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Method and apparatus for dynamic Big Data enabled insights based personalised IVR:

IP.com Disclosure Number: IPCOM000243546D
Publication Date: 2015-Sep-30

Publishing Venue

The IP.com Prior Art Database

Abstract

The current limitation of normalized static IVR can be overcome by the method and apparatus we propose in this publication. The solution is to include a GUI based apparatus, referred to as Big data enabled insights based personalized IVR Steward, to reside in Call center based applications to perform the following: [1]. Identify the type of user. For existing user prompt options of normal or personalized offering [2]. For personalized insights based offering, overlay the existing IVR framework with personalized framework and allow the user to choose options [3]. For new users enable IVR engine to leverage public/private API-fication to retrieve at least minimum insights [4]. Prompt the user of personalized IVR option, based on derived insights and caller’s location and calling time [5]. Overlay existing IVR framework with thin insights framework and provide options

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Method and apparatus for dynamic Big Data enabled insights based personalised IVR :

Background

The business value of interactive voice response (IVR) systems or voice portals are their ability to service customers without the need for a live person. These provide advantages in terms of speed, cost efficiency, and resource allocation. However, these systems cannot replace the emotional support and friendly demeanor of a live operator.

Traditional interactive voice response systems have the following key limitations:


 Traditional implementations use relatively static models to represent reference data such as user profile information, which typically do not provide a mechanism to evolve based on changing behaviours and situational patterns.


 While these interactions can be made more personal, inaccurate caller/ segment identification or over-familiarity can repel customers.

The capabilities of "Big Data" encompass the handling and processing of both structured and unstructured data, including text and voice.
. The increase in social media participation by both individuals and enterprises has created a significant new source of sentiment data for organizations. Organizations that leveraged data from various internal sources (like call center logs/network data/ customer profiles) can now enrich this data with an individual's entries on social blogs, purchasing preferences, demographic disposition derived from various social interactions. This enriched data provides the organization with more insights into an individual's preferences, wants and needs. This can be used to align and optimize the actions of an organization toward a customer in the form of targeted advertising, mailings or direct calling or improved problem resolution
If a prospective customer calls the IVR to enquire about a company's products, or an existing customer who contacted the call center previously were to use the IVR, he or she may need to explain their preferences, wants, needs, questions or problem.

Current rules and data available in IVR systems may not adequately address the concerns or requests a client brings to a customer interaction. The sales representative or service representative serving the call might better serve the customer if they were able to complement the deeper insights on the customers preferences provided by Big Data engines with real time inputs from the call center conversation.

This aside IVRs is one of the key channels used by Organizations for serving its callers (could be totally new- yet to become prospective customers), Prospective customer, existing or separated customers. IVRs today are personalized primarily at a high level but not blended with Insights based big data to provide uniquely personalized service (could be offers, Information or better service).

Such demerits in the current solution are overcome by the method and apparatus we propose in this invention.

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Known solutions to the above cited problem...