Tame the Monster

If you work in marketing at an enterprise company, you know how enormous your customer database is. It could easily contain half a million to several million leads. And the size of the database is constantly growing. Research by SiriusDecisions revealed that the volume of information in the average B2B customer database doubles every 12 to 18 months. Maintaining control over enterprise customer databases is a massive challenge!

Unfortunately, database decay is unstoppable. The people and organizations represented by the records in your customer database are constantly changing. As a result, the customer database is never 100% up to date. Database decay can come from many different sources. ZoomInfo has quantified how often changes occur and the results are startling:

  • The lead’s title and responsibilities may change: Two thirds of people change jobs annually.
  • The lead’s phone number changes: Over one third of people (43%) change phone numbers each year.
  • The name of the lead’s company changes, perhaps due to a merger, acquisition, or some other business event: Around one third of companies (34%) change their names annually.
  • The lead leaves the company entirely for another job: Around one third of people (30%) change jobs each year.

The bigger the customer database, the bigger the decay problem becomes. Considering all these sources of churn, a 50% annual decay rate for a customer database is not unreasonable. For an enterprise company, that means hundreds of thousands or even millions of useless leads in the customer database after a year.

Figure 1: The Scope of Decay in Enterprise Customer Databases

What if there was a simple, cost-effective way to combat enterprise customer database decay?

Unaddressed database decay has a major impact on marketing and sales effectiveness. Research by Dun & Bradstreet NetProspex discovered that 62% of companies felt the email deliverability of their customer database information was “questionable” at best and over one third of contacts (41%) lacked a working phone number.

Research has found that close to one third of organizations (30%) have no strategy to update inaccurate or incomplete records, and at least one third leave inaccurate or incomplete records in their databases.

Fortunately, there is a better approach. Every time you launch an email campaign, you receive a slew of auto-responses, ranging from out of office messages, to “left the company” and “my email address has changed” notices. Rather than ignoring them or mining them manually, savvy enterprise marketing teams analyze the responses in an automated way and update the customer database records with the dates that leads will be on vacation, changes in title, new phone numbers, and email addresses.

Assume that your organization runs two email campaigns a month. Based on our experience, the automated returns generated range between 2% and 4%, with peak auto response times during the summer vacation months and around the December holidays. If 3% of your emails generate an automated return, over the course of a year it’s possible to automatically generate hundreds of thousands of enhanced leads based on information in the auto responses.

Figure 2: Analyzing Automate Responses Combats Database Decay

Figure 2: Analyzing Automate Responses Combats Database Decay

This approach to continuous data cleansing just makes sense, but it’s often overlooked because companies aren’t aware that automated tools exist that make it possible. One of the biggest challenges facing enterprises is keeping data clean after (and during) large data cleansing projects. Given the continuous nature of data decay, a “one and done” approach simply doesn’t work for customer data cleanliness. A best practice is to combat continual data decay with continual data cleansing.

What if there was a way to turn organic database decay into new leads?

Even if your company has a plan for dealing with database decay, do you have a strategy for turning seemingly lost leads into new contacts? Let’s say that a lead has changed jobs internally or left the company entirely. Based on the research, we know that’s a common occurrence. How do you update that information and proactively keep Sales abreast of who the new contact is? Or do you just let the information remain stagnant until Sales discovers that they have a new contact person and rely on them to update the customer database?

Once again, leading marketing teams take auto responses from email campaigns and use automation to analyze them. This reveals when leads have moved to a new position and who their replacements are. Automation can also proactively create new records in the customer database that Sales can reach out to.

In our experience, close to 60% of auto responses results in a new contact within an account. This is powerful for organizations that have embraced account based marketing. It’s also a significant source of new contacts for an enterprise customer database. Let’s expand our example and assume that just 50% of our enhanced leads result in new contacts. That translates into hundreds of thousands to millions of new contacts.

Figure 3: Analyzing Automated Responses Generates New Leads

Figure 3 - Analyzing Automated Responses Generates New Leads

Take a moment to consider how extracting information from campaign reply emails could help your company take control of its customer database. If you’d like to evaluate how an automated approach to this process could impact your enterprise customer database, download our Excel simulator.

Matt BenatiToday’s post is by guest author, Matt Benati, Co-Founder & CEO for LeadGnome, a web service that mines email replies to your campaigns.