5 Tips for Effective Multichannel Campaigns

Your audience is in more than one place—you need to be too. Multichannel marketing means reaching your audience across more than one channel. A good multichannel campaign could be the key to your marketing success. Customers these days rarely communicate with any brand through one channel alone. These tips will help you craft your multichannel marketing campaign to not only include the latest digital channels, but also direct mail.

Your audience is in more than one place—you need to be too. multichannel marketing means reaching your audience across more than one channel. A good multichannel campaign could be the key to your marketing success. Customers these days rarely communicate with any brand through one channel alone. These tips will help you craft your multichannel marketing campaign to not only include the latest digital channels, but also direct mail.

5 Tips for effective multichannel campaigns:

  1. Start with your goal: Some common goals are to promote a product or service, increase sales, generate inquires or leads, brand awareness, build relationships, etc. Have your goal or goals clearly in mind so you can plan every stage of your campaign to best meet them. Laser focus on your goals will give you better results.
  2. Who is your audience: Before you start building your campaign, know who you’re talking to. Use sources such as previous campaigns, customer feedback, demographic information, and website or social media metrics to build a clear image of your audience. Make use of customer profiles to focus on your ideal customer, their wants and needs, and the kind of message that appeals to them. The more targeted the message the better your response will be.
  3. Choose channels carefully: A multichannel campaign doesn’t mean using every possible channel. Rather, figure out which combination of channels is likely to resonate best with your target audience. Use what you know about their past interactions with you, to help you make that decision. Pick the channels that will give you the most bang for your buck. Remember that the newest channels that have a lot of buzz, may not be the best channels to reach your audience.
  4. Consistent messaging: A good multichannel campaign gives your audience a consistent experience across channels. The value you offer them and your brand voice should remain consistent across channels. After all, your customers don’t think in terms of channels, they think in terms of what your message means to them. Make their transition between channels (such as scanning a QR code to go from direct mail to online, or clicking your email link) seamless.
  5. Vary delivery: Consistency is important in a multichannel marketing campaign, but that doesn’t mean saying the message the exact same way in each medium. Each channel has its own best method of communication. The essence of your message will be the same, but the way you convey it in a 140 character tweet will differ greatly from how you say it on your direct mail piece.

Planning a multichannel campaign takes time and effort. By breaking the process down into clear steps and always keeping your goals and your audience in mind, you can plan a campaign that will put your message in front of the right people, at the right time and in the right way. One reason that direct mail is so effective in a multichannel campaign, is that it facilitates the cross over from print to online. Direct mail can drive online engagement and still have all of the tangible benefits. The fact that it can be highly targeted, kept for long periods of time, used over and over, and then easily shared with others is a real bonus. Get started on your multichannel marketing campaign today.

Beyond RFM Data

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

In the world of predictive analytics, the transaction data is the king of the hill. The master of the domain. The protector of the realm. Why? Because they are hands-down the most powerful predictors. If I may borrow the term that my mentor coined for our cooperative venture more than a decade ago (before anyone even uttered the word “Big Data”), “The past behavior is the best predictor of the future behavior.” Indeed. Back then, we had built a platform that nowadays could easily have qualified as Big Data. The platform predicted people’s future behaviors on a massive scale, and it worked really well, so I still stand by that statement.

How so? At the risk of sounding like a pompous mathematical smartypants (I’m really not), it is because people do not change that much, or if so, not so rapidly. Every move you make is on some predictive curve. What you been buying, clicking, browsing, smelling or coveting somehow leads to the next move. Well, not all the time. (Maybe you just like to “look” at pretty shoes?) But with enough data, we can calculate the probability with some confidence that you would be an outdoors type, or a golfer, or a relaxing type on a cruise ship, or a risk-averse investor, or a wine enthusiast, or into fashion, or a passionate gardener, or a sci-fi geek, or a professional wrestling fan. Beyond affinity scores listed here, we can predict future value of each customer or prospect and possible attrition points, as well. And behind all those predictive models (and I have seen countless algorithms), the leading predictors are mostly transaction data, if you are lucky enough to get your hands on them. In the age of ubiquitous data and at the dawn of the “Internet of Things,” more marketers will be in that lucky group if they are diligent about data collection and refinement. Yes, in the near future, even a refrigerator will be able to order groceries, but don’t forget that only the collection mechanism will be different there. We still have to collect, refine and analyze the transaction data.

Last month, I talked about three major types of data (refer to “Big Data Must Get Smaller“), which are:
1. Descriptive Data
2. Behavioral Data (mostly Transaction Data)
3. Attitudinal Data.

If you gain access to all three elements with decent coverage, you will have tremendous predictive power when it comes to human behaviors. Unfortunately, it is really difficult to accumulate attitudinal data on a large scale with individual-level details (i.e., knowing who’s behind all those sentiments). Behavioral data, mostly in forms of transaction data, are also not easy to collect and maintain (non-transaction behavioral data are even bigger and harder to handle), but I’d say it is definitely worth the effort, as most of what we call Big Data fall under this category. Conversely, one can just purchase descriptive data, which are what we generally call demographic or firmographic data, from data compilers or brokers. The sellers (there are many) will even do the data-append processing for you and they may also throw in a few free profile reports with it.

Now, when we start talking about the transaction data, many marketers will respond “Oh, you mean RFM data?” Well, that is not completely off-base, because “Recency, Frequency and Monetary” data certainly occupy important positions in the family of transaction data. But they hardly are the whole thing, and the term is misused as frequently as “Big Data.” Transaction data are so much more than simple RFM variables.

RFM Data Is Just a Good Start
The term RFM should be used more as a checklist for marketers, not as design guidelines—or limitations in many cases—for data professionals. How recently did this particular customer purchase our product, and how frequently did she do that and how much money did she spend with us? Answering these questions is a good start, but stopping there would seriously limit the potential of transaction data. Further, this line of questioning would lead the interrogation efforts to simple “filtering,” as in: “Select all customers who purchased anything with a price tag over $100 more than once in past 12 months.” Many data users may think that this query is somewhat complex, but it really is just a one-dimensional view of the universe. And unfortunately, no customer is one-dimensional. And this query is just one slice of truth from the marketer’s point of view, not the customer’s. If you want to get really deep, the view must be “buyer-centric,” not product-, channel-, division-, seller- or company-centric. And the database structure should reflect that view (refer to “It’s All About Ranking,” where the concept of “Analytical Sandbox” is introduced).

Transaction data by definition describe the transactions, not the buyers. If you would like to describe a buyer or if you are trying to predict the buyer’s future behavior, you need to convert the transaction data into “descriptors of the buyers” first. What is the difference? It is the same data looked at through a different window—front vs. side window—but the effect is huge.

Even if we think about just one simple transaction with one item, instead of describing the shopping basket as “transaction happened on July 3, 2014, containing the Coldplay’s latest CD ‘Ghost Stories’ priced at $11.88,” a buyer-centric description would read: “A recent CD buyer in Rock genre with an average spending level in the music category under $20.” The trick is to describe the buyer, not the product or the transaction. If that customer has many orders and items in his purchase history (let’s say he downloaded a few songs to his portable devices, as well), the description of the buyer would become much richer. If you collect all of his past purchase history, it gets even more colorful, as in: “A recent music CD or MP3 buyer in rock, classical and jazz genres with 24-month purchase totaling to 13 orders containing 16 items with total spending valued in $100-$150 range and $11 average order size.” Of course you would store all this using many different variables (such as genre indicators, number of orders, number of items, total dollars spent during the past 24 months, average order amount and number of weeks since last purchase in the music category, etc.). But the point is that the story would come out this way when you change the perspective.

Creating a Buyer-Centric Portrait
The whole process of creating a buyer-centric portrait starts with data summarization (or de-normalization). A typical structure of the table (or database) that needs to capture every transaction detail, such as transaction date and amount, would require an entry for every transaction, and the database designers call it the “normal” state. As I explained in my previous article (“Ranking is the key”), if you would like to rank in terms of customer value, the data record must be on a customer level, as well. If you are ranking households or companies, you would then need to summarize the data on those levels, too.

Now, this summarization (or de-normalization) is not a process of eliminating duplicate entries of names, as you wouldn’t want to throw away any transaction details. If there are multiple orders per person, what is the total number of orders? What is the total amount of spending on an individual level? What would be average spending level per transaction, or per year? If you are allowed to have only one line of entry per person, how would you summarize the purchase dates, as you cannot just add them up? In that case, you can start with the first and last transaction date of each customer. Now, when you have the first and last transaction date for every customer, what would be the tenure of each customer and what would be the number of days since the last purchase? How many days, on average, are there in between orders then? Yes, all these figures are related to basic RFM metrics, but they are far more colorful this way.

The attached exhibit displays a very simple example of a before and after picture of such summarization process. On the left-hand side, there resides a typical order table containing customer ID, order number, order date and transaction amount. If a customer has multiple orders in a given period, an equal number of lines are required to record the transaction details. In real life, other order level information, such as payment method (very predictive, by the way), tax amount, discount or coupon amount and, if applicable, shipping amount would be on this table, as well.

On the right-hand side of the chart, you will find there is only one line per customer. As I mentioned in my previous columns, establishing consistent and accurate customer ID cannot be neglected—for this reason alone. How would you rely on the summary data if one person may have multiple IDs? The customer may have moved to a new address, or shopped from multiple stores or sites, or there could have been errors in data collections. Relying on email address is a big no-no, as we all carry many email addresses. That is why the first step of building a functional marketing database is to go through the data hygiene and consolidation process. (There are many data processing vendors and software packages for it.) Once a persistent customer (or individual) ID system is in place, you can add up the numbers to create customer-level statistics, such as total orders, total dollars, and first and last order dates, as you see in the chart.

Remember R, F, M, P and C
The real fun begins when you combine these numeric summary figures with product, channel and other important categorical variables. Because product (or service) and channel are the most distinctive dividers of customer behaviors, let’s just add P and C to the famous RFM (remember, we are using RFM just as a checklist here), and call it R, F, M, P and C.

Product (rather, product category) is an important separator, as people often show completely different spending behavior for different types of products. For example, you can send me fancy-shmancy fashion catalogs all you want, but I won’t look at it with an intention of purchase, as most men will look at the models and not what they are wearing. So my active purchase history in the sports, home electronics or music categories won’t mean anything in the fashion category. In other words, those so-called “hotline” names should be treated differently for different categories.

Channel information is also important, as there are active online buyers who would never buy certain items, such as apparel or home furnishing products, without physically touching them first. For example, even in the same categories, I would buy guitar strings or golf balls online. But I would not purchase a guitar or a driver without trying them out first. Now, when I say channel, I mean the channel that the customer used to make the purchase, not the channel through which the marketer chose to communicate with him. Channel information should be treated as a two-way street, as no marketer “owns” a customer through a particular channel (refer to “The Future of Online is Offline“).

As an exercise, let’s go back to the basic RFM data and create some actual variables. For “each” customer, we can start with basic RFM measures, as exhibited in the chart:

· Number of Transactions
· Total Dollar Amount
· Number of Days (or Weeks) since the Last Transaction
· Number of Days (or Weeks) since the First Transaction

Notice that the days are counted from today’s point of view (practically the day the database is updated), as the actual date’s significance changes as time goes by (e.g., a day in February would feel different when looked back on from April vs. November). “Recency” is a relative concept; therefore, we should relativize the time measurements to express it.

From these basic figures, we can derive other related variables, such as:

· Average Dollar Amount per Customer
· Average Dollar Amount per Transaction
· Average Dollar Amount per Year
· Lifetime Highest Amount per Item
· Lifetime Lowest Amount per Transaction
· Average Number of Days Between Transactions
· Etc., etc…

Now, imagine you have all these measurements by channels, such as retail, Web, catalog, phone or mail-in, and separately by product categories. If you imagine a gigantic spreadsheet, the summarized table would have fewer numbers of rows, but a seemingly endless number of columns. I will discuss categorical and non-numeric variables in future articles. But for this exercise, let’s just imagine having these sets of variables for all major product categories. The result is that the recency factor now becomes more like “Weeks since Last Online Order”—not just any order. Frequency measurements would be more like “Number of Transactions in Dietary Supplement Category”—not just for any product. Monetary values can be expressed in “Average Spending Level in Outdoor Sports Category through Online Channel”—not just the customer’s average dollar amount, in general.

Why stop there? We may slice and dice the data by offer type, customer status, payment method or time intervals (e.g., lifetime, 24-month, 48-months, etc.) as well. I am not saying that all the RFM variables should be cut out this way, but having “Number of Transaction by Payment Method,” for example, could be very revealing about the customer, as everybody uses multiple payment methods, while some may never use a debit card for a large purchase, for example. All these little measurements become building blocks in predictive modeling. Now, too many variables can also be troublesome. And knowing the balance (i.e., knowing where to stop) comes from the experience and preliminary analysis. That is when experts and analysts should be consulted for this type of uniform variable creation. Nevertheless, the point is that RFM variables are not just three simple measures that happen be a part of the larger transaction data menu. And we didn’t even touch non-transaction based behavioral elements, such as clicks, views, miles or minutes.

The Time Factor
So, if such data summarization is so useful for analytics and modeling, should we always include everything that has been collected since the inception of the database? The answer is yes and no. Sorry for being cryptic here, but it really depends on what your product is all about; how the buyers would relate to it; and what you, as a marketer, are trying to achieve. As for going back forever, there is a danger in that kind of data hoarding, as “Life-to-Date” data always favors tenured customers over new customers who have a relatively short history. In reality, many new customers may have more potential in terms of value than a tenured customer with lots of transaction records from a long time ago, but with no recent activity. That is why we need to create a level playing field in terms of time limit.

If a “Life-to-Date” summary is not ideal for predictive analytics, then where should you place the cutoff line? If you are selling cars or home furnishing products, we may need to look at a 4- to 5-year history. If your products are consumables with relatively short purchase cycles, then a 1-year examination would be enough. If your product is seasonal in nature—like gardening, vacation or heavily holiday-related items, then you may have to look at a minimum of two consecutive years of history to capture seasonal patterns. If you have mixed seasonality or longevity of products (e.g., selling golf balls and golf clubs sets through the same store or site), then you may have to summarize the data with multiple timelines, where the above metrics would be separated by 12 months, 24 months, 48 months, etc. If you have lifetime value models or any time-series models in the plan, then you may have to break the timeline down even more finely. Again, this is where you may need professional guidance, but marketers’ input is equally important.

Analytical Sandbox
Lastly, who should be doing all of this data summary work? I talked about the concept of the “Analytical Sandbox,” where all types of data conversion, hygiene, transformation, categorization and summarization are done in a consistent manner, and analytical activities, such as sampling, profiling, modeling and scoring are done with proper toolsets like SAS, R or SPSS (refer to “It’s All About Ranking“). The short and final answer is this: Do not leave that to analysts or statisticians. They are the main players in that playground, not the architects or developers of it. If you are serious about employing analytics for your business, plan to build the Analytical Sandbox along with the team of analysts.

My goal as a database designer has always been serving the analysts and statisticians with “model-ready” datasets on silver platters. My promise to them has been that the modelers would spend no time fixing the data. Instead, they would be spending their valuable time thinking about the targets and statistical methodologies to fulfill the marketing goals. After all, answers that we seek come out of those mighty—but often elusive—algorithms, and the algorithms are made of data variables. So, in the interest of getting the proper answers fast, we must build lots of building blocks first. And no, simple RFM variables won’t cut it.

Extended Coverage: USPS – Will It Disappear?

When your editor makes a decision to defend you in the comments section below a feature article, then the article must have hit a nerve! I talked to several mailers, and association leaders who represent them, in a feature this month in the magazine … as I should: mailers have a lot to say about goings-on at the Postal Service

When your editor makes a decision to defend you in the comments section below a feature article, then the article must have hit a nerve!

I talked to several mailers, and association leaders who represent them, in a feature this month in the magazine … as I should: mailers have a lot to say about goings-on at the Postal Service (and not-goings-on in Congress) leading some mail marketers to re-evaluate the medium. I’d say it is a timely premise—particularly with the recent exigent postage hike on top of the inflation-indexed hike.

Far more was offered than I could include in the feature. However, “Marketing Sustainably” has a bit of room and—with my editor’s permission—allow me to share a few more observations.

Let me be clear, every mailer I talked to wants the Postal Service to succeed. The prescriptions may vary. What may be unclear is how it will succeed…

Always the Postologist, Charley Howard of Harte-Hanks had these points to share on a future path:

“If the Postal Service is allowed to manage its own healthcare, get the pre-retirement funding relief from Congress that it is due, and get Congress to back off on leaning in on operations, I believe that we would have a USPS that is both viable and competitive. We should close post offices that only see 1.5 people a day, limit some mail delivery to five days (keep the parcels moving) and have the USPS become more sensitive to pricing. These outcomes require enabling legislation—and that’s a big ‘if’ and certainly not likely in an election year, never mind by 2020 or 2025.”

“I believe the leadership of the USPS, Postmaster General Patrick Donahoe in particular, has made the right decisions to try and save the post office,” says Paul Ercolino of U.S. Monitor. “Cost cutting, Network Rationalization and five-day delivery are all controversial decisions, but they are essential if the Post Office is to survive in the coming years.”

Hamilton Davison of the American Catalog Mailers Association spoke about innovation—but still sees challenges because of the process of oversight:

“Innovation on the revenue side, or improvements to [the Postal Service’s] cost structure, will only occur if it is given the freedom to experiment free from regulatory or political concerns. While it is right and proper that the enormous market power of the Postal Service not be unchecked, it should be given greater freedom in advancing markets or improving its cost structure without undue concern about these regulatory and political pressures. Management today is handcuffed in too many areas. Barriers to experimentation on a modest scale must be removed so the USPS can demonstrate pathways for greater innovation that can then be rolled out system-wide under the review of a regulator. Getting the regulator involved in early stage exploration of potential innovation is much more cumbersome.”

And Joel Quadrucci of Quad-Graphics spoke to mail’s role in a multichannel, digital-savvy world:

“We live in a multichannel media world, and print is—and will continue to be—a critical marketing and communications channel,” he said. “Print is especially powerful when connected with other channels. Direct mail is a critical channel because of its ability to drive action to numerous other media channels. Direct mail and digital marketing channels will move forward hand in hand, with direct mail creating a compelling call to action and digital marketing channels giving consumers a way to act.”

“The entire world of logistics is evolving along with retail,” Quadrucci continued. “More and more consumers are opting for the convenience of shopping online. We already see it with Amazon building distribution centers all over the country with the goal of facilitating same-day delivery of its products. The USPS could play a pivotal role in this evolving world of logistics; it is has many strengths. But in order to be competitive with alternative delivery systems, it must address its current challenges head-on.”

Clearly marketers must stay engaged with the Postal Service—and with Congress—as we tackle these challenges together. The Postal Service clearly has my support, too. Now if I could only sate Denny Hatch.

The Future of Online Is Offline

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

I find it offensive when marketers call anyone an “online person.” Let’s get this straight: At the end of some not-so-memorable transaction with you, if I opt in for your how-bad-can-it-be email promotions, or worse, neglect to uncheck the pre-checked check-box that says “You will hear from us from time to time” (which could turn into a daily commitment for the rest of my cognitive life, or, until I decide finding that invisible unsubscribe link presented in the font size of a few pixels is a better option than hitting the delete key every day), I get to be an online person to you? How nice.

What if I receive an email offer from you, research the heck out of the product on the Internet, and then show up at a store to have instant gratification? Does that make me an offline person now? Sorry to break your channel-oriented marketing mind, but hey, I am just a guy. I am neither an online person nor an offline person; which, by the way, happens to be a dirty word in some pretentious marketing circles (as in “Eew, you’re in the offline space?!”).

Marketers often forget to recognize that all this “Big Data” stuff (or any size data, for that matter) and channel management tools are just tools to get to people. In the age of Big Data, it shouldn’t be so hard to know “a lot” about a person, and tailor messages and offers for that person. Then why is that I get confusing offers all the time? How is that I receive multiple types of credit card offers from the same bank within weeks? Don’t they know all about my banking details? Don’t they have some all-inclusive central data depository for all that kind of stuff?

The sad and short answer to all this is that it really doesn’t matter if the users of such databases still think only in terms of her division, his channel assignment, and only through to the very next campaign. And such mindsets may even alter the structure of the marketing database, where everything is organized by division, product or channel. That is how one becomes an online person, who might as well be invisible when it comes to his offline activities.

What is the right answer, then? Both database and users of such databases should be “buyer-centric” or “individual-centric” at the core. In a well-designed marketing database, every variable should be a descriptor for the individual, regardless of the data sources or channels through which she happens to have navigated to end up in the database. There, what she has been buying, her typical spending level, her pricing threshold, channels that she uses to listen, channels that she employs to make purchases or to express herself, stores she visited, lapsed time since her last activities by each channel, contact/response history, her demographic profile, etc. should all be nicely lined up as “her” personal record. That is how modern marketing databases should be structured. Just putting various legacy datasets in one place isn’t going to cut it, even if some individual ID is assigned to everyone in every table. Through some fancy Big Data tools, you may be able to store and retrieve records for every transaction for the past 20 years, but such records describe transactions, not people. Again, it’s all about people.

Why should marketing databases be “buyer-centric”? (1) Nobody is one-dimensional, locked into one channel or division of some marketer, and (2) Individualized targeting and messaging can only be actualized through buyer-centric data platforms. Want to use advanced statistical models? You would need individualized structure because the main goal of any model for marketing is to rank “people” in terms of your target’s susceptibility to certain offers or products. If an individual’s information is scattered all over the database, requiring lots of joins and manipulations, then that database simply isn’t model-ready.

Further, when I look into the future, I see the world where one-click checkout is the norm, even in the offline world. The technology to identify ourselves and to make payment will be smaller and more ubiquitous. Today, when we go to a drug store, we need to bring out the membership card, coupons and our credit card to finish the transaction. Why couldn’t that be just one step? If I identify myself with an ID card or with some futuristic device that I would wear such as a phone, glasses or a wristwatch, shouldn’t that be enough to finish the deal and let me out of the store? When that kind of future becomes a reality (in the not-too distant future), will marketers still think and behave within that channel-centric box? Will we even attempt to link what just happened at the store to other activities the person engaged in online or offline? Not if some guy is in charge of that “one” new channel, no matter how fancy that department title would be.

I have been saying this all along, but let me say it again. The future of online is offline. The distinction of such things would be as meaningless as debating if interactive TV of the future should be called a TV or a computer. Is an iPhone a phone or mobile computer? My answer? Who cares? We should be concentrating our efforts on talking to the person who is looking at the device, whether it is through a computer screen, mobile screen or TV screen. That is the first step toward the buyer-centric mindset; that it is and always has been about people, not channel or devices that would come and go. And it is certainly not about some marketing department that may handle just one channel or one product at a time.

The Big Data movement should about the people. The only difference this new wave brings is the amount of data that we need to deal with and the speed in which we need to operate. Soon, marketers should be able to do things in less than a second that used to take three months. Displaying an individually customized real-time offer built with past and present data through fancy statistical model via hologram won’t be just a scene in a science fiction movie (remember the department store scene in “Minority Report”?). And if marketing databases are not built in a buyer-centric structure, someone along the line will waste a lot of time just to understand what the target individual is all about. That could have been OK in the last century, but not in the age of abundant and ubiquitous data.

6 Factors to Align Direct Marketing Channels With Your Customers

Studies abound about which channels consumers prefer for receiving direct marketing messages. Some studies say consumers prefer direct mail. Others say it’s email. Then, there is the growing use of personalized web experience, social media, text messaging and other forms of messaging. The proliferation of devices and channels seems to be

Studies abound about which channels consumers prefer for receiving direct marketing messages. Some studies say consumers prefer direct mail. Others say it’s email. Then, there is the growing use of personalized web experience, social media, text messaging, and other forms of messaging. The proliferation of devices and channels seems to be unending.

In reality, your customers and prospects will demonstrate to you which channel they prefer, based on their actions. That’s what makes direct marketing what it is. But we are going to offer five qualitative factors, and one bottom line quantitative factor, to internally evaluate and align your message delivery strategy and channel with your customer and prospect’s preferences.

Qualitative factors for customer preference can include:

  1. Pure-play Sales Marketing vs. Content
    As customers and prospects are presented with marketing messages, do they view it as pure-play marketing (i.e., they see through it as your attempt to sell something), or as information and content that will be helpful to them? For example, publishers have succeeded for years when their messaging felt more like helpful information than a pitch to sell a subscription.
  2. Time Sensitivity
    Clearly an email can feel more time sensitive than direct mail, yet, experienced direct mail copywriters have for years been able to convey urgency in copy. But for your customers and prospects, other channels can be perceived as more time sensitive. Email, social media, telesales and even texting are channels that may feel most urgent.
  3. Shelf Life
    Email can vanish in a click. Direct mail can disappear in the trash bin (although it can be fished out of the trash). Higher production value catalogs and direct mail may be held onto longer than down-and-dirty printed packages. And higher production values (such as colors, textures, folds, tip-ons, stickers, die-cuts,and the visual impact of an 11×17 fold-out brochure) are impossible to convey in an email.
  4. How Did They Get My Name?
    Customers probably won’t be as concerned about privacy, but prospects can be much more sensitive. This can be especially the case if your offer touches on information such as health of personal finances. The trust factor is huge in prospects taking an action to pursue learning more about you, or making a purchase decision.
  5. How Do I Know You?
    Prospecting via email can be challenging to get opens and clicks. Run the numbers first (see our post on how to run the numbers). Direct mail for prospecting is getting more and more costly. Social media followers opt-in when they see you on various platforms or are referred to you by a friend. But consider that consumers often identify with social media as a personal platform, not necessarily as a place, to interact with marketing organizations. Better: Your prospect initiates the contact with you, and thus, become a lead. How do you do that? Content marketing, using those other online channels, can be a game-changer for you.

Quantitative Factors: As for quantitative factors you can use to align direct marketing to the media, there is really only one set of numbers to evaluate: Sales and cost per order (or per thousand). As an internal metric, when you evaluate your sales and cost per thousand, you can identify the ultimate metric to assess how your marketing messaging aligns with results.

Bottom line: Be aware of the studies that claim to have answers about which media channel customers prefer. But consider that you know your product better than anyone, you know the channel (or channels) that work for you, and you know your numbers. In a time when we’re awash in devices, channels, and choices, balance how you use each one so you’re aligned with how to drive cost-efficient sales.

5 Things to Do Now to Prepare for the Next Stage of Email Marketing

The email channel is well known for being a low cost high performance marketing machine. Generating revenue requires little more than the ability to acquire opt-in permission and change content in a template. It’s so easy that someone with no experience could create a successful email program. But the email marketing world is changing. Evolution has already begun. Companies have to adapt or lose the effectiveness of a channel that has served well as a cash flow king

The email channel is well known for being a low cost high performance marketing machine. Generating revenue requires little more than the ability to acquire opt-in permission and change content in a template. It’s so easy that someone with no experience could create a successful email program.

And, they do. This is one of the reasons that spam continues to grow. Someone with access to thousands of addresses can fill his or her coffers by blanketing the list with promotional messages or scams. Those emails keep coming because they work. If people didn’t respond to them, the spammers would find a new source of income.

The minimal requirements for success also contribute to the cookie cutter emails sent by established brands. Subject lines, images and content change, but the layout and offers are strikingly similar. When asked why they do this, marketers claim that testing has proven that their subscribers respond best to this presentation and offers.

The problem is that they decided to stop testing once a solution was found. Any halfway decent direct marketer will tell you that testing shows what works best AT THAT TIME. The winner becomes the control that is used to gauge the effectiveness of future tests. Email marketing lulls marketers into complacency because it works so well at consistently generating revenue. Following the “don’t fix it if it’s not broke” theory keeps them from finding strategies that work better.

In fairness, the demands on marketing teams are continuously increasing. Participation in high maintenance, continuously changing channels requires time and effort that might have been dedicated to improving email campaigns if the world were different. Resources have to be allocated by need and email campaigns do not require much to be successful.

The email marketing world is changing. Evolution has already begun. Companies have to adapt or lose the effectiveness of a channel that has served well as a cash flow king. That adaptation has to start now because it takes time to establish the relationships required for continued success. Waiting until campaigns start losing their effectiveness will be too late.

There are two shifts creating the need for change. The first is increased competition. According to the Radicati Group’s email statistics report for 2012 – 2016, 144.8 billion emails were sent in 2012. By 2016, that number is expected to increase to 192.2 billion. Business emails account for 61 percent of the emails today, increasing to 75 percent in 2016. Consumer emails are decreasing. In 2012, 55.8 billion emails were sent. By 2016, consumer emails will drop to 48.4 billion. More marketing messages mean that company emails have to fight harder for recipients’ attention.

The second shift is the ongoing effort to provide a personalized universal search experience. Google is the first search engine to test adding emails to results. It’s only a matter of time before the field trial rolls out and other search providers follow the lead. This changes the rules of engagement for the email marketing game.

Email campaigns will need to work overtime to deliver the best results. In addition to generating immediate cash flow, they need to have a “save for later” appeal that keeps recipients from deleting them. The saved emails will appear when people search the web for similar products or services.

Fortunately, preparing for increased competition and universal search has immediate benefits. The same tactics that position your emails for success in the future also make them work better today. To get started:

  1. Improve your customer relationships: Loyal customers are more likely to ignore increased competition and save your emails. Including emails that make it easier for people to use your products and services solidifies relationships and adds life to your messages.
  2. Optimize emails for search: Adding alternative text to images provides information that can be accessed by search bots. Balancing text and images makes your messages more readable by recipients and bots. It also improves deliverability.
  3. Use personalized trigger emails to improve the shopping and service experience: Trigger emails are a low cost way to keep customers informed about order status and new products or services.
  4. Customize emails by customer behavior: Sending everyone in your database the same marketing message works. Sending customized message to individuals based on their shopping and communication preferences works better.
  5. Keep everything simple and easy: The easier you make it for your customers, the more loyal they tend to be. Work to eliminate as many steps as possible between the marketing message and sale. People keep coming back when the process is simple.

Does Channel Even Matter Anymore? Prove It With an ECHO!

I’ve heard it said, and I believe it, that the consumer has gone “omnichannel” on us. As customers have taken all the power in which brands they choose to interact with, we’ve awakened to find ourselves in a world where we—the brands and the marketers behind them—need to be everywhere the customer is. Digital created a real-time, on-demand environment where communities could easily tap and share knowledge. There is a collective intelligence there that, in tandem, empowers individual customers who use it. The result has affected all channels

I’ve heard it said, and I believe it, that the consumer has gone “omnichannel” on us. As customers have taken all the power in which brands they choose to interact with, we’ve awakened to find ourselves in a world where we—the brands and the marketers behind them—need to be everywhere the customer is. We need to be ready on demand, easily accessed, relevant but not intrusive, poised with an offer, with an ability to listen and interact accordingly, all on top of a product or service that demonstrates value to the customer.

The shift to customer centrism—the growth of customer power—probably began before the digital age, but certainly digital was the game-changer. Digital created a real-time, on-demand environment where communities could easily tap and share knowledge. There is a collective intelligence there that, in tandem, empowers individual customers who use it. The result has affected all channels.

It’s been said that the sole purpose of a business is to create a customer and grow the value of that customer over time. (Using this same reasoning, I doubt that the sole purpose of a charity is to create a donor, but it is to show a need to create a donor, and to make that donor relationship happen and grow.)

So in this brave new world, does channel even matter? Former Direct Marketing Association Chief Executive Officer Larry Kimmel (now with hawkeye) once told direct marketers we need to be “channel-agnostic.” That is, we need to be willing to understand and accept that our prospects and customers could be anywhere, with wants and needs, so we need to be able to recognize these individuals and communicate with them with relevance and permission—and deliver value to them when and where they are ready to engage.

(By the way, relevance—always interpreted from a consumer’s perspective—trumps permission. Discuss.)

I’ve always preferred the descriptor “channel fluent” to communicate this same message. Be channel agnostic, yes, but also have the best practices know-how to deploy any channel in an all-channel mix.

So BAM! Now we have all these channels, and all this channel data to deal with, and the customer wanting brand interaction and engagement in real time, her wants and needs met, and to move on until she’s ready to interact again.

How does a chief marketing officer navigate all this … with success? How should channels be deployed in concert with each other—around the customer? What unique attributes, if any, does any single channel bring to the brand engagement mix? What successful results have been achieved? How can we learn from each other?

I believe it’s time we take a page from the consumer to establish and share collective intelligence, this time among advertisers and marketers. Enter, the DMA 2013 International ECHO Awards Competition.

Does Your Marketing Have What it Takes?
Prove It With an ECHO Entry

Since its debut in 1929, the ECHOs have evolved with direct-response advertising—in all its channels and all of direct marketing’s manifestations. Today, the ECHOs are about the world’s best data-driven marketing campaigns—with data informing both strategy and creative, and producing results. Winning campaigns in 2012 came from Australia, Brazil, Canada, Denmark, Germany, India, Mexico, New Zealand, Spain, the United Kingdom and the United States. The winners represent today’s direct marketing—and the winners truly showcase the best in channel-fluency performance.

For 2013, Winners will be selected in 15 business categories, including three new categories in consumer products, education, and professional services, as well as automotive; business and consumer services; communications and utilities; financial products and services; information technologies; insurance; nonprofit; pharmaceutical and healthcare; product manufacturing and distribution; publishing and entertainment; retail and direct sales; and travel and hospitality/transportation.

Channels represented among the winning campaigns will cover the media landscape: alternative media, catalog, direct mail, email, mobile, print, search engine marketing, social media, telemarketing, television/video/radio, Web advertising and Web development. Entries may represent single channel success—but increasingly entries reflect integrated marketing deployments, not necessarily “omnichannel,” but moving toward this customer expectation.

This year’s call for entries is now open, under the theme “The Ultimate Team Award” (campaign credits to Quinn Fable Advertising, New York, NY). Information on the ECHOs is posted at http://dma-echo.org/index.jsp.

The deadline is May 3, so let’s get started on building 2013’s version of marketing excellence collective intelligence—to share how and when channels matter. I’ll have more to share on the ECHOs in future posts here at “Marketing Sustainably,” but get started today on proving how direct marketing matters, and matters most, in creating and engaging customers everywhere.

Overwhelmed by the Complexity of Mobile Marketing? Start Here

When talking to small business owners,  I hear a lot of reasons as to why they haven’t added mobile to their marketing mix … These excuses illustrate why it’s important to educate folks on the benefits and use cases of mobile and to demystify how it all works in order to eliminate the fear and uncertainty that prevent businesses from moving forward with mobile.

When talking to small business owners, I hear a lot of reasons as to why they haven’t added mobile to their marketing mix …

“I don’t have time to manage one more thing … ”

“I’m not sure where to start … ”

“I feel like my competition has already done that … ”

“I can’t keep up with how fast the technology is advancing … ”

“I can’t afford to use mobile for my small business … ”

These excuses illustrate why it’s important to educate folks on the benefits and use cases of mobile and to demystify how it all works in order to eliminate the fear and uncertainty that prevent businesses from moving forward with mobile.

As those businesses begin to understand that mobile is just a piece of the puzzle they become less confused and you hear more of …

“OK, well. There are so many options. So how can it work for MY business?”

Well, I can tell you that if you’re asking yourself that question, you’re already two steps ahead of most business owners.

And you know what? It’s OK to be confused. The truth is, it’s overwhelming.

Mobile websites, responsive design, SMS marketing, MMS marketing, mobile optimized email, QR Codes, location-based services, augmented reality, smarpthone apps, tablets, NFC, the mobile wallet, mobile commerce …

Holy smokes!

Warning: If you try to jump into all of these areas at once, you will most definitely fail.

If you break down your mobile strategy into smaller parts, integrating one aspect at a time, it will become less overwhelming and you’ll be in a position for a successful mobile program without disrupting the rest of your business.

Remember … mobile is just one part of your marketing strategy. Take it one step at a time:

1. Start by planning how it will play a part into your existing initiatives. Mobile is the most dependent marketing channel to-date. You can’t view it as a solo initiative.

Plan accordingly and make sure it will play nice with your other channels, meaning there is one voice and one message. Chasing the “latest shiny object” thinking it will save your business will get you nowhere fast.

2. Focus on what works and what will delivers results to your business.
You’ll most likely start with your mobile site.

The most important thing to work on is making sure your mobile website is friendly. You’ve probably heard people say that having a mobile-friendly website will give you a competitive advantage.

To some degree, this is true—if your competitors are slow to execute. But, to be honest, a mobile-friendly website is now a cost of doing business.

As a small business owner you’re foolish if you don’t have a mobile friendly site. Let’s say you own a restaurant … A recent Google study stated that 88 percent of total search volume for the keyword “restaurant” comes from mobile devices. Do you own a bar? About 97 percent of search volume for the keyword “bar” is coming from mobile devices.

In fact, “restaurants near me” receives 10,000 searches a month from desktops. Guess what? It’s four times more on mobile devices.

This is the reason that you see restaurants and bars listed in the top of search results in Google from your mobile device but not from your desktop.

Small business owners seem slow to adopt mobile. Surprisingly, a restaurant study stated that 95 percent of independent restaurants do not have a mobile website, and only about half of chain restaurants have some sort of mobile site.

This means a lot of unhappy mobile searchers and no repeat visits.

3. You see, mobile searchers have a different intent than those on a desktop. They are looking for different things. When it comes to local locations like a restaurant or bar they most often look for your location, hours, directions and how to contact you.

4. What’s the cost of not offering these folks a mobile friendly version?
That’s easy … a whole lot of sales.

The same Google study found that 94 percent of U.S.-based smartphone users look for local information on their phones and 90 percent take action as a result, such as making a purchase or contacting the business.

90 percent take action …

Read that again.

Basically, if your site is not mobile friendly when a prospective customer is looking for you, the odds of you losing a sale are close to 100 percent.

5. Speaking of being more “findable” … If you list your business in the various directories AND location-based services, such as Google Places, Foursquare, Yelp, Facebook, etc., you’ll put yourself in a better position to be found. It’s like adding your listing to the Yellow Pages.

6. OK. So you built a mobile-friendly website. Now what?

Your mobile website is what many would consider a “pull” channel. This means that it doesn’t offer you the level of outreach that other channels do, but allows you to be right there when your customer needs you.

So next time, we’re going to dive into the second aspect of your mobile strategy to put in place. It’s actually the most overlooked part of mobile, in my opinion.

Seeing as how you are going to start mobilizing your website right now, you have time to prepare for the second part of your small business mobile strategy … mobile-friendly email.

Attribution and the ‘Mail Moment’ in the Multichannel Mix

At its Sept. 13 meeting, the Direct Marketing Club of New York hosted an engaging panel discussion regarding the use of direct mail in a multichannel world, and the panelists included representatives from Citigroup, Gerber Life and The Agency Inside Harte-Hanks. … Hearing from two financial service brands, and an agency that services brands in several markets, packed the house. I’m not sure if it was the topic or the brands who spoke, or both, that was the draw—but the information imparted prompted lots of audience interest and questions.

At its Sept. 13 meeting, the Direct Marketing Club of New York (DMCNY) hosted an engaging panel discussion regarding the use of direct mail in a multichannel world, and the panelists included representatives from Citigroup, Gerber Life and The Agency Inside Harte-Hanks.

The representatives included Linda Gharib, senior vice president, digital marketing, for Citi’s Global Consumer Marketing & Internet division; David Rosenbluth, vice president, marketing, Gerber Life Insurance Company; and, from the agency side, panel moderator Pam Haas, who is both vice president, sales, for agency services at Harte-Hanks (and first vice president for DMCNY), and Michele Fitzpatrick, senior vice president, strategy and insight, The Agency Inside Harte-Hanks.

Hearing from two financial service brands, and an agency that services brands in several markets (tech, consumer package goods, automotive, insurance, pharma and more), packed the house. I’m not sure if it was the topic or the brands who spoke, or both, that was the draw—but the information imparted prompted lots of audience interest and questions.

First, customer acquisition—at least in the financial services area—still appears to be very dependent on mail. At Gerber, Rosenbluth said, as many as a third of new business policies are still generated by direct mail, even as the brand is “omni-channel”—digital (including web site, search, display ads, email), direct-response television, as well as direct mail. For Citi, the brand is positioned No. 2 in the nation by Target Marketing in its “Top 50 Mailers” ranking for 2012 (which is ranked by overall revenue, not mail volume), Gharib said, solidifying its importance in both acquisition and retention.

Fitzpatrick agreed, noting that in financial services, where marketing is modeled most precisely for risk and performance, direct mail remains an acquisition workhorse, particularly on new product launches. For automotive and pharma verticals, however, where as much as 80 percent of transactions are researched anonymously beforehand online, digital media is used for hand-raising, and direct mail may be then used to deliver a brochure of other information in a highly segmented way to close the deal. “Consumer preferences [for media] are situational,” Fitzpatrick said.

Who gets credit for attribution, when a multichannel communications mix produces a desired response? At Citi, Gharib said, such discussions are a “work in progress,” where the final interaction point currently gets the credit, whether that is chat, direct mail, email or some triggered communication. Adding to the multichannel attribution discussion is the mix of advertising purposes—some are pure branding messages, while others are intended to elicit a response, but both may compel or influence customer behavior in some discernible (or indiscernible) manner. Hence, there is complexity in the attribution discussion.

Yes, indeed, says Rosenbluth, where “allowances” are given for each channel in regard to the brand’s most importance metric to manage: total costs to convert a policy. Currently, “last touch” gets the attribution on response, but the policy conversion metric is the bigger-picture measurement, where everyone gets to take some credit.

Fitzpatrick pointed to recent Forrester research where “fractional attribution”—first touch, mid-touch and last-touch on the path to purchase share credit—and “engagement” is modeled, rather than response (alone). Every brand should undertake a channel impact study to determine, as best it can, the impact of incremental sales as a result of a multichannel customer experience, while also researching receiver reaction research. Clearly, direct mail, email, chat and other channels can be both or either “conversation starters” and “conversation extenders,” but analytics is the only way to know the role of the channel for any given customer.

“There’s credibility in paper,” Gharib remarked, “that helps with both the brand and its consideration.” Where email is cluttered, direct mail largely is not.

At Gerber, Rosenbluth, there really is no brand spend, all market spending is intended to produce engagement.

Fitzpatrick sees almost all “below the line” spending getting a branding blend—branding and direct marketing have come together. All the panelists agreed: it’s really about the consumer experience across channels, and having a database that enables customer recognition and a full customer view. Having tons of data is not enough—it’s having technology and processes in place for customer data integration and analytics to create smart engagement rules.

The verdict? Direct mail is and will remain a vital part of the media mix—because it’s an anchor in the consumer’s experience and brand consideration mix. As digital gets more clutter, boy that mailbox is looking pretty.

4 Attribution Models in the Age of Big Data

For marketers, attribution is the Holy Grail. For those unfamiliar with the term, attribution means determining what marketing channel or budget was responsible for generating a particular action. Without proper attribution, it’s pretty darn difficult to perform any kind of meaningful ROI calculations on your marketing spend. In fact, I wrote another post about attribution earlier this year or so ago titled “The ‘A’ Word—Learn It, Love It, Live It!,” which pointed out that in today’s marketing world, attribution isn’t always what it’s cracked up to be.

For marketers, attribution is the Holy Grail. For those unfamiliar with the term, attribution means determining what marketing channel or budget was responsible for generating a particular action. Without proper attribution, it’s pretty darn difficult to perform any kind of meaningful ROI calculations on your marketing spend. In fact, I wrote another post about attribution earlier this year or so ago titled “The ‘A’ Word—Learn It, Love It, Live It!,” which pointed out that in today’s marketing world, attribution isn’t always what it’s cracked up to be.

Now it’s no secret that attribution analysis is rather difficult to perform in an age of proliferating media, multichannel customers and, drum roll … Big Data. Think about it, how do you gauge which marketing channel was responsible for generating a sale when a customer was sent and read an email, received a direct mail piece and visited a microsite, Googled the company name and found the homepage, but clicked on a sponsored link leading to a landing page, went to and Liked a Facebook page, became a follower on Twitter, tweeted about it to his friends … and ultimately made a purchase using an App on an iPhone. Which channel gets credit? Email, direct mail, organic SEO, mobile, social? All of them? None of them? Some of them? It’s enough to make your head spin.

Now enter Big Data. In this column, I’ve written extensively about the challenge to marketers posed by Big Data. I know, it’s the meme du jour … seems like you read about it everywhere you go these days. Basically, Big Data is the massive accumulation of information that’s taking place across organizations as they market and engage with their customers and prospects across an ever-expanding proliferation of channels.

As customers and prospects interact with firms across different channels, the data continue to pile up. It’s this deluge of information and how to make sense out of it that is being referred to as Big Data. But, as I’ve written before, Big Data is really the problem—not the solution, per se. The fact that organizations are collecting all of this information is great. It’s what they are doing (or not doing, as you’re about to see) with it that’s most important.

I recently read a study done by the Columbia Business School and the American Marketing Association titled “Marketing ROI in the Era of Big Data.” The study was a survey of 253 corporate marketing decision-makers, director-level and above, at large companies. The results were striking.

They found that 91 percent of senior corporate marketers believe that successful brands use customer data to drive marketing decisions. OK, fair enough … couldn’t agree more. But, among those who are collecting data, a measly 39 percent admit they’re actually unable to turn this information into actionable insight. Pretty surprising, huh?

That’s not all. A whopping 65 percent of marketers admitted that comparing the effectiveness of marketing across different digital media is “a major challenge” for their business. An astounding 57 percent of marketers are not basing their marketing budgets on any ROI analysis whatsoever. And to add insult to injury, 22 percent are using brand awareness as their sole measure to evaluate their marketing spend. That’s right, as their sole measure. A direct marketer by trade, I almost spit out my coffee when I read that last stat.

But the shocking thing is based on my experience, I do not find this to be out of the ordinary. In fact, I met with one client recently and was shocked to learn that the client had basically thrown in the towel when it come to defining attribution, and had created hyper-simplistic ROI analysis by using a control customer group to whom the client didn’t market at all, and compared how much this group bought against the rest. Sounds pretty wonky, right? The crazy part is that even the simplistic model is astronomically better than the 57 percent who don’t even bother with ROI in the first place.

So, what are some solutions to the attribution conundrum? Well, there are several popular models that marketers are experimenting with, and each one of course has its plusses and minuses.

1. First-click attribution—credits the channel where a customer first engaged with the firm. On the plus side, this model actually attempts to discern where the customer journey actually began. The downside is that in today’s environment where marketing is often run in silos, it can be challenging to track customer engagement in a multichannel manner.

2. Last-click attribution—credits the channel where the last action took place (i.e., where the conversion occurred). On the plus side, this model is super easy to track. The downside is that it only measures the channel that’s best at generating the sale itself, and completely disregards how the prospect was initially brought into the fold.

3. Equal-weighting attribution—tracks all of the touchpoints where the customer engaged with the firm, and gives them all equal weight in terms of generating the conversion. The advantage of this model is that it takes a holistic view of the customer-vendor relationship. At the same time, this model overlooks the disproportionate role one channel may play over another.

4. Custom-credit attribution—a hybrid model created by the marketer based on its marketing strategy, customer base, and so on. If done right, a custom model can be highly effective, as it’s designed based on facts on the ground. The only downside is, well, you’ve got to create and test it—which is often easier said than done!

Okay, guess I’m out of room for this post, so I’ll end it here. In any event, I’d love to hear about what if any attribution model you’re been using, how it has worked out, and so on. Let me know in your comments.

— Rio