The Product Recommendation Framework provides a blueprint to implement personalized product recommendations that deliver measurable impact on revenue generation. The recommender engine leverages Azure cloud technologies and sophisticated reinforcement algorithms to intelligently predict which products or content should be suggested to customers.

The unique coding framework on which the recommender engine is based, allows companies to leverage their customer and sales data at scale to generate customized recommendations. Studies show that 91% of consumers are more likely to shop with brands that remember their preferences and provide relevant recommendations.

Attainable KPI’s

This Recommender engine plays a pivotal role in driving key performance indicators (KPIs) across various dimensions of business success.

Increasing Sales and Conversion Rates
Enhancing Customer Engagement and Loyalty
Improving overall customer Sentiment

Solving Critical Modelling Challenges

Addressing issues such as the cold start problem, data sparsity and scalability in this recommender engine is crucial for maximizing the effectiveness and impact of personalized recommendations.

The Cold Start Problem/ Data Sparsity: Recommenders typically struggle when dealing with new users or items that lack sufficient historical data. The cold-start problem occurs when there isn’t enough information to make accurate recommendations for these entities.
Scalability: As the digital landscape continues to expand and the volume of data generated by consumers escalates exponentially, the demand for efficient recommender systems capable of handling large datasets becomes increasingly imperative.

The Solution Approach

The recommendation engine uses diverse data sources, including sales data, browsing behavior, demographics, and temporal factors like seasonality. Its main aim is to deliver personalized recommendations to customers by considering various contextual factors, ensuring that recommendations are relevant and effective. As part of the implementation process, we utilize an A/B testing or Champion-Challenger framework, augmented by the inclusion of potential hold-out groups. By leveraging these testing frameworks, we ensure the effective monitoring and evaluation of the recommendation engine's performance.

Product Features

The recommendation engine offers a comprehensive recommender framework aimed at enhancing customer engagement and boosting overall company revenue. Notable features encompass:

Customer segmentation and analytics for targeted marketing strategies.
Tailored product recommendations specific to individual segments or users.
Monitoring of recommendation engine KPIs and quantification of value generated.
Flexibility through Rest API or Batch serving capability, facilitating seamless integration with business applications.

Choose from a Core or Enhanced Implementation

The Core plan includes the ingestion, processing and parsing of customer data. Historical analysis and trends as well as customer segmentation.

The Enhanced plan includes everything in the Core plan PLUS the development of the Recommender Engine.

Both plans include model monitoring and maintenance services, documentation as well as frequent updates/ ecosystem improvements.

TrueNorth Group - Prosum Azure Recommendation Engine

by TrueNorth Investment Holdings (Pty) Ltd