I am the VP of Data and Analytics for Showcase CRM at
Salesforce.com.
My team and I develop data-driven systems that enable us to
assess the state of Salesforce.com deployments, help account teams to
improve enterprise wide adoption, ensure renewals and reduce attrition,
and assist customers to get the best possible value from their investment.
Previously, I was VP of Recommender Technology at
Strands Labs, where I was in charge
of revising the recommendation technology behind the Strands products: the
Strands Recommender,
moneyStrands and
strands.com.
Before Strands, I was Research Director at PricewaterhouseCoopers' Center for
Advanced Research where I led a group of researchers, developers,
usability and subject matter experts to develop advanced tools and
technologies that solve previously unsolvable practice problems and give
PwC a major competitive advantage.
During my time at PwC, my team and I implemented two projects:
Insight and the Connection Machine. Insight is a
Recommender System which determines and predicts issues companies
are facing by mining internal and external data sources and allows
PwC to offer services proactively. The project involves linking
previously decoupled internal data sources to analyze PwC’s prior
relationship with a client, mining publicly available documents to
extract relevant market, industry and company trends, predicting
potential issues and solutions for the client and visualizing the
results. Project Insight leverages techniques from Data Mining,
Social Network Analysis, Case-Based Reasoning, Feature Extraction,
and Data Warehousing to develop an intuitive to use “Sales
Intelligence System.” The application is able to predict services of
interest with up to 80% accuracy and has been deployed to the US
firm at the end of October 2009.
The Connection Machine is an
Expertise Locator that helps PwC partners and staff solve problems
by connecting people. The application allows information seekers to
enter their question in free text, finds knowledgeable colleagues,
forwards the question to them, obtains the answer and sends it back
to the seeker. In the course of this interaction, the application
unobtrusively learns and updates user profiles, and thereby
increases its routing accuracy. The project applies techniques from
Information Retrieval, User Modeling, Recommender Systems and Social
Network Analysis to develop a “Virtual Information Concierge.” The
Connection Machine has been incorporated in the application
portfolio of the PwC Knowledge Services Organization and is being
used by 30,000 employees of the U.S. firm. PwC applied for patents
on the user profiling and information retrieval technology as well
as the application workflow.
Prior to joining PwC, I was the Vice President of Professional
Services at Kaidara. My
professional services team and I delivered Kaidara’s Case-Based
solutions for Knowledge Management, technical self-service and
e-commerce to Fortune 500 clients. We acquired and modeled expert
knowledge and know-how from key personnel and information systems,
customized and integrated applications and provided training as well
as post sales support. Our charter was to develop systems that
facilitate intelligent access to information while adapting
themselves to the level of experience of the user. I set up the
professional services organization of the US Branch of Kaidara.
During the time I was there, we developed and delivered tens of
knowledge management projects as well as pre-sales prototypes to
clients such as Cisco, DaimlerChrysler, General Motors, National
Semiconductor and Rhodia. We supported pre-sales with prototypes and
pilots and provided training as well as post sales support to
clients.
Before joining Kaidara, I was a senior research scientist at
DaimlerChrysler's Research
and Technology Center in Palo Alto, California (Adaptive Systems
Group, Head: Prof. Pat Langley) and in Ulm, Germany (Data Mining Group,
Head: Prof. Gholamrheza Nakhaeizadeh).
While in Palo Alto, I designed and developed intelligent systems to
provide personalized in-car services (user adaptive recommendations)
and to perform proactive diagnosis and maintenance of vehicles. The
Adaptive Place Advisor was the first voice enabled, in-car,
personalized, conversational recommendation system. It allowed
drivers to verbally specify their preferences for a restaurant,
asked questions to narrow down the options, and by leveraging the
experience it had gathered in the course of previous conversations
with the driver, came up with a recommendation. The system would use
each interaction to refine the user model, thereby reducing the
number of necessary questions and improving the recommendation
quality. The COMO project leveraged Neural Networks to predict
the expected cooling water temperature range of trucks several
minutes in advance and has been patented by DaimlerChrysler.
During my time in Ulm, I was the primary subject matter expert and
researcher on Case-Based Reasoning technology within DaimlerBenz AG.
I designed and managed the development of the Case-Based Help-Desk
support tool HOMER (Hotline mit Erfahrung). I
was the primary contact person for the ESPRIT research project
INRECA-II (funded by the European Union). The project produced
guidelines for the development of industrial strength knowledge
management applications using case-based reasoning. The result of
this work has been published in a book (currently in its second
edition).
