The Problem
Large chain retailers have a goldmine of customer data but it often lives in silos. While a retailer may have a robust loyalty program and understand how customers behave around key promotions and offerings, that data is siloed in various systems such as the POS, Paytronix, the retailer's ERP system, and the back-office applications that run the day-to-day functions of the business. The retailer's supply chain systems also store important customer information. The result: a treasure trove of customer insights that live in disparate systems where different teams only have access to select portions of the customer's buying behavior in real time.
Many organizations are finding out that they don't have enough analysts to service the reporting needs of the organization. With a small team of analysts responsible for data preparation and reporting as well as answering ad hoc questions from senior executives to support strategic planning across multiple lines of business, pressure mounts as the volume of requests continues to rise, creating longer and longer cycles to deliver the information they need.
We find that there is a large cost associated with the current approach: leaders require timely and accurate data to optimise the loyalty rate, but the reporting is manual, fragmented and too slow.
The Solution
Instead of relying on IT teams to manually parse out numbers for executives to interpret, AI-powered reporting uses three core features to empower business operators with unparalleled insight: data unification, agent-based orchestration, and natural language generation.
No more lengthy, custom reports for senior executives. Instead, the system integrates customer and operational data on an ongoing basis from a retailer's core retail systems and Jarvis Chat enables users to receive on-the-spot answers in words and pictures by asking natural language questions.
The result is rapid insights, significant cross functional alignment, and better strategic decision making to grow your loyalty program.
ROI & Business Value
| Outcome | Impact |
|---|---|
| Improved loyalty strategy execution | Leaders can quickly identify retention drivers, churn risks, and campaign performance |
| Faster executive decision cycles | Questions that once required analyst queue time can be answered on demand |
| Higher analytics team leverage | Analysts shift from repetitive report generation to high-value strategic analysis |
| Better data consistency | Unified reporting layer reduces conflicting metrics across departments |
| Greater campaign precision | Teams can segment and optimize promotions using fresher, connected data |
| Operational efficiency | Fewer manual reporting handoffs across business and technical teams |
Analyze this: The biggest change is that Organizations must make decisions at business speeds, not report-cycle speeds.
How We Solved It with Jarvis AI
We built an agent from the ground up on top of the Jarvis Registry that submits telemetry from various data sources.
The architecture is built on three coordinating layers: an agent gateway that secures and routes every data request, an agent registry that catalogs all approved agents and connectors, and an agent orchestrator that sequences those agents to produce a complete, executive-ready answer. For a deeper look at how these components work together, see our guide on agent gateway, agent registry, and agent orchestrator architecture.
In the backend, our agentic approach to flow connects to a POS system, Paytronix, an ERP system, back-office software, supply chain management and more. The Agents continuously collect and normalize information from various systems of record to create a solid reporting foundation upon which executives and operations leaders can build to gain better insights into their businesses.
Jarvis Chat on the frontend provides intuitive analytics for non-technical business users to ask any question they wish, phrased naturally, such as:
- "Which customer segments are dropping in repeat purchase rate this month?"
- "How did loyalty redemption change after last weekend's promotion?"
- "Show top stores by loyalty growth and basket size impact."
Once a conversation with Jarvis Chat has taken place, Jarvis Chat then provides a quick and interactive answer supplemented with charts and graphs, allowing for fast analysis and decision making without having to go through the additional step of building a BI report.
By orchestrating back-end agents using artificial intelligence, and analyzing conversations, chain retailers can obtain improved loyalty, with reduced wait times and less administrative burden.
Why This Matters for Future Retail Customers
Retailers don't have a loyalty data problem. They have a loyalty decision speed problem.
By unifying and making customer signals easily accessible through conversational AI, executive teams can act more quickly on churn risk, campaign performance, and product and store-level behavior signals that drive loyalty. This tempo in action reduces the organization's dependence on expensive and over-allocated analytics resources and improves the likelihood of achieving loyalty objectives.
What's next for this seasonal retail cycle? Converting existing data into meaningful customer retention metrics through the implementation of AI reporting. To understand the underlying architecture that makes this possible, explore our deep dive on agent orchestrator workflows for retail analytics.