Sap Se

Abhijit Mitra

Prerna Makanawala

Venkitesh Subramanian

Kedar Shiroor

Niyanth Kudumula

Preeta Kulavil

Karan Sood

Praveen Kalla

Jean-Francois Desjeans Gauthier

Madhubala Vanam

Sushant Potdar

Brian Yip

Ke Sun

Ponmurughu S

Sebastine Augustine

Nayaki Nayyar

Peter Pieruschka

Sangita Purkayastha

A sales application is described that includes multiple automated sales tools that can be combined to help improve sales within a sales team. A deal finder sales tool is described to help identify sales opportunities. A deal playbook sales tool is also described to help structure the sales opportunity into a sales play by recommending products to sale in conjunction and also constructing a sales team. An influencer sales tool is also described to help identify business relationships that can be utilized to influence members of the sales team.

Automated software tools for improving sales

In one embodiment, feedback data of a software object is received through a sequence of cascaded GUIs. The cascaded GUIs include an interaction portion to receive the feedback data from users at a plurality of feedback levels. Further, user role weightings of the users, account weightings of enterprises associated with the users and a time weighting corresponding to a life-cycle phase of the software object are retrieved. Furthermore, average rating of the software object corresponding to each feedback level is determined as a function of the user role weightings, the account weightings, the time weighting, the feedback data corresponding to a feedback level and a number of users submitted the feedback data. The determined average ratings and rating distribution corresponding to each feedback level are graphically displayed on the interaction portion associated with a next feedback level.

Assessment of users feedback data to evaluate a software object

Systems and method for identifying individuals who may be potentially influential on a particular identified contact for a particular sales opportunity are disclosed. The individuals identified as potential influencers are then ranked according to a predicted level of influence and displayed in a graphic representation. In one embodiment, the ranked potential influencers are displayed as icons in a spiral formation relative to an icon representing the identified contact. The closer an icon representing a particular potential influencer is to the icon representing the identified contact, the greater the level influence is that that influencer might have on the identified contact.

Predicting levels of influence

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Palo Alto, CA, United States of America

Abhijit Mitra