Keywords: Address quality, Data quality, Addresses, Address database, CRM, Duplicates, Postal address check, Address correction, Software.

Data quality

Data quality (DQ) may be perceived as a process or as an evaluation of a condition. It is certain that the high address quality is only achieved and maintained with the help of many measures.

Basic cases are concerned with preparing address lists for mailings or other marketing activities. Simple tests are usually sufficient here: Are the addresses correct? Do the salutatory addresses match? Were duplicates eliminated? q.address Stand-Alone executes these tasks safely.

Despite the similarity of tasks the basic cleansing of address inventory is much more complex. Data formatting and duplicate handling regularly cause maximum complexity; particularly data structures developed in legacy systems and uncontrolled data contents concerning names and company names complicate data formatting. Generally it is not enough to just merge addresses in case of duplicates, since more objects like offers, orders and invoices are connected with the duplicates which makes merging difficult. q.address Stand-alone is a valuable tool for basic database cleansing.

Basic database cleansing is definitely recommended at regular intervals, most certainly before a data transfer to new CRM- or Marketing applications.

A range of measures is required to maintain the once acquired data quality even after basic database cleansing – the quality assurance will be a permanent process. q.address provides effective tools which aid this process: Ready-to-use q.address-Integrations for numerous standard products, as well as the q.address Quality Server with its interfaces (APIs) for the integrations of q.address-functionality in individual applications.

What are the expenses for?

The costs of inadequate address quality can seldom be quantified accurately.

  • Cost savings in case of mailings: Cost savings by avoiding undelivered letters (returns) and double mails alone finance the purchase costs of q.address Stand-alone often already during the first mailing. Costs not only arise due to saved postal charges but also due to the services of the letter shop and the content of the advertising mail drawn up with a high cost.
  • A loss of image is suffered in case the recipient of an advertising letter receives the mail repeatedly and with an incorrect address or a completely incorrect salutatory address. If it is not strived at to use correct addresses while initiating the contract, then customers might fear that even the product quality and contract execution will be unsatisfactory.

There are more reasons speaking for themselves. Data quality is a necessary pre-requisite of correct data processing:

  • Consistent standardization of names and addresses are essential for many data evaluations, for instance:
    • For matching with external reference inventories for data enrichment (e.g. bedirect, D&B, Hoppenstedt, Schober) or data filtering (e.g. relocation file, Robinson list, insolvencies).
    • For synchronizing addresses maintained in various systems, e.g. in CRM and in ERP.
    • For data cleansing (for e.g. de-duplication).
  • The expected and verified information in the correct data field is mandatory for a successful interaction of software applications.
  • Consistently standardized names and addresses and complete contact details facilitate quick and smooth communication and cumbersome time-consuming researches can be prevented.
  • The error-tolerant address search saves time for the user and provides a better service to the customers.
  • Customers receive offers as per their requirements. The marketing department is pleased about smooth operations, more effective and accurate marketing actions.
  • Preventing errors:
    • Supply of customers reluctant in paying, because red-flags are attributed to unnoticed and overlooked duplicates in the system.
    • Marketing information remain unaddrassed in customer discussions because the relevant information is stored in unnoticed and overlooked duplicates.
    • Unreliable evaluations: Profitable customers are not identified if the turnovers are distributed to several duplicate addresses.
    • Errors in accounting if customers and/or suppliers are not merged and balanced properly.
    • Incomplete and garbled data due to missing or insufficiently validated data entries.

Quality offensive

Since data quality is a priority, bad data quality discourages employees and impairs quality awareness in companies and it also impedes the impact of marketing and sales.

ACS advises and assists in

  • Preparing data for mailing
  • Preparing data for transfer to new CRM- or Marketing applications,
  • Cleansing data in CRM-, Marketing- or ERP-Systems (“Basic database cleansing”),
  • Planning and implementing a quality management for data.

Further links