You'll have a better chance of direct mail success when you start out with good data. That's a simple fact. Depending on your definition of success, it's certainly possible to see some positive results out of "lesser quality data", but what will that cost you in added time, money, effort and missed opportunity? This post explores the nature and impact of bad data, and why it should be avoided.
What is Bad Data?
Considering the nature of the community association marketplace (HOAs, condominiums, cooperatives, timeshares, mobile home parks, etc.), "the data" you need originates with multiple sources (covering the corporate information, property information, director information and related demographics). And that's where the problem begins. All this data is not located in one place, and it's certainly not all in a single, consistent format. Much of the data originates when associations are first created, and entries are made by people involved with registering that association, not by people necessarily trained in data entry.
For that person, this is just the data needed to get the association and the property set-up for all required licensing and operation. Make the entries, pay the fees and move on. It might not matter whether the correct titles are used, whether names are spelled correctly, whether the address is complete (or consistent), or whether special "name" characters are properly placed. After all, it's just one entry. But when you add up all the entries, and try to compile the data for marketing and analytical purposes, these individual anamolies add up, and create havoc.
And before you know it - you've got bad data.
What are the Costs?
When it comes to mailing lists, bad data makes successful marketing more difficult, time-consuming and certainly more costly. You can't set appropriate strategies based on incomplete data. You can't know that your message will reach the intended recipients if addresses are incomplete or inaccurate. You can't know that your intended recipients will take your mailing seriously if their name or association name is misspelled. This all adds up to significant costs and consequences:
- Missed opportunities and wasted dollars due to undeliverable mailings.
- Ineffective decision making due to incomplete or inaccurate analytics.
- Lost time and productivity sorting through data and trying to keep your lists clean and current.
- Diminished return on investment for your marketing dollars and related work efforts.
How do you know your data is "bad" (or at least not what it should be)? Just look for all the warning signs (returned mail, low response rates, and blank stares when you ask potential prospects if they have received your mailings). In all, "bad data" is more than an inconvienience. It's an obstacle and a burden, defeating the intended purpose of your marketing efforts. If you're going to invest the time, money and effort into finding new customers and marketing your business, it's best to start off with quality data, designed to make the most of every dollar invested and every hour spent.