Institutional Markets: Eliminating the Data Headache of an Untapped Goldmine

Hospitals, libraries, and schools…what do all these entities have in common?

Besides providing important services for our nation, these non-commercial organizations represent approximately 1/3 of the U.S. economy, and nearly $4 trillion of the GDP, according to MCH Strategic Data. This is an important fact for business to business marketers, who may want to consider the untapped potential of this huge market. In fact, these institutions actually have more buying power than most commercial businesses due to size and scope, and have been growing faster than many businesses for the last 50 years.

From a data quality perspective, this is critical information for marketers who want to work with this large segment. Unfortunately, many databases don’t treat these institutions as the large potential revenue generators that they are due to the quality of the information provided, often leading to very poor, inaccurate data!

While these institutions can be a great source of business for an organization, treating these non-commercial entities like businesses in databases creates huge problems with data quality for several reasons:

  • The SIC system used to classify businesses is out of date and doesn’t work appropriately for institutions
  • Many institutions share the same physical addresses and may have similar names
  • Many typical business attributes do not work for institutions

From irrelevant records and duplicates to typographical and spelling errors, having poor,inaccurate data on this large group of prospects can be very unsettling from a data quality perspective. Institutions represent a large group of potential revenue, and it is important to have this data cleaned and appropriately segmented for use.

Using the appropriate attributes can help clean up some of the data. For example,using “number of employees” as a business attribute may be misleading for an institution such as a church, where a majority of the employees are actually volunteers.

Another issue arises with name similarity. Many institutions have similar names due to the fact they are publicly funded and may use their city as part of the name. This can be a challenge indata matching.

Data Ladder can help your organization sort through the data. From data cleansing to data matching services, we work with all types of data and can provide the right services make your databases work for you. Contact us for a consultation.