HealthcareT-order • Seoul, Korea

Tablet Ad Targeting Strategy

We turn tablet ads into an auditable targeting policy—matching creatives to table-level context to improve and validate click-through rates (CTR).

Problem

In T-order environments, ads are not just 'impressions'—they are decision problems that change choices at the moment of ordering (menu/drinks/add-ons). However, most venues show the same ads to all tables or operate based on simple time slots/popular items. As a result, table-level click probability differences (party size/order context/time/group composition) are not reflected, leading to low click-through rates (CTR). Validation and optimization of which ads actually drove clicks are impossible, and campaign operations remain stuck on experience and intuition.

Decision to Make

Which ad should be shown to this table right now to increase click-through rate? How to target based on table segment and context (time/order context) to maximize ad click-through rate (CTR)?

Inputs (Data)

1Table-level features: visit time, party size, order composition (drinks/appetizers/mains), ordering speed, payment amount

2Ad-level features: product category, margin, inventory, promotion conditions, time restrictions

3Repeat visit status (if available)

Approach

Define table-level context and generate targeting candidate rules (policies) (e.g., '2-person table without drinks + evening peak → value appetizer/set ad'). Estimate incremental revenue uplift by comparing before/after exposure and similar tables (incrementality perspective), and validate through A/B or switchback experimental design. Deploy not as 'dashboard' but as operational rules: segment definition → top-N recommended ads → guardrails (frequency/duplication/fatigue/inventory) → exception handling.

Deliverables

Tablet Ad Targeting Playbook (1p) - Ad rules by table/context

Segment × Creative Matrix - Recommended ads/copy/exposure timing by segment

CTR Performance Report (Before/After + control) - Click-through rate comparison by segment

Guardrail & Ops Rules - Over-exposure prevention, repeat limits, inventory/time restrictions, substitution rules for stockouts

Next experiment design - Segment refinement/creative replacement/timing adjustment for higher CTR

Result

Converted table-context targeting policies into deployable operational rules to improve ad click-through rates (CTR) in measurable form. Track CTR differences by table segment and compare which targeting rules generate higher click rates to build a repeatable decision optimization loop.

Time-to-Value

2-week diagnostic + 6-week pilot

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