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How a QSR Chain Increased Sales by 18% with Craveva AI Enterprise

Real results from implementing **Craveva AI Enterprise** AI kiosk upselling agents across 15 outlets in Singapore.

Craveva AI Enterprise Team · Mar 20, 2025 · 5 min read
Supported today (auto-updated)
Deployments
  • Web widget (JavaScript embed)
  • WhatsApp Business
  • E-commerce: Shopify, WordPress, WooCommerce, Magento, BigCommerce
Data sources & integrations
  • Offline files + Google Drive
  • Databases: PostgreSQL, MySQL, MongoDB, BigQuery, Snowflake, Redshift, Athena, ClickHouse, Trino, SQL Server, Oracle, DuckDB
  • Online APIs: REST, GraphQL, Webhook
  • POS (Singapore): Qashier, Eats365 (others appear in roadmap/partials)
Note: Some connectors may exist as base classes/framework but are not yet available as production deployments.

18% Sales Lift Without Extra Headcount: A Franchise Playbook for AI Kiosk Upselling (Craveva AI Enterprise)

For founders and CXOs, “upselling” is not a marketing tactic. It is a unit economics lever: more revenue per transaction without adding labor or slowing service.

This applies across F&B verticals: QSR, casual dining, fine dining, cloud kitchens, catering, bakeries, and franchise groups.

Executive Snapshot

  • Outcome: +18% total sales and +22% average order value across 15 outlets (case example)
  • Operational win: higher throughput at peaks without adding cashier headcount
  • Commercial win: higher attach rate on sides, drinks, desserts, and upgrades
  • Why it worked: recommendations were driven by live POS data, availability, and promo rules, not static kiosk screens

The real constraint is throughput, not demand

Most multi-outlet brands do not lose money because customers refuse add-ons. They lose money because queues and slow screens create friction. If you add more prompts, you often slow down ordering and lose volume.

The growth problem becomes: increase AOV while keeping time-to-checkout flat.

What was deployed

The chain rolled out a kiosk upselling agent in Craveva AI Enterprise that sits inside the kiosk flow and makes a small number of high-probability suggestions.

Data connected (typical setup):

  • POS: menu, pricing, modifiers, and item availability
  • Promotions: daypart rules, bundles, and limited-time offers
  • Operations rules: outlet-specific stock constraints and prep cutoffs

Recommendation logic that protects margin

The agent does not “push random add-ons.” It follows governed logic that a CFO and COO can sign off on:

  • Suggest only items that are in stock at that outlet
  • Prioritize high-margin, fast-to-prepare attachments during peaks
  • Respect promo logic and upsell only when it increases contribution margin
  • Avoid “bad combos” that increase refunds, remakes, or kitchen load

Operating model: who owns what

This is where most kiosk projects fail: nobody owns the decision logic.

  • Marketing: defines bundles, promo windows, and product positioning
  • Operations: owns outlet rules, prep constraints, and fulfillment capacity
  • Finance: sets guardrails (margin floors, discount limits, approval thresholds)
  • IT: monitors kiosk uptime, POS integration health, and incident response

With Craveva AI Enterprise, the logic is centralized so a franchise group can run one playbook across outlets with controlled local overrides.

Rollout plan that avoids guesswork

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  1. Pilot 1–2 outlets first (2 weeks)
  2. Run three simple experiments: best attachments, best phrasing, best timing
  3. Roll out by cluster once KPIs stabilize (regional constraints often differ)
  4. Review weekly: conversion and margin impact, not vanity "suggestions shown"

What CXOs should measure

  • Attach rate by item category (sides, drinks, desserts)
  • AOV uplift and incremental gross profit (not just revenue)
  • Time-to-checkout and peak throughput (orders per minute)
  • Refund/remake rate (recommendations should not increase errors)
  • Cost to serve: labor minutes saved vs. any incremental complexity

Next steps

  • Architecture: /solutions/architecture
  • Data layer: /solutions/data-layer
  • Deployment: /solutions/deployment
  • Contact: /contact

Craveva AI Enterprise helps multi-outlet brands scale revenue per transaction with controlled, data-governed upselling that does not slow down service.

KPIs to track

MetricArea
Channel conversion (WhatsApp/web/kiosk) and drop-off pointsSales
Menu engineering: low-margin items share and driftOther
Menu availability accuracy across POS + delivery channelsOther
Receiving errors and reconciliation timeOther
Delivery cancellations, prep-time variance, and late-order rateOther
Agent adoption rate (active users) and resolution timeOther

Connect Now: AI Enterprise Consultants

Ready to transform your F&B operations with Craveva AI Enterprise? Book a meeting with our AI Enterprise Consultants to discuss how we can help your business.

Technical Glossary

Artificial Intelligence (AI)

AI/ML

The simulation of human intelligence in machines that are programmed to think and learn like humans. In F&B, AI is used to automate decisions, analyze data, and provide insights.

Machine Learning (ML)

AI/ML

A subset of AI that enables systems to learn and improve from experience without being explicitly programmed. ML algorithms identify patterns in data to make predictions or decisions.

Large Language Model (LLM)

AI/ML

Advanced AI models trained on vast amounts of text data that can understand and generate human-like text. Used in chatbots, content generation, and natural language processing.

RAG (Retrieval-Augmented Generation)

AI/ML

An AI technique that combines information retrieval with text generation. RAG systems retrieve relevant information from a knowledge base and use it to generate accurate, context-aware responses.

AI Agents

AI/ML

Autonomous software programs that use AI to perform tasks, make decisions, and interact with systems. In F&B, agents can automate customer service, procurement, inventory management, and more.

Embeddings

AI/ML

Numerical representations of text, images, or other data that capture semantic meaning. Embeddings enable AI systems to understand relationships and similarities between different pieces of information.

Vector Database

AI/ML

A specialized database designed to store and query high-dimensional vectors (embeddings). Used in RAG systems to quickly find relevant information based on semantic similarity.

Neural Networks

AI/ML

Computing systems inspired by biological neural networks. They consist of interconnected nodes (neurons) that process information and learn patterns from data.

Natural Language Processing (NLP)

AI/ML

A branch of AI that enables computers to understand, interpret, and generate human language. Used in chatbots, sentiment analysis, and text analysis.

Deep Learning

AI/ML

A subset of machine learning that uses neural networks with multiple layers to learn complex patterns in data. Particularly effective for image recognition, speech recognition, and natural language processing.

Data Centralization

Data

The process of consolidating data from multiple sources (POS systems, databases, files, APIs) into a single unified platform. Essential for AI systems to work effectively with all business data.

Data Integration

Data

The process of combining data from different sources into a unified view. Enables businesses to access and analyze all their data in one place.

ETL (Extract, Transform, Load)

Data

A data integration process that extracts data from source systems, transforms it to fit business needs, and loads it into a target database or data warehouse.

Data Warehouse

Data

A centralized repository that stores integrated data from multiple sources. Designed for querying and analysis rather than transaction processing.

API (Application Programming Interface)

Data

A set of protocols and tools that allows different software applications to communicate and share data. APIs enable integration between systems.

Database

Data

An organized collection of data stored and accessed electronically. Common types include relational databases (PostgreSQL, MySQL) and NoSQL databases (MongoDB).

Data Pipeline

Data

A series of data processing steps that move data from source systems to destination systems, often with transformations along the way.

Data Governance

Data

The overall management of data availability, usability, integrity, and security. Ensures data quality and compliance with regulations.

Data Quality

Data

The measure of data's fitness for its intended use. High-quality data is accurate, complete, consistent, and timely.

Business Intelligence (BI)

Data

Technologies and strategies used to analyze business data and provide actionable insights. Includes reporting, analytics, and data visualization.

POS (Point of Sale)

Operations

The system where customers complete transactions. POS systems record sales, manage inventory, process payments, and generate receipts. Examples include Qashier, Eats365, and Dinlr.

Inventory Management

Operations

The process of ordering, storing, and using inventory. Effective inventory management ensures the right products are available at the right time while minimizing waste and costs.

Supply Chain

Operations

The network of organizations, people, activities, and resources involved in moving products from suppliers to customers. Includes procurement, logistics, and distribution.

Procurement

Operations

The process of finding, acquiring, and managing goods and services needed for business operations. Includes supplier selection, negotiation, and purchase order management.

Food Cost

F&B

The cost of ingredients used to prepare menu items. Food cost percentage is calculated as (cost of ingredients / menu price) × 100. A key metric for profitability.

Labor Cost

F&B

The total cost of employee wages, benefits, and related expenses. Labor cost percentage is calculated as (total labor cost / total revenue) × 100.

Menu Engineering

F&B

The analysis of menu items based on profitability and popularity. Helps restaurants optimize menu offerings to maximize revenue and profit.

Average Order Value (AOV)

F&B

The average amount spent per customer transaction. Calculated as total revenue divided by number of orders. Increasing AOV is a key revenue growth strategy.

Customer Lifetime Value (CLV)

F&B

The total revenue a business can expect from a single customer over their entire relationship. Helps prioritize customer retention and acquisition strategies.

Waste Reduction

Operations

Strategies and processes to minimize food waste, inventory spoilage, and operational inefficiencies. Reduces costs and improves sustainability.

Cloud Computing

Technology

The delivery of computing services (servers, storage, databases, software) over the internet. Provides scalability, flexibility, and cost efficiency.

SaaS (Software as a Service)

Technology

A software delivery model where applications are hosted by a vendor and made available to customers over the internet. Users access software through web browsers.

API Integration

Technology

The process of connecting different software systems using APIs. Enables data sharing and workflow automation between applications.

Microservices

Technology

An architectural approach where applications are built as a collection of small, independent services. Each service handles a specific business function.

Automation

Technology

The use of technology to perform tasks with minimal human intervention. In F&B, automation can handle repetitive tasks like order processing, inventory updates, and reporting.

Workflow

Technology

A series of steps or tasks that need to be completed to achieve a business goal. Workflow automation uses technology to streamline and automate these processes.

Real-time Processing

Technology

The processing of data immediately as it is received, without delay. Enables instant insights and responses, critical for operational decision-making.

Scalability

Technology

The ability of a system to handle growing amounts of work or to be easily expanded. Critical for businesses that plan to grow or handle variable workloads.

Dashboard

Technology

A visual display of key business metrics and KPIs. Provides at-a-glance views of performance and helps identify trends and issues quickly.

KPI (Key Performance Indicator)

Technology

Measurable values that demonstrate how effectively a business is achieving key objectives. Common F&B KPIs include food cost percentage, labor cost percentage, and AOV.

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