RT Pastry — AI Analytics Dashboard

AI automatically identifies common customer profiles based on visit timing and purchase patterns. Each segment shows peak activity hours and average spend. Use this to optimize baking schedules, product availability, and time-based promotions for each group.

Stacked bar chart showing when each customer segment is most active throughout the day. For example, Morning Regulars dominate 8–10AM while After-Work Commuters and Families peak at 6–8PM. Helps plan which products to bake and display at different times.

Outlet-specific analysis ranking each product by performance score. Green "Hero" products are high sellers that deserve prime shelf space. Red "Laggard" products underperform and take valuable display space — consider repositioning, repricing, or replacing them.

Daily comparison of total items produced (blue), items sold (green), and items wasted (red line). The gap between produced and sold represents waste. A declining red line means the AI forecasting is improving production planning over time.

AI projects next week's sales trends to help RT Pastry move from reactive baking to proactive production planning. Solid bars show actual revenue; transparent bars show AI-predicted revenue. Benefits include reduced food wastage, better stock planning, and improved operational efficiency.

Links customer movement heatmaps with SKU sales to measure how well a product's shelf position translates into actual sales. A high shelf score with low sales means the placement isn't working. A low shelf score with high sales means the product deserves a better spot. Helps optimize product display for maximum revenue.

Top-selling products during the 6–8PM rush. Quick-grab items outperform during peak. Ensure these are freshly baked and prominently displayed before the rush.

Overall daily sales volume per product. Full-day rankings differ from peak because morning items accumulate volume over more hours.

Products with low sales volume, poor sell-through rate, and high wastage. These items occupy shelf space that could be used for better performers.

How quickly each product sells out vs how much is left over at closing. Green = high sell-through (hero). Red = high leftover (laggard). Products that sell out early = lost revenue, increase production. Products always left over = waste, reduce production.

AI estimates customer age range using facial analysis. The 25–34 bracket dominates (32%), indicating a young professional customer base. Useful for product development and marketing targeting.

AI detects gender distribution among store visitors. Female customers dominate at 58%, which is typical for bakery retail. Helps tailor product presentation and in-store experience.

AI classifies visitors into Malay, Chinese, Indian, and Others. Reflects the Malaysian demographic mix and helps plan culturally relevant product offerings (e.g., halal-certified items, festive specials for Raya, CNY, Deepavali).

During peak hours (6–8PM), the age skews younger with 18–34 making up 60% vs 47% all-day. After-work commuters and young families drive this shift.

Female ratio increases to 62% during peak hours, driven by mothers picking up after-work/school treats. Male ratio drops from 42% to 38%.

Chinese customer share increases from 35% to 38% during peak, while Malay dips from 45% to 42%. Correlates with after-work commuter patterns in urban areas.

34.2% of customers who enter leave without purchasing. AI tracks their dwell time to categorize: short dwell (<2 min) = nothing caught their eye, medium (2–5 min) = interest but no conversion, long (>5 min) = pricing or queue issues prevented purchase.

Strong positive correlation (r=0.82): customers who spend more time in-store spend more money. Each additional minute adds ~RM 3.20 to the average transaction. Validates the strategy of creating an engaging in-store experience.

Zones where customers spend the most time. High dwell = strong engagement — prime spots for upselling, cross-selling, and placing new launches.

Zones with the fewest visitors per day. These represent wasted retail space. Solutions: better signage, lighting improvements, or relocating popular items to draw traffic.

Where customers go first after entering. Entry → Main Display is the strongest flow (78%). Understanding these paths helps optimize product placement along high-flow corridors.

Areas that customers rarely visit. Low flow = poor visibility, unappealing layout, or no draw products. These dead zones represent lost sales opportunities.

Destination (58%): Regulars who beeline to products and checkout quickly. 1–2 zones, 4.5 min avg.

Discovery (42%): Explorers who browse multiple zones. 4–6 zones, 11.2 min avg, 28% higher basket size.

How Destination vs Discovery customers distribute across zones. Destination customers concentrate at Main Display and Checkout. Discovery customers spread more evenly and explore zones that Destination customers skip.