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Here are the key takeaways from this paper:
1. Cluster-wise recommendations are based on peer group analysis.
Our models output recommendations for customers based on the customers’ relationships with their peers and the cluster to which they belong. If a specified customer is from a specific cluster, our model provides recommendations that are most popular within that cluster. In other words, customers from the same group tend to like similar products. Further, for a given customer the model also looks into the behaviors of peer account customers and selects relevant service offers as recommendations for that customer.
2. These recommendations include analytics-based insights.
In contrast to other recommendation models, Dell Technologies models make recommendations for compelling reasons, reducing the risk that customers will consider those recommendations as marketing products and not view them seriously. Here are some examples: