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Apparatus and Method for Improving Customer Satisfaction and Sales

IP.com Disclosure Number: IPCOM000245018D
Publication Date: 2016-Feb-06
Document File: 4 page(s) / 78K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a system to enhance existing technologies in order to provide a sales force with the most complete and up-to-date information about a customer when the customer enters a retail establishment.

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Apparatus and Method for Improving Customer Satisfaction and Sales

The current retail environment is highly competitive. Retailers are constantly searching for new methods and technologies to build customer rapport, increase selling value, and increase add-on sales. Each of these approaches increases revenue for the retailer.

Current technologies for this purpose include facial recognition, primarily for security purposes, and the use of data to improve shopping experiences.

The novel contribution is a system to enhance existing technologies in order to provide a sales force with the most complete and up-to-date information about a customer when the customer enters a retail establishment.

The novel system uses the following known technologies: cogitative computing, analytics, real time natural language processing (NLP), video processing, big data analytics, and push data communication services. In addition, it integrates the existing technologies for facial recognition. These provide a sales consultant with real time customer data that the sales person can use to provide personalized customer service. For example, with repeat customers, this personalized service can begin by greeting the customer, and each of the people accompanying the customer, by name, and then continue with the sales person having the right information to be able to immediately establish a rapport with a customer (which is a proven method of enhancing customer satisfaction and sales).

The core novel features are the use of real-time natural language processing to analyze the conversations between the sales person and the customer, followed by the combination of the NLP results with any available structured and/or unstructured data to generate sales information. This sales information is then sent to the sales person in real-time. Information sent to the sales person can include (but is not limited to):

• The customer's current intended purchase(s)

• The customer's future (near or later) intended purchase(s)

• The customer's likes, dislikes, interests, etc. (e.g., hobbies, designers, collections, etc.)

As illustrated in the process flow figure below, all conversations between the sales staff and the customer are processed in real-time using NLP and cogitative computing. This means any new insights or new sales information that is discovered during the course of a conversation is sent to the sale person real time.

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Figure: Process flow

Figure 1 shows two possible initial paths: one for a group of customers and one for a single customer. This is necessary because when a group arrives it is not readily apparent which person(s) is the actual customer and which people are simply accompanying the customer.

From the left-side flow:
102: In this state, multiple people enter an establishment.

104: The facial recognition process is performed to determine the names of everyone in the group who enter...