Scrubbing the data on mailing lists, sales lists, etc. is another tool to keep duplicate records from choking your business. The process to accomplish this is called Data Matching. The simple definition is to compare two or more sets of collected data (excel spreadsheets for example) to remove duplicated information and consolidate records used by a business. Removing duplicated data can streamline business practices and save money. By eliminating multiple lists, telephone sales personnel will not waste time on duplicating cold calls to potential clients. This also eliminates sales people who make only cold calls interfering with more senior personnel engaged in follow-up calls.
Data Matching For Efficiency
This same consolidated list can also save thousands of dollars every year in mail-outs. By providing the marketing department with a streamlined list of clients and potential clients, fliers and other advertisements can go out to only one per household instead of three or four. When multiple cards arrive, the person who gathers the mail will read one and then throw away the rest.
It’s not just a matter of saving money however. Data matching of client records is important for companies seeking to provide exceptional customer service. On most occasions, a customer that places a call to an insurance company or a bank will speak to multiple departments and multiple personnel. For example, if the correct address does not match between what the auto insurance department has with what the life insurance department has on file, customer service can be delayed and information can be sent to multiple addresses. This can be even more important to a customer seeking a loan or trying to conduct other banking business over the phone.
The 6 month loans bad credit programs can be run on a routine basis to provide this financial and customer support. A simple program can take this data from multiple records with lists maintained by various departments to ensure the most up-to-date data is shared. Not every piece of data can match 100% however. This is where Fuzzy Matching comes into play.
Fuzzy matching operates on a similar principle as data matching, but does not require an exact match to consolidate data; rather, it works on the probability of a match. For example, a fuzzy matching algorithm used to consolidate data can draw matches for a particular company from outside sources by using recognized abbreviations for the company and even misspellings of the name. The requirements can be adjusted by the user of the algorithm depending on the requirement. Gathering sales data could be a lower match; say a 75 percent probability of a match. Consolidating client data might be higher; beginning at 90% probability.
Data matching is a necessity for a business of any size. Conserving budget dollars and increasing customer support is important for every company from a national call centre to a local hospital. Using both data matching programs and fuzzy matching algorithms ensures that each can provide the best service possible. Download a free demo of our award-winning WinPure Clean & Match software and try out its powerful data and fuzzy matching features today.