The following is a drill-down on a portion of the Data Quality Bootcamp session that Inga Romanoff and I conducted at the Marketing Nation Summit 2015. We'll provide more drill-downs in future blog posts.
According to a SiriusDecisions study, up to 25 percent of the data in the average B2B company includes critical data errors ranging from incorrect demographic data to a lack of current disposition1. And in our experience, B2C companies may have an even larger percentage of bad data (as high as 50%), especially if e-commerce is the primary revenue channel. This affects both the numerator and the denominator in the ROI calculation.
Bad data causes your effective lead acquisition cost to increase
For example a 250,000 lead database with an average cost of $50/lead has a total cost of acquisition of $12.5mm. However, if 25% of your leads are bad, your effective cost of acquisition is 33% higher and you fundamentally wasted over $3mm!
Bad data causes your revenue to decline
According to the same SiriusDecisions study, strong data organizations that actively manage their data quality have only 10 percent bad data and are realizing nearly 70 percent more revenue purely based on data quality. This is the result of simple funnel metrics – fewer good leads in results in fewer won opportunities at the end.
Bad data impacts your job performance
Finally, 36% of marketers say that data quality is the biggest obstacle to MA success2, which for Marketo users can be a career limiting event.
Inexpensively ensure clean data
The least expensive way to ensure clean data is at the point of data capture and here are a few tips on how Marketo can help you be a strong data quality organization.
- When possible use Marketo forms (embedded or in an iframe) on your website. You can use your own forms, but use the server-side form post method to send them to Marketo. Try to avoid using the SOAP or ReST API as they do not create a ‘Fill Out Form’ activity in the lead record Activity Log, which is much easier to use for triggering and filtering.
- Use Select Fields, Input Masking and Required Fields when possible to ensure consistent data and be sure that they are the same as those used in your CRM to ensure no failed syncs.
- Typos are easy to make and the result can be a lead with an invalid phone number and/or email address. So use field validation rules or real-time email and phone validation services from third parties to ensure this vital data is accurate.
- Use field pre-population to allow returning leads to update incorrect data, but don’t allow the lead to directly change the value in their email address field or a new lead record will be created. Instead provide a link to a different form that will allow the lead to enter the new email address in a different field. Then you can have a smart campaign change the value of the email address field on the existing record.
- Restrict List Import role permission to only trained staff. Create and use an import template and field aliases if necessary.
There are many other tips that will help you be a strong data quality organization that we’ll share in future blog posts.
1 Sirius Decisions - The Impact of Bad Data on Demand Creation (11/25/08)
2 Ascend2 Marketing Automation Benchmark Survey, July 2014