System and Method for customer identification using a real time unique string

The method is to create a unique real time method to deliver customer information to various customer touch-points by way of a real-time unique value.  This value is re-calculated after every action a customer makes. The value is calculated using a scoring system built into our analytical Engine.  All interactions made by the customer is sent to this Analytical Engine using agents located at the touch-points.  The new calculated value is updated back at all touch –points.

The method helps the business in optimizing customer value by use of real-time information. The method also helps in automating processes, which helps in saving time and resources, considerably.

Unique customer identifier is a unique alphanumerical string.  This String contains real-time calculated insights of a single customer, derived from scoring models.  Scores are assigned to customer behavior, interactions and transactions at all Internet enabled customer touch-points.  With this system & Method the customer Net Present Value is always up-to-date.

Drawbacks in existing state-of-art

Currently, customer data is available across various sources and enterprises have to integrate these data sources to create unified profiles.  Unification of Customer Data is necessary to understand and retain customers and their value in terms of revenue, profits and risks .

Since customer touch-point applications are offered by various vendors, integration of any two data sources is an external activity, thus making real-time performance weaker.  Since the sources are different, difficulty in scoring interactions and behavior. Thus, no accurate calculations of customer value in real-time

And for calculating customer retention, the RFM Model used currently, does not scope in customer sentiment value, which is a main contributor in calculating retention rate


  • Marketers can know customer value in real-time and execute their retention programs in real-time
  • Improved Calculation of CLV, as we have a improved way to calculate customer retention
  • Customer Retention Rate is calculated based on RFM Model + Customer Sentiment across all touch-points, along with Churn trends
  • Marketers can automate processes to optimize customer conversions and eliminating human intervention
  • Customer touch-points can have real-time customer information, thus helping customer executives to perform better with available insights

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