Direct Mail: Finding Lift in Your Data
Every direct mail campaign—whether it's a solicitation letter seeking new customers, offers for existing customers or a catalog full of products—should be managed for maximum response. Often, however, in the rush to meet deadlines with limited resources, organizations choose to run the same campaigns over and over knowing they're not getting the response they could.
Here's how to break those ruts by applying data-driven processes to your campaigns.
The Real Baseline
It makes sense to start with some rules of engagement for data-driven campaigns. There aren't very many, but they are important.
1. Know where you are. In most organizations the assumption is that, because there are marketing plans and agreed goals, everyone knows the baseline for response rates on current campaigns. In many cases, that assumption is not supported with fact. To know where you are, you must be able to answer the following questions:
→ How many individuals are you trying to communicate with?
→ How many individuals have responded in some way to each of the last three campaigns?
→ Did each of the last three campaigns grow in response rate?
2. Know where you are trying to go. Most people don't head to a train station or airport without a plan for where they are going and what they will do when they get there. Oddly, many companies embark on direct mail campaigns without defining an engagement goal. In some companies, the direct mail campaign is even a legacy event—it's done because it has always been done.
To be effective, a direct mail campaign must have a real goal that identifies engagement expectations by customer segment, anticipated response rates, target revenue goals and a means for tracking each of the anticipated data points. The basic questions are:
→ What is the revenue goal for the campaign? You might express it as revenue per mail address, revenue per neighborhood or region or revenue by type of customer. But you need a goal.
→ What are your target customer profiles? Are you going after your usual suspects, or are you looking carefully at customer spending patterns? Can you identify your top spenders and your non-spenders? Start with three segments: 1) customers who do not spend regularly or in high enough dollar amounts, 2) customers who spend regularly and in high dollar amounts, and 3) everyone else.
→ What offers can you make to each of these groups? What is likely to intrigue each individual group enough to respond?
Most organizations build basic profiles of their customers based on their spending patterns with the company, as just outlined. But if that is all you use, you could be making big mistakes.
Consider a customer who has four primary credit cards. In many cases, that customer uses different cards to manage specific expenses—one might be used to manage school expenses for children, one might be for household expenses and another for items related to a hobby. There might also be a corporate credit card for business entertainment and travel. If you profile this customer using the data from any single set of transactions, you will miss key attributes that could give you insight into better ways to market to that customer.
That is where a variety of data enhancement methods can come into play—talk to your vendors about what kind of data they have available to append to your file.
One of the easiest data enhancement methods to access is based on taking ZIP+4 postcodes and drilling down into the socio-geodemographic data available from a variety of sources to build an overlay to the data that you maintain in your transaction database.
For example, for the customer with four primary credit cards—where each provides a specific view based on the available transactions—the deeper view provided by overlaying this socio-geodemographic data may show a new opportunity to market to that customer. You might learn that the customer in question lives near a golf course, on a lake or in an urban enclave. You can purchase data that will help you zero in on those with larger disposable incomes or new home owners. Every additional data point overlaid helps you to build a better profile to target your offers.
That's just one example of scrutinizing data to understand customer behavior and drive higher response rates. There are others. For example, transaction billers use some of these techniques and variable data printing (VDP) to add personalized marketing offers to transaction bills and statements that get higher response rates because customer data was used to inform the offers.
One company specializing in the non-profit market, Savannah, Ga.'s The Kennickel Group, did a deep dive into the data of The Citadel's donor database and used the insights gained and VDP to increase membership more in 60 days than it would normally increase in a year. And Fenske Media (see sidebar) has had great success with ZIP+4 socio-geodemographic data.
The common thread is taking what you already know and what you can learn by appending additional available data to that, and using the result to inform how you converse with your many customer constituencies. That will provide differentiation for your clients.
Pat McGrew, EDP, is the Data-driven Communication Evangelist at Kodak. She can be reached at Pat.McGrew@kodak.com Follow her on Twitter @PatMcGrew, and read her blog at http://patmcgrew.growyourbiz.kodak.com
Pat McGrew, M-EDP, CMP is the Director and Evangelist for the Production Workflow Service at InfoTrends. As an analyst and industry educator, McGrew works with InfoTrends customers and its clients to promote workflow effectiveness. She also has a background in data-driven customer communication, and production printing with offset, inkjet, and toner. Co-author of eight industry books, editor of "A Guide to the Electronic Document Body of Knowledge," and regular writer in the industry trade press, McGrew won the 2014 #GirlsWhoPrint Girlie Award for her dedication to education and communication in the industry, and the 2016 Brian Platte Lifetime Achievement Award from Xplor International. Find Pat on Twitter as @PatMcGrew and LinkedIn.