Marketing Campaign Results Directly Correlate with Customer Data Quality
As marketing becomes more data-driven 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, compromises your marketing automation system, degrades campaign performance, misspends the budget and harms your customer relationships. In fact, it's somewhere between ineffective and impossible to deliver highly targeted or precision marketing campaigns without clean data.
And recognize that because customers change jobs, get new emails, and incur other events, customer data depreciates at 2 percent per month on average. That means if you don't have a data quality program, about half the data you rely on for campaigns will be inaccurate or bogus.
Campaign Data Challenges
To maximize campaign effectiveness, your need to associate each unique prospect or customer record to a marketing campaign or promotional offer based on highly targeted variables that most closely correlate to propensity to buy.
This is harder than it sounds as detailed and accurate marketing data is a precursor to subsequent marketing steps. Even with modern MarTech, savvy marketers still need to devote effort to make sure that their contact and related data is both clean (not containing any redundant or incorrect entries) and accurately reflects up to date customer insights and behaviors.
But even the simple challenge of redundant data can be difficult. 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 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 cleansing all of those records would have been treated as separate businesses, thereby, increasing marketing costs and blurring performance results.
CRM apps can help. They can automatically enforce fields prone to data entry error and check for duplicate accounts or contacts. They know that St. and Street are the same and neither of them equates to, say, St. as in St. Louis.
But those processes are only the basics of marketing data quality challenges. To apply data with the necessary integrity for assured campaign performance, consider the following six best practices.
Measure Your Data Quality
The first step to getting clean data is to validate the data manually or systemically by applying data integrity rules. Many rules will be simple, for example, designed to catch transposition errors (such as “srteet” for “street”). For more advanced rules and use cases, you may want to consider using a data validation service such as Dun & Bradstreet, WinPure or Trillium which will bring increased intelligence and automation, and more comprehensive results. These third party systems can integrate with your marketing automation platform to automate the process.
Set Data Standards
Increased data structure and decreased variation results in much higher data quality. Set standards (such as “St” for street) and create CRM workflow rules 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.
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. CRM system can provide alerts for what may be similar words, such as in names and addresses, perhaps something like “Pati”, “Patti”, and “Patty”.
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.
Harvest Data From the Source Where Possible
For example, many times you can capture emails or customer data from the source. 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.
Seek Out Duplicate Records
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. CRM systems normally have automation routines to do this periodically.
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.