Using Data to Increase Business Intelligence and Revenue


Online businesses generate overwhelming volumes of data. If correctly harnessed, this data could be used to better understand and target customers based on past behaviors. For instance, by presenting more relevant online ads to customers, additional revenue from pay per click and keyword searches could be achieved. With this in mind, an international technology company approached Alacer to design a solution that would gather and distribute intelligent data to its online business groups (advertising, publishing, finance, etc.). The goals were to increase customer satisfaction and marketing effectiveness, and to increase the company’s ROI with advertisers and business partners.


Alacer tackled four challenges, the most critical one being the creation of a highly scalable system that would gather large amounts of data from the corporate network and external sources. The second was to provide timely and reliable insights by lowering the latency for the massive amounts of data that needed to go through phases such as extraction/transformation/loading/presentation. Thirdly, Alacer experts needed to present the data in such a way that it was personalized by role of the business user and provide a design flexible enough to support evolving business models and needs. The last challenge was to create a consistent nomenclature for data validation.


Alacer designed and implemented a comprehensive business and customer intelligence platform that catered to multiple business groups and their users, benefiting business and financial analysts to marketing and data quality managers. Data miners could now reap the benefits of data at the raw, granular level while business analysts and managers derived benefits from aggregated data and drill down capabilities. Managers and executives were served with the high level insights and user interfaces that provided rich visualizations. In short, the new platform solved very specific critical business questions by eliminating the lack of alignment between various groups, better management of large amounts of data and improvement in data quality.