Customer Analytics

Customer Analytics helps businesses to define a Customer Lifetime Value (CLV) strategy for both topline and bottom-line growth. This endeavor involves understanding and enhancing the CLV of most profitable customer segments through Customer Analytics and deploying focused Marketing Strategies and Optimizing offer responsiveness.

Advanced Analytics and Customer Analytics models help organizations in CLV (Customer Lifetime Value) based Micro-segmentation of customers by leveraging Machine Learning techniques. They can use a combination of CLV Estimation Modelling and Supervised Machine Learning to segment the customer basis their CLV. This Micro-segmentation, in turn, helps organizations create specific targeted marketing campaigns for selective customer segments.

Customer Analytics helps the retailer decipher the possible causes, such as not offering the right product to the right consumer and unjust treatment of valued customers. The solution approach involves adopting a CLV-based Customer Micro-Segmentation for increased response to marketing programs, leading to a sizeable increase in hot/warm leads and improved profitability.

If organizations track critical data, Customer Analytics helps them to identify the right leads. Customer Acquisition models, powered by Data Science, identify the best potential leads and set up the best strategies to convert these leads into active customers. It is a crucial element in driving growth in any company. A good Customer Acquisition model also helps maximize your RoI by focusing efforts on the prospects most likely to convert. Some of the most sophisticated Analytics models allow you to -

-  Prioritize customer segments according to size and ability to win and identify the core target.

- Identify propositions that resonate with target customers.

-  Increase efficiency of sales and marketing investments, and focus resources on the most qualified leads.

-  Identify preferred communication channels by customer segment, and use them for interactions.

Successful Contact Centers use Advanced Call Center Analytics, Customer Journey Analytics, and Customer Experience Analytics solutions to monitor and review agent performance, not only from a customer lens but also from the perspective of both employees and management. These solutions also offer multiple insights to improve Customer Loyalty and even increase revenue through enhanced customer experiences and meaningful conversations. These agile data-driven solutions lead to superior customer engagement throgh identification of key determinants, in a multi-variate environment, and their effect on customer satisfaction instead of focusing on all factors.

Customer Analytics and Predictive Analytics solutions have the ability to help Banks, FIs, and lenders gain valuable insights into borrowers' profiles and drive informed business decisions. Such Analytics are powered by pairing mortgage loan-level datasets with recommendation engines that span the entire loan life cycle to minimize mortgage fraud, reduce regulatory compliance risk, perform due diligence, detect changing market conditions, and predict foreclosures.