Summary
Dun & Bradstreet is a multinational data, insights, and marketing consulting firm that does business with many companies in the Fortune 500. Its primary focus is providing deep insights and data about businesses.
Dun & Bradstreet’s former solution was acceptable in the proof-of-concept phase, but there were issues related to the volume of data. The company was also missing some of the empirical data related to home-based businesses.
Korem compared data and software vendors and recommended geospatial data and Spectrum technology from Precisely. Because of the completeness and currency of address data, Dun & Bradstreet was able to geocode more accurately and have a 93% match rate of its existing internal database.
The client
Dun & Bradstreet is a multinational data, insights, and marketing consulting firm that does business with many Fortune 500 companies. It focuses primarily on branding and responding to standard issues related to customer satisfaction by understanding people and inspiring growth. The Syndicated Technologies division of Dun & Bradstreet works almost exclusively within the telecommunications and technology industries.
The Syndicated team creates predictive scores and modeling to determine the likelihood of customers purchasing telecommunication and IT products, which allows companies to perform market sizing and prospect prioritization when marketing their products to businesses.
Since last year, the company has welcomed many more datasets, such as cell phone data and geographic polygons, and has started to integrate customer data into its products. The goal is to be more empirical with its end clients, especially considering that many of them have data science teams and that predictive modeling has gotten increasingly ubiquitous in the industry.
The Challenge
Dun & Bradstreet wanted to improve its empirical datasets to predict customer behavior, know their physical location, and have more insights surrounding that location. The company had been working with cell phone data for a few months and was developing a proof-of-concept (POC), which involved going to county GIS departments and downloading the building polygons for each city and county that were available. This was an extremely cumbersome process as not every county published its data, and the accuracy of the data was suspect.
In addition, while its previous solution was acceptable in the POC phase, there were data volume issues from cell phones. Indeed, the data was being collected faster than could be handled by its existing IT infrastructure. Dun & Bradstreet’s workflow needed to process the 30 billion cell phone records that it received each month. It took approximately 157 hours, running at full capacity, to process data for a single month from Cook County, Illinois, which itself includes many of Chicago’s suburbs.
“I did not have the time to go to 3,000 county websites and pull down 3,000 county websites’ worth of GIS data, and then spin that into something.”
Ben Edelman, Principal Consultant at Dun & Bradstreet
Dun & Bradstreet decided to utilize a third-party firm to design some of the advanced algorithms and offload the data processing to a cloud-computing services company. It considered doing business with a data marketplace, but it would have been expensive to run the algorithms. The firm concluded that leveraging the cloud was the best solution, but it did not have time to compare data from the numerous vendors in the market.
The Solution
Dun & Bradstreet was introduced to Korem after attending its webinar on rural broadband deployment. Korem piqued Dun & Bradstreet’s curiosity by presenting information related to data from its partner network that had access to most building footprints in the United States. The company needed a big-picture view of the data marketplace in order to make a thoughtful decision and to attract key clients. It wanted to conduct data science internally and exclude predictive datasets.
Thanks to its extensive portfolio of partners, Korem compared and recommended high-quality data vendors, thus saving Dun & Bradstreet considerable time. The result: Korem provided Dun & Bradstreet with 144 million polygons, updated on a quarterly basis, and various sets of data from the U.S., including an address fabric and building data, as well as school, college, and hospital data. It also provided shopping center boundaries, master location and street data, and property attributes.
Finally, to prepare all the data in a suitable and workable format, Korem offered a Data as a Service (DaaS) solution as well as advice on geospatial data integration and business logic.
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Key results
93% match rate of the database
Improved geocoding accuracy
Data quality checks
The results
Dun & Bradstreet can now blend its own empirical data with building footprint polygons and other ancillary data provided by Korem to determine who goes into which building, when, and why. It also helps to answer the questions of where the network of telecom companies and of their competitors are, and how that has evolved over a certain period of time. Indeed, Dun & Bradstreet can take geospatial data points and do a spatial join to find out which Internet provider serves each physical structure at a particular date and time. This is a “game changer” for the telecommunications industry.
Korem has enabled Dun & Bradstreet to identify where certain companies are physically located, which is a huge asset for its end clients. They are now able to locate the best places to build fiber-optic lines, for example. With this competitive intel, they can know when to be on the defensive or on the offensive to maintain their share of the market.
In addition, Dun & Bradstreet has improved its existing product, the “Enhanced Building File” through the use of the geocoding solution provided by Korem. The firm has since been able to match 93% of its internal business records with an Enhanced Building File (EBF) record. Before doing business with Korem, the company was only matching 40% of its data files.
Moreover, Dun & Bradstreet now benefits from data quality checks, which have been very helpful for its teams in reaching new clients and creating new growth opportunities for the company. As a result, the company is getting more traction with clients than it has ever had before. Originally, most of Dun & Bradstreet’s clients were in the telecommunications industry. With the improvement in the quality of the building data, it is now possible to attract a new audience for services such as robotics, HVAC, solar, and equipment companies.
With its predictive scores and Korem’s data, Dun & Bradstreet has also begun offering DaaS products, such as hosted analytics. The company is growing as a marketplace and is offering various data delivery mechanisms, including APIs and web applications.
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