TrueNorth Group - Prosum Azure Recommendation Engine

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.