Mehmet H. Goker

Mehmet H. Göker

mehmet at

Academic Work



I am the VP of Data and Analytics for Showcase CRM at My team and I develop data-driven systems that enable us to assess the state of 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


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).


Home | Expertise | Education | Patents | Academic Work | Resume

This site was last touched 01/31/2012 and is in dire need of a re-design