Cost Effective Direct Marketing
Many Small businesses and organizations lack the expertise and capital to effectively communicate with their target audience through direct marketing. These organizations tend to invest in direct marketing campaigns that target an all-encompassing universe of their client base while hoping for a positive response and seeing a return on investment. In a large number of these cases the response is too small to justify the expense of executing the campaign.

Most of today’s unsuccessful direct mail campaigns executed by small organizations focus more on the mail package instead of the customer. Due to a lack of data mining skills and IT resources, many small organizations are unaware that they can reduce mailing size and cost while increasing positive response by mining into their customer base to extract customers with favorable transactions and characteristics. Technology is also available to apply additional demographic information to existing client bases such as interests in reading or gardening, household income ranges, age, presence of children and many, many more. The marrying of in-house customer data with outsourced demographic data is known as Customer Data Integration (CDI). Customer data elements combined with demographic data elements gives organizations more opportunity to segment their market into smaller targeted prospects who will receive their message personalized to the customers’ interests. Taking this approach will allow the organization to reduce mailing costs while increasing positive response.

To strengthen the data approach, a combination of a low cost email marketing campaign strategically timed with a follow up direct mail campaign will reinforce the message to the customer. The email campaign acts as a foreshadowing mechanism; notifying the customer for what is about to arrive in their mailbox. The email stimulates the interest and the direct mail piece drives home the message.


Let us take a look back at the early days of direct marketing; pre-internet. These were the days when organizations had encountered a large expense in storing and maintaining customer data. Computers and disk space needed to store data were not easily affordable. It was also a time when email marketing was non-existent, campaign management systems were basically unheard of and target marketing wasn’t a meaningful term.

Direct marketing consisted of extracting universes of customers from in-house databases for direct mail campaigns in a very straightforward manner. In most cases, Direct Marketers would segment their customer file based on recent purchases of existing products, the frequency of these purchases and the hunch that these customers may have an affinity to the new product that was about to be launched. If a direct marketer didn’t have enough in-house customers to market to or needed to acquire new customers, they would have to rent the mailing lists of organizations whose customer base was similar to their own and put those names through a costly merge/purge process to avoid duplicating customer names. As time went on customer databases began to grow while the mailing costs associated with the execution of the direct mail campaigns were also on the rise. A need arose to mail smarter; mail to less prospects while also increasing response rates.

The good news was that computers were becoming more affordable and disk space was doubling in size and decreasing in cost. This allowed organizations to not have to rely on large, expensive mainframe computers or to the contrary, PC’s in small organizations that didn’t have the storage capacity to maintain a growing customer file. Since disk space was becoming cheaper and the medium which stored the data became smaller, companies and organizations were able to capture and store an abundance of data about their customers in smaller more cost effective computers.

The client/server environment began replacing mainframes and the data stored in these high capacity servers was easy to access and manipulate through the client. And, since disk space became so plentiful the data did not have to be stored in a packed or binary format as it was on mainframes. Data in files began to look more “English like” as opposed to mainframe data which looked very much like machine language and not easily understood by the untrained eye.

Along with the client/server environment came a whole new set of applications to access this data. The database operating system that ran on these servers such as Oracle conformed to a standard called Open Database Compliance (ODBC). With ODBC, data could be stored in a format that could be accessed by any application of a users fondness (as long as the application, like Microsoft Access was ODBC as well). Data processing migrated from sequential file processing on mainframes to relational data processing in the client/server environment. What this meant was that data could now be stored in tables of rows and columns with a unique identifier on each record such as a customer account number. When a unique identifier on one table matched a unique identifier on another table or tables, a relationship would be formed.

Creating and understanding these relationships allowed marketers to get a better “view” of their customers. As the customer data relationships grew, the need to efficiently access this data also grew and so was born the campaign management system. Campaign management systems in most cases were and still are today, easy to use applications that allow users to ask questions (known as queries) of the customer relationships. Queries in the database sense are synonymous with customer segments and these queries would be grouped together in a hierarchy of mutually exclusive segments to form a campaign.

These advancements have allowed Marketers to segment more efficiently; to target smaller or rather more qualified universes to receive a direct marketing offer while keeping costs at bay. However, the need to keep improving target marketing and acquiring new customers had kept growing as well.

Acxiom Corporation in Little Rock, AR developed a technology that assigns a unique identifier called a link to every individual and address in the United States. This technology has allowed Acxiom to acquire and compile customer demographic/lifestyle information about millions of consumers in the United States from a multitude of sources based on assigning this link across the sources of the information. For a fee, organizations can send their customer base to Acxiom where a link will be assigned to the customer base and then joined to their demographic/lifestyle database in order to form a relationship and to overlay these characteristics onto the customer base. Acxiom has literally hundreds of these characteristics available for overlay. This technology has opened up new avenues to segment customer bases into unforeseen marketing groups.

In recent years many organizations have been implementing statistical modeling within their customer base. In its basic form, this involves mailing to a small cross section of a particular customer segment. Calculated data elements such as date of last order and total number of purchases, created at the time the cross section was mailed, are applied to the mailed customers and a file is created and stored for later statistical analysis. Once all responses to the campaign have been accumulated, the customers who responded are matched by their unique identifier to the stored data characteristic file in order to isolate the customers’ characteristics at the time of the mailing. The customers that have responded to the campaign, along with their data characteristics are run through a statistical analysis program to identify the statistically significant characteristics of an individual that would have the tendency to respond to the type of offer mailed to the cross section segment. Once these characteristics are identified a full universe is then selected by the same definition as the original cross section. The full universe is then run back through another statistical analysis program and the customers are ranked by their propensity to purchase based on the previously identified statistically significant characteristics and are subsequently mailed the same offer as the cross section sample.

Statistical modeling and analysis is a very advanced strategy that allows the organization to market within budget constraints to the most likely responders. Statistical modeling is used mainly in organizations where the customer base gets into the millions of customers. However, the point to small organizations is to make them aware of how a customer base can grow from hundreds of customers to thousands and even millions through the advances in segmentation and statistical analysis. Smarter segmentation leads to more purchases and a growing customer base while the application of demographic overlays and statistical modeling support drilling into the customer base to select highly qualified prospects to receive messages.

All of these marketing strategies are still in use today; some separately as standalone processes and in some cases all of these strategies are applied together in a systematic approach. These strategies can be applied across industries. They can be applied to the publishing industry, the non profit industry and to almost any small business or organization that is looking to target customers in a cost effective manner through direct marketing.

So, how does an organization go about implementing and executing these strategies? One solution is to contract with a small business outsourcer capable of bringing all of these pieces together. There are consultants such as Hopewell Data Services (HDS) (http://www.hopewelldataservices.com) that have years of experience in the direct marketing data processing field who act as liaisons between marketers with customer bases and mailing houses that create email and direct marketing offers.

Companies such as HDS meet with organizations to get an understanding of the particular goal the organizations may have with regard to sending messages/offers to prospective customers. An analysis of the current customer base is done to identify the state of the data within the customer base. The data within the customer base is then extracted and normalized (cleaned and formatted). Once the data is normalized, recommendations are made to the client as to possible segments to target or possible demographic data overlays that can be purchased and appended to the existing customer base which can further help the client segment and target.

After the segments are identified the transition of electronically prepared mailing files begins. This could entail transferring an email address file to an email vendor or a name and address file to a print vendor.

One of the new strategies to make contact with the segmented customers is to send identified prospects with email addresses, an email blast. The email blast is just what it sounds like; an email message is created and targeted at the specified segments of customers and is blasted out through the internet by an email marketing service provider. This email message is usually directed at informing prospects that a direct mail piece will be arriving in the coming days with a special offer or message. Depending on the timing, the direct mail piece arrives at the prospects home and an ongoing relationship is formed between the organization and the prospect.

It should be noted that because of the advances in customer segmentation, the email and direct mail pieces can provide different messages, tailored to each particular segment. The data vendor provides a message code on each customer’s record and the email or print vendor creates the marketing piece based on the message code.

The email and direct mail strategy can be an iterative process meaning that it can be done multiple times with a predefined amount of time in between efforts to contact customers a second, third or fourth time. In between each effort, the customers that have already responded are suppressed from the subsequent follow up efforts.

This is just one example of how target segmentation and direct marketing strategy can work together. Organizations can be as creative as they want with these approaches because there are segmentation specialists, email vendors and direct mail vendors out there that can help.

A company like Hopewell Data Services manages all of the file preparation; from cleaning the existing database to applying demographic and lifestyle information to the actual segmentation and key coding of the market segments. HDS then manages the transfer of the customer files to the respective email and/or print vendor. The email and print vendors manage the process of creating targeted direct mail and email messages and specialize in the prompt delivery of such messages.