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.
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.
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.
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.
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.
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.
eCommerce growth creates three compounding profitability challenges most retailers manage separately — with manual processes, fragmented data, and no closed loop back to operations.
Fake returns, serial abusers, and employee collusion go undetected without cross-event analytics. Every undetected fraud event is a direct margin hit.
True cost of a return is 2–3× the refund amount. Pickup, transport, and QC costs are invisible — so return policies are set on instinct, not economics.
Promotions underperform or over-discount. Trading teams lack real-time SKU and city-level data to distinguish effective discounts from margin destruction.
Answer five questions and we'll tell you which module to lead with — and why.
Five enterprise capabilities that platforms built on traditional data stacks cannot match — running inside your existing Snowflake contract, activated by Jarvis as part of platform delivery.
Everything below runs inside your existing Snowflake contract. Jarvis activates and deploys each capability as part of platform delivery.
Most fraud detection tools pull data out of the warehouse, score it externally, then push results back. That round trip takes time — in returns fraud, time is money already out the door. With Snowpark ML, RefundGuard's scoring model lives inside Snowflake. Scoring happens where the data sits. No separate ML platform to buy, manage, or secure. That's the difference between catching fraud before the refund fires and catching it on a report the next morning.
Most retailers run batch loads — data lands in S3 every few hours, dashboards refresh overnight. That's fine for reporting. It's useless for fraud. By the time a batch job flags a serial abuser, the refund is processed. Snowpipe Streaming changes that. Return events land in Snowflake within seconds. RefundGuard scores them immediately. The decision reaches the agent before they've approved anything. That's a fundamentally different capability.
Risk scores create a problem nobody discusses — your agent gets a flagged return and has no idea what to tell the customer. So they override the flag or give a vague answer that escalates. Cortex reads the risk signals and generates a plain-English explanation automatically: "This return was flagged because three returns were made in seven days across two accounts linked to the same address." Auditable, explainable, defensible — and it directly answers the GDPR question procurement always raises.
Every retailer has the same story. They build their infrastructure for normal volume, then Black Friday arrives and everything slows down. The analytics team is the last priority when trading is screaming. Snowflake's virtual warehouses scale up automatically when query load spikes — no manual intervention, no re-architecture, no weekend on-call. PromotionIQ and RefundGuard keep running at full speed on the days when the data matters most.
CostLens only works if you can see the true cost of a return — and that means you need carrier billing data from DHL, Evri, Aramex, whoever. The problem is carriers won't hand over their raw rate cards, and retailers won't expose customer records. Both sides have legal and commercial reasons to say no. Clean Rooms remove the blocker. Both parties connect their Snowflake accounts, the analysis runs on the joined data, and neither side ever sees the other's raw records. The retailer gets their true cost-per-return figure. Legal on both sides can sign off.
Deploy individually or together. Shared Snowflake architecture means modules deployed together share pipeline costs and reduce total delivery time.
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.
Model scores each event inside Snowflake within seconds of arrival. Cortex generates the agent explanation automatically. No external ML platform required.
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.
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.
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.
Promotion data flows in real time. Black Friday volume scales automatically — no manual intervention or re-architecture during peak trading.
Virtual warehouses auto-scale on query load. Peak trading performance maintained without manual intervention or weekend on-call for the data team.
Carrier billing data joined to retailer cost records without raw data exposure. CostLens true-cost model enabled without legal risk on either side.
No Tableau licence, no external ML platform. Streamlit dashboards run natively inside your Snowflake tenancy — inside your existing contract.
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.
Fraud is rising and manual review can't scale
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.
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 · £25–75k · Quantify annual exposure before platform commitment
The board wants fees — the data isn't there to justify it
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.
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 · £25–60k · Show segment cost before commercial discussion begins
Trading teams run on gut feel; margin erosion arrives at month end
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.
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
One retailer, all three modules — the anchor account model
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.
Snowflake edge: All five advanced capabilities active simultaneously. Shared pipeline architecture reduces combined delivery cost vs. three separate builds.
Programme value: £350k–£1.5M · Validated POC exists for all three modules against real omnichannel retail data
Highest online retail penetration in Europe; first-mover on return fees; most analytically mature retail sector
Germany: returns a cultural norm. India: COD refund fraud at scale. UAE: growing eCommerce, return economics under-managed.
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.
Our retail intelligence team responds within one business day
A Jarvis retail intelligence specialist will be in touch within one business day.
Or email: sales@jarvisbusiness.io