The client, an e-commerce company in the furniture sector, commissioned us to develop a comprehensive plan to implement an advanced recommendation engine. 

The project included analyzing user data, integrating learning algorithms, optimizing the existing IT infrastructure and conducting training for users.

During the first phase, we conducted a detailed analysis of the company's site visitor data. This data included information about users' historical purchasing behavior and user journeys. Our analysis showed that the accuracy and relevance of the recommendation engine could be significantly improved. Previously, this data was collected in an unstructured way, which made it difficult to generate personalized recommendations. We cleansed the data and merged purchases and user journeys of the same users. This allowed us to effectively use the historical data to speed up the delivery of the recommendation engine.

While the rapid integration of user data enables fast deployment of recommendation systems, the structuring of this data offers several advantages:

• Improved filtering options in the recommendation engine.
• More detailed and precise data analysis, such as user preferences by product category, price range, brand, etc.

We proposed a phased approach using Google Cloud infrastructure and the Apriori algorithm to improve the Recommendation Engine:

1. Data collection and pre-processing::
   • Conducting user interviews to determine existing processes and identify weaknesses.
   • Analyzing the current data model and comparing it to industry standards to identify gaps.
   • Collection and cleansing of historical user data to create a robust data set for training the recommendation engine.
2. Implementation of the Apriori algorithm:
   • Development of a proof of concept (PoC) to evaluate the feasibility of using the Apriori algorithm to generate association rules based on user buying behavior.
   • Implementation of the Apriori algorithm on Google Cloud to identify items frequently purchased together and generate rules for the recommendation engine.
   • Evaluation of the quality and relevance of the generated recommendations and fine-tuning of the algorithm parameters.
3. Integration with Google Cloud:
   • Migration of cleansed and structured data to Google Cloud's BigQuery for efficient storage and retrieval.
   • Leverage Google Cloud's infrastructure to provide a scalable API endpoint for the Apriori algorithm.
   • Implementation of a real-time data pipeline to continuously update the recommendation engine with new user data.
4. User training and deployment:
   • Providing training to the client's IT and marketing teams on how to manage and optimize the new recommendation engine.
   • Deploying the enhanced recommendation engine on the e-commerce platform and monitoring its performance.

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1. Improved data quality and consistency:
   • Structured and cleansed user data, resulting in more accurate and relevant recommendations.
2. Improved recommendation engine performance:
   • The new recommendation engine, powered by the Apriori algorithm and Google Cloud infrastructure, delivered more personalized and relevant product recommendations.
   • The platform saw an 18% increase in average order value and a 15% increase in user interaction metrics such as click-through rate and session duration.
3. Scalability and efficiency:
   • The automated data pipeline and real-time updates allowed the recommendation engine to scale efficiently with the growing user base.
   • The new system processed and integrated user data 50% faster than the previous manual processes.
4. User satisfaction and revenue growth:
   • Improved user experience through personalized recommendations led to higher customer satisfaction and loyalty. The size of shopping baskets increased by 16% on average.
   • The client reported a 25% increase in sales within six months of implementing the new recommendation engine.

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