I have a pet peeve: companies that claim their products will improve your bottom line, but can’t or won’t back it up with numbers. If you are a regular reader of our blog, you know that we are big fans of quantifying return-on-investment, because market research drives our development priorities among many others.
This is why a recent Gartner Research article was so on point for us: it talks about making a business case for improving data quality. It outlined a multi-step process for cost-justifying data quality efforts, starting with understanding your business priorities, selecting business performance metrics, choosing a desired target state, and estimating the financials.
In this article, I would like to lay out some considerations for making your business case for lead data quality, and how it affects your ultimate goals of lead generation and sales.
Factors in lead data quality ROI
Start your analysis with your incremental cost per lead – e. g. what does each lead cost you when you run a campaign? Consider things like printing, postage and labor costs for direct mail, telecommunications and labor costs for outbound telemarketing, lead counts in your CRM and marketing automation platforms, and even the contribution of leads to your fixed costs (for example, in staffing and infrastructure).
Now, factor in the impact of common types of bad lead data. Each of these, of course, affect your incremental cost per lead, plus other related costs as follows:
Bogus leads. Few companies have ever sold products to someone named Donald Duck, beyond fictitious ones in Disney cartoons. But people often submit fake names to get marketing and research offers for free. Estimate the percentage of these bogus leads, and the costs of keeping and processing them on your campaign lists forever.
Opportunity loss. What percentage of leads are potentially valid but unserviceable due to data entry errors, changes in address or contact information, or problems with address formatting? Each of these has a cost in lost business, based on your normal conversion percentages, as well as processing costs.
Fraud. Some marketing leads are intended as an entry point for fraudulent orders or credit card fraud, particularly if you don’t detect red flags such as contacts in high-risk areas or addresses that are inconsistent with the IP address used. In these cases, consider potential merchandise or financial losses, credit card processing rate increases, as well as processing expenses.
Compliance penalties. For many businesses, this is one of the biggest cost factors of all – recent consumer data privacy legislation has ushered in stiff penalties for mis-directed marketing contacts. These include violations of the Telephone Consumer Protection Act (TCPA) for telemarketing, the strict new General Data Protection Regulation (GDPR) for marketing to consumers in Europe, the CAN-SPAM Act for e-mail marketing, and more.
Beyond these core factors, think about intangible costs from bad data as well – for example, bad business intelligence for marketing decisions, incorrect ROI estimates from marketing efforts, and more. Also, don’t forget to count in the human costs of dealing with fixing and removing bad or out-of-date contact data, versus using an automated solution. Pulling all of these factors together now gives you a real, quantitative base for cost-justifying your data quality efforts.
Making the case for automated data quality
Of course, automated solutions exist for evaluating, cleaning and updating lead data, such as our composite Lead Validation tools or our flagship of Address Validation product suite. But as marketing technology expert Christopher Penn noted in a recent podcast, managers often look at data quality efforts as a cost center and don’t always think in terms of ROI.
One analyst report from SiriusDecisions (now part of Forrester) links better data quality with a nearly 70 percent increase in revenue. (The same report, by the way, also describes one of our favorite statistics: the 1-10-100 rule, where it can take $1 to validate and correct a contact record at data entry time, $10 to clean it up from an existing database, and $100 to do nothing.) By arming yourself with your own numbers as well as industry research, you can make a rational argument that automated data quality tools pay for themselves many times over.
Finally, feel free to use us as a resource to help make a better business case for your own business. Contact us any time, and tap into our knowledge of these products, the industry, and the literature – we’re always happy to help.