The Department 3 Blog

Direct Marketing Data Matching: The Smarter, the Better

October 23 2018, Carolyn Hart

Direct Marketing Data Optimization

When it comes to Direct Marketing campaigns, success is demonstrated by one crucial measurement – ROI. However, this can be far more nuanced than it appears at first glance. Especially when direct marketing data is involved.

Before you next sit down to allocate budget to your Direct Marketing campaigns, make sure you’re getting the most out of them by optimizing your database and maximizing ROI. And a precise matching process is part of that.

When combining data from multiple sources, we need to identify records that should be suppressed from a campaign. Duplicate records have to be eliminated. A merge/purge has the goal of producing a single unique record containing all the useful data about a target, without redundancy.

Many times, duplicate records still contain bits of important information, and each record has a unique piece of information that should be saved. For example, duplicate customer records may contain the same name and address, but one record includes the customer’s email address while the other record includes the customer’s phone number.

This can be a pretty complex challenge, but vital to address. Suppressing duplicates or ineligibles from a direct marketing database through matching can cut costs (like printing and postage) and elevate efficiency. If a business or organization is trying to assemble a sizable mailing list, the costs of duplication obviously rise in proportion to the scale of that list.

Direct Marketing Data Optimization

The exact matching logic being applied can differ greatly from one company to another, depending on their respective goals. But in almost all cases, good matching is absolutely imperative to DM success.

Here’s a scenario that demonstrates exactly how this works, and the part matching plays in it.

The danger of duplicate data

If you’ve moved anytime in the last decade or so, chances are you’ve received ‘Sign Up Specials’ from local Internet providers in your mailbox. Internet and cable providers are notably fast when it comes to prospecting, but this haste can result in overlooked errors, particularly in mailing lists.

The following scenario isn’t that uncommon, as Internet provider XYWebServe launches a user acquisition campaign that targets two people at the same address:

Direct Marketing Data Optimization
Direct Marketing Data Optimization

Although the fact both of them are receiving the same offer may not dissuade the Takes from signing up with XYWebServe, it illustrates a larger problem for the company. Filling up the Takes’ recycling bin with redundant mailings is an expensive mistake, and one that could have been easily avoided with household-level matching. By flagging the Takes as a duplicate in their database, XYWebServe could have targeted one prospect in the household – thus increasing their conversion rate and saving wasted resources.

Advanced data-matching techniques, like those we use for our own clients, generate that unique record retaining all the valuable data, while removing the duplicated data. This goes beyond simple household-level matching, though, because the challenges are getting more complex – and direct marketers are under constant pressure to wring the most return from their campaign spend.

The convenience of customer suppression

Fast forward six months…

The Takes are content with their Internet service, but the data optimization department at XYWebServe neglected to refresh their database. They send another prospecting mail to the Takes. So not only is Miss Take wondering why she’s received a second Sign-Up offer, but those printing and processing costs? They’re being dumped in that recycling bin.

Merge/purge processing with sophisticated matching is key to avoiding such accidents. Ongoing data hygiene means clean data, and clean data ensures that you suppress everyone you don’t want to target – whether that be suppressing current clients from prospecting mail, to suppressing deceased individuals from all mail.

Of course, some marketers are happy to send multiple prospect mailings to the same house. For a health club or a magazine subscription service, their aim is to maximize the number of individual accounts they can convert. So the matching logic employed has to be adjusted accordingly.

Cleaning up in other ways

Eliminating duplications with less-sophisticated merge/purge processes can be stymied by simple differences in how a target’s name or address might be entered in different records. State-of-the-art matching, though, lets us clean up lists by being able to actually recognize and reconcile instances where nicknames or different naming conventions have been used, as with this company:

AA Adams & Associates

37 Lincoln St

Anytown, CA, 123

AA Adams and Associates

37 Lincoln St

Anytown, CA, 123

Here’s an example of how such a matchback processing tool can link records and cause a change in historical matching. Let’s start with data from Week One that includes two records that don’t seem to match, though they share an address:

John Smith

123 Main Street

Anytown, CA  12345

June Jones

123 Main Street

Anytown, CA  12345

In Week Two, we’ve obtained a new record that allows us to link all the records at this household together.

Jane Smith-Jones

123 Main Street

Anytown, CA, 12345

To do this, though, requires that the data processing engine being used receives not just the newest record, but all historical records, often going back for many years. In looking back over years’ worth of direct marketing data, naturally, the chance a customer has moved over time increases. Here are two records separated by several years: Are they the same person?

Mark Median

456 Central Ave,

Anytown, CA  12345

Mark Median

789 Midway

Everyburgh, OR  54321

By applying National Change Of Address (NCOA) to the customer file and matching it to the most recent campaign history, we’re able to identify customers like Mark Median who were previously mailed at a different address.

Here’s another example. Let’s say that on January 1, we mailed a business at the following address:

ABC Company

123 Main Street, 3rd Floor

Anytown, CA, 12345

Six months later, on July 1, we determined a new DM match as a result of applying NCOA to the customer table:

ABC Company

456 East Main Street

Newtown, CA, 23456

Over those six months, this company had moved to a new location. We were able to map the old address to the new address and update customer lists accordingly. Timely list cleansing like this ensures our clients’ campaigns are reaching their intended audience; running precise matchings means their campaign spend gets maximized. It’s also an example of how our process is customized to the specific business needs of a client.

Counting any response as a match

There are cases where clients may not have as specific a set of matching criteria as others, and are open to seeing matches that are the result of any response to a DM piece – regardless of whether or not the response was from the addressed individual. It was, clearly, the mailer that drove response.

Imagine an apartment complex: Multiple mailers might have gone to the same complex, and were delivered to the tenants’ mailroom. Some might not have gone into mailboxes, or were discarded, but another tenant could have picked one up and responded. Or the new tenant in Suite 3, below, might respond even though Carolyn Doe’s name was on the mailer. For this marketer, they’re all direct mail success points.

John Smith

123 N Main Street, Ste 10

Anytown, CA  12345

Carolyn Doe

123 N Main Street, Ste 3

Anytown, CA  12345

Optimized matching takes specialized expertise

These are just a few examples. To obtain optimal results in any direct marketing data matching scenario, from simple to complex, it takes a resource combining highly specialized data optimization expertise with purpose-built technologies.

In our own case, we optimize merge/purge by combining our long history in data optimization with a data processing architecture employing sophisticated matching techniques, paired with databases that are custom-built for each client.

Together, they empower merge/purge processes that are extremely fast, precise, and reliable.

Direct Marketing Data Optimization

With the right resource to partner with, data optimization can be painless. While sparing you the pain of duplicate data, improper segmentation, and poor matching practices, as these are just some of the many factors that can muddy your DM campaign results and undercut your ROI.  

Learn about Department 3 data optimization

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