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System & Method to recommend a 'best-sellers' team

IP.com Disclosure Number: IPCOM000243842D
Publication Date: 2015-Oct-20
Document File: 4 page(s) / 144K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a system and method to recommend a best-sellers team to meet customer executives and/or pursue a given opportunity in a way that maximizes the collective potential to win the deal. The technique creates comprehensive profiles of executives and uses them to find and rank salespeople.

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System & Method to recommend a 'best-sellers' team

One of the key reasons attributed to winning or losing a deal is linked directly to the relationships with key decision makers in the client's organization. Often the question is: How to identify a team of sellers best suited to improve the odds of winning a given opportunity/deal? Additionally, how to make the team better informed of the client executives' profile? Usually, sales teams are selected manually for a given opportunity/deal, leading to a sub-optimal selection that is not positioned for winning the deal. Often times, they do not have relationship with client decision makers and/or have limited insight of their background/personality/outlook/behavior before the meeting (ex: aggressive, pro- or against some technology, professional history like education, work experience, skills & honors etc.).

At the core of our system is an Analytics & Insights system, which when given the name of a decision maker and client company, it can (in real-time) query open and/or paid data sources to generate a profile of the person. The profile of a person can include images, videos, personality, education, affiliations, skills, professional work experience, honors & awards, personal life & history, salary, contact info etc. The system then finds the decision maker's and his/her company's connections with the seller organization in terms of people, products, partners etc. It then ranks by weighing various core attributes of sellers like size of past deals etc. and attributes in common with decision makers like geographical proximity/cultural familiarity, past relationships with the same client or other peers in the industry, past interactions with one or more decision makers, commonality with their profiles, depth and breadth of seller's domain/ industry expertise etc. Finally, it then suggests a 'best sellers team' that is well-positioned to win the deal.

Fig. 1 shows a system diagram where profiles are created from open and/or paid da...