ML Recommendation engine: 6 weeks implementation

A deep understanding between customer interests and purchasing patterns is a critical component for a business that wants to run intelligent operations.

Making decisions based on insights is a journey. First, you should understand your current and historic data. A typical business collects customer data through various channels. These channels include web-browsing patterns, purchase behaviors, demographics, and other session-based web data. Some of the data originates from core business operations, ERPs, CRMs. However, other data could be pulled and joined from external sources, such as social media platforms, the public domain, and so on. Because the data is disparate, many businesses apply only a portion of available data. Unifying your data into one common data platform can help you understand what happened and why it happened.

R Systems’ ML recommendation engine can help you in the following use-cases:

• Improved customer engagement and satisfaction: By providing personalized recommendations, the ML recommendation engine can help your business to engage with your customers in a more meaningful way and improve the overall customer experience.

• Increased sales and revenue: By providing personalized recommendations, the ML engine can help your business to increase the likelihood that customers will make a purchase. This can lead to increased sales and revenue for the business.

• Better targeting of marketing efforts: By analyzing customer data and behavior, the ML recommendation engine can help your business to better target your marketing efforts. This can help to improve the effectiveness of marketing campaigns and reduce the amount of money spent on ineffective marketing efforts.

• Improved efficiency: By automating the process of making recommendations, the ML recommendation engine can help your business to save time and resources. This can help to improve the overall efficiency of the business and allow employees to focus on other tasks.

R Systems’ ML recommendation engine consulting service has the following workflow and deliverables:

• Discovery (Data Discovery)

• Requirements Gathering, Business Entities identification

• Data Collection & Standardization

• Perform de-duplication of customer data.

• Offers & Recommendations based on ML Algorithms

• Content Management

• Marketing Analytics & Automation

• Reporting/BI

• Testing

• Access, Training

• Managed services

R Systems’ ML recommendation engine will be implemented using modern cloud solutions available in Microsoft Azure and will be designed following cloud adoption framework. An example of such architecture can include the following:

• Data Lake Storage: Central repository for all raw data imported from ERP and e-commerce platform

• Azure Data Factory/Azure Synapse: Will be used to create the ETL and data flow

• Azure Databricks/Apache Spark: Will be used to train, deploy, automate, manage, and track machine learning models for product recommendation

• Dedicated SQL Pool/Serverless SQL Pool: will be used for reporting and query OLAP database.

• Power BI for visualizing data (licenses are not included in the consulting service offering)