Every e-commerce brand we speak to describes the same operational reality. Their post-purchase experience is held together by manual processes, disconnected systems, and a CX team that spends more time tracing orders across tabs than actually helping customers.
The average prospect we engage has 5 to 8 disconnected systems touching the customer promise: storefronts, ERPs, warehouses, carriers, returns platforms, and helpdesks. None of them talk to each other. When something goes wrong with an order, the CX team has to manually investigate across all of them. That investigation takes 15 to 45 minutes per ticket, and most of those tickets could have been prevented entirely if the issue had been detected before the customer noticed.
That is what Keeyu solves. We connect to every system in the post-purchase chain, detect issues before they generate complaints, and either resolve them automatically or surface them to the right person with full context. The result is fewer tickets, lower operational cost, and retained revenue from customers who would otherwise churn silently.
Every business case we build follows the same three-pillar framework. Each pillar captures a distinct, measurable source of value.
Pillar 1: Ticket Reduction. Direct CX cost savings from preventing inbound tickets before they occur. When Keeyu detects a delayed shipment, a payment anomaly, or a fulfillment error and resolves it proactively, the customer never needs to contact support. Average annual savings: $107,000 across modeled prospects.
Pillar 2: Operational Efficiency. Cost savings from eliminating manual operational work outside of ticket handling. Report generation, data reconciliation, manual returns processing, management investigation time. Average annual savings: $37,000 across modeled prospects.
Pillar 3: Revenue Uplift. Incremental revenue retained from customers who would otherwise churn due to poor post-purchase experiences. Average annual savings: $54,000 across modeled prospects.
The methodology is deliberately conservative. Where we do not have confirmed data from discovery, we use industry benchmarks. In every case where a prospect subsequently provides their actual data, the confirmed figures exceed the benchmarks. The business cases we present are floors, not ceilings.
In 2026, we have built and presented detailed business cases to 18 prospects across a range of sizes, verticals, and operational complexity levels. Every single business case shows a positive return.
Average ROI: 4.6x. For every dollar a prospect spends on Keeyu, the business case shows $4.60 in measurable savings. The median is 4.5x. Not skewed by outliers.
Average payback: 2.7 months. Prospects recover their full annual Keeyu investment in under three months on average. Fastest: 1.3 months (Mecca). Longest: 6 months (The Oodie, conservative multi-geography assumptions).
Total annual savings modeled: $3.0M. Across all 18 prospects, combined annual savings total $3,007,000. Against a combined ACV of $603,000, that represents an aggregate portfolio ROI of 5.0x.
The more systems, warehouses, carriers, and storefronts a prospect manages, the more manual work Keeyu eliminates and the higher the ROI.
#### Enterprise (50,000+ orders per month)
Mecca (In Pipeline). ~100,000 orders/month, 50 onshore CX FTEs, Salesforce Commerce Cloud + Service Cloud + Blue Yonder WMS + multiple carriers. Business case: $852,000 annual savings, 9.5x ROI, 1.3-month payback. Largest driver: Pillar 1 at $14.58 cost per ticket with 180,000 annual tickets.
Brand Collective (In Pipeline). 42,000 orders/month, multiple retail brands, omnichannel (online, wholesale, retail). Business case: $313,000 annual savings, 4.5x ROI, 2.7-month payback. Revenue uplift (Pillar 3) dominant at $182,000.
#### Mid-Market (5,000 to 50,000 orders per month)
Boardriders (Billabong, Quiksilver, RVCA). 40,000 orders/month, complex omnichannel. Business case: $153,000 annual savings, 3.5x ROI. Signed contract 26 March 2026. Validating the business case through commercial commitment.
The Oodie (Davie Group). 90,000 orders/month across AU, NZ, US, CA, UK with 4 x 3PLs and 22 carriers. Business case: $126,000 annual savings, 2.0x ROI. Signed.
#### SMB (1,000 to 5,000 orders per month)
Petzyo (In Pipeline). 3,333 orders/month. Business case: $37,000 annual savings, 3.8x ROI.
The swivel-chair problem is universal. Every CX team describes the same workflow: receive complaint, open helpdesk, open Shopify, open carrier, open ERP, open returns platform. Piece together what happened. 15-45 minutes per ticket. The customer has already had a negative experience before the agent begins.
Nobody is doing proactive post-purchase today. When we ask "how do you currently detect issues before customers complain?", the answer is consistently "we don't."
The build-vs-buy objection is fading. One prospect described their internal build attempt as "three months of engineering time that covered about 5% of what we actually needed."
Peak season is the forcing function. Multiple prospects cite BFCM, EOFY, or seasonal peaks as the trigger. They know manual processes will break under volume.
| Metric | Value |
| Prospects with business cases | 28 |
| Prospects with full ROI modeling | 18 |
| Average ROI multiple | 4.6x |
| Median ROI multiple | 4.5x |
| Average annual savings per prospect | $167,000 |
| Median annual savings per prospect | $92,000 |
| Average payback period | 2.7 months |
| Total modeled annual savings | $3,007,000 |
| Total ACV (modeled prospects) | $603,000 |
| Aggregate portfolio ROI | 5.0x |
| ROI range | 2.0x to 9.5x |
| Order volume range | 1,000 to 300,000 per month |
| ACV range | $12,000 to $107,000 |
| Negative business cases | 0 |
Statistics reflect signed customers and active pipeline only.