Jarvis Business Solutions · Snowflake-Native

eCommerce Intelligence —
Protect Margin. Reduce Fraud. Optimise Promotions.

Three analytics modules — returns fraud detection, reverse logistics cost governance, and promotion effectiveness — running natively on Snowflake. No data movement. No extra tooling. First insight in eight weeks.

Find Your Module →See the Snowflake Edge
RefundGuardCostLensPromotionIQ Snowpark MLCortexSnowpipe StreamingData Clean Rooms
Live platform · 3 modules active
RefundGuardActive
847 returns scored · 23 flagged · 4 blocked
CostLensActive
True avg. cost per return: £84.20 · 15 high-cost SKUs
PromotionIQActive
12 promotions live · 4 over-discounting · 2 stalled
Snowpipe Streaming · Events land in <5s · Snowpark ML scoring inline
20–40%
Reduction in refund fraud loss identified at-risk
£75+
True cost per return made visible for the first time
15–25%
Promotion revenue uplift per promotional £ spent
8 wks
From project start to first live insight
Three Modules · One Platform

eCommerce Intelligence: Three Named Solutions

Each module addresses a distinct margin threat. All three share a common Snowflake RAW → SILVER → GOLD data architecture and can be deployed individually or as a combined platform on a single data pipeline.

RefundGuard
RefundGuard
Returns & Refunds Risk Analytics

Classifies every return event across eight risk dimensions — fake empty-box returns, serial abusers, wardrobing, refund-before-pickup, store-level and employee abuse. Three-tier decision output feeds directly into your OMS and operations layer.

AUTO_APPROVE MANUAL_REVIEW BLOCK_REFUND
  • 8-dimension returns risk scoring model
  • Customer-level risk profile & historical pattern analysis
  • Store and employee-level risk monitoring
  • Value-at-risk quantification by risk type
  • Power BI: CEO Risk Overview, Customer Risk, Audit & Store Risk
CostLens
CostLens
Reverse Logistics Cost Intelligence
£100K–£500K

Calculates the true economic cost of every return — refund plus pickup plus reverse transport plus QC and reprocessing. A £50 item with standard logistics generates closer to £75 in total loss. CostLens makes that number visible at customer, SKU, and geography level.

ALLOW_FREE_RETURN CHARGE_RETURN_FEE BLOCK_FREE_RETURNS
  • True cost-per-return: refund + pickup + logistics + QC
  • Customer-level reverse cost profiling (LOW / MEDIUM / HIGH)
  • SKU and location-level cost analysis
  • Policy recommendation engine with three-tier output
  • Power BI: Reverse Logistics Cost Risk overview
PromotionIQ
PromotionIQ
Pricing & Promotion Analytics

Delivers SKU-level promotion effectiveness, city-level behaviour analysis, real-time margin-at-risk alerting, and promotion quality scoring. Replaces gut-feel promotion decisions with live Snowflake analytics — within 24 hours of a campaign launching.

STRONG MODERATE · WATCH OVER_DISCOUNT
  • Promotion effectiveness scoring by campaign & SKU
  • SKU-level uplift analysis — incremental vs cannibalised
  • City-level promotion behaviour analysis
  • Margin-at-risk real-time alerting
  • Power BI: Promotion Overview, SKU, City, Quality Alerts
Full Platform

All Three Modules. One Snowflake Pipeline.

Deploy RefundGuard, CostLens, and PromotionIQ on a shared RAW → SILVER → GOLD architecture. Significantly faster and more cost-effective than building separately — a single data pipeline feeds all three modules.

Full platform deployment
Talk to the Retail Practice →
Platform Modules

Three modules. One shared pipeline.

Deploy individually or together. Shared Snowflake architecture means modules deployed together share pipeline costs and reduce total delivery time.

Returns & Refunds Risk Analytics

Eight risk dimensions scored per return event. Every transaction classified and fed back to operations in real time — before the refund fires, not after it's processed.

Risk type
Fake returns
Risk type
Serial abusers
Risk type
Wardrobing
Risk type
Refund-before-pickup
Risk type
High-value exposure
Risk type
Payment mismatch
Risk type
Store-level abuse
Risk type
Employee collusion
Decision outputs
Auto-approveManual reviewBlock refund
⚡ Snowpark ML + Snowpipe Streaming + Cortex

Model scores each event inside Snowflake within seconds of arrival. Cortex generates the agent explanation automatically. No external ML platform required.

Fraud loss reduction
20–40%
Identified at-risk value for retailers with returns rates above 15%
Entry engagement
2-week value-at-risk assessment on 3 months of returns data — before any platform commitment
Dashboards
CEO risk overviewCustomer riskAudit & SLAStore & employee

Reverse Logistics Cost Intelligence

Calculates the true economic cost of every return — refund + pickup agent + reverse transport + QC — aggregated by customer, SKU, and geography. Converts an invisible cost into an actionable return policy engine.

Cost component
Refund value
Cost component
Pickup agent
Cost component
Reverse transport
Cost component
QC & restocking
Analysis
By customer segment
Analysis
By SKU / category
Analysis
By geography
Analysis
By carrier
Policy outputs
Allow free returnCharge return feeBlock free return
⚡ Data Clean Rooms

Carrier billing data joined without either party exposing raw records. Legal-safe collaboration that unlocks the true cost figure your policy team has never had.

True cost revealed
£75+
Average true cost per return once pickup, transport and QC are included
Entry engagement
Reverse cost pilot on logistics billing data — show segment cost before commercial discussion
Risk tiers
Low cost tierMedium riskHigh cost tierSKU-level

Pricing & Promotion Analytics

SKU-level uplift scoring, city-by-city promotion response analysis, and margin-at-risk alerting — derived from order data already flowing through Snowflake. Trading teams get a live signal, not a post-mortem report.

Scoring
SKU uplift score
Scoring
City-level response
Alerting
Margin-at-risk flags
Alerting
Over-discount detection
Intelligence
Incremental revenue
Intelligence
Promotion cannibalism
Intelligence
Baseline vs. uplift
Intelligence
Competitor price context
Promotion quality flags
StrongWatchOver-discount
⚡ Snowpipe Streaming + Elastic Compute

Promotion data flows in real time. Black Friday volume scales automatically — no manual intervention or re-architecture during peak trading.

Revenue uplift
15–25%
Per promotional £ spent when over-discounting is detected and corrected early
Entry engagement
Promotion analytics assessment + CEO dashboard on 6 months of order data
Dashboards
Promo overviewSKU effectivenessCity-wise responseQuality alerts
Architecture

One pipeline. Three outputs.
Five Snowflake capabilities.

SourcesOMS / WMS / POS
Snowpipe StreamingReal-time ingest
MedallionRAW → SILVER → GOLD
Snowpark MLIn-warehouse scoring
CortexExplanation layer
OutputStreamlit / Power BI

Elastic Compute — always-on

Virtual warehouses auto-scale on query load. Peak trading performance maintained without manual intervention or weekend on-call for the data team.

Data Clean Rooms — carrier join

Carrier billing data joined to retailer cost records without raw data exposure. CostLens true-cost model enabled without legal risk on either side.

Zero data egress — no extra tooling

No Tableau licence, no external ML platform. Streamlit dashboards run natively inside your Snowflake tenancy — inside your existing contract.

Customer Scenarios

Which modules fit which situation.

Click each scenario to see the recommended module, the Snowflake capability that makes it possible, and the conversation starters your Jarvis consultant will use on the discovery call.

The head of loss prevention knows serial abusers and fake returns are costing millions — but has no data infrastructure to detect patterns, quantify exposure, or act at speed. Manual review backlogs are growing. Legitimate customers are being delayed.

Lead: RefundGuard+ CostLens as phase 2

Snowflake edge: Snowpipe Streaming + Snowpark ML means the risk score exists before the refund approval button is clicked. Cortex explains the flag to the agent automatically — eliminating override culture.

Entry: 2-week value-at-risk assessment · Quantify annual exposure before any platform commitment

💬 Discovery conversation starters for this scenario
Q1How do you currently identify fraudulent or abusive returns — is the process manual, rule-based, or do you have any automated detection?
Q2Can you today see which individual customers, stores, or cashiers are generating disproportionate return volumes — and act on that view?
Q3Have you had any significant fraud incidents — empty box returns, coordinated abuse campaigns, or employee collusion — in the last 12 months?
Q4If we could show you — using your own last 90 days of returns data — the estimated annual value at risk from refund fraud and abuse, what would that be worth to the business?
Listen for: "We know things are slipping through but we can't prove it" — that's your entry. A named incident (coordinated campaign, Christmas fraud) is an immediate green light for the value-at-risk assessment.

Following moves by ASOS, Zara, and H&M to charge for returns, leadership is under pressure. But the commercial team cannot show which customers generate disproportionate reverse costs — or how to target a fee without damaging loyal customers.

Lead: CostLens+ RefundGuard as complementary

Snowflake edge: Data Clean Rooms unlock carrier billing data without legal exposure on either side. The true cost-per-return figure — the number the policy decision actually needs — becomes available for the first time.

Entry: Reverse cost pilot on logistics billing data · Show segment cost before commercial discussion begins

💬 Discovery conversation starters for this scenario
Q1Beyond the refund amount itself, do you track the total cost of a return — pickup agent, reverse transport, quality check, and reprocessing — per return event?
Q2Have you considered introducing or tiering return fees, and if so, what has prevented you from implementing that policy?
Q3Do you know which SKU categories or geographies drive your highest reverse logistics cost per return event?
Q4If we could classify every customer by their reverse logistics cost profile and recommend a return policy per segment, how would your commercial team use that?
Listen for: "We've discussed fees but we're scared of customer backlash" — that's your opening. The fear of getting it wrong is the buying trigger. CostLens gives them precision to target surgically rather than blanket the customer base.

The CFO is challenging the commercial director on promotion ROI. Buying teams are running the same promotions quarter after quarter without measuring incremental revenue. The insight lag is 4–6 weeks. Over-discounting is invisible until the P&L arrives.

Lead: PromotionIQ+ RefundGuard / CostLens if returns context exists

Snowflake edge: Snowpipe Streaming turns a 6-week lag into a live signal. Elastic Compute keeps PromotionIQ running at full speed on Black Friday — no infrastructure panic during peak trading.

Entry: Promotion analytics assessment + CEO dashboard · £30–80k · STRONG / OVER_DISCOUNT flags from your own data

💬 Discovery conversation starters for this scenario
Q1How do you currently measure whether a promotion drove incremental revenue, or simply discounted demand that would have existed anyway?
Q2How long after a promotion launches does your team see performance data — and what format does it arrive in?
Q3What percentage of your current promotions do you estimate are over-discounting — and is that number visible to your CFO before the month-end P&L?
Q4If we could show you promotion performance at SKU and city level within 24 hours of a campaign launching, what decisions would that change?
Listen for: "We run the same promotions every quarter because nobody has data to challenge them" — that's the stagnation signal. CFO pressure on promotion ROI without a data answer is your strongest close.

A large omnichannel retailer with fashion and food categories. Returns fraud in clothing, reverse logistics complexity across both, and a promotion engine spanning seasonal ranges and everyday pricing. All three problems exist — and all three share the same underlying data.

RefundGuardCostLensPromotionIQ

Snowflake edge: All five advanced capabilities active simultaneously. Shared pipeline architecture reduces combined delivery cost vs. three separate builds.

Validated POC exists for all three modules against real omnichannel retail data

💬 Discovery conversation starters for this scenario
Q1Of the three problems we've discussed — returns fraud, reverse logistics cost, and promotion margin — which one carries the highest urgency for the board right now?
Q2Is there an executive sponsor — CFO, CCO, or CDO — who owns the commercial outcome you're trying to solve, and are they actively engaged?
Q3If we ran a two-week proof of concept using your own data — showing live risk scores, cost profiles, or promotion flags before any commercial commitment — would that give you what you need to decide?
Q4What are the two or three things that could prevent this project from moving forward in the next 90 days?
Listen for: A named CFO or CDO with budget authority and a Q-deadline. If all three pain points are real and the exec is engaged, the POC is the natural close — it de-risks the decision and creates a reference client across all three modules.
Who This Is For

eCommerce retailers and direct-to-consumer brands selling at scale — with a margin problem data can solve.

eCommerce Intelligence is not a generic analytics platform. It is built specifically for online retailers and DTC brands that have Snowflake (or are evaluating it), have meaningful transaction volume, and face measurable profitability pressure from returns, logistics costs, or promotions — but lack the data infrastructure to act on it surgically.

Head of Loss Prevention

Knows fraud is happening — lacks cross-event analytics to prove it, quantify it, or act on it before the refund fires. RefundGuard is built for this conversation.

CFO / Finance Director

Under board pressure to justify return policy decisions — but the true cost per return has never been calculated. CostLens gives them the number the policy team needs.

CDO / Head of Data

Has Snowflake. Needs a production-grade analytics use case with commercial impact. eCommerce Intelligence deploys in 8 weeks — with code they own from day one.

You are the right fit if —
£50M+ online GMVReturns rate above 15% of ordersFree returns policy under board pressure10+ concurrent promotions runningPromotion performance tracked in ExcelOMS and finance data not joinedeCommerce margin declining year-on-yearCFO scrutiny on return economicsSnowflake active or in active evaluationNo current fraud detection on returns
Primary markets
UK — Primary market
UK-based eCommerce retailers

Highest online retail penetration in Europe. First-mover on return fees (ASOS, Zara, H&M). Most analytically mature retail sector — CDOs and CFOs understand the problem immediately.

Secondary markets
Germany · India · UAE

Germany: returns are cultural — the economics are severe. India: COD refund fraud at scale. UAE: fast-growing eCommerce with return economics still under-managed.

Request a Demo

Talk to a Snowflake retail specialist.

Tell us about your data environment and the margin challenge you're facing. We'll come back within one business day with a specific view of what the platform would surface — using your numbers, not ours.

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Value-at-risk assessment on your own returns data — before any platform commitment
✓
60-minute live demo of RefundGuard, CostLens, or PromotionIQ on representative retail data
✓
Architecture walkthrough — how the five Snowflake capabilities fit your existing stack
✓
POC proposal scoped to one module — live insight in 8 weeks from sign-off

Request a Demo

Our retail intelligence team responds within one business day

Your information is never shared with third parties.
✓

Request received.

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Or email: sales@jarvisbusiness.io