Bridgei2i BRIDGEpersona vs KPMG Data & Analytics comparison

Grow Existing Customers:
Recommendation from BRIDGEpersona is aligned to customer needs and preferences, and finds relevance in their immediate context. Improved relevance of marketing action – product, information or service – increases cross sell conversions many fold thereby boosting customer lifetime value.

Orchestrate personalization across channels and products and services:
Using APIs, insights from BRIDGEpersona get seamlessly embedded into your marketing stack. Contextualized and personalized recommendation on product or service is available at all times on any digital or human assisted channel that the customer chooses to arrive at.

Scale Personalization across the entire customer base:
BRIDGEpersona computes recommendations for the entire customer base. It allows you to accomplish real time contextualization of marketing actions for all concurrent inbound customer visits across all channels.

Improve Marketing ROI:
Campaigns can be designed with customer at the center rather than product centric or channel centric campaigns. Targeting customers with multiple competing product offers is replaced by a single most suited offer that removes the redundancy in marketing spend. This combined with improved cross sell conversions gets more out of your marketing dollars.

Deepen Customer Engagement and lower churn:
Delivering meaningful experiences through personalized marketing action builds customer trust. Customers see value in the relationship with the bank/ FI and start relying on you for their future needs

Terms such as Machine Learning, Analytics and Big Data have become a part of the commonly used language of the Advanced Data Analysis field, or Analytics in short. This field has received attention and acknowledgment in recent years and become recognized as a valuable asset for every organization in any sector.

Analytics have changed the way we approach data in organizations. In the past, organizational information was replicated and transformed to match a format of the organization’s database. In order to access and analyze that information users created queries, however, not all organizational data could be processed that way since the database only worked with structured data.

Nowadays, we know that the numbers and types of data sources available to us are enormous, and we must take the internal and external information into consideration in order to get the full picture.

The Data & Analytics practice provides holistic solutions by collecting ALL available data and directing it towards interesting issues by raising anomalies and trends in the data.