For most collections companies and departments, one of the key performance indicators is the ability to locate and enter a constructive dialogue with as many customers as possible.
Your ability to locate customers is typically dependent upon the customer data quality that has been presented and its age, the quality and currency of the data sources being searched upon to locate customers, and the often transient nature of the customers that you are trying to locate.
The role of a collector in this context is not likely to be replaced by technology in the foreseeable future, however it does have a critical role to play in terms of providing efficiencies and ensuring that the collector receives a complete picture with minimum time and effort.
This must all be achieved while keeping an eye on the regulatory framework that we all exist within.
This article outlines some of the key considerations for determining the most appropriate data sources to search when trying to locate customers. It also identifies some of the tools that are available to assist in ensuring that the maximum efficiency is achieved by your collection staff.
Legitimacy, availability, quality and currency of data sources
There are two key aspects that require consideration when looking at the legitimacy and availability of the data sources.
The second is whether the use of that data source when collecting and skip tracing would be considered fair in the context of the debt collection guidelines that have been published by the ACCC and ASIC.
In both instances, understanding the data sources being used, how the data is compiled and whether it will stand scrutiny from the relevant authorities are critical considerations. This is relevant, simply because most companies would prefer to avoid the scrutiny and brand damage that would be afforded a company that has been named by a government authority as having acted in an unfair or unethical manner.
Once you are comfortable with the legitimacy of a data source, the quality and currency are the next most important factors.
Data Quality simply recognises that different data sources have different strengths and weaknesses. The strengths and weaknesses can generally be identified by looking at the primary purpose of the data source, which is typically not for credit and collections. Some data sources will not contain any full first names (initials only), which produce problems when dealing with common names. Other data sources do not store any unit or apartment numbers, which typically raises issues when searching in apartment blocks, particularly when looking in areas with high population density of particular ethnicities or cultures.
Data Currency refers to how up-to-date the data is, and how it is maintained. Not many people realise that often higher match rates against an aged customer list is often a result of a poorly maintained data source rather than a good system. The quality of a data source cannot be measured by match rates unless the matches are verified.
In my own experience, I am often asked why we don’t have a record that might be listed elsewhere. My reply of ‘have you checked to see if the record is connected?’ typically is resolved in the negative.
Obviously high match rates is an important measure of a system, but only if the data sources are properly maintained and have measures in place to actively retire records.
Ease of Access
The ability to easily search data sources and the appropriate application of technology is often the difference between finding and not finding a customer. There are a variety of tools that users should be aware of, and should apply based on the appropriate set of circumstances.
The technology described below revolves around understanding the strengths and weaknesses of the various data sources, and managing the human and technical error that is generally involved in creating, managing and updating the data source and the customer list.
Furthermore, the technology, if implemented correctly can resolve some of the peculiarities in the general population and the geography to produce specific results that may not be obvious to the collector.
Fuzzy Surname Searching
A competent collector will typically look at a surname like Johnston, and maybe look for a Johnson and a Johnstone at the same address. They will probably be less likely to look for a Jonson. There are two types of errors that appear in Surnames. The first type are spelling or typographical errors, and the second are phonetic (or sounds like) errors.
Collectors when searching for records will often try to resolve some of these errors, however, it is impossible for a human to be fully aware of all variations of spelling of all surnames.
Fuzzy surname searching allows the system to pick up all matches that are typographically and phonetically close to the entered name. It will also return likely results where the collector has mistyped the customer’s surname into the search application.
The benefit of this to you of course is that a collector will perform a single search to achieve the same results as they would otherwise be required to perform a number of searches to achieve a lesser outcome.
Surrounding Suburb Searching
It is well known that many customers that live near the border of a more desirable suburb will use ‘vanity’ suburb names. If your customer address data has not been parsed through an address parsing system, collectors will often search on the wrong locality.
Additionally, most people when they move tend to move short distances. However, as you move from city to rural areas, the move distances tend to be slightly larger.
A surrounding suburbs search function that will intelligently look for matches on neighbouring localities, based on population density will help collectors resolve these discrepancies in a single search. This functionality can also be used to find leads for customers with relatively uncommon names, or who live in sparsely populated areas.
It is not well known that there are over 60 different spellings of the name ‘Mohammad’. Further, the western translation of the Spanish name ‘Enrique’ can be ‘Henry’ or ‘Hank’, meanwhile the Italian version is ‘Enrico’.
It is not uncommon for a person to deal with an institution like a bank using their legal name, but wish for their telephone number listing to contain a western version of their name. This is quite easy to pick up with relatively uncommon surnames, however, for surnames that are quite common this can be a powerful tool for reducing or prioritising a candidate list of leads to follow up.
For the uninitiated, when referring to leads, we are generally trying to provide alternate addresses and telephone numbers for a customer. Leads technology does not provide a confirmed move of a customer, rather a list of candidate records that are potentially the same person.
Most collections cases have been handed over to a collector simply because the customer has moved, so attempting to find the customer’s new address and or telephone number is the main task of the collector. The idea of leads functionality is to perform all of the searches that the collector will typically perform to try to find likely candidates in a single search.
The power that this style of search can bring is twofold. The first is that it can take into consideration the specific geographic factors, in that it can behave differently in rural as opposed to metropolitan areas. The second is that can include in its calculations the pervasiveness of the names being searched upon.
Again the benefit of this functionality is that it provides the best candidates to the collector, and performs multiple searches in a single operation, which improves efficiency and effectiveness.
Tying it all together
Obviously the best use of technology will be dependent upon the specific scenario; however, an efficient approach for many collections businesses will be to leverage all tools available. For short term debt collection, or where you want to verify whether a customer still exists at an address, a batch offering can be the best, most convenient and cost effective way to follow up the easier cases with minimum effort regardless of the size of the customer list.
For skips, longer term debt and for customers that fail to be discovered through the batch process, a more considered, investigative approach is typically required. In this instance search tools that allow users to search against multiple data sources including a substantial history to trace people wherever possible between addresses is critical to the skip tracing process.
Additional tools like demographic information (especially those that are tailored to the financial health of the area) and maps can help a collector determine the most appropriate strategy to approach a customer. Determining whether a customer is likely to have the funds immediately available to meet their debt or whether they are more likely to require some form of payment plan before contacting the customer can change your approach to the customer and will improve your chances of entering into a constructive dialogue with your customer.