How did my data quality get so bad?

As a general rule in marketing we believe that static data decays in quality at the rate of 1% per week.

Why is this important? Consider the CANSPAM laws. Your Salesforce.com account allows you to have multiple copies of the same person based on their email. Your Inside Sales Rep flags one of the copies as an “unsubscribe” at the request of the individual. Are you sure that is the same contact record your email vendor is using? Or consider the budget you are wasting by marketing to a database that you know is riddled with data problems, duplicates, invalid data, and then you get nailed for having lower open rates and click-through rates than the industry average! It’s the old 60:20:20 rule. 60% of the success of a marketing campaign is on the quality of the list, 20% on the quality of the offer, and 20% on the packaging. Good data is the keystone of successful marketing campaigns that bridge the gap between you and your prospect.

Good information quality will allow you to get the right data, to the right people, in the right place, at the right time, in the right form, at the right cost, so they can make the best business decisions. There is one thing worse than a lack of information, and that is bad information, because the latter can lead to bad decisions, whereas the former will generally stimulate more consideration before making a decision. Marketing Automation Systems can certainly help ensure that the data flowing into the system is much higher quality and does no more harm. But there are multiple ways that data can decline in accuracy and result in poor information quality. The following examples will help explain:

• Information quality decay (gradual loss of accuracy, loss of consistency)
  • People and companies change their email, addresses, phone numbers, names
  • Fortunes change, peoples titles change, and corporation sizes vary with time
  • Someone renames a campaign, or changes filters in a report and the trend data no longer has a common referential basis
• Data that becomes ‘corrupted’ by being overwritten with incorrect data (loss of integrity)
  • Marketing fills in a demographic field, followed by a sales person revising with improved data, followed by Client Services changing it back to original value
  • User with incorrect permissions accidentally changes a record
• Data that is a duplicate and is therefore double counted in reports (loss of cleanliness)
  • Sales rep adds new account or contact because they didn’t find it in the system
  • Marketing imports leads from show and accidentally adds duplicates
  • Prospect uses two different email addresses
  • Syndicated content provider uses Salesforce.com Web2Lead interface and introduces duplicates
• New data added that is just plain wrong  (loss of accuracy, lack of completeness)
  • Leads entered directly by Sales may not go through the field validation rules that marketing has access to with the Market2Lead Marketing Automation System
  • Auto-spammers fill in forms on website (increasing problem)
  • Prospect mistypes their own email address
  • Incomplete fields (the absence of some required members of a data set)
  • No common defined referential set for data exchange between ERP system and Salesforce.com  results in data being uploaded to the wrong field.
  • Person entering the data gets lazy and picks random values from pick lists for things like industry and role
  • Form attempts to collect BANT data far too early in the relationship with a prospect and as a result the prospect enters bogus data.

Over the coming months I plan to blog more about ways to tackle the data quality issue, especially if you have Salesforce.com.  Dealing with bad data requires a mix of process, people and technology. The activities can be divided into those that prevent data decay, and those that detect data quality issues and subsequently help remediate those issues.  The Market2Lead Marketing Automation Technology has a terrific response management engine that validates addresses, rejects junk like asdf.com, suppresses public domain email addresses, normalizes data, and ensures all required fields have data in the required format. But you still need to work on the People and Process to get really get a handle on data quality because your Sales Reps and Client Services folks still get to put data directly into Salesforce.com and  Garbage In means Garbage Out (GIGO).

That's all for now, more on detection and remediation later, Kevin