Retail marketing agents that remember what worked and why
A global retail brand replaced its stack of reporting tools, spreadsheets and reactive judgement calls with a fleet of retail marketing agents on the Decision Execution Layer. Every promo, product brief, shopper message and spend decision now carries a Context Object and every campaign outcome across stores and channels feeds back into decision memory that sharpens the next one.
Background
The retailer operated across 12 markets, 2,000+ stores and 5 digital platforms. Every tool in the stack POS, CDP, e-commerce, loyalty, ad platforms, influencer CRMs was a System of Record. None of them answered the one question the CMO kept asking: which promos, SKUs and channels moved basket size last quarter, and what should we do next?
Challenge
Campaign and merchandising decisions were made in silos. Regional teams cloned promo playbooks that had “worked before” without evidence. Shopper DMs and store comms went out off-brand. Escalations happened by Slack tap. There was no shared memory of which offers, price points, SKUs or timings moved basket size and no guardrails to keep agents on brand at speed across stores and channels.
Solution
We built a Decision Execution Layer between the retailer’s Systems of Record and its retail marketing agents. Vertical agents own specific jobs promo briefs, shopper outreach, store-comms response, escalation and all of them read and write the same Context Object: entities (shopper segment, channel, store, SKU, campaign), state (live basket, inventory and performance signals), decisions (offer, price, tone, spend), outcomes (conversion, basket size, sentiment, churn).
What we did:
Unified Context Repo across 12 markets, 5 platforms and 4 currencies
Planning agents that learn from decision memory not just historical reports
Brand-consistent response agents across email, TikTok and WhatsApp with guardrails enforced at the layer
Escalation logic: high-risk or brand-sensitive responses route to humans, everything else is handled automatically
Technologies
Decision Execution Layer with Context Repo and brand guardrails, vertical marketing agents with shared memory, outcome pipelines that feed every decision back into the graph.
Governed NLP for outreach, briefs and community response tone and policy enforced pre-send
Outcome-weighted forecasting: budget and mix models trained on decision memory, not just historic spend
Multi-agent orchestration with shared Context Object planning and comms agents reason over the same state
Cloud pipelines unifying Systems of Record across 12 markets and 5 platforms into one governed layer
Channel integrations (WhatsApp, TikTok) wired through guardrailed agents with full audit trail
Impact
Once campaign outcomes started compounding into memory, marketing stopped running on instinct and started running on evidence.
Decision memory
Every campaign decision and outcome captured in the Context Repo next quarter’s plan starts from evidence, not guesswork.
Brand-consistent execution
15 hours per person per week reclaimed from reporting and coordination; brand-consistent responses handled by agents with escalation to humans when risk spikes.
Margin-aware spend
Acquisition cost down 12.7% as spend decisions became margin-aware the agent learned when a discount won revenue and when it eroded it.
Questions & Answers
Short answers on retail marketing decision memory, guardrails and how the Context Object pattern keeps agents on brand across stores and channels.
AI relies entirely on the quality of your data. Without consistent, clean, and connected data, AI models produce unreliable outputs. A data foundation ensures your data is accurate, trustworthy, and structured in a way that supports scalable AI.
Timelines vary depending on your current maturity, number of data sources, and governance needs. Most organisations see a functional foundation in 6–12 weeks, with continuous improvement over time.
No. CDP, ad platforms and CRM stay as Systems of Record. The Decision Execution Layer wraps them, reading state and writing decisions and outcomes back so existing investments keep paying.
Governance covers everything from ownership and access control to data quality rules, cataloging, lineage, and compliance. It ensures your data stays reliable, auditable, and secure essential for any AI system.
We assess each system, map its structure, and build automated pipelines that clean, transform, and centralise your data. This eliminates manual extraction and creates a single, consistent source of truth across the organisation.
Map your retail marketing Decision Execution Layer
Show us your stack, your brand guardrails and your agent ambitions. We’ll map where campaign decisions leak today and where the Context Repo will compound tomorrow.