarding hide.
PlanogramVisual merchandising blueprint defining product placement on retail shelves. Goes out of compliance at 10% per week without verification.
OTP verificationOne-time password sent to retailer phone number to confirm existence and ownership. Primary defence against duplicate and phantom onboarding.
EXIF metadataData embedded in digital photographs including GPS, timestamp, device. Stripped by WhatsApp in standard mode.

Run a free audit of your last campaign

Pick one BTL or retail campaign closed in the last 90 days. We will run it through gOGig's verification engine and show you which of these five fraud types showed up in your data. First audit is free.

₹15,000–20,500 Cr

Annual industry fraud cost

60–70%

Year-1 exposure reduction with FEI

Real-time

Detection speed under FEI

Book a free campaign audit →

Written by

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gOGig Editorial

Field Execution Intelligence Research

The gOGig Editorial team covers Field Execution Intelligence, BTL verification, and the future of India's physical marketing ecosystem.

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