Client

Cluster Call Planning (CCP)

Period

2002 – 2005

Client Bio

In 2002 Pfizer merged with Pharmacia to become the world’s largest pharmaceutical company.

Personal History

This story reads like a tale of David and Goliath. Thirst was asked to quote on this project but we were up against several large well-known companies but we knew why were asked to bid and we were glad we did. Because of internal restructuring, Pfizer needed help building this sales application from the ground up, they did not want to use any of their previous tools and thought that the right approach would be a fresh one. Thirst worked with various third-parties, large and small, to create this application that was basically a moving target because of the need for new functionality that crept into the scope on a weekly basis. This was an open source project based on the LAMP standard applications that worked within the confines of proprietary software. As a special point of interest the sales tool was an ‘AJAX’ application before AJAX was even defined or coined.

Objective

In response to the merger, Pfizer developed the CCP methodology to bring together its sales analytics and valuable first hand sales force knowledge. Pfizer turned to Thirst to transform this abstract methodology into a powerful sales targeting application for use by its 45,000 strong US sales force.

Noteworthy

An over 11% upsurge in sales directly resulted from this new application. CCP evolved from a rapid prototyping development process conducted over a 9 month period with four representative sales force teams. CCP was built using an open source toolset including Linux, Apache, PostgreSQL, PHP, AJAX and DHTML. CCP included a local data store that was automatically synchronized with targeting data from fellow teammates and Pfizer analytics. In 2005, Thirst’s CCP application was merged into Pfizer’s S3 flagship sales platform.

Vendor

Thirst

Client

EngageMe

Period

2006 – Present

Client Bio

Startup internet venture.

Personal History

This project was slowly evolved during countless brainstorming sessions on how to fix the inaccurate and problematic tracking of user surfing patterns that companies use now-a-days to dictate a user’s online interests. Most of you can probably recognize the issues everyone faces with examples of your own. For myself it was the fact that many companies were tracking my movements across the internet and defining me incorrectly on very narrow terms, and one day it culminated frustratingly on Amazon where I was served up an offer for yet another children’s book just because I bought one for my niece over one year earlier. For my business partner it was the fact that Google kept returning billions of results on such a wide array of topics because they feel that giving you everything is what you deserve but not necessarily what you really wanted.

Keyword advertising had just taken off and Google was not yet the beast it is today. Other search engines were trying to radically change the direction of what search meant and delivered but unfortunately the simple one page of ten results that Google put out in a couple of micro seconds seemed to gain popularity too fast for other companies to challenge. I would bet that now-a-days, since our connections are generally much faster, that what has seemingly become Google’s trademark results would be drastically better.

So how do you make them better? I would argue the answer is to know and understand the user’s interests better and to deliver information that is skewed towards those interests. But to do this you would have to ask the user about themselves and behave honestly and track them across the web. You would also have to make sure that you did not take in any ‘personally identifiable information’ as that isn’t a trustworthy action.

This is what EngageMe intends to be: open, honest and accurate.

Objective

To create a proof of concept for an interest profile toolset using open source tools (LAMP) based on an AJAX model. EngageMe enables a user-centric profile ownership model that leverages non-personally identifiable information (PII) for security reasons and fast adoption and interoperability with emerging open profile standards.

Noteworthy

An interest taxonomy was developed with over 700k+ interests. A bookmark analyzer allows users to create an initial profile that can then be shared across social networks. Advertisers using EngageMe can target users based on their interests rather than tracking.

Vendor

Thirst for EngageMe