Triplet RYME

Customer Flow Analytics for Retail

Triplet RYME structurally analyzes where people look, where they pause, and where they drop off — providing the benchmarks operators need.

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Recurring Problems

High foot traffic, but hard to explain why actual entry is low
or where customers drop off.

  • Visitors increased, but conversion didn't—and you don't know why.
  • People crowd certain zones, but you can't tell if they converted
  • Non-purchasing customer flow goes unrecorded.

How Triplet AI understands retail

Triplet RYME records the flow of visitors within a space and organizes it into a format operators can act on immediately.

Not complex numbers — an intuitive view of what’s happening in the space right now.

  • Real-time customer movement tracking
  • Cumulative inflow/dwell/conversion data
  • Movement organized into understandable patterns
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From path analysis to monthly reports and comparative analytics

We provide benchmarks for operational decisions.

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Conversion Analytics
built for retail

Design the flow that connects visits to purchases

  • Measure entry rate vs foot traffic by hour and day.
  • Analyzes the correlation between zone-level dwell time and purchase conversion by hour, day, and promotion period.
  • Verify promo and layout changes with data.
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Building benchmarks for decision

We make the changes happening inside a space understandable — so operators can act on them.

1. We observe movement within the space. ryme_c_card_01.png
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1. We observe movement within the space.

Not raw camera footage — movement abstracted onto floor plans for clear visualization.

2. We remember the patterns. ryme_c_card_02.png
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2. We remember the patterns.

We store daily flow as comparable data. Not one-off stats—accumulated by day, hour, session, and season to build operational benchmarks.

3. We interpret why things change — as patterns. ryme_c_card_03.png
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3. We interpret why things change — as patterns.

We distill a space’s rhythm into explainable patterns.

Real-World Applications

Field Challenge 01

Foot traffic outside is high, but we don’t know why so few people actually walk in.

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Triplet's Interpret

Measures the ratio of outside foot traffic to actual store entry by hour and day of week.

Compares conditions between low-entry and high-entry time slots — weather, promotions, display changes — to isolate contributing factors.

Operators can determine what to adjust to convert passers-by into visitors.

After Implementation
  • Visualize entry rate vs foot traffic trends by hour
  • Increased target demographic visit rate
  • Storefront strategy basis: experience → data
Field Challenge 02

We want to understand the relationship between dwell time and purchase conversion to shape our operations strategy.

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Triplet's Interpret

Analyzes the correlation between zone-level dwell time and purchase conversion by hour, day, and promotion period.

Distinguish zones with high dwell/low conversion vs low dwell/high conversion.

Operators can determine which zones to change and how to boost conversion.

After Implementation
  • Visualize dwell-to-conversion correlation by zone
  • Auto-identify priority zones for conversion improvement
  • Verify changes after promo and layout updates

USE CASE

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GUESS

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Mill Studio

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Barneys New York

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Ovinomio

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New World Mart

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Retro Moon

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Sweet Spot

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Today's Glasses

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Try it out with a guided demo walkthrough.

Some features may be limited.

Request a Demo

What answers does your space need?

Data without interpretation piles up and disappears. With Triplet, turn your data into answers that lead to the next action.

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