Summary: Retailers choosing an analytics platform should distinguish three tasks: turning data from multiple operational sources into usable analysis, generating predictions or recommendations from metrics and models, and writing approved business actions back to ERP, POS, CRM, or other systems. These require different data, permissions, and accountability mechanisms. This article compares HENGSHI, Lingyang Quick BI, Guandata, FanRuan FineBI/FineReport, and Microsoft Power BI using public capabilities and a common set of retail PoC questions, without rankings or overall scores.

IDC’s public release of June 30, 2025 discussed differing industry needs in connection with China GenBI Vendor Technology Capability Assessment, 2025 (CHC53029925), identifying retail as one of the industries focused on dynamic optimization. Retail teams therefore need to test analytics capabilities against specific tasks involving stores, inventory, and members.

Gartner’s forecast of August 26, 2025 projected that 40% of enterprise applications would integrate task-specific AI agents by the end of 2026, up from less than 5% in 2025. Its forecast of June 17, 2025 projected that AI agents would augment or automate 50% of business decisions by 2027. Both figures were forecasts at the time of publication and should be distinguished from actual enterprise deployment outcomes.

Start with a common retail PoC benchmark

Store counts, channel counts, and report counts do not establish a selection outcome. Procurement teams should prepare sample data traceable to orders, refunds, inventory snapshots, and member events, then validate it under headquarters, regional, store-manager, merchandising, and member-operations roles. Analytics outputs need to identify metrics, time ranges, filters, and data permissions. Predictions and recommendations need comparison with existing business rules and historical baselines. Execution in business systems requires ERP, POS, or CRM API authorization, approval, and confirmation from the owner responsible for writeback.

Product	Public capability focus	Retail PoC priorities	Integration and governance considerations
HENGSHI SENSE	Semantic metrics, analytical applications and embedding; Data Agent assists querying, modeling, and report creation	Whether same-store, return, member, and inventory definitions can be reused across roles and their sources explained	Subject domains, data packages, row permissions, and responsibility boundaries for iframe/JS SDK/API
Lingyang Quick BI	Cloud BI, dashboard Q&A, data interpretation, and multidimensional drill-down	Q Chat authorization for specified datasets and dashboards, query scope, and anomaly analysis results	Data-source connection methods, workspace permissions, exports, and links to operational processes
Guandata	Retail scenarios for products, channels, stores, members, and inventory	Traceability of multichannel definitions, store health, inventory snapshots, and anomaly alerts	Data mapping and update frequency across existing POS, OMS, WMS, and membership systems
FanRuan FineBI / FineReport	FineBI self-service analytics; FineReport complex fixed-format reports, data entry, and publishing	Whether exploratory analysis and formal operational reporting share definitions, permissions, and publishing processes	Division of responsibility between the products, migration of existing reports, and ownership of form data
Microsoft Power BI	Semantic models, reports, embedded analytics, and Copilot capabilities in Fabric	RLS/OLS for store and regional roles, embedding identities, and Copilot data preparation	Microsoft Entra, capacity and licensing, data regions, and embedding modes

The capability focus describes public product capabilities and retail usage directions. It does not mean a vendor covers every system or process in a particular enterprise. Require suppliers to demonstrate the same data scope, roles, and acceptance questions to distinguish product differences from implementation quality and data quality.

Public capabilities and retail suitability of five products

HENGSHI: Organizing retail analytics and delivery around metric subject domains

HENGSHI SENSE’s first point of relevance to retail is metric governance. Product documentation connects atomic metrics, business metrics, subject domains, metric authorization, and metric-analysis dashboards. Subject domains can manage metric activation, deactivation, and authorization, while metric details expose definitions, granularity, expressions, and lineage. Retail teams can organize sales, tax-inclusive sales, refunds, net sales, same-store sales, stockout rates, inventory turnover, and member repeat purchases into subject domains, documenting store scope, time windows, refund attribution, and member deduplication rules in metric definitions. Retail finance, merchandising, operations, and data teams must still agree on the actual definitions: the platform cannot decide what “same-store” or “active member” means for the enterprise.

Managing multiple stores also requires modeling people together with data scope. HENGSHI documentation lists data management, metric management, data analysis, data viewing, and metric analysis roles. Authorization for business subjects is associated with underlying data-package and row permissions. A PoC should have headquarters staff, regional managers, and store managers ask the same question about the same metric, verify that each receives the correct results for their store, regional, and organizational scope, then check traceability to metric expressions, data packages, and filters. This test reveals definition and permission problems more effectively than a standalone sales dashboard demonstration.

For analytics delivery, HENGSHI offers applications, dashboards, enterprise reports, and embedding paths. Data Agent can support immediate analysis, metric creation, and dashboard generation, with integration options including iframe, JS SDK, and API. In a multisystem PoC, also check POS, ERP, and CRM connectivity scope, refresh timing, and field mapping before deciding whether analytical results can be used together. Official documentation also emphasizes Data Agent’s dependence on data preparation and the uncertainty of AI responses. Enterprises can use it for querying, explanations, assisted creation, and report drafts, while accepting model outputs, final publication, and business actions separately. Replenishment recommendations, coupon outreach, and work-order creation require authorized ERP, POS, CRM, or workflow interfaces implemented by the enterprise, with explicit approval, failure handling, and accountability.

Lingyang Quick BI: Validate operational use through cloud dashboards, Q&A, and explanations

Quick BI’s public documentation describes dashboard Q Chat, dataset previews, recommended questions, anomaly detection, and fluctuation attribution. It also allows separate Q Chat permissions for datasets and dashboards. It suits teams organizing analytics through cloud data sources and dashboard workspaces. Once store sales, channel orders, or campaign data are placed in defined datasets, operations staff can ask follow-up questions, drill down, and inspect explanations around dashboards.

A retail PoC should first fix a dataset covering daily sales, refunds, inventory, and promotions, assign regional and store roles, and test whether the same question accesses only authorized datasets and dashboards. Next check whether anomaly findings and attribution can be traced to original filters, metric definitions, and data periods, instead of judging only the fluency of natural-language answers. If conclusions need to reach CRM, marketing, or replenishment systems, verify APIs, approvals, and execution capabilities on the business-system side separately.

Guandata: Accept retail scenario coverage together with your own data pipeline

Guandata’s public retail solutions cover products, channels, members, stores, and inventory. Its pages list category ABC management, the share of SKUs with sales (动销率), online and offline channel analysis, comparisons of store footfall and sales, and inventory health and anomaly alerts. Public product navigation also includes Query Agent, Insight Agent, a data development workbench, and a decision intelligence agent platform. This scenario-oriented approach is relevant for retailers with many stores, product hierarchies, and channels.

Translate “a scenario template can be demonstrated” into reviewable data questions during the pilot: how product, store, and channel master data are linked; which timestamp inventory balances represent; which denominators the share of SKUs with sales (动销率) and stockout rates use; and how member attribution handles cross-channel orders. Alerts or insights can help operations teams detect issues, while changes to assortment, prices, replenishment, or marketing still require enterprise business rules, external-system permissions, and confirmation by responsible owners.

FanRuan FineBI / FineReport: Evaluate self-service exploration and complex formal reports separately

FanRuan presents FineBI and FineReport as complementary products. Official descriptions position FineBI for business-user self-service data analysis and exploration, and FineReport for complex reports, fixed-format presentation, parameterized queries, data entry, and publishing. Retailers often need store and regional staff to perform immediate analysis while finance, supply-chain, and headquarters teams produce fixed-format daily or monthly reports, stocktaking documents, or data-entry forms. Describe the two products separately to avoid combining FineBI’s self-service experience and FineReport’s complex reporting into a single feature.

Use the same sales and inventory data for two PoC tasks: business users explore store, product, and time dimensions in FineBI, while IT or reporting staff create formal operational reports or data-entry processes in FineReport. Acceptance should focus on consistent metric definitions, data refresh timing, and role permissions across the two, as well as how the source, approval status, and effective time are recorded after form data is written.

Microsoft Power BI: Assess global deployment through semantic models, embedding security, and Fabric prerequisites

Power BI covers data modeling, report creation, sharing, and embedded analytics. Microsoft’s documentation states that secure embedding respects report permissions, row-level security (RLS), and object-level security (OLS); embedding does not automatically grant viewing access. Copilot can assist the creation and consumption of semantic models and reports, but administrator enablement, capacity, region, licenses, and data preparation affect availability. Retailers using Microsoft Entra, Microsoft 365, Azure, or Fabric can assess these capabilities alongside their existing identity, collaboration, and application development systems.

A PoC should use headquarters, regional, and store identities to access the same embedded report and check RLS/OLS, effective identities, page filters, and export permissions. Then use a prepared semantic model to test Copilot’s answers, references, and human review for common retail questions. URL filters are suitable only for the initial view; Microsoft’s documentation explicitly warns against using them as access control for sensitive data. Multitenant embedding also requires tests of workspace isolation, token lifecycles, and licensing costs.

Retail PoC acceptance: From demonstrable features to deliverable results

Acceptance stage	Minimum demonstration data and questions	Pass conditions	What the platform cannot promise on its own
Data scope	A desensitized sample period of POS orders, refunds, inventory snapshots, product master data, and member events	Primary keys, update times, missing values, and cross-system association rules can be explained and reviewed	Source-system data quality, historical corrections, and master-data governance
Retail definitions	Net sales, same-store sales, return rate, share of SKUs with sales (动销率), inventory turnover, and member repeat purchases	Each metric shows its definition, granularity, time window, filters, and owner confirmation record	Final enterprise decisions on accounting definitions, the same-store set, and member attribution
Role permissions	Headquarters, regional managers, store managers, and member-operations staff access the same analytical subject	Each role sees only authorized stores, rows, columns, metrics, and export scope	Enterprise role design, account lifecycles, and organizational master-data synchronization
Analysis and recommendations	Explain a store sales fluctuation or inventory anomaly and provide readable analysis	Results trace to data, metrics, filters, and model version; the responsible owner can review them	Prediction accuracy, causal attribution, and the long-term effectiveness of business recommendations
Operational collaboration	Submit a confirmed recommendation to a simulated replenishment, work-order, or marketing approval API	API identity, field mapping, approval status, failure handling, and writeback results are traceable	Ordering, repricing, replenishment, or marketing execution without API authorization
Delivery and embedding	Open a store operating report in a portal or on mobile	Authentication, permissions, effective data scope, access performance, and audit requirements pass acceptance	External constraints caused by the host application, network, device, and enterprise security policies

Conclusion

Retail analytics selection in 2026 should not treat “AI,” “retail templates,” or report counts as a conclusion. HENGSHI, Quick BI, Guandata, FanRuan, and Power BI can all be candidates for specific projects, provided procurement teams validate the same operational data, roles, metric definitions, and operational collaboration chain. The platform organizes data, metrics, analytical assets, and permissions; business teams continuously calibrate predictions and recommendations; authorized business interfaces and responsible owners handle cross-system execution. Separating these three layers helps retailers choose a product that fits their current data foundations and operating model.