A practical handbook
for retail AI.

Loyalty

Is your retail data ready for AI-powered loyalty?

Prepare retail data for AI-powered loyalty with practical checks for purchases, identities, returns, consent, reward ledgers and measurable decisions.

Loyalty

Design a loyalty journey that earns the second purchase

Build a retail second-purchase journey around customer needs, category timing and equal-age cohorts, with a worked example and practical measurement plan.

Loyalty

Measure whether loyalty changes customer behaviour

Separate loyalty participation from incremental value. Define fair comparison groups, repeat-purchase denominators and contribution with a worked example.

Loyalty

Design loyalty rewards around contribution, not redemption alone

Evaluate loyalty rewards using incremental contribution, redemption costs and offer stacking. Includes a rupee-based example and a reward approval checklist.

Loyalty

Connect customer identity without losing permission context

Connect store and online customer records carefully. Build identity evidence, channel permissions and withdrawal checks into an omnichannel retail workflow.

Campaigns

Select AI campaign audiences that are eligible, useful and testable

Build AI campaign audiences with eligibility gates, transparent ranking and a fair test population. Includes a practical selection funnel and retail checklist.

Campaigns

Run a campaign holdout that answers a real business question

Plan a retail campaign holdout with stable assignment, clean denominators and equal follow-up. Learn how to interpret lift with a worked experiment example.

Campaigns

Turn next-best-action campaigns into reviewable retail decisions

Design next-best-action retail campaigns with eligible choices, contribution-based comparisons and feedback records, including a practical worked example.

Marketplace intelligence

Marketplace settlement reconciliation: explain every rupee before raising a claim

Reconcile marketplace orders, refunds, fees and bank receipts with a practical exception workflow, a checked example and controls for Indian retail teams.

Marketplace intelligence

Marketplace SKU contribution: what remains after fees, returns and advertising?

Build a SKU contribution view that includes marketplace fees, returns, fulfilment and ad spend, with clear denominators and a worked retail example.

Marketplace intelligence

Marketplace catalogue and SKU mapping: build a dependable product crosswalk

Map marketplace listings to the right retail variants, bundles and units with a governed product crosswalk, validation checks and a worked example.

Marketplace intelligence

Returns and RTO economics: distinguish the failure before choosing the fix

Separate customer returns from return-to-origin shipments, track their true costs and choose better interventions using a practical retail measurement guide.

Marketplace intelligence

A marketplace intelligence morning brief that leads to accountable decisions

Design a marketplace morning brief around evidence, urgency, ownership and measured actions, with a practical priority framework and illustrative example.

MBO

MBO intelligence: distinguish sell-in growth from real consumer sell-through

Separate shipments to multi-brand outlets from consumer sales, reconcile stock movement and prioritise MBO replenishment using clear measures and examples.

MBO

MBO scheme and claims reconciliation: connect the promise, the sale and the deduction

Reconcile MBO incentives and deductions against approved scheme rules, eligible sales and prior credits, with an evidence workflow and worked example.

SIS

Shop-in-shop sales and stock visibility: see what each concession can actually sell

Build shop-in-shop visibility across sales, receipts, transfers and stock states, with variant-level controls, reporting coverage and a worked example.

SIS

SIS concession settlement reconciliation: connect store sales to the amount received

Reconcile shop-in-shop sales, returns, concession charges and bank receipts using contract versions, transaction evidence and a checked settlement example.

EBO

An EBO daily performance dashboard that changes the store conversation

Build an EBO daily performance dashboard with clear sales definitions, return treatment, conversion denominators and a practical store review checklist.

EBO

EBO clienteling with AI recommendations: from suggestion to useful service

Design EBO clienteling with grounded AI recommendations, current product context, associate review, clear customer choices and meaningful pilot measures.

Social commerce

Social commerce opt-in and message governance for retail teams

Build a practical social commerce messaging process with permission evidence, purpose checks, suppression updates, accountable approvals and measurable controls.

Social commerce

Turning social conversations into reconciled retail orders

Connect social enquiries to verified orders, payment events, fulfilment and refunds with clear identifiers, reconciliation rules and a practical handover checklist.

Social commerce

Social-commerce attribution versus incrementality: measure different questions

Separate attributed orders from incremental sales in social commerce using clear identity rules, holdouts, return-adjusted outcomes and contribution measures.

Malls

How malls should measure repeat visits from loyalty programmes

Measure mall loyalty with mature visitor cohorts, distinct visit days, bill validation, tenant coverage and a clear distinction between association and lift.

Malls

Mall tenant data quality: the foundation of a credible trading brief

Improve mall trading intelligence with controlled tenant masters, explicit reporting states, comparable sales, correction history and a daily quality checklist.

Governed retail AI

How to evaluate a governed retail AI workflow with Fundle Brain

Evaluate Fundle Brain with a clear retail decision, verified source scope, role tests, evidence checks and separate controls for recommendations and actions.