Revolutionzing
Pricing Strategies
for Enterprises
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Category Generative AI
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Client FMCG
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Start Date February 2024
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Handover June 2024
What's the
Challenge
A top-tier marketing company in the United Kingdom was on a mission to transform the way they estimated the cost of production for various services, campaigns, and assets. Their goal was to develop a SaaS platform that could not only provide accurate price recommendations but also leverage conversational AI to enhance user engagement. The key challenges they faced included:
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Data Collection: Collecting and tagging existing datasets with the necessary key identifiers for the price estimation algorithm was complex and time-consuming.
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Accuracy: Ensuring consistent accuracy across various domains in the calculation engine was critical, especially when dealing with dynamic multipliers.
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Data Cleaning: Managing large datasets posed a risk of redundancy and bias, which could impact the calculation engine’s reliability.
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Algorithm Development: The complexity of designing algorithms that could adapt to various factors such as geography, industry, and campaign focus required meticulous attention to detail.
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AI Integration: Fine-tuning the Llama3 LLM for context-based learning and ensuring it could effectively understand and respond to user queries was a significant challenge.
Introduction
In the ever-evolving landscape of marketing, accurate price estimation is crucial for staying competitive. A leading marketing firm in the UK recognized the need to innovate and improve its cost estimation processes. To address this, they partnered with GrowthGear to develop Alex MVP—a revolutionary SaaS platform powered by advanced algorithms and conversational AI. This platform aimed to streamline price estimation, enhance user engagement, and ultimately, improve operational efficiency.
Solution Overview & Methodology
To meet the client’s objectives, GrowthGear designed and developed Alex MVP, a cutting-edge SaaS platform. The solution was built with a focus on scalability, efficiency, and precision, leveraging state-of-the-art technology and AI-driven processes.
The platform’s architecture was designed to be highly scalable and efficient, utilizing microservices, distributed processing, advanced load balancing, and a robust logging system. Each microservice was tailored to handle specific tasks, such as calculation algorithms, rate card bifurcations, and conversational AI.
Alex, the platform’s conversational AI, was integrated to gather user requirements through natural language interactions. By understanding and translating user inputs into relevant data points, Alex was able to provide accurate and interactive price estimations, enhancing user experience.
The platform included comprehensive user management features, allowing administrators to manage accounts, track platform usage, and provide detailed analytics and reporting. Users could easily create and manage marketing campaigns, save drafts, and adjust account settings.
See the Impact:
Results That Speak
The project successfully achieved its primary objective of developing a price recommendation engine as a SaaS platform. The solution integrated sophisticated algorithms and a conversational AI agent, providing users with accurate and interactive price estimations.
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Data Collection and Cleaning: Despite challenges, the data collection process was successful, with all datasets meticulously tagged and cleaned to ensure accuracy. This thorough approach prevented redundancy and bias, ensuring reliable performance of the calculation engine.
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Algorithm Development and Accuracy: The algorithms were developed with a focus on precision, resulting in consistent accuracy across various domains. The use of the BeRT model further enhanced the engine’s ability to adapt to different variables, making it a significant success.
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Database Design: The database was designed to be secure, efficient, and cost-effective, serving as the backbone of the platform. The careful planning involved ensured that it met all application requirements, contributing to the platform’s robustness.
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AI Integration: The successful integration of the Llama3 LLM allowed the AI to understand and respond to user queries effectively. This context-aware AI was crucial for the platform’s conversational capabilities, enabling natural interactions with users.
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Architectural Efficiency: The platform’s architecture proved to be highly scalable and efficient, supporting the various microservices responsible for different tasks. This modular design ensured that the system was maintainable and capable of handling high loads.
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User Engagement: The implementation of user-friendly features such as campaign management, profile creation, and secure login contributed to a comprehensive and engaging user experience. Users could efficiently create and manage campaigns, making the platform highly usable.