Chapter 1

Data Driven Marketing 101: It’s All About the Customer

In This Chapter

Understanding what data driven marketing is

Compiling, managing, and analyzing customer data

Understanding the components of database marketing campaigns

Measuring and learning from your marketing campaigns

Data driven marketing means using data about customers to drive marketing communications. The use of consumer data in advertising was pioneered back in the 1930s by Arthur Nielsen of TV ratings fame. Nielsen’s data was, and still is, largely derived from survey research. In this book, I approach the subject of data driven marketing primarily, though not exclusively, from the perspective of database marketing.

Database marketing uses direct communication to individuals, like direct mail and e-mail campaigns. What sets database marketing apart is, not surprisingly, the data it uses. Even though the phrase data driven marketing refers more broadly to all types of marketing, I often use the phrase interchangeably with database marketing.

Database marketers rely on customer databases that contain more than just customer contact information. These databases typically contain purchase history, age, income, and a host of other customer attributes. These attributes are analyzed with an eye toward understanding how to effectively drive sales to different types of customers.

Everything you do as a database marketer begins and ends with the customer. Your effectiveness at driving sales is fundamentally dependent on your understanding of your customer base. This understanding is driven not by intuition or guesswork but by rigorous analyses of facts. Virtually every decision you make is rooted in what your database says about your customers.

Database marketing involves a hybrid set of skills. It requires some level of understanding of database technology. It also involves some degree of analytic capability, particularly some familiarity with statistics. Business acumen is also key. And finally, it requires having some degree of appreciation for the psychology of the consumer. This diversity of skill sets is reflected in the teams that populate database marketing departments. They tend to be very interesting places to work.

In this chapter, I present an overview of the various components of an effective database marketing strategy. Clearly the database is one important component. Your approach to designing communications, or campaigns, is another. And one of the defining characteristics of database marketing is the use of analytic methods to measure and learn from your campaigns. But first, I want to prevent some potential confusion about the focus of this book.

What Data Driven Marketing Is (and Isn’t) About

A number of marketing sub-disciplines involve the use of customer data. Web marketing and social media marketing come to mind. I touch briefly on some aspects of web marketing in this book. And I touch even more briefly on social media marketing. Both of these subjects are explored in depth in their own For Dummies volumes.

Small business marketing and business-to-business marketing have also evolved to include substantial use of data. Many of the ideas in this book can be applied in these arenas. In this book, however, I focus specifically on business-to-consumer marketing. I outline my reasons for that in this section.

Database marketing versus direct marketing

Database marketing, as I’ve said, is a sub-discipline of direct marketing. Direct marketing refers broadly to any communication that is sent directly to a consumer. It does not imply any sophisticated, or even unsophisticated, targeting or audience selection. The “coupon clipper” circulars that show up in your mailbox every week are an example of direct marketing that is not database marketing. Sure, they’re sent directly to your mailbox. But they’re sent to everyone else’s as well.

I call this approach to direct marketing carpet bombing. Its online equivalent is spam. And it’s probably the easiest way to distinguish direct marketing in general from database marketing as I use the terms. Database marketing is the opposite of carpet bombing.

These carpet bombing campaigns typically involve low-quality mail pieces and messages. They’re frequently addressed to Resident rather than to an individual. And they’re not targeted.

Database marketing campaigns on the other hand reflect the use of customer data in all these areas. The mail piece is personalized and often customized to resonate with the recipient. And most importantly, these campaigns are targeted at specific audiences rather than entire zip codes.

Database marketing and customer ­relationship management

Customer relationship management, more commonly shortened to CRM, has become a marketing industry staple. You hear about CRM in the context of systems, software, sales force automation, and corporate marketing strategies. It refers generally to the use of customer data throughout the enterprise.

In this sense, database marketing is a component of a company’s CRM strategy. By definition, if you’re doing database marketing, you’re using customer data. But CRM also involves other forms of customer contact. Salespeople, call center agents, customer service reps, and company employees all have contact with customers. CRM attempts to use customer data to support all these functions.

This book focuses on only the database marketing component of customer contacts. Most of the discussion and examples I give stress communications through direct mail or e-mail or other electronic media. The only exception to this is a short section in Chapter 17 where I talk about integrating customer data with your company’s broader CRM strategy.

Marketing to businesses

As I say earlier, I don’t focus on business-to-business marketing in this book. One reason for this is that it’s not my area of expertise. But I do know enough to know that business-to-business marketing differs in many significant ways from consumer marketing.

For one thing, in consumer marketing the fundamental notion is a household. Communications are targeted at and customized around individual households. On the business side, one business can have multiple contacts that you need to keep track of.

This difference affects fundamentally the way a business-to-business database needs to be structured. The consumer database model won’t work. And the types of data that’s available on businesses differs dramatically from that on the consumer side. Presence of children and marital status — typical marketing variables — don’t make any sense in context of businesses.

For another thing, the different contacts in a business need to be treated differently when designing communications. For example, when trying to get a mail piece read by an executive, you need to think about how to keep the assistant who screens the mail from throwing it in the recycling bin.

That’s not to say that the ideas in this book are completely irrelevant to business-to-business marketing. The basic structure of database marketing campaigns is the same in both contexts. Much of my discussion on analytics and measurement also applies.

Database marketing and small businesses

Again, many of the ideas in this book are applicable to small businesses. But small businesses have some features that make robust database marketing difficult, if not impossible.

First, small businesses are often geographically limited in their customer base. This makes the use of simpler and less expensive direct-mail programs more feasible than targeted programs. It’s more cost effective for them to just carpet bomb zip codes than spend a lot of money on buying targeted lists.

Small businesses also have limited marketing and technology budgets. The cost of setting up elaborate databases, purchasing customer demographic data, and buying analytic tools can be prohibitive.

Having said that, it’s possible for a clever and industrious business owner to take a more low-budget approach. The typical suite of office software contains presentation software along with spreadsheet and database software. Moreover, the spreadsheet software is a fairly powerful reporting and statistical tool if you know what you’re doing. You could go a long way with just a PC and the occasional college intern.

Focusing on the Customer

Everything you do as a database marketer depends on understanding your customers. This in turn depends on having clean and accurate customer information. Customer databases aren’t static entities. You don’t just build one and hang up the tool belt. These databases require constant maintenance.

Getting customer data from your ­companies systems

Your customer data is spread out across a variety of sources. You have transaction systems that keep track of sales, orders, service requests, customer complaints, and everything else it takes to run your business. These systems include call center databases, cash registers, your website, and your e-commerce system.

When building a customer database, you face two main challenges. The first is simply getting the data out of these systems and collected in one place. Because the data is always being updated, this is an ongoing process.

Most customer databases that I’ve seen involve regularly scheduled data downloads from various source systems. I’ve seen this done monthly, weekly, and even daily or hourly. The frequency of these downloads defines how fresh the data in your database is.

It’s important for you to understand how fresh your data is. Very likely, not all systems are loaded to your database on the same schedule, so this freshness factor may vary depending on what data you’re looking at. The freshness of you data limits what you can do with it. For example, you can’t send daily thank you e-mails for purchases if your purchase data is only loaded to your database on a weekly basis.

Integrating data at the customer level

The second, bigger, challenge is integrating the data. Database marketing focuses on the customer. Your database needs to be organized around your customers as well. Everything needs to tie back to individual customers.

One thing that makes this difficult is that much of the transaction data in your company’s operational systems is anonymous. It’s not actually associated with customers at all. If I go into a grocery store and hand the cashier a dollar bill for a candy bar, that sale can’t be tied back to me.

Throughout this book, I talk about a number of ways to overcome this challenge. Technology — particularly the Internet and other electronic media — are making it much easier to get information about your customers’ behavior, attitudes, and preferences. In the Part of Tens, I include a chapter specifically about this and other ways of linking data to customers.

Managing contact information

Your database marketing campaigns involve contacting your customers directly. That means your customer contact information needs to be accurate. Whether it’s e-mail addresses, home addresses, or mobile device numbers, if these addresses aren’t accurate, you’re wasting your time.

Address quality

Managing your customer contact information is an ongoing maintenance chore. People move all the time. So addresses need to be regularly updated. The U.S. Postal Service can help with this. The USPS maintains and makes available a mover database that contains the change of address information it receives.

E-mail addresses aren’t as easy to update, though a growing number of services can help with this. It is important, however, to recognize when an e-mail address is no longer in use. You can tell when one of your e-mails bounces, or is returned as undeliverable. These need to be removed from your database. If not, you risk the nasty repercussions of being labeled a spammer.

Respecting opt-outs

Customers can and do request to be taken off mailing lists. Another ongoing maintenance chore involves managing these opt-outs. Active management is required because these opt-outs can come from a variety of sources.

In the case of physical mail, there’s a national database that contains global opt-outs. By global I mean these are folks who’ve asked to be excluded from any direct-marketing campaign. This database is managed by the Direct Marketing Association (of which you should be a member.) But people can also contact you directly to ask that you not market to them.

In the case of e-mail, legislation requires you to provide customers with the ability to opt out of hearing from you. Another piece of legislation applies to phone numbers. A national Do Not Call registry is operated by the FTC.

You need to respect opt-out requests. In some cases, you’re legally obligated to do so. In the case of mail, the Direct Marketing Association takes a dim view of database marketers who don’t comply with their standards. If the DMA blackballs you, you’ll have a difficult time finding credible vendors to help with your direct mail. Plus, bottom line, it’s just good business. Why waste money communicating with people who don’t want to hear from you?

Communicating with the household

Most database marketing campaigns are directed at households rather than individuals. This is partly to reduce mailing costs. It usually doesn’t make sense to mail multiple copies of your message to the same address.

But focusing on the whole household serves another purpose as well. Many of the demographic traits that drive the way you target and communicate your marketing messages are household-level traits. Things like presence of children and household income are classic examples. In fact, all the data collected by the U.S. Census Bureau is collected at the household level.

Actually building a household record is a little bit tricky. It involves a good bit of data cleansing and data processing to group customers together. Luckily, a variety of companies out there can do this for you. In fact, because they have a lot more data than you do, they can do it far more effectively than you can. I talk about householding services in Chapter 19.

The Database Marketing Campaign

Database marketing campaigns all have the same basic structure. And as the title of this chapter suggests, that structure revolves around the customer. As you develop your campaigns, you’ll be using customer data to answer four basic questions about your campaign:

Who?

What?

How?

When?

Your attention needs to stay focused on the customer. A common trap that marketers fall into is to start with an offer or promotion and attempt to find an audience to sell it to. This “I’ve got something to sell, find me someone to buy it” approach is completely backwards and not particularly effective. The power of database marketing comes from an understanding of the customer’s needs, preferences, and even financial means. An understanding of your customer’s perspective is what drives effective offers.

The target audience

Your database marketing campaigns begin with a particular business challenge or opportunity in mind — a goal. Typically your goal involves generating revenue. But sometimes your goal may not generate revenue directly. For example, you may be asked to increase web registrations, with the thought that these registrants may be more likely to make purchases in the future.

Once you’re presented with this goal, you need to figure out whom you’re going to communicate with. This target audience will drive everything else you do in your campaign development process. Much of this book revolves around techniques for defining this target audience. The heart and soul of database marketing is the ability to use data and analytic techniques to choose audiences that will help you meet your business goal.

Through the process of selecting your target audience, you’ll develop a clear picture of the traits that are shared by that audience. You’ll have a profile of that audience which might include past purchases, age, income, marital status, presence of children, and a host of other characteristics that describe them. This profile — your understanding of the who — will be used in answering the what, how, and when questions about your campaign.

The offer

The core of your marketing message is the offer that you put in front of the customer. Your goal is to make this offer relevant to members of your target audience. The audience profile is key in designing the offer.

You’ll probably find, for example, that you have two distinct groups of customers as it relates to discounts. You have some that are extremely price sensitive and others who are more interested in getting premium benefits. By understanding which is which, you can design different offers that will appeal to different groups.

The price-sensitive group is probably going to need a discount. So you might offer them 20 percent off their next purchase. But the benefit-driven customer may be enticed by simply informing them of your new top-of-the-line product, no discount needed.

Whatever your offer, you need to make it clear and concise. It’s generally a bad idea to make several offers in a single communication. The offer needs to be the star of the show.

Closely related to your offer is the call to action. It’s not enough to make the offer. You need to explicitly tell the customer how to take advantage of it. Visit our website now or Visit our store today. Like your offer, your call to action should be simple and prominent. It should also be crafted to convey a sense of urgency.

Your marketing message

The how of your database marketing campaign involves what you’re sending them. This is frequently referred to as your marketing collateral. The first decision you have to make is how you’re going to deliver it. For example, will it be an e-mail or direct mail piece?

Again, your database can help. By looking at past campaigns, you can get a sense of who is responding through which channels. People who often purchase online may be enticed by an e-mail with a link to your site. Those who typically buy in stores may respond better to a direct-mail piece that includes a coupon.

Once you’ve selected the channel, you can proceed with designing your communication. Here, yet again, the profile of your target audience can help you. You can craft your message with that particular audience in mind. Often you do that with the help of an advertising or creative partner. But that partner will definitely want to understand the target audience profile.

Timing your message

Your database marketing campaigns may be executed in conjunction with broader marketing initiatives, like mass media advertising. In these cases, they clearly need to be timed to take full advantage of the combined effects.

But you also need to understand when the message is most likely to be effective. If you’re announcing a weekend sale, when is the right time to be in market? You don’t want to wait until the last minute, but should you send your communication a week in advance? Two weeks?

These same sorts of questions arise when you’re marketing products that require some degree of planning on the part of the customer. Things like vacations and mortgages don’t tend to be last-minute impulse purchases. If you’re trying to fill empty hotel rooms, you can’t wait until the last minute to drop a campaign.

Your database can help yet again. By looking at past campaigns, you can get a sense of where the sweet spot lies. In the case of hotel reservations, your database can tell you exactly how far in advance reservations are typically made.

Analyzing Customer Data

A good portion of this book is about the types of analysis that are performed in database marketing. Much of this analysis requires some advanced knowledge of mathematics and statistics.

In this book, I largely avoid the more technical aspects. I concentrate instead on pointing out some pitfalls to look out for and how to evaluate the results of advanced analysis. In this section, I introduce you to a couple of the more common types of insights that can be coaxed from your customer data.

Grouping customers into segments

Database marketing relies fundamentally on the process of identifying the target audience. This is essentially an exercise in finding groups of customers who share similar traits.

A great deal of the analytic work that goes on in marketing in general has to do with finding such groups, called segments. Such work is generally referred to as segmentation. In this section, I outline a few approaches to segmentation that are commonly used in database marketing.

Geographic segments

Perhaps the easiest to understand is geographic segmentation. Households in the U.S. are divided up into a hierarchy of geographic groupings. These groups are defined by the Census Bureau. They range from individual city blocks, to block groups all the way up to what are called Metropolitan Statistical Areas, or MSAs, which are essentially large population centers.

The reason that these segments are important to marketers is that they reflect the way mass media is distributed. The viewership of local TV network affiliates is closely aligned with these MSAs. The same is true of radio audiences and the subscription bases of local newspapers.

What’s more, these are the groupings that the Census Bureau uses to report census data. That means that marketers have access to a wealth of demographic and economic data on these areas.

Weather, culture, and even school calendars vary geographically. This means that you’ll often have reason to use geography in developing your marketing campaigns. Regional differences can help drive decisions about what products to offer, how to craft your message, and even when to communicate.

Demographic segmentation

Demographic data is a core part of your database. This data generally needs to be purchased if you want to have it at the individual customer level. There are a number of vendors out there who provide a wide range of customer level information.

Lifestage traits such as age, marital status, and number of children all affect the product needs of consumers. They also affect the types of messages that will make an emotional connection. Income drives affordability and price sensitivity. Homeowners need a wide range of products and services that aren’t needed by renters.

As an ongoing part of your job as a database marketer, you’ll be involved in trying to define and refine a demographic segmentation scheme for your customers. Because every business is different, there really isn’t a one-size-fits-all segmentation scheme you can universally apply. By combining your customer purchase history with demographic data, you can develop a segmentation scheme tailored for your particular business needs.

Behavioral segmentation

Another common approach to segmentation involves analyzing transaction data. The idea is to group customers together based on the way they interact with you.

A standard behavioral segmentation in the credit-card industry involves looking at how customers use their cards. They’re grouped into transacters, revolvers, or inactive according to how much they use the card and whether they pay off their balance every month. This is a useful distinction, because the nature of the revenue these groups generate is very different. Marketing campaigns are dramatically different depending on which group is being targeted.

Behavioral segmentation is particularly fruitful with web data. When customers visit your website, they generate a huge virtual paper trail. You can tell what pages they’re looking at, what they’re searching and shopping for, and even how long they stay on your site. This data is extremely useful in developing relevant database marketing campaigns.

Building response models

It’s possible to use the results of past campaigns to define target audiences for future campaigns. This exercise is known as response modeling. It involves looking at the various customer attributes of responders. Some of these attributes turn out to be closely associated with how likely a customer is to respond to a given campaign.

When advanced statistical techniques are applied, it’s actually possible to throw a bunch of different attributes into the mix all at once. These statistical procedures then tease out the combination of attributes that best predicts a customer response.

Again, I avoid getting into the deep statistical detail that underlies the model building process. Instead I focus on how to approach working with a statistician to build models. I also give you some tools to help interpret and evaluate response models.

Measuring Results

As I say at the beginning of this chapter, your job as a database marketer begins and ends with the customer. Focus on the individual customer is the defining principle of database marketing.

But there’s another defining characteristic of database marketing that’s a close second. Database marketing is measurable. Everything about it is measurable. Messages, offers, response rates, and even revenue can all be measured.

I don’t mean to imply that every one of these things can be measured for every marketing campaign. You need to pick and choose your battles, so to speak. But there is nothing about these campaigns that you can’t measure or test if you so choose.

There is an age-old adage among marketing executives that laments their inability to really understand marketing effectiveness. They know, so the saying goes, that only half their marketing is working. They just don’t know which half. This adage does not apply to the database marketer.

The secret lies in the fact that you know who is responding to your campaigns. You can tie responses back to individuals in your target audience. This allows you to essentially design marketing experiments.

These experiments can test the success of one offer over another. You can scientifically compare the response rates of different messages. And you can even confidently calculate the revenue that is specifically due to your efforts.

In this book, I give you a primer on how to use the scientific method to go about designing your marketing experiments. By properly setting up your campaigns as experiments, you can not only learn a great deal about what works and what doesn’t — you can also take credit for quantifiable contributions to your company’s bottom line.