Introduction

In response to the task of developing an Automated Marketing Recommendations System for restaurants, this document outlines a comprehensive approach to conceptualizing, designing, and implementing such a system. The goal is to empower restaurant owners, especially those with limited digital marketing expertise, by providing actionable insights derived from customer data to enhance personalization and increase customer retention.

Questions & Assumption

To ensure clarity and alignment, several critical questions and assumptions must be addressed before proceeding:

Key Questions

Purpose and Alignment with Business Goals:

• What is the primary purpose of the Automated Marketing Recommendations System? Is it a core feature or complementary to existing offerings?
• How does this feature align with our company's strategic goals, such as increasing market share, improving customer lifetime value, or enhancing operational efficiency?

<aside> 💡

Assumption

The primary purpose of the recommendation feature is to assist restaurant owners in making data-driven marketing decisions, thereby improving customer engagement and boosting sales. This feature is envisioned as a complementary enhancement to our existing offerings, aiming to provide added value to our clients.

</aside>

Development Status

<aside> 💡

Assumption

This is a new initiative, the development will commence from the ground up, involving stages from data collection to system deployment.

</aside>

Revenue Generation and Cost Impact:

• How will we monetize this feature? Will it be part of a premium subscription plan, offered as an upsell, or included in the base product?
• What are the direct and indirect costs associated with developing, maintaining, and scaling this system? How will these costs impact pricing models?

<aside> 💡

Assumption

The implementation will incur costs related to data infrastructure, AI model development, ongoing maintenance, and operation cost. Revenue generation is anticipated through subscription models or premium service offerings that provide advanced analytics and personalized marketing tools to restaurant owners.

</aside>

Data Availability and Acquisition:

• Do we have sufficient historical data (e.g., customer demographics, purchase history, app behavior) to train robust machine learning models? If not, what steps will we take to acquire or generate this data?
• Can our white-label system serve as a centralized database for enriching the recommendation models across clients?

<aside> 💡

Assumption

It's assumed that we have access to essential data, including customer demographics, purchase history, and app browsing behavior. If data is insufficient, strategies such as customer surveys, loyalty programs, or third-party data partnerships will be considered to enrich our datasets.

</aside>

Resource Allocation:

• Do we have the necessary resources, including skilled AI/ML engineers, data scientists, operations teams, and tools, to develop and maintain this system?
• What external partnerships or third-party services might be required to supplement our capabilities?

<aside> 💡

Assumption

We have an in-house AI team equipped with the necessary tools and expertise to develop and maintain the recommendation system.

</aside>

System Architecture

• Do we have a centralized database even for the whitelabel apps?
• Will we have an integration with the social media channels such as Meta ads, …etc?

<aside> 💡

Assumption

Our platform operates on a centralized database system, facilitating efficient data collection and model training. our system is designed to seamlessly integrate with various social media channels, including platforms like Meta Ads (formerly Facebook Ads). This integration allows restaurant owners to manage and optimize their social media marketing campaigns directly from our platform, providing a cohesive and efficient marketing strategy.

</aside>