Carriyo Intelligence: Revenue at Risk and CX Score Are Built, and Open to Customers in Q4 2026
Back in July we wrote about where Carriyo Intelligence was heading, and the short version was that the reporting inside Carriyo was going to move beyond operational dashboards into questions a finance or CX lead would actually ask. Since then two of those reports have been built end to end, and we are now able to say when they open: Revenue at Risk and CX Score will be available to Carriyo customers in Q4 2026. This post is about what is actually in them, how we expect teams to use them, and why they sit on the Carriyo platform rather than in a separate BI tool.
One thing to be clear about upfront, because we would rather be precise than exciting: both reports are built, and they are not yet switched on for customers. We are adding more reports to the same library before we open it, so the honest framing is that they are opening in Q4, not that they are live today.
Why we built these two first
Most of the reporting we see merchants run on delivery is descriptive. It tells you how many shipments went out, how many were delivered on time, and which carrier was late last week. That is useful, and Carriyo has had that kind of reporting for a long time. But it usually stops one step short of the question the business actually has, which is something like "what is this costing us right now?" or "are our customers getting a good experience, and how would we even know?"
Those two questions are the ones we picked. Revenue at Risk answers the first one in money. CX Score answers the second one with a number that can be trended, compared by carrier, and checked against what customers actually said.
Revenue at Risk: the revenue you can still save
Revenue at Risk is a daily view of the order revenue that is currently exposed because something in the delivery journey has gone wrong or is about to. It is not a report on revenue you have already lost. The point is that most of this exposure is still recoverable if someone acts on it today, so the report is organised around what can still be done.
It groups the exposure into five buckets:
- Shipments that are running late against the promise you gave the customer.
- Returns that have been requested and are waiting, which is refund liability sitting on the books.
- Orders that are stuck in fulfillment and have not moved.
- Deliveries that failed today and need another attempt or a decision.
- Customers who look likely to churn, because they gave negative feedback after a bad experience, ranked by how valuable they have been to you.
Each bucket shows a value in your currency, and each one drills down to the shipments and the customers behind the number, so an operations lead can go from "we have a problem" to the list of orders that make up the problem in a couple of clicks. There is a 30-day trend so you can see whether the exposure is growing or shrinking, and there is a short written commentary on top of the numbers, generated by AI inside the analytics layer, which reads a bit like an analyst's note on what moved and why. We think that last part matters more than it sounds, because most people do not have time to read a dashboard every morning, but they will read three sentences.
One design decision worth mentioning: an order is only counted once. If the same order is late and also has a return pending, it sits in the more severe bucket and not both, so the total is something you can actually take to a finance meeting without someone pointing out that it double counts.
CX Score: delivery experience, as a number
CX Score is a 0 to 100 score for every shipment, built from what actually happened on that shipment rather than from a survey alone. The components are delivery speed against the promise, whether the first delivery attempt succeeded, the quality of the communication the customer received along the way, the return experience if there was one, direct customer feedback, and how the customer engaged with the tracking experience. Where a signal is not available for a particular merchant, the remaining components take up the weight, so the score still means something.
From there it aggregates in the ways you would expect: an average, a distribution, a trend over time, and a comparison by carrier. The comparison by carrier is the one we expect people to use most, because it turns "we think carrier X is worse for our customers" into a number that can be put in front of the carrier.
We also built a calibration view that puts the score next to the ratings customers actually gave. That was partly to keep ourselves honest, because a score nobody has checked against real ratings is not much use to anyone, and partly because it gives a merchant a way to see that the score reflects their customers and not a generic model.
If you run more than one brand
A good number of our customers are groups running several brands on Carriyo, and the usual situation is that every brand ended up with its own post-purchase setup, so nobody can answer a group-level question without rebuilding it in a spreadsheet. On Carriyo each brand keeps its own tracking experience, its own carrier rules and its own numbers, but all of it is recorded the same way underneath. Revenue at Risk and CX Score follow the same logic: you can read one brand on its own or the group as a whole, on the same definitions.
The boundary is worth stating plainly. This works across brands inside one group account. Brands that were set up as separate accounts stay separate, which is usually what a group wants for the brands it does not manage centrally.
What is already there today
None of this replaces the reporting that is live in Carriyo now, and it is probably worth restating what that covers, so the Q4 framing does not make it sound like the analytics layer is empty until then. Today customers have operational reports over orders, shipments and returns, delivery performance measured by carrier and by lane, analytics on the branded tracking pages, and reporting on how accurate the delivery promise has been against what actually happened. The two new reports sit on top of that, and the historical views of orders and shipments, with longer windows and year-over-year comparisons, are being built out on the same foundation.
Why this lives on the platform and not in a BI tool
The honest reason these reports are possible is that Carriyo already holds the data they need in one model. Orders, shipments, carrier scans, fulfillment status, returns, notifications and customer feedback all land in the same place, recorded the same way, because they are all produced by the same platform. Revenue at Risk needs all of those signals joined to the underlying order. CX Score needs most of them joined to the shipment. If those signals live in four systems, the report is a stitching project before it is a report.
That is also the plain argument for not building this yourself. An analytics layer you build in-house starts with zero history, and it starts with the joining problem unsolved. Carriyo's starts with the delivery-performance data accumulated across 100+ brands and enterprise customers, $5B in GMV, 130+ carriers and shipments spanning 200 countries, already normalised into a model that reports and AI can reason over. We would rather say that as a benefit to the merchant than as a boast: your data compounds from day one, because the foundation is already there.
Three ways to use it
Like everything else on Carriyo, the Intelligence layer works in three modes, and most customers will probably use all three.
The traditional mode is that a person opens the dashboard, reads the number, and drills into it. The automated mode is that rules watch the same data and alert on breaches and exceptions, so nobody has to check. The third mode is AI agents. The written commentary in Revenue at Risk is one early example of AI working inside the analytics layer, but the bigger point is that a governed, normalised data layer like this is what makes AI agents effective in the first place. An agent that is asked to review stuck orders or flag a carrier problem, through Carriyo's native MCP layer, needs exactly this kind of trustworthy foundation underneath it, and that is the connection between this launch and the agent-ready platform we wrote about earlier this year.
A note on privacy
The analytics layer is built to hold numbers, not people. Customer names, email addresses and phone numbers stay in the operational platform. The warehouse carries a one-way hashed customer identifier alongside order values, ratings and counts, and a name is only looked up from the operational platform when a person opens a record there. The AI commentary is generated from the aggregated figures alone. We think that is the right way to build this, and it is also, frankly, a reasonable thing to ask of anyone who offers you delivery analytics.
Opening in Q4
Revenue at Risk and CX Score open to Carriyo customers in Q4 2026, alongside an expanding library of reports on the same foundation. If you would like to see them on your own data when they open, or you want to talk through what the analytics layer could do for your operation before then, get in touch with our team at carriyo.com/contact and we will walk you through it. You can read more about the analytics that are live today at carriyo.com/platform/analytics.
Carriyo is The Intelligent Commerce Platform, from checkout to doorstep.
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Sources
1. Carriyo. "From Tracking to Insight: How Carriyo Intelligence Surfaces Revenue at Risk and Carrier Performance" (Carriyo blog, July 2026). https://carriyo.com/resources/blog/tracking-insight-carriyo-intelligence-surfaces-2026-07-21/ 2. Carriyo. "Carriyo's MCP Server Turns AI Agents into Operators" (Carriyo blog, May 2026). https://carriyo.com/resources/blog/carriyos-mcp-server-turns-ai-2026-05-23/ 3. Carriyo. Analytics & Control Tower platform page. https://carriyo.com/platform/analytics