| By Rick Cook
Marketing Performance is Directly Correlated to Data Quality
As marketing becomes more focused, pervasive and customer centric, and as campaigns become more sophisticated in using more intelligent data points, it becomes more critical than ever to leverage clean, complete and accurate data in order to achieve successful results.
Incorrect data, often called dirty data, in your marketing database can do anything from waste money to annoy, and possibly lose, customers. In fact the more highly targeted and selective your marketing campaign becomes, the more important it is to use clean data. A nation-wide mailing to every house in targeted ZIP codes offering a special at a chain pizza parlor will suffer a lot less from dirty data than a highly targeted B2B campaign aimed at prime customers.
To maximize campaign effectiveness and marketing budget ROI, your goal is to associate each unique prospect or customer record to a marketing campaign or promotional offer based on highly targeted criteria that most closely correlates to their most likely propensity to buy. This is harder than it sounds as detailed and accurate marketing data is a precursor to subsequent marketing steps. Even with Customer Relationship Management software, savvy marketers will devote effort to making sure that their lists are both clean (not containing any redundant or incorrect entries) and accurately reflect promotional offers.
The problem is that computers are not at all good at “almost”. Two pieces of data which are “almost” the same to the human eye are completely different to your marketing system.
The simplest example involves basic names and addresses. For example, one company with a nationwide direct mail marketing campaign found that their purchased address lists included more than 17 different spellings for McDonald's, the fast food chain. Without data cleaning all of those records would have been treated as separate businesses, thereby, increasing marketing costs and blurring marketing performance results.
There are data scrubbing features in CRM software systems which can fix some of these problems and specialized programs and services which can fix more of these problems automatically. Generally the services and software concentrate on cleaning up lists of names and addresses, although some other fields can be checked for common errors at the same time. Other cases will need human intervention.
Most CRM mailing list modules, for example, know that St. and Street are the same and neither of them equates to, say, St. as in St. Louis.
Be especially cautious with purchased marketing lists and doubly so when combining lists from different sources. The lists you buy are notoriously prone to duplication and need to be scrubbed carefully. Beyond that, consider the six best practices to maximize your data quality.
1) Check Your Data
The most elementary step in getting clean data is to validate the data manually or systemically according to simple business rules. For mailing or email lists at least run it through a scrubbing program which will catch obvious errors (such as “srteet” for “street”). You may want to consider using a data validation service for the checking. Many companies such as Dun & Bradstreet, WinPure and Trillium offer both software and services and can advise you on which best suits your needs.
2) Set Data Standards
Increased data structure and decreased variation results in much higher data quality. Set standards (such as “St” for street) and create internal process reviews to enforce them. Companies should establish rules for how company names are entered to a system. For instance, is IBM entered as “IBM” or “International Business Machines”? Is Microsoft entered as “Microsoft” or “Microsoft Corp” or “Microsoft Corporation”? The way your CRM system collects data can also help keep it clean. For example, numeric fields should be checked for the reasonableness of the entries (possibly using number ranges) and not permit alpha characters. Also instead of having the user enter fields with a limited number of values, consider using drop down menus or radio buttons in order to improve data consistency.
3) Enforce Spelling
Remember that most CRM software cannot spot even an obviously misspelled word. But don't trust spell checkers to find and correct there errors. In addition to words that have alternate spellings for different meanings, variant spellings are common, especially in names and addresses. (“Pati”, “Patti”, and “Patty”) for example. Also use drop down lists to enforce the spelling of repeat terms. Take the job title field for example. Rather than have staff manually enter CEO, C.E.O., chief executive officer or another variant, allow them to select a pre-defined title from a dropdown list.
4) Get Correct Email Addresses at the Source
These are easy to capture from emails and other sources. Pay special attention to email addresses entered on forms by the prospect. It's often a good idea to ask for the email address twice.
5) Periodically Cross-Check Entries Looking for Duplicates
Duplicate contact records will usually possess more than a single field of duplicate data. Use the search function on your database to find entries which match in most or all but one field and subject them to manual scrutiny. Certain fields, such as email address, can be used to quickly identify duplicate records and then consolidate the records or discard one of the entries.
6) Monitor Returned Mail and Bounced Emails
Failures such as bounced emails and returned mail usually mean a failure to sufficiently clean your database. Monitor the number of returns or bounces and use that as a metric to understand how effective your data cleaning procedures are. Also be sure to correct the information in your database as timely as possible. That can include removing it completely if you can’t fix the error.
Tags: Dirty Data, Scrubbing Data, Data Quality
Author: Rick Cook
||— Comments for this page are closed —
||Good points, however, marketing data management is getting even more difficult as we try to engage with consumers and customers across more channels and more devices. I think the upcoming solution to help address this challenge is master data management (MDM), effectively a central system to aggregate customer interactions and synthesize the data in a way that it can be understood, put into context and effectively used for service, campaigns, customer experience programs or other customer interactions.