Why Attribution Matters in Content Marketing

Why does attribution matter in content marketing? Money, that’s why. More pointedly, attribution matters because the denizens of the C-suite don’t care about clicks, likes, follows or friends. They care about business outcomes and you need to be able to show that your content marketing is contributing to your firm’s profitability. If you’re just another cost center, you’re going to get cut.

Why does attribution matter in content marketing? Money, that’s why.

More pointedly, attribution matters because the denizens of the C-suite don’t care about clicks, likes, follows or friends. They care about business outcomes, and you need to be able to show that your content marketing is contributing to your firm’s profitability. If you’re just another cost center, you’re going to get cut.

But what exactly is attribution in this context? It’s the ability to know how prospects found you and once they did, what influenced their decision to become a client.

Determining Lead Source

Sounds easy enough, but determining a lead’s source can be tricky. Determining what influenced the lead’s decision can be even tougher. There are steps you can take to help increase the degree of certainty with which you identify lead sources and their paths to purchase.

Let’s start with a look at your website. If you think you’re being helpful by cheerfully having your email address accessible on every page of the site — or even just on the contact page — you should re-evaluate what your website is supposed to do. It has to help your prospective clients, of course, but if it’s not helping your marketing, it shouldn’t be part of the program.

Instead, each page of your site — or perhaps just the contact page — should have a simple mail form through which visitors can contact you. This allows you to track what page prospects were on when they were motivated to reach out to you.

Depending on the sophistication of your site’s coding, it may enable you to see what other pages the prospect spent time on, as well. If not, you may was to discuss the possibility with your web developers, as this is valuable information for your sales team. And it’s valuable to your marketing team, too. It can guide what content to present to the prospect as you move that prospect toward the hand-off to the sales team.

Mail forms also cut down on the spam you receive through your website, which is a nice side benefit. They can also be coded to help automate the marketing process, by routing messages to the appropriate team member depending on the prospect’s needs and interests. Again, check with your web dev team if this isn’t happening already.

Phone numbers can similarly be tracked. Various services allow you to replace your “real” phone number with one that will automatically ring through to the appropriate department and can be tracked as having come from your website. (Or anywhere else the number is published.)

Some services also offer the ability to record calls so you can get a sense of whether your telephone reps are a strong or weak link in your marketing process. Even just tracking call length can provide valuable insights.

Other Content Attribution Tools

There are other attribution tools, as well. The key to use them effectively and to managing the attribution chain well include:

•    Plugging the leaks — know where every lead is coming from
•    Connecting the dots online and off — not everything happens on your website or in your inbox
•    Integrate sales and marketing and your CRM tools in the process
•    Create a consistent data framework

The last bullet may be the most important. Tracking attribution over time helps smooth over the inevitable inaccuracies by allowing you to view trends rather than just individual data points. You’re never going to get to 100 percent accuracy of all lead generation online, offline, and via all branding activity,  so trends may be as useful as the data itself.

Now, there are always going to be imperfections in any attribution attempts you make. You simply have to embrace the imperfection, be aware that you don’t know it all and likely never will, and use the data you’ve gathered to guide your decision making. 80 percent certainty is a lot better than 0 percent. The bottom line is that if you can’t attribute any of your firm’s revenue or profit to your content marketing, you shouldn’t be doing it.

Why Don’t Millennials Use Cash?

When’s the last time you saw a Millennial pay with cash? Even convenience store purchases of less than $5 are paid with a debit card. Coffee in Starbucks is paid via cell phone. Money is exchanged between friends using PayPal and Venmo.

As I paid a dinner check, my Millennial daughter affectionately quipped, “You old people and your cash!”

My response was, “Everybody likes cash!” I was wrong of course, (and perhaps prejudiced by my South Philly roots, where some businesses are still “cash only” for one reason or another).

When’s the last time you saw a Millennial pay with cash? Even convenience store purchases of less than $5 are paid with a debit card. Coffee in Starbucks is paid via cell phone. Money is exchanged between friends using PayPal and Venmo.

Many of the Millennials I give birthday gifts to prefer gift cards to specific retailers, like Home Depot or Banana Republic, rather than cash that they can spend anywhere.

A survey by TD Bank of 1,300 Americans, reported in ABA Bank Marketing last month, found that 25 percent of Americans either currently use or have used a reloadable prepaid card in the past two to three years. But among Millennials (ages 18 to 34), this proportion jumps to 33 percent. According to FICO, more than one-third of Millennials are expected to use a mobile wallet in 2015. (Opens as a PDF)

Professor Bernardo Batiz-Lazo of Bangor University, Wales, speculates that Millennials’ predisposition for non-cash transactions could eventually result in the demise of ATMs. His blog post reprinted by Newstex last month states:

“Perhaps the biggest issue shaping ATMs in the near future will concern the choices of Millennials, those for whom the Internet, mobile phones and plastic cards are a fact of life, checks are unknown and cash is quaint. They challenge financial institutions and their business models to do more, faster because they have easier and faster access to better technology than offered by the banks’ legacy systems through the multitude of apps on their smartphones, wearables, tablets and elsewhere. Left to their own devices, Millennials could spell the end of the ATM by 2035 or thereafter.”

Now of course the use of electronic payment methods is not limited to just Millennials. Boomers and Silents are also moving away from cash transactions, but Millennials are certainly leading the charge. If your business requires a minimum purchase to use a card, you’re probably losing customers among the largest demographic group. Millennials represent 24.6 percent of the population vs. 23.3 percent for the Baby Boomers.

I’m waiting to see the first panhandler with a card reader. Let me know if you spot one.

Money Loves Speed

“Money loves speed.” This phrase has been quoted so often that it’s difficult to know who should be credited for coining it. In an “always-on” digital world, it’s a saying that reminds us that we need to encourage fast action to make a sale, and to act fast when a customer needs help. Today, I contrast the customer service of two digital companies—both household names and both who serve direct marketers—and suggest four money-attracting recommendations

“Money loves speed.” This phrase has been quoted so often that it’s difficult to know who should be credited for coining it. In an “always-on” digital world, it’s a saying that reminds us that we need to encourage fast action to make a sale, and to act fast when a customer needs help. Today, I contrast the customer service of two digital companies—both household names and both who serve direct marketers—and suggest four money-attracting recommendations.

One of the many aggravations for any customer is the inability to get fast answers from a company when help is needed. It’s especially a problem with online merchants. In the digital age, it’s too easy to hide behind an online form.

The contrast of service and responsiveness from Facebook and Google, in my experience, is significant. Both are digital mega-corporations, both provide advertising platforms for marketers, and both are tremendous resources of online metrics for direct marketers.

Facebook is a content marketer’s dream. Gain a fan following at little or no cost, share news, videos, how-to information and much more to your audience. In social media, your audience does your work of sharing and evangelizing for you. Facebook has evolved and requires “pay-to-play” if you want your fans to see your posts. In my view, it’s completely acceptable for Facebook to say that if you want your post to float toward the top of a newsfeed for a day that you’ll need to spend a few bucks.

I pay for posts often for an organization with a vibrant social media presence. The Facebook promoted post budget isn’t huge, but over a year’s time it runs into the thousands of dollars.

The rules for including an image with a promoted post allows up to 20 percent of the image to contain text. Recently, one of my promoted posts was rejected because Facebook technology image scanners thought there was more than the 20 percent amount allowed. But with the human eye, it was apparent looking at the photo and text that we were not over the allowed amount of text. Surely Facebook would reconsider, I thought. My credit card was ready to be charged.

The only way I’ve found to contact Facebook is via an online form. So I filled one out, asking them to reconsider the image for my promoted post expecting a quick response. After all, it took them only about 15 minutes to reject the ad, so surely as an “always-on” social media platform with thousands of employees, someone will respond quickly. Well, it took nearly 24 hours to get a reply to my request. They agreed with me and approved it. But by that point, the timeliness of the news item had passed and myself, and our followers, had moved on.

But then another rejection happened a few days ago. This time, a photo of sheet music didn’t fly. The culprit? Apparently treble clefs, staffs and rests. Once again the rejection was in minutes. I immediately asked Facebook to reevaluate it, thinking that my prior experience of 24 hours for a reply may have been a fluke. It wasn’t. The reply to this second request came in at 1:51 AM the next day, more than 24 hours later, with an approval. But again, the news cycle for this event had ended.

Bottom line: Facebook customer service is pokey. They are leaving advertising money on the table with an apparently cumbersome internal review process.

Contrast Facebook with Google. I manage Google Adwords for another client with a respectable budget. Google has assigned a representative to me. We talk. They rotate representatives every few months so I get different points of view and ideas. And if I need to contact Google, they offer a phone number for me to call where I can actually talk with someone in just minutes, enabling the ads to continue without delay.

Facebook repels money. Google attracts money.

Bottom line points for marketers:

  1. Give the customer options, such as phone, online forms, chat and more to contact you.
  2. Don’t hide behind an online form. Sure, a call center may be more expensive to operate, but it’s surely less expensive than losing sales.
  3. Be responsive. If you decide an online form is less expensive than a call center, fine. But then make sure you have a customer service representative available 24/7 who can quickly answer customer questions.
  4. Remove internal bureaucracy. Sometimes movement is brought to a halt because the internal process is too cumbersome.

In an “always-on” digital age, customers can be impatient. And for goodness sakes, if your business is in technology, act fast! It’s expected.

Money loves speed.

Making LinkedIn Sales Navigator Work for You

LinkedIn Sales Navigator can be great investment. But recovering the money you invest means having an effective, repeatable way to get buyers asking about your product/service.

LinkedIn Sales Navigator can be great investment. But recovering the money you invest means having an effective, repeatable way to get buyers asking about your product/service.

Having a reliable way to provoke response from buyers is the piece most sales reps and recruiting professionals are overlooking. Today, I’ll give you that piece and three templates to take action on—start improving your ROI with Sales Navigator.

“What Does Navigator (Alone) Give Me?”
Sales Navigator provides more access to the LinkedIn database.

Navigator also:

  • makes automated lead suggestions for you (however, my clients rarely get quality leads this way);
  • allows 700 search results (vs. 100) when querying the database;
  • lets you access prospects you don’t know—via InMail messages.

InMail Rules Totally Changed in 2015
Since Jan. 1, 2015 LinkedIn gives “credits” (you buy) back—but only for InMails that earn a response in 90 days.

This is NEW!

Remember the old system? If you did not receive a response within a week, it was credited back to you. You were rewarded for your success AND for failures. Whoops! This encouraged way too much spam.

Today you receive a credit (get your money back) for each InMail receiving a response within 90 days.

What the New InMail Rules Mean to You
Your money is wasted when your potential buyer:

  • hits the “Not interested” button this COUNTS as a response!
  • replies negatively or
  • ignores your message.

Hence, InMail is not guaranteed to be effective. Plus, if it’s not you’re punished by LinkedIn.

InMail also is monitored and rated by LinkedIn—and you must maintain an InMail reputation score in order to send messages. If enough prospects mark you as spam, you’re out of the game.

That’s another reason why you need a reliable communications process that sparks customers’ curiosity in InMails you’re sending.

Do This Right Now
When writing InMails, be sure to state a clear reason the other side will benefit from hitting reply. Make inviting you to speak an attractive idea. Sound crazy? It’s not. Give it a try. It works.

Here are simple guidelines to follow:

  • Be brief, blunt and basic: Write four to five sentences MAX.
  • After drafting, reduce the number of “I’s” and “my’s” in your message.
  • State a clear reason you want a reply in your InMail.
  • Conclude with the customer’s name again. (hyper-personalize)

This will help you put an insane amount of focus on the prospect.

A Few (Proven) Templates for You
For example:

Subject line: Let’s decide?

Hi, [prospect first name].

Are you looking for a better way to ________ [insert goal]? If so may propose a short email exchange—to decide if a deeper conversation is warranted? I __________________[insert description of you] who helps businesses like _______ [insert target business name]. If not, thanks for your time in considering. Please let me know your decision, [prospect first name]?

Sincerely,
[your name]

Why does this template work? For a handful of reasons. If you’re curious ask me in comments and I’ll explain.

When you write, make taking the next step:

  • rewarding to the prospect;
  • predictable and
  • crystal clear to them.

Want to learn this system now? Here are two more free templates to get you started.

Will You Waste Time and Money on LinkedIn?
LinkedIn Sales Navigator can be a good investment, but you are only buying access. Knowing what you do now … having invested time in reading this … what will you do?

Will you rely on a systematic approach this year? Or will you struggle and risk failing?

Will you make quick work of prospecting—or will this feel like slave labor? It’s in your hands. Let me know if I can help.

Smart Data – Not Big Data

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

So, why would I stay on the troubled side? Well, because, for now, this Big Data thing is creating lots of opportunities, too. I am writing this on my way back from Seoul, Korea, where I presented this Big Data idea nine times in just two short weeks, trotting from large venues to small gatherings. Just a few years back, I used to have a hard time explaining what I do for living. Now, I just have to say “Hey, I do this Big Data thing,” and the doors start to open. In my experience, this is the best “Open Sesame” moment for all data specialists. But it will last only if we play it right.

Nonetheless, I also know that I will somehow continue to make living setting data strategies, fixing bad data, designing databases and leading analytical activities, even after the hype cools down. Just with a different title, under a different banner. I’ve seen buzzwords come and go, and this data business has been carried on by the people who cut through each hype (and gargantuan amount of BS along with it) and create real revenue-generating opportunities. At the end of the day (I apologize for using this cliché), it is all about the bottom line, whether it comes from a revenue increase or cost reduction. It is never about the buzzwords that may have created the business opportunities in the first place; it has always been more about the substance that turned those opportunities into money-making machines. And substance needs no fancy title or buzzwords attached to it.

Have you heard Google or Amazon calling themselves a “Big Data” companies? They are the ones with sick amounts of data, but they also know that it is not about the sheer amount of data, but it is all about the user experience. “Wannabes” who are not able to understand the core values often hang onto buzzwords and hypes. As if Big Data, Cloud Computing or coding language du jour will come and save the day. But they are just words.

Even the name “Big Data” is all wrong, as it implies that bigger is always better. The 3 Vs of Big Data—volume, velocity and variety—are also misleading. That could be a meaningful distinction for existing data players, but for decision-makers, it gives a notion that size and speed are the ultimate quest. But for the users, small is better. They don’t have time to analyze big sets of data. They need small answers in fun size packages. Plus, why is big and fast new? Since the invention of modern computers, has there been any year when the processing speed did not get faster and storage capacity did not get bigger?

Lest we forget, it is the software industry that came up with this Big Data thing. It was created as a marketing tagline. We should have read it as, “Yes, we can now process really large amounts of data, too,” not as, “Big Data will make all your dreams come true.” If you are in the business of selling toolsets, of course, that is how you present your product. If guitar companies keep emphasizing how hard it is to be a decent guitar player, would that help their businesses? It is a lot more effective to say, “Hey, this is the same guitar that your guitar hero plays!” But you don’t become Jeff Beck just because you bought a white Fender Stratocaster with a rosewood neck. The real hard work begins “after” you purchase a decent guitar. However, this obvious connection is often lost in the data business. Toolsets never provide solutions on their own. They may make your life easier, but you’d still have to formulate the question in a logical fashion, and still have to make decisions based on provided data. And harnessing meanings out of mounds of data requires training of your mind, much like the way musicians practice incessantly.

So, before business people even consider venturing into this Big Data hype, they should ask themselves “Why data?” What are burning questions that you are trying to solve with the data? If you can’t answer this simple question, then don’t jump into it. Forget about it. Don’t get into it just because everyone else seems to be getting into it. Yeah, it’s a big party, but why are you going there? Besides, if you formulate the question properly, often you will find that you don’t need Big Data all the time. If fact, Big Data can be a terrible detour if your question can be answered by “small” data. But that happens all the time, because people approach their business questions through the processes set by the toolsets. Big Data should be about the business, not about the IT or data.

Smart Data, Not Big Data
So, how do we get over this hype? All too often, perception rules, and a replacement word becomes necessary to summarize the essence of the concept for the general public. In my opinion, “Big Data” should have been “Smart Data.” Piles of unorganized dumb data aren’t worth a damn thing. Imagine a warehouse full of boxes with no labels, collecting dust since 1943. Would you be impressed with the sheer size of the warehouse? Great, the ark that Indiana Jones procured (or did he?) may be stored in there somewhere. But if no one knows where it is—or even if it can be located, if no one knows what to do with it—who cares?

Then, how do data get smarter? Smart data are bite-sized answers to questions. A thousand variables could have been considered to provide the weather forecast that calls for a “70 percent chance of scattered showers in the afternoon,” but that one line that we hear is the smart piece of data. Not the list of all the variables that went into the formula that created that answer. Emphasizing the raw data would be like giving paints and brushes to a person who wants a picture on the wall. As in, “Hey, here are all the ingredients, so why don’t you paint the picture and hang it on the wall?” Unfortunately, that is how the Big Data movement looks now. And too often, even the ingredients aren’t all that great.

I visit many companies only to find that the databases in question are just messy piles of unorganized and unstructured data. And please do not assume that such disarrays are good for my business. I’d rather spend my time harnessing meanings out of data and creating values, not taking care of someone else’s mess all the time. Really smart data are small, concise, clean and organized. Big Data should only be seen in “Behind the Scenes” types of documentaries for manias, not for everyday decision-makers.

I have been already saying that Big Data must get smaller for some time (refer to “Big Data Must Get Smaller“) and I would repeat it until it becomes a movement on its own. The Big Data movement must be about:

  1. Cutting down the noise
  2. Providing the answers

There is too much noise in the data, and cutting it out is the first step toward making the data smaller and smarter. The trouble is that the definition of “noise” is not static. Rock music that I grew up with was certainly a noise to my parents’ generation. In turn, some music that my kids listen to is pure noise to me. Likewise, “product color,” which is essential for a database designed for an inventory management system, may or may not be noise if the goal is to sell more apparel items. In such cases, more important variables could be style, brand, price range, target gender, etc., but color could be just peripheral information at best, or even noise (as in, “Uh, she isn’t going to buy just red shoes all the time?”). How do we then determine the differences? First, set the clear goals (as in, “Why are we playing with the data to begin with?”), define the goals using logical expressions, and let mathematics take care of it. Now you can drop the noise with conviction (even if it may look important to human minds).

If we continue with that mathematical path, we would reach the second part, which is “providing answers to the question.” And the smart answers are in the forms of yes/no, probability figures or some type of scores. Like in the weather forecast example, the question would be “chance of rain on a certain day” and the answer would be “70 percent.” Statistical modeling is not easy or simple, but it is the essential part of making the data smarter, as models are the most effective way to summarize complex and abundant data into compact forms (refer to “Why Model?”).

Most people do not have degrees in mathematics or statistics, but they all know what to do with a piece of information such as “70 percent chance of rain” on the day of a company outing. Some may complain that it is not a definite yes/no answer, but all would agree that providing information in this form is more humane than dumping all the raw data onto users. Sales folks are not necessarily mathematicians, but they would certainly appreciate scores attached to each lead, as in “more or less likely to close.” No, that is not a definite answer, but now sales people can start calling the leads in the order of relative importance to them.

So, all the Big Data players and data scientists must try to “humanize” the data, instead of bragging about the size of the data, making things more complex, and providing irrelevant pieces of raw data to users. Make things simpler, not more complex. Some may think that complexity is their job security, but I strongly disagree. That is a sure way to bring down this Big Data movement to the ground. We are already living in a complex world, and we certainly do not need more complications around us (more on “How to be a good data scientist” in a future article).

It’s About the Users, Too
On the flip side, the decision-makers must change their attitude about the data, as well.

1. Define the goals first: The main theme of this series has been that the Big Data movement is about the business, not IT or data. But I’ve seen too many business folks who would so willingly take a hands-off approach to data. They just fund the database; do not define clear business goals to developers; and hope to God that someday, somehow, some genius will show up and clear up the mess for them. Guess what? That cavalry is never coming if you are not even praying properly. If you do not know what problems you want to solve with data, don’t even get started; you will get to nowhere really slowly, bleeding lots of money and time along the way.

2. Take the data seriously: You don’t have to be a scientist to have a scientific mind. It is not ideal if someone blindly subscribes anything computers spew out (there are lots of inaccurate information in databases; refer to “Not All Databases Are Created Equal.”). But too many people do not take data seriously and continue to follow their gut feelings. Even if your customer profile coming out of a serious analysis does not match with your preconceived notions, do not blindly reject it; instead, treat it as a newly found gold mine. Gut feelings are even more overrated than Big Data.

3. Be logical: Illogical questions do not lead anywhere. There is no toolset that reads minds—at least not yet. Even if we get to have such amazing computers—as seen on “Star Trek” or in other science fiction movies—you would still have to ask questions in a logical fashion for them to be effective. I am not asking decision-makers to learn how to code (or be like Mr. Spock or his loyal follower, Dr. Sheldon Cooper), but to have some basic understanding of logical expressions and try to learn how analysts communicate with computers. This is not data geek vs. non-geek world anymore; we all have to be a little geekier. Knowing Boolean expressions may not be as cool as being able to throw a curve ball, but it is necessary to survive in the age of information overload.

4. Shoot for small successes: Start with a small proof of concept before fully investing in large data initiatives. Even with a small project, one gets to touch all necessary steps to finish the job. Understanding the flow of information is as important as each specific step, as most breakdowns occur in between steps, due to lack of proper connections. There was Gemini program before Apollo missions. Learn how to dock spaceships in space before plotting the chart to the moon. Often, over-investments are committed when the discussion is led by IT. Outsource even major components in the beginning, as the initial goal should be mastering the flow of things.

5. Be buyer-centric: No customer is bound by the channel of the marketer’s choice, and yet, may businesses act exactly that way. No one is an online person just because she did not refuse your email promotions yet (refer to “The Future of Online is Offline“). No buyer is just one dimensional. So get out of brand-, division-, product- or channel-centric mindsets. Even well-designed, buyer-centric marketing databases become ineffective if users are trapped in their channel- or division-centric attitudes, as in “These email promotions must flow!” or “I own this product line!” The more data we collect, the more chances marketers will gain to impress their customers and prospects. Do not waste those opportunities by imposing your own myopic views on them. Big Data movement is not there to fortify marketers’ bad habits. Thanks to the size of the data and speed of machines, we are now capable of disappointing a lot of people really fast.

What Did This Hype Change?
So, what did this Big Data hype change? First off, it changed people’s attitudes about the data. Some are no longer afraid of large amounts of information being thrown at them, and some actually started using them in their decision-making processes. Many realized that we are surrounded by numbers everywhere, not just in marketing, but also in politics, media, national security, health care and the criminal justice system.

Conversely, some people became more afraid—often with good reasons. But even more often, people react based on pure fear that their personal information is being actively exploited without their consent. While data geeks are rejoicing in the age of open source and cloud computing, many more are looking at this hype with deep suspicions, and they boldly reject storing any personal data in those obscure “clouds.” There are some people who don’t even sign up for EZ Pass and voluntarily stay on the long lane to pay tolls in the old, but untraceable way.

Nevertheless, not all is lost in this hype. The data got really big, and types of data that were previously unavailable, such as mobile and social data, became available to many marketers. Focus groups are now the size of Twitter followers of the company or a subject matter. The collection rate of POS (point of service) data has been increasingly steady, and some data players became virtuosi in using such fresh and abundant data to impress their customers (though some crossed that “creepy” line inadvertently). Different types of data are being used together now, and such merging activities will compound the predictive power even further. Analysts are dealing with less missing data, though no dataset would ever be totally complete. Developers in open source environments are now able to move really fast with new toolsets that would just run on any device. Simply, things that our forefathers of direct marketing used to take six months to complete can be done in few hours, and in the near future, maybe within a few seconds.

And that may be a good thing and a bad thing. If we do this right, without creating too many angry consumers and without burning holes in our budgets, we are currently in a position to achieve great many things in terms of predicting the future and making everyone’s lives a little more convenient. If we screw it up badly, we will end up creating lots of angry customers by abusing sensitive data and, at the same time, wasting a whole lot of investors’ money. Then this Big Data thing will go down in history as a great money-eating hype.

We should never do things just because we can; data is a powerful tool that can hurt real people. Do not even get into it if you don’t have a clear goal in terms of what to do with the data; it is not some piece of furniture that you buy just because your neighbor bought it. Living with data is a lifestyle change, and it requires a long-term commitment; it is not some fad that you try once and give up. It is a continuous loop where people’s responses to marketer’s data-based activities create even more data to be analyzed. And that is the only way it keeps getting better.

There Is No Big Data
And all that has nothing to do with “Big.” If done right, small data can do plenty. And in fact, most companies’ transaction data for the past few years would easily fit in an iPhone. It is about what to do with the data, and that goal must be set from a business point of view. This is not just a new playground for data geeks, who may care more for new hip technologies that sound cool in their little circle.

I recently went to Brazil to speak at a data conference called QIBRAS, and I was pleasantly surprised that the main theme of it was the quality of the data, not the size of the data. Well, at least somewhere in the world, people are approaching this whole thing without the “Big” hype. And if you look around, you will not find any successful data players calling this thing “Big Data.” They just deal with small and large data as part of their businesses. There is no buzzword, fanfare or a big banner there. Because when something is just part of your everyday business, you don’t even care what you call it. You just do. And to those masters of data, there is no Big Data. If Google all of a sudden starts calling itself a Big Data company, it would be so uncool, as that word would seriously limit it. Think about that.

San Diego Dreamin’ – Charging Through ‘The DMA’

The last time the Direct Marketing Association held its annual conference in San Diego, it was 2009, we were all amid The Great Recession, and having been recently thrown out of a job, money was just too tight to attend on my own. Since then, marketing has changed—a lot—and the U.S. economy overall is in better shape than it was. Folks, looking back, we avoided a Depression

The last time the Direct Marketing Association held its annual conference in San Diego, it was 2009, we were all amid The Great Recession, and having been recently thrown out of a job, money was just too tight to attend on my own. Since then, marketing has changed—a lot—and the U.S. economy overall is in better shape than it was. Folks, looking back, we avoided a Depression.

I endured, and so did DMA. It’s 2014: The conference offering is as good as ever, and there’s simply no better place in the world for data-driven marketers to gather, learn and exchange. While I might argue, all of marketing, and all of advertising, has become data-driven, let’s not forget that measurability and accountability had its historic home in direct marketing … going back to at least 1917. ROI lives here.

It’s always good to get to The DMA early, to support Marketing EDGE (note, a client) and its Annual Awards Dinner, this year honoring Michael Becker and Google. If you didn’t make it Saturday night, you can still contribute via Marketing EDGE’s first foray into social fundraising. Literally hundreds of thousands will be raised this quarter to help build a bridge from students to market-ready marketing professionals.

Come Monday (today), it’s full-on with the conference: and I won’t be missing Magic Johnson giving “Part 3” of the opening keynote, right after DMA Chairman JoAnne Dunn, CEO of Alliant, gives the association address (can’t recall when a DMA Chairman has taken on this role at the conference), with KBM Group, joining Shell and Air Canada, on “The Evolution of Engagement: The Modern Reality of One-to-One.”

I also can’t miss “Data-Driven Marketing Genius: Google, Xerox and a Foreign Film Festival”—the first-time actual International ECHO Award Winners (they don’t know what they’ve won yet) get a main stage to tell the story behind the marketing campaigns that “Wowed” this year’s ECHO judges (including me). Happy Halloween: I’m still shaking over that Horror Festival campaign.

And since I can’t wait ’til January for my “Downton Abbey” fix, I plan to listen in on “Big Data Helps Keep Downton Abbey Alive for its Fans,” which I’m hopeful gives insight on how a popular TV program gives public television more fundraising lift through brand engagement. I’m curious about the Big Data angle.

“What’s the role of the Agency?” seems to have captured a La Jolla wave. Sessions such as “The New Engagement Agency: A Real-Time Revolution,” and “Agency A-List: The Changing Face & Role of the Agency in 2015” speak to some of the digital disruption that is going on, while Brian Fetherstonhaugh of OgilvyOne Worldwide addresses “E-Commerce: The Crucible of Customer Engagement” (all the more interesting, given Ogilvy’s creation of a new analytics agency, OgilvyAmp.)

By the time Wednesday comes, I will be exhausted, inspired and ready to put some newly learned know-how to the test—and I hope to come home with new business contacts, too—but only after I catch a wave and a libation at the Coronado.

Hottest 2014 Marketing Tip for Small Business? Put Aside a Budget!

Over 50 percent of the working population (120 million) work in a small business, and that trend is growing. According to the SBA definition, there were nearly 28 million small businesses in 2013, and 6 million of those had employees beyond just the owner

Over 50 percent of the working population (120 million) work in a small business, and that trend is growing. According to the SBA definition, there were nearly 28 million small businesses in 2013, and 6 million of those had employees beyond just the owner.

Judging from the number of small business attendees at webinars, online and at events and conferences on how to grow your business, they’re craving solid marketing advice. But unfortunately, it seems no one told them that marketing takes time, costs money (more than you’d think!), and doesn’t pay out instantly.

So I dedicate this blog post to all the small business owners out there who want some solid marketing advice—for free. Here are eight marketing tips that every business, no matter what size, should take to heart:

  1. Create a Clear USP
    This is the secret sauce missing for many companies—your Unique Selling Proposition. What makes your business different from the next guy’s? Why should I do business with you at all? If you’re a dry cleaner, it’s all about location and ease of access (including parking). But if you’re an accountant, how do you distinguish yourself from every other accountant? Are you more current on tax codes? Are you faster and therefore more efficient on the preparation of my tax return? Think about why you started your business in the first place and what makes your customers loyal—those can be good foundations for a marketing platform.
  2. Build and Maintain a User-Friendly Website
    Your website is the face of your business, and too often there hasn’t been enough time, effort or thought given to this critical calling card. Broken links, typos, lengthy copy that rambles on and on (without a point), too many navigation options, poor design choices (tiny type, or worse, tiny type reversed on a dark background) are all the hallmarks of a bad first impression.
  3. Create Marketing Solutions Based on the Business Problem You’re Trying to Solve.
    There’s too much emphasis these days on generating new leads. Take a look at your existing customer base—is there an opportunity to sell them more product or additional services? Examine your sales funnel—where’s the drop-off point? Why do people start a dialogue with you and then discontinue? I worked with one company recently who focused their entire effort on “new lead” volume, but after auditing their sales funnel discovered they weren’t adequately following up with leads after a key decision-making point in the conversion process. Once we adjusted that process, they doubled their sales (even though they maintained their lead level).
  4. Smart Marketing Costs Money
    Many smaller businesses hire a marketing person and expect them to understand marketing strategy, planning, art direction, copywriting, HTML, SEO, SEM, printing techniques, database design and management, analysis, web design, and email blasting. But the reality is, most marketers at this level are simply good project managers. As a result, the creative work is unsophisticated and the strategy non-existent. Different aspects of marketing should be handled by different professionals—I have yet to meet that “jack-of-all-trades” who is also “master-of-all-trades.”
  5. Respect Marketing Professionals
    If you do hire help, fight the urge to ask your friends and neighbors to review/assess the marketing advice/creative recommendations you’ve received. If you’ve spent the time and energy to vet professional help before you hired them, then trust them do their job. If they know what they’re doing, they should be able to develop a strategy (which you approve) and work with other professionals to develop the media and creative to support that strategy (with rationale). You don’t need to rewrite headlines, make color change recommendations or choose your favorite font. That results in bad, disjointed work … period.
  6. Social Media Is Not the Answer
    While there’s plenty of good that can come from social media marketing, it’s highly unlikely that it will keep your business thriving by itself. You can spent a lot of energy generating Facebook “likes” which result in $0 sales. Instead, think about your target audience and their media consumption habits. Then, as the cliché goes, fish where the fish are. And yes, that means spending some money.

So here’s the big “AHA!” that’s missing in most marketing efforts: It’s called a marketing budget.

Want folks to find your website? That means your site needs to be optimized for Search (SEO)—and you need to some money on key words, banner ads, etc. Think about the role that Yelp might play in your business and consider an ad campaign on Yelp.

Just because you build a business doesn’t mean they will come. It will take time—and money—to drive traffic to your doorstep. Visiting your website is only half the battle, so you’d better be diligent about figuring out how to convert that traffic into buyers or all the effort you’ve spent to drive them there will be wasted.

Big Data Must Get Smaller

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Like many folks who worked in the data business for a long time, I don’t even like the words “Big Data.” Yeah, data is big now, I get it. But so what? Faster and bigger have been the theme in the computing business since the first calculator was invented. In fact, I don’t appreciate the common definition of Big Data that is often expressed in the three Vs: volume, velocity and variety. So, if any kind of data are big and fast, it’s all good? I don’t think so. If you have lots of “dumb” data all over the place, how does that help you? Well, as much as all the clutter that’s been piled on in your basement since 1971. It may yield some profit on an online auction site one day. Who knows? Maybe some collector will pay good money for some obscure Coltrane or Moody Blues albums that you never even touched since your last turntable (Ooh, what is that?) died on you. Those oversized album jackets were really cool though, weren’t they?

Seriously, the word “Big” only emphasizes the size element, and that is a sure way to miss the essence of the data business. And many folks are missing even that little point by calling all decision-making activities that involve even small-sized data “Big Data.” It is entirely possible that this data stuff seems all new to someone, but the data-based decision-making process has been with us for a very long time. If you use that “B” word to differentiate old-fashioned data analytics of yesteryear and ridiculously large datasets of the present day, yes, that is a proper usage of it. But we all know most people do not mean it that way. One side benefit of this bloated and hyped up buzzword is data professionals like myself do not have to explain what we do for living for 20 minutes anymore by simply uttering the word “Big Data,” though that is a lot like a grandmother declaring all her grandchildren work on computers for living. Better yet, that magic “B” word sometimes opens doors to new business opportunities (or at least a chance to grab a microphone in non-data-related meetings and conferences) that data geeks of the past never dreamed of.

So, I guess it is not all that bad. But lest we forget, all hypes lead to overinvestments, and all overinvestments leads to disappointments, and all disappointments lead to purging of related personnel and vendors that bear that hyped-up dirty word in their titles or division names. If this Big Data stuff does not yield significant profit (or reduction in cost), I am certain that those investment bubbles will burst soon enough. Yes, some data folks may be lucky enough to milk it for another two or three years, but brace for impact if all those collected data do not lead to some serious dollar signs. I know how the storage and processing cost decreased significantly in recent years, but they ain’t totally free, and related man-hours aren’t exactly cheap, either. Also, if this whole data business is a new concept to an organization, any money spent on the promise of Big Data easily becomes a liability for the reluctant bunch.

This is why I open up my speeches and lectures with this question: “Have you made any money with this Big Data stuff yet?” Surely, you didn’t spend all that money to provide faster toys and nicer playgrounds to IT folks? Maybe the head of IT had some fun with it, but let’s ask that question to CFOs, not CTOs, CIOs or CDOs. I know some colleagues (i.e., fellow data geeks) who are already thinking about a new name for this—”decision-making activities, based on data and analytics”—because many of us will be still doing that “data stuff” even after Big Data cease to be cool after the judgment day. Yeah, that Gangnam Style dance was fun for a while, but who still jumps around like a horse?

Now, if you ask me (though nobody did yet), I’d say the Big Data should have been “Smart Data,” “Intelligent Data” or something to that extent. Because data must provide insights. Answers to questions. Guidance to decision-makers. To data professionals, piles of data—especially the ones that are fragmented, unstructured and unformatted, no matter what kind of fancy names the operating system and underlying database technology may bear—it is just a good start. For non-data-professionals, unrefined data—whether they are big or small—would remain distant and obscure. Offering mounds of raw data to end-users is like providing a painting kit when someone wants a picture on the wall. Bragging about the size of the data with impressive sounding new measurements that end with “bytes” is like counting grains of rice in California in front of a hungry man.

Big Data must get smaller. People want yes/no answers to their specific questions. If such clarity is not possible, probability figures to such questions should be provided; as in, “There’s an 80 percent chance of thunderstorms on the day of the company golf outing,” “An above-average chance to close a deal with a certain prospect” or “Potential value of a customer who is repeatedly complaining about something on the phone.” It is about easy-to-understand answers to business questions, not a quintillion bytes of data stored in some obscure cloud somewhere. As I stated at the end of my last column, the Big Data movement should be about (1) Getting rid of the noise, and (2) Providing simple answers to decision-makers. And getting to such answers is indeed the process of making data smaller and smaller.

In my past columns, I talked about the benefits of statistical models in the age of Big Data, as they are the best way to compact big and complex information in forms of simple answers (refer to “Why Model?”). Models built to predict (or point out) who is more likely to be into outdoor sports, to be a risk-averse investor, to go on a cruise vacation, to be a member of discount club, to buy children’s products, to be a bigtime donor or to be a NASCAR fan, are all providing specific answers to specific questions, while each model score is a result of serious reduction of information, often compressing thousands of variables into one answer. That simplification process in itself provides incredible value to decision-makers, as most wouldn’t know where to cut out unnecessary information to answer specific questions. Using mathematical techniques, we can cut down the noise with conviction.

In model development, “Variable Reduction” is the first major step after the target variable is determined (refer to “The Art of Targeting“). It is often the most rigorous and laborious exercise in the whole model development process, where the characteristics of models are often determined as each statistician has his or her unique approach to it. Now, I am not about to initiate a debate about the best statistical method for variable reduction (I haven’t met two statisticians who completely agree with each other in terms of methodologies), but I happened to know that many effective statistical analysts separate variables in terms of data types and treat them differently. In other words, not all data variables are created equal. So, what are the major types of data that database designers and decision-makers (i.e., non-mathematical types) should be aware of?

In the business of predictive analytics for marketing, the following three types of data make up three dimensions of a target individual’s portrait:

  1. Descriptive Data
  2. Transaction Data / Behavioral Data
  3. Attitudinal Data

In other words, if we get to know all three aspects of a person, it will be much easier to predict what the person is about and/or what the person will do. Why do we need these three dimensions? If an individual has a high income and is living in a highly valued home (demographic element, which is descriptive); and if he is an avid golfer (behavioral element often derived from his purchase history), can we just assume that he is politically conservative (attitudinal element)? Well, not really, and not all the time. Sometimes we have to stop and ask what the person’s attitude and outlook on life is all about. Now, because it is not practical to ask everyone in the country about every subject, we often build models to predict the attitudinal aspect with available data. If you got a phone call from a political party that “assumes” your political stance, that incident was probably not random or accidental. Like I emphasized many times, analytics is about making the best of what is available, as there is no such thing as a complete dataset, even in this age of ubiquitous data. Nonetheless, these three dimensions of the data spectrum occupy a unique and distinct place in the business of predictive analytics.

So, in the interest of obtaining, maintaining and utilizing all possible types of data—or, conversely, reducing the size of data with conviction by knowing what to ignore, let us dig a little deeper:

Descriptive Data
Generally, demographic data—such as people’s income, age, number of children, housing size, dwelling type, occupation, etc.—fall under this category. For B-to-B applications, “Firmographic” data—such as number of employees, sales volume, year started, industry type, etc.—would be considered as descriptive data. It is about what the targets “look like” and, generally, they are frozen in the present time. Many prominent data compilers (or data brokers, as the U.S. government calls them) collect, compile and refine the data and make hundreds of variables available to users in various industry sectors. They also fill in the blanks using predictive modeling techniques. In other words, the compilers may not know the income range of every household, but using statistical techniques and other available data—such as age, home ownership, housing value, and many other variables—they provide their best estimates in case of missing values. People often have some allergic reaction to such data compilation practices siting privacy concerns, but these types of data are not about looking up one person at a time, but about analyzing and targeting groups (or segments) of individuals and households. In terms of predictive power, they are quite effective and results are very consistent. The best part is that most of the variables are available for every household in the country, whether they are actual or inferred.

Other types of descriptive data include geo-demographic data, and the Census Data by the U.S. Census Bureau falls under this category. These datasets are organized by geographic denominations such as Census Block Group, Census Tract, Country or ZIP Code Tabulation Area (ZCTA, much like postal ZIP codes, but not exactly the same). Although they are not available on an individual or a household level, the Census data are very useful in predictive modeling, as every target record can be enhanced with it, even when name and address are not available, and data themselves are very stable. The downside is that while the datasets are free through Census Bureau, the raw datasets contain more than 40,000 variables. Plus, due to the budget cut and changes in survey methods during the past decade, the sample size (yes, they sample) decreased significantly, rendering some variables useless at lower geographic denominations, such as Census Block Group. There are professional data companies that narrowed down the list of variables to manageable sizes (300 to 400 variables) and filled in the missing values. Because they are geo-level data, variables are in the forms of percentages, averages or median values of elements, such as gender, race, age, language, occupation, education level, real estate value, etc. (as in, percent male, percent Asian, percent white-collar professionals, average income, median school years, median rent, etc.).

There are many instances where marketers cannot pinpoint the identity of a person due to privacy issues or challenges in data collection, and the Census Data play a role of effective substitute for individual- or household-level demographic data. In predictive analytics, duller variables that are available nearly all the time are often more valuable than precise information with limited availability.

Transaction Data/Behavioral Data
While descriptive data are about what the targets look like, behavioral data are about what they actually did. Often, behavioral data are in forms of transactions. So many just call it transaction data. What marketers commonly refer to as RFM (Recency, Frequency and Monetary) data fall under this category. In terms of predicting power, they are truly at the top of the food chain. Yes, we can build models to guess who potential golfers are with demographic data, such as age, gender, income, occupation, housing value and other neighborhood-level information, but if you get to “know” that someone is a buyer of a box of golf balls every six weeks or so, why guess? Further, models built with transaction data can even predict the nature of future purchases, in terms of monetary value and frequency intervals. Unfortunately, many who have access to RFM data are using them only in rudimentary filtering, as in “select everyone who spends more than $200 in a gift category during the past 12 months,” or something like that. But we can do so much more with rich transaction data in every stage of the marketing life cycle for prospecting, cultivating, retaining and winning back.

Other types of behavioral data include non-transaction data, such as click data, page views, abandoned shopping baskets or movement data. This type of behavioral data is getting a lot of attention as it is truly “big.” The data have been out of reach for many decision-makers before the emergence of new technology to capture and store them. In terms of predictability, nevertheless, they are not as powerful as real transaction data. These non-transaction data may provide directional guidance, as they are what some data geeks call “a-camera-on-everyone’s-shoulder” type of data. But we all know that there is a clear dividing line between people’s intentions and their commitments. And it can be very costly to follow every breath you take, every move you make, and every step you take. Due to their distinct characteristics, transaction data and non-transaction data must be managed separately. And if used together in models, they should be clearly labeled, so the analysts will never treat them the same way by accident. You really don’t want to mix intentions and commitments.

The trouble with the behavioral data are, (1) they are difficult to compile and manage, (2) they get big; sometimes really big, (3) they are generally confined within divisions or companies, and (4) they are not easy to analyze. In fact, most of the examples that I used in this series are about the transaction data. Now, No. 3 here could be really troublesome, as it equates to availability (or lack thereof). Yes, you may know everything that happened with your customers, but do you know where else they are shopping? Fortunately, there are co-op companies that can answer that question, as they are compilers of transaction data across multiple merchants and sources. And combined data can be exponentially more powerful than data in silos. Now, because transaction data are not always available for every person in databases, analysts often combine behavioral data and descriptive data in their models. Transaction data usually become the dominant predictors in such cases, while descriptive data play the supporting roles filling in the gaps and smoothing out the predictive curves.

As I stated repeatedly, predictive analytics in marketing is all about finding out (1) whom to engage, and (2) if you decided to engage someone, what to offer to that person. Using carefully collected transaction data for most of their customers, there are supermarket chains that achieved 100 percent customization rates for their coupon books. That means no two coupon books are exactly the same, which is a quite impressive accomplishment. And that is all transaction data in action, and it is a great example of “Big Data” (or rather, “Smart Data”).

Attitudinal Data
In the past, attitudinal data came from surveys, primary researches and focus groups. Now, basically all social media channels function as gigantic focus groups. Through virtual places, such as Facebook, Twitter or other social media networks, people are freely volunteering what they think and feel about certain products and services, and many marketers are learning how to “listen” to them. Sentiment analysis falls under that category of analytics, and many automatically think of this type of analytics when they hear “Big Data.”

The trouble with social data is:

  1. We often do not know who’s behind the statements in question, and
  2. They are in silos, and it is not easy to combine such data with transaction or demographic data, due to lack of identity of their sources.

Yes, we can see that a certain political candidate is trending high after an impressive speech, but how would we connect that piece of information to whom will actually donate money for the candidate’s causes? If we can find out “where” the target is via an IP address and related ZIP codes, we may be able to connect the voter to geo-demographic data, such as the Census. But, generally, personally identifiable information (PII) is only accessible by the data compilers, if they even bothered to collect them.

Therefore, most such studies are on a macro level, citing trends and directions, and types of analysts in that field are quite different from the micro-level analysts who deal with behavioral data and descriptive data. Now, the former provide important insights regarding the “why” part of the equation, which is often the hardest thing to predict; while the latter provide answers to “who, what, where and when.” (“Who” is the easiest to answer, and “when” is the hardest.) That “why” part may dictate a product development part of the decision-making process at the conceptual stage (as in, “Why would customers care for a new type of dishwasher?”), while “who, what, where and when” are more about selling the developed products (as in “Let’s sell those dishwashers in the most effective ways.”). So, it can be argued that these different types of data call for different types of analytics for different cycles in the decision-making processes.

Obviously, there are more types of data out there. But for marketing applications dealing with humans, these three types of data complete the buyers’ portraits. Now, depending on what marketers are trying to do with the data, they can prioritize where to invest first and what to ignore (for now). If they are early in the marketing cycle trying to develop a new product for the future, they need to understand why people want something and behave in certain ways. If signing up as many new customers as possible is the immediate goal, finding out who and where the ideal prospects are becomes the most imminent task. If maximizing the customer value is the ongoing objective, then you’d better start analyzing transaction data more seriously. If preventing attrition is the goal, then you will have to line up the transaction data in time series format for further analysis.

The business goals must dictate the analytics, and the analytics call for specific types of data to meet the goals, and the supporting datasets should be in “analytics-ready” formats. Not the other way around, where businesses are dictated by the limitations of analytics, and analytics are hampered by inadequate data clutters. That type of business-oriented hierarchy should be the main theme of effective data management, and with clear goals and proper data strategy, you will know where to invest first and what data to ignore as a decision-maker, not necessarily as a mathematical analyst. And that is the first step toward making the Big Data smaller. Don’t be impressed by the size of the data, as they often blur the big picture and not all data are created equal.

Postal Delivery: Which Will It Be—5 Days or 6 Days?

I just had a great exchange with my letter carrier while at my mailbox today. Of course, I brought up the likelihood of five-day delivery come August, to which she gave a candid response, “Well, we’ve been losing money.” That’s why it’s easy to be indignant when some members of Congress, perhaps predictably, jumped onto the current appropriations bill with mandates for six-day delivery. Yet, one has to ask, where are the means for real relief from some of the costliest demands of the 2006 postal reform law?

I just had a great exchange with my letter carrier (as I sometimes do) while at my mailbox today, and I wonder how many times a day my carrier is interrupted in her work, as I interrupted her, to politely chit-chat. Of course, I brought up the likelihood of five-day delivery come August, to which she gave a candid response, “Well, we’ve been losing money.”

Most Americans—and maybe even some carriers—don’t know the full story—or any story—about how the United States Postal Service endures pre-funding retirement benefit mandates from Congress, as well as other cost-drivers that have nothing to do with the digital age, electronic bill payments and multichannel communication trends. Nor do they know that both The White House and Congress spend these mandated monies on their own programs, even as the federal deficit spirals.

That’s why it’s easy to be indignant when some members of Congress, perhaps predictably, jumped onto the current appropriations bill (a continuing resolution to fund the government beyond March 27) with mandates for six-day delivery. Yet, one has to ask, where are the means for real relief from some of the costliest demands of the 2006 postal reform law? Making the Postal Service stick with Saturday delivery isn’t the action we need Congress to take.

Is it really enough, or correct, to just counter USPS management efforts to cut costs and right-size the network? Why not delve deeper into the ills that Congress and the Administration—both parties involved here—have heaped onto the Postal Service’s bottom line? Why not revisit real postal reform? How many more years must the Postal Service get squeezed, and default on payments, before Congress and the Obama (or next) Administration take seriously its cause, its future, its sustainability?

Late last month, National Public Radio discussed, in a piece regarding postal services around the globe, how these services are coping with lower demand of an increasingly electronic society: http://www.npr.org/templates/story/story.php?storyId=172932914

It’s funny how much of “Socialist” Europe already has privatized its posts (not that citizens or businesses are the better for it). On the other hand, it’s very serious to say our quasi-public U.S. Postal Service still runs the most efficient ship of all, universal delivery at a fair price, despite its tethers to political whims …

… and despite my “stealing” of expensive carrier street time! five-day or six-day delivery is a concern for many mailers—but it’s really not the most important postal operations issue that needs to be addressed.

4 Direct Mail Tips for a Great Yard Sale

One of the best things about this time of year is that it’s perfect for yard sales. But judging by what I’ve seen around suburbia over the last few weeks, a lot of folks are missing a good opportunity to unload their old stuff and make some money. If you adapt some tried-and-true direct marketing tips to fit your yard sale, you can attract lots of prospects, and then, get them to buy.

One of the best things about this time of year is that it’s perfect for yard sales. But judging by what I’ve seen around suburbia over the last few weeks, a lot of folks are missing a good opportunity to unload their old stuff and make some money. If you adapt some tried-and-true direct marketing tips to fit your yard sale, you can attract lots of prospects, and then, get them to buy.

Catch their attention. Like a great teaser on an envelope or a good subject line, your sale sign should be just as hard to ignore. You don’t really need posterboard — an 8-1/2 “x 11” sheet of paper, in a bright color, is good enough. In block lettering big enough to be easily read by someone on foot, bicycle or car, announce the street address, date and time. Maybe insert two or three keywords like “furniture” and “clothing.” That’s it.

Post it on telephone poles on the heavily trafficked streets near your home, and to be more helpful, draw an arrow on it to point people in the right direction. To help guide them further, hang some balloons or maybe another sign with a big “X” on a tree or utility pole closest to your house.

Also, going multichannel couldn’t hurt. List your sale with some specific details about what you’re selling on Craigslist and in local newspapers. And, for a personal touch, hand-deliver your sign to your neighbors.

Think like a retailer. Like a well-organized catalog or website with lots of high-quality photos, you need to place your merchandise so it can be easily seen. If at all possible, don’t use the ground, which can be difficult for some people to bend down and reach.

Borrow some tables if you don’t have any. After making sure everything is clean, group like items together, such as books or housewares. Retailers, no matter how many ways they sell, sort clothing by size, and you should, too.

Some things, like furniture or appliances, are big and attractive enough to be put up front by themselves. A kid at a small table selling lemonade, water or pretzels is another great way to generate traffic and further sales.

If you’re worried that you don’t have enough to sell, you probably don’t. So, bring neighbors and relatives in to be part of your sale. It’s like those co-op mailings everyone gets; the greater the variety and amount of goods that’s out there for buyers to see, the more likely it is that people will stop, then linger.

The offer rules. Maybe you have some idea of what your market will bear. So make it easy for everybody by charging a flat rate price for things like silverware, or CDs ans DVDs, and clearly mark individual ones for the rest.

But, as in direct mail, if you’re not getting enough (or any) results, dramatically improve your offer. Try “buy one, get one” deals for big volume items like baby clothes, and get ready to bargain further with larger discounts. That is, unless you really want to haul everything back to the garage or attic.

Like any good marketer, you’ll also want to give people options when paying you, so they don’t walk away. You’ll need to have small bills and coins on hand to make change.

Sell the sizzle. One great marketing rule is to talk about benefits, not features. Present yourself as having the answer to what other people need. Be polite and friendly as potential buyers approach you. Try not to be demanding or hovering, as that tends to scare people way.

Instead, BE the testimonial. That still-in-the-box belt sander over there? “I only used it once and it did a great job of refinishing my dining room floor.”

And show — don’t just say that your stuff still works. Run a heavy-duty extension cord to your house so your customers can try any electrical items before buying.

End your day on a high note. Of course, you can sell your leftover stuff another day. But instead, maybe donate them to the Salvation Army or Goodwill stores, or list them on Freecycle. Chances are that someone will be able to use them.

Be a good neighbor by taking down all of your signs after you pack up. Then, sit back and reflect: You’ve enjoyed the fresh air and sunshine, cleared your clutter, maybe made some new friends, and hopefully, brought in some decent money. Not too bad!

What ideas do you have for successful yard sales?

Paul Bobnak is the research director of Direct Marketing IQ and runs the Who’s Mailing What! Archive. He can be reached at pbobnak@napco.com.