Smart Signage Optimization: Using ML to Transform Retail Store Communication & Experience.pdf

Smart Signage Optimization: Using ML to Transform Retail Store Communication & Experience
This presentation explores how machine learning and AI-driven automation are reshaping in-store communication — closing the gap between online and offline customer journeys.
From predictive maintenance to dynamic content delivery, retailers can now use data and ML models to optimize signage placement, messaging, and timing for maximum engagement. The deck highlights:
The role of smart signage in omnichannel retail strategy
AI applications for real-time content optimization
Operational and maintenance challenges at scale
Case examples of measurable ROI from connected store systems
Developed by Katadhin Consulting, this framework helps retailers transform physical environments into intelligent media platforms that adapt, learn, and drive results.

1. 10 August 2025
Smart Signage Optimization: Using ML to Transform
Retail Store Communication & Experience
Duke AI For Product Managers
This project was developed as part of a peer-graded assignment for the Machine Learning for Product Managers
course from Duke University on Coursera, applying the CRISP-DM methodology to design, validate, and assess a
real-world ML solution from opportunity evaluation through production risk analysis.

2. Project Overview
Smart Signage Optimization uses machine learning to help
retailers plan the right quantity, type, and placement of both
printed and digital signage for each store and campaign. By
forecasting print demand and optimizing digital screen locations
and content based on shopper traffic patterns, the solution reduces
waste, lowers costs, and enhances the customer experience,
turning in-store communications into a measurable driver of
engagement and sales.
Omnichannel Impact:
Smart Signage Optimization unites print and digital in-store, using shopper and sales data to deliver
relevant, measurable experiences , bringing the adaptability of online retail into the physical store.

3. Opportunity Evaluation
Problem:
•Retailers overproduce/misallocate printed signage → waste & higher costs
•Digital signage is often placed without data-driven optimization → low engagement
•Missed opportunity to connect signage strategy to customer journey, not just ads
Why is ML a good fit?
1. Data-rich → sales history, campaign data, store layouts, traffic flow, weather/events
2. Complex patterns → many factors influence optimal signage decisions
3. High business value → 25–30% cost savings, sales lift, improved CSAT

4. CRISP_DM Business Understanding
What’s the Problem:
Predict optimal quantity, type, and placement of both printed and digital signage for each store and campaign cycle.
Success Metrics:
•Outcome:
◦Reduce signage waste by 25–30%
◦Increase signage engagement rate (CV/dwell time)
◦Drive measurable sales lift in featured products
•Output:
◦Forecast accuracy for print demand
◦Engagement prediction accuracy for digital signage
Relevant Factors:
•Store size/layout
•Product mix/category
•Historical sales during promos
•Creative type & campaign goals
•Traffic flow patterns
•Local events & weather
•Seasonal patterns
Right Sign, Right Place, Right Time.
Using ML to ensure every piece of signage,
print or digital is optimized for impact,
ef
fi
ficiency, and the customer journey.

5. Solution Validation Plan
ML Concept:
•Model A (Batch): Print demand forecasting → historical campaign + store-level data
•Model B (Near Real-Time): Digital signage placement & content → traffic & engagement data
Validation:
•Pilot in 10–15 stores
•A/B test optimized plan vs. control
•Measure waste reduction, engagement rate, and sales lift
•Iterate models with pilot feedback

6. ML System Design
1. Batch vs. Online:
◦Print → Batch updates monthly
◦Digital → Near real-time updates daily/hourly
2. Data Sources:
◦Internal: campaign history, POS, layouts, signage orders
◦External: weather, events
3. Model Updating:
◦Print model retrained monthly
◦Digital model retrained weekly with online learning for anomalies

7. Potential Risks in Production
•Data Drift: Seasonality, store remodels, product changes
•Concept Drift: New customer behaviors (e.g., QR code adoption)
•Training–Serving Skew: Layout changes not in data pipeline
•Latency: Digital adjustments too slow for daily events

8. Optimizing every in-store
touchpoint, where data
meets design.
John Andrews
Katadhin Consulting