Duplicate data is a big concern for most organizations. Everyone has duplicate or contradictory data in their databases stemming from unconnected departments, mergers, and acquisitions. As companies access outside databases, the problems worsen. Most organizations would benefit from a strengthened data matching and consolidation strategy that improves data quality.
When companies think about data matching, they often focus on ways to connect two separate data sources, such as matching customer profile information with transactional details. Organizations might line up data such as this by comparing something like account numbers that appear in both files. In an application like monthly account statements, matching details with master records is essential.
But software that handles matching can do much more.
Matching logic allows companies to eliminate duplicates. This lowers costs for outbound communications like direct mail marketing or shareholder communications by saving on paper, production, and postage. Duplicate removal saves time whenever files are searched, transmitted, or processed. Clean corporate databases also help companies make better informed judgements about capital investments, promotional campaigns, store locations, and many other decisions.
Better Campaign ROI
Marketing campaign performance improves when duplicates are dropped or merged. Data merged from multiple sources provides the “single customer view” companies crave. With a merged view of all the interactions and relationships each customer has with a company, the organization can make their communications more relevant and interesting. Consolidated data allows companies to present each customer with messages more likely to elicit a positive response.
Customer Experience Improvements
A single customer view made possible by matching data from unconnected databases allows companies to customize messages and communicate with customers on a personal level. When companies recognize individual customer relationships, the customer experience improves. One of the most popular uses for data matching software is merging information to achieve single customer views.
Unveil New Opportunities
Multi-buyer identification allows companies to reach new markets. A company preparing to introduce a new running shoe, for example, might examine subscriber lists from several fitness or running magazines. Subscribers appearing on multiple lists are the best prospects for a new product aimed at serious runners. These consumers are more likely to buy high-priced equipment than individuals who read only one publication. The shoe manufacturer can direct more advertising dollars towards the buyers who are reading multiple publications covering running.
The Science of Data Matching
Many decisions affect how data is matched. Not all data matching software provides the level of sophistication companies require to manipulate data to meet their objectives. General-purpose tools like Excel or text editors lack the functionality to perform the matching and consolidation logic most companies need. Programs like Excel only recognize matches if every character in every compared field is exactly the same. A small difference prevents general-purpose programs from finding matches. When comparing highly structured fields like account numbers an exact match is preferred, but often a more intuitive approach is more appropriate. Some of the best data matching products use “fuzzy matching” to allow users to control the tightness of match and consolidate operations.
When projects call for working with only parts of a large database, include/exclude parameters can shorten run times and eliminate extra steps such as manually extracting subsets.
The criteria used to determine a matched record will change depending on the application, even when using the same databases. Look for software that allows users to build match keys within the program. It shouldn’t be necessary to use outside tools or custom programming to create new match key fields.
In some applications, companies want to skip records that also appear on another list. Suppressing current customers from a customer acquisition campaign based on a purchased mailing list is a good example. Other common suppression lists are deceased persons, prisoners, nursing home residents, bankruptcy filers, and the DMA Choice “Do Not Mail” list.
When records appear on two or more databases, users should be able to tell the system which sources have priority. With some matching/consolidation software, this choice is random, depending on which file was accessed first when a match is found.
As customer communications and customer experiences become ever more personalized, high quality data is essential, even as data sources expand. High quality matching and consolidating software should be part of every company’s data quality toolbox.
Ken Kucera is the managing principal of Firstlogic Solutions delivering world-class address and data quality software to data-driven companies across the USA. With 38 years of industry experience, Ken leads the teams that innovate and deliver address correction, data cleansing, data matching and consolidation software at Firstlogic. Ken has been an active member of the National Postal Forum (NPF), Direct Marketing Association (DMA), and the National Etailing & Mailing Organization of America (NEMOA). Reach Ken at firstlogic.com or follow him at Twitter @FirstlogicDQ and LinkedIn.