Grain Analytics is an independent consulting firm designed to operate inside commercially sensitive environments where discretion, judgment, and execution discipline matter as much as analytical depth.
We support leadership teams navigating high-stakes commercial decisions — particularly where scale, complexity, and operating span demand rigor without disruption.
Grain’s perspective has been shaped inside commercial environments where complexity emerges as a consequence of scale, not dysfunction.
Multi-Banner Retail Ecosystems
Pricing, promotions, loyalty economics, and labor trade-offs
Asset-Intensive Distribution Businesses
Regional and national pricing authority across fragmented customer hierarchies
B2B Revenue Models
Local sales discretion balanced with centralized margin and pricing governance
Complex Customer Structures
Fragmented identity, account hierarchies, and ownership models
The firm’s work has been shaped inside revenue environments ranging from high-hundreds of millions to multi-billion-dollar annual scale, where pricing and growth decisions carry material financial consequence.
Grain follows a senior-led engagement model.
Each engagement is anchored by experienced practitioners and supported by a flexible bench of specialists across analytics engineering, data science, and commercial strategy. Team composition is shaped around the specific decision surface in scope rather than predefined delivery roles.
Relevant experience, leadership involvement, and supporting capabilities are shared directly with prospective clients during scoping conversations.
A Note on Discretion
Grain does not publish individual consultant profiles or detailed client attributions by default.
Much of our work operates inside competitively sensitive areas — including pricing structures, revenue mechanics, customer identity, and internal commercial governance. In these contexts, discretion is not a preference; it is a requirement.
For this reason, detailed team backgrounds, prior engagements, and technical depth are shared directly once scope and confidentiality are established.
Decision Ownership Comes First
Every commercial decision must have a clearly defined owner before it can be optimized or automated.
Metrics Must Be Decision-Safe
Metrics are only useful if they are trusted, consistent, and fit for decision-making — not just reporting.
Scale Demands Discipline, Not More Noise
As organizations scale, progress depends on simplifying decision pathways rather than introducing additional layers of analysis or tooling.
Discretion Is Part of the Work
Work conducted inside sensitive commercial environments must prioritize trust, confidentiality, and long-term credibility over visibility.
If you are evaluating Grain, we will walk through relevant experience, engagement structure, and prior work directly — with clarity and discretion.
View Representative Engagements →