Arc Customer360 is a business intelligence tool designed for retail marketers for a customer analytics solution that provides a combined view of customer topography. It gives a detailed analysis of what they buy, when and where, which products they prefer along with the frequency of their purchase and what type of offer will attract them to the same store during their next purchase. The main features of Arc Customer 360 are highlighted as follows: 1) Market Basket Analysis (MBA): This analysis provides access to basket characteristics, popularity of an item, tracking of marketing events and product affinity analysis. MBA is considered an inexpensive technique for identifying cross-selling opportunities. It gives an overview of the customer's profile, who they are and the main purchases they make each time they visit the store. This helps determine which products tend to be purchased together and which products need more marketing promotions or should be put on sale or at a discount. This analysis can be applied in different ways: • Provide an offer on products purchased together. • Control inventory based on product demand. • Place related products together inside the store. Market basket analysis is a great way to understand customers and their behaviors 2) Churn Prediction: Churn rate is the number of customers who have severed their relationship with the business (store) during a period of given time. Tracking churn rate will be helpful for the business to avoid losing the relationship with an existing customer by taking appropriate measures. Churn Prediction focuses on predicting the likelihood that a customer will stop purchasing from the store and...... middle of paper...... 360, as a customer analytics solution , can bring benefits such as Understanding Customers • Customer Profiling • Customer Trend and Scope• Segment Migration and Profiling Marketing Effectiveness• ROI Measurement Performance Driver Analysis• Sales Trend Campaigns• Response of the campaign, its frequency and reach• ROI analysis Promotions• Channel effectiveness • Experimental analysis design • Historical promotion analysis  Purchasing behavior • Purchasing trends by individual customer or others segments • Sales channel analysis • Store migration analysis  Loyalty • Loyalty program performance • Points flow analysis • Member activity analysis • Customer lifecycle analysis Product mix • Products having a maximum affinity and concentration.• Analysis of brand migration• Analysis of seasonality.