Built to work as a full loop, or deployed where the need is greatest.
Most organizations cannot see their market clearly, not because the data doesn't exist, but because it has never been consolidated into a single reliable base. Arm A resolves that before anything else can be attempted.
Arm A does not assume the data exists. First it consolidates the information the operation already has, scattered and unharmonized; then it captures what is not yet visible.
The transactional, POS, and distributor data the operation already has: scattered, in different formats, and unharmonized. We capture, clean, harmonize, and consolidate it into a single reliable base.
The external market made observable: competition, prices, and what happens at the point of sale.
The physical floor made observable: shopper, journey, shelf, and the sales conversation, read with computer vision and audio.
ERP, sales systems, and billing records, the data the client already owns but rarely consolidates.
Point-of-sale capture from fragmented traditional trade where traceability is typically low.
Transactional data from structured retail chains and modern trade formats.
Structured connections to receive and map sales, coverage, pricing, and promotional data.
Third-party aggregated sources for benchmarking and market-level reads.
SKU master data, price lists, promotional calendars, and inventory records.
In-field capture for prices, inventory, promotions, visibility, competitive presence, and compliance.
Route-level intelligence from commercial teams operating at the point of sale.
Assortment compliance, share of shelf, pricing execution, and promotional display tracking.
Observed competitor pricing at point of sale by channel, region, and format.
External research and category benchmarks that provide context beyond internal data.
Demand-side intelligence to complement supply-side and transactional data.
Direct capture from POS systems, surveys, apps, and integrations. The entry point for all commercial information.
Technical loading of multiple sources into a common architecture, regardless of format, cadence, or origin.
Correction, validation, deduplication, and resolution of inconsistencies. A reliable base starts here.
Standardization of SKUs, customers, channels, and geographies so data becomes comparable across sources.
Generation of structured, usable datasets for analytics, benchmarking, and eventual monetization.
Thirteen analytical capabilities that turn structured commercial data into prioritized decisions, organized in four families. Trade-offs made explicit. Decisions, not reports.
Most engagements stop at the recommendation. Arm C is where the work becomes real, intelligence translated into field action, measured against baseline, and fed back into the operation.
The distance between a good analysis and a measurable commercial result is where most consulting engagements fail. We do not hand off a deck and disengage.
Execution is delivered in two modes, depending on where it is needed.
In capabilities like Concierge or Market Signal, Imberion operates the intelligence engine end-to-end. Concierge runs omnichannel customer interaction (takes orders, recommends, and serves) through the Companion, a conversational layer with live dashboards; Market Signal runs the market radar. AI does the work; we operate the engine. Nothing to operate on the client side.
When the work belongs inside your organization, we build the capability: ways of working, RGM playbooks, dashboards, and governance, so your team runs the operation and the decisions stick.
B output converted into a specific, sequenced action list, what gets done first, what gets done second, what does not get done at all, with responsible owners and timelines.
Decisions structured into field-ready plans by commercial, trade, key account, and territory, with the specificity required for execution, not just communication.
We do not hand off the plan. We work alongside the teams responsible for execution until the logic operates on its own, training, real-time support, and adaptation when field signals reveal gaps.
Performance dashboards and cycle-by-cycle variance analysis built in from the start. Results measured against a pre-defined baseline, not against internal perception of progress.
Structured review cadence, escalation paths, and accountability frameworks to maintain commercial discipline beyond the initial deployment, institutionalizing the logic, not just the output.
What Arm C learns in execution is not lost. Structured and returned to the analytical models, the data infrastructure, and the priority framework, so every cycle is more informed than the last.
Every cycle returns structured learning to Arm A and Arm B, making the data richer, the models sharper, and the decisions more precise. The operation compounds.
Market data structured and consolidated
Data converted into decisions
Decisions captured in the field
Results feed back into A & B