It’s All About Ranking

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

The decision-making process is really all about ranking. As a marketer, to whom should you be talking first? What product should you offer through what channel? As a businessperson, whom should you hire among all the candidates? As an investor, what stocks or bonds should you purchase? As a vacationer, where should you visit first?

Yes, “choice” is the keyword in all of these questions. And if you picked Paris over other places as an answer to the last question, you just made a choice based on some ranking order in your mind. The world is big, and there could have been many factors that contributed to that decision, such as culture, art, cuisine, attractions, weather, hotels, airlines, prices, deals, distance, convenience, language, etc., and I am pretty sure that not all factors carried the same weight for you. For example, if you put more weight on “cuisine,” I can see why London would lose a few points to Paris in that ranking order.

As a citizen, for whom should I vote? That’s the choice based on your ranking among candidates, too. Call me overly analytical (and I am), but I see the difference in political stances as differences in “weights” for many political (and sometimes not-so-political) factors, such as economy, foreign policy, defense, education, tax policy, entitlement programs, environmental issues, social issues, religious views, local policies, etc. Every voter puts different weights on these factors, and the sum of them becomes the score for each candidate in their minds. No one thinks that education is not important, but among all these factors, how much weight should it receive? Well, that is different for everybody; hence, the political differences.

I didn’t bring this up to start a political debate, but rather to point out that the decision-making process is based on ranking, and the ranking scores are made of many factors with different weights. And that is how the statistical models are designed in a nutshell (so, that means the models are “nuts”?). Analysts call those factors “independent variables,” which describe the target.

In my past columns, I talked about the importance of statistical models in the age of Big Data (refer to “Why Model?”), and why marketing databases must be “model-ready” (refer to “Chicken or the Egg? Data or Analytics?”). Now let’s dig a little deeper into the design of the “model-ready” marketing databases. And surprise! That is also all about “ranking.”

Let’s step back into the marketing world, where folks are not easily offended by the subject matter. If I give a spreadsheet that contains thousands of leads for your business, you wouldn’t be able to tell easily which ones are the “Glengarry Glen Ross” leads that came from Downtown, along with those infamous steak knives. What choice would you have then? Call everyone on the list? I guess you can start picking names out of a hat. If you think a little more about it, you may filter the list by the first name, as they may reflect the decade in which they were born. Or start calling folks who live in towns that sound affluent. Heck, you can start calling them in alphabetical order, but the point is that you would “sort” the list somehow.

Now, if the list came with some other valuable information, such as income, age, gender, education level, socio-economic status, housing type, number of children, etc., you may be able to pick and choose by which variables you would use to sort the list. You may start calling the high income folks first. Not all product sales are positively related to income, but it is an easy way to start the process. Then, you would throw in other variables to break the ties in rich areas. I don’t know what you’re selling, but maybe, you would want folks who live in a single-family house with kids. And sometimes, your “gut” feeling may lead you to the right place. But only sometimes. And only when the size of the list is not in millions.

If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You’d have to scroll to the right for quite some time just to see what’s included in the chart.

In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn’t it be? Now you can just start calling the folks in the model group No. 1.

But let me throw in a curveball here. Let’s go back to the list with all those transaction data attached, but without the model scores. You may say, “Hey, that’s OK, because I’ve been doing alright without any help from a statistician so far, and I’ll just use the past dollar amount as their primary value and sort the list by it.” And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.

Relational databases are optimized to store every transaction and retrieve them efficiently. In a relational database, tables are connected by match keys, and many times, tables are connected in what we call “1-to-many” relationships. Imagine a shopping basket. There is a buyer, and we need to record the buyer’s ID number, name, address, account number, status, etc. Each buyer may have multiple transactions, and for each transaction, we now have to record the date, dollar amount, payment method, etc. Further, if the buyer put multiple items in a shopping basket, that transaction, in turn, is in yet another 1-to-many relationship to the item table. You see, in order to record everything that just happened, this relational structure is very useful. If you are the person who has to create the shipping package, yes, you need to know all the item details, transaction value and the buyer’s information, including the shipping and billing address. Database designers love this completeness so much, they even call this structure the “normal” state.

But the trouble with the relational structure is that each line is describing transactions or items, not the buyers. Sure, one can “filter” people out by interrogating every line in the transaction table, say “Select buyers who had any transaction over $100 in past 12 months.” That is what I call rudimentary filtering, but once we start asking complex questions such as, “What is the buyer’s average transaction amount for past 12 months in the outdoor sports category, and what is the overall future value of the customers through online channels?” then you will need what we call “Buyer-centric” portraits, not transaction or item-centric records. Better yet, if I ask you to rank every customer in the order of such future value, well, good luck doing that when all the tables are describing transactions, not people. That would be exactly like the case where you have multiple lines for one individual when you need to sort the leads from high value to low.

So, how do we remedy this? We need to summarize the database on an individual level, if you would like to sort the leads on an individual level. If the goal is to rank households, email addresses, companies, business sites or products, then the summarization should be done on those levels, too. Now, database designers call it the “de-normalization” process, and the tables tend to get “wide” along that process, but that is the necessary step in order to rank the entities properly.

Now, the starting point in all the summarizations is proper identification numbers for those levels. It won’t be possible to summarize any table on a household level without a reliable household ID. One may think that such things are given, but I would have to disagree. I’ve seen so many so-called “state of the art” (another cliché that makes me nauseous) databases that do not have consistent IDs of any kind. If your database managers say they are using “plain name” or “email address” fields for matching or summarization, be afraid. Be very afraid. As a starter, you know how many email addresses one person may have. To add to that, consider how many people move around each year.

Things get worse in regard to ranking by model scores when it comes to “unstructured” databases. We see more and more of those, as the data sources are getting into uncharted territories, and the size of the databases is growing exponentially. There, all these bits and pieces of data are sitting on mysterious “clouds” as entries on their own. Here again, it is one thing to select or filter based on collected data, but ranking based on some statistical modeling is simply not possible in such a structure (or lack thereof). Just ask the database managers how many 24-month active customers they really have, considering a great many people move in that time period and change their addresses, creating multiple entries. If you get an answer like “2 million-ish,” well, that’s another scary moment. (Refer to “Cheat Sheet: Is Your Database Marketing Ready?”)

In order to develop models using variables that are descriptors of customers, not transactions, we must convert those relational or unstructured data into the structure that match the level by which you would like to rank the records. Even temporarily. As the size of databases are getting bigger and bigger and the storage is getting cheaper and cheaper, I’d say that the temporary time period could be, well, indefinite. And because the word “data-mart” is overused and confusing to many, let me just call that place the “Analytical Sandbox.” Sandboxes are fun, and yes, all kinds of fun stuff for marketers and analysts happen there.

The Analytical Sandbox is where samples are created for model development, actual models are built, models are scored for every record—no matter how many there are—without hiccups; targets are easily sorted and selected by model scores; reports are created in meaningful and consistent ways (consistency is even more important than sheer accuracy in what we do), and analytical language such as SAS, SPSS or R are spoken without being frowned up by other computing folks. Here, analysts will spend their time pondering upon target definitions and methodologies, not about database structures and incomplete data fields. Have you heard about a fancy term called “in-database scoring”? This is where that happens, too.

And what comes out of the Analytical Sandbox and back into the world of relational database or unstructured databases—IT folks often ask this question—is going to be very simple. Instead of having to move mountains of data back and forth, all the variables will be in forms of model scores, providing answers to marketing questions, without any missing values (by definition, every record can be scored by models). While the scores are packing tons of information in them, the sizes could be as small as a couple bytes or even less. Even if you carry over a few hundred affinity scores for 100 million people (or any other types of entities), I wouldn’t call the resultant file large, as it would be as small as a few video files, really.

In my future columns, I will explain how to create model-ready (and human-ready) variables using all kinds of numeric, character or free-form data. In Exhibit A, you will see what we call traditional analytical activities colored in dark blue on the right-hand side. In order to make those processes really hum, we must follow all the steps that are on the left-hand side of that big cylinder in the middle. Preventing garbage-in-garbage-out situations from happening, this is where all the data get collected in uniform fashion, properly converted, edited and standardized by uniform rules, categorized based on preset meta-tables, consolidated with consistent IDs, summarized to desired levels, and meaningful variables are created for more advanced analytics.

Even more than statistical methodologies, consistent and creative variables in form of “descriptors” of the target audience make or break the marketing plan. Many people think that purchasing expensive analytical software will provide all the answers. But lest we forget, fancy software only answers the right-hand side of Exhibit A, not all of it. Creating a consistent template for all useful information in a uniform fashion is the key to maximizing the power of analytics. If you look into any modeling bakeoff in the industry, you will see that the differences in methodologies are measured in fractions. Conversely, inconsistent and incomplete data create disasters in real world. And in many cases, companies can’t even attempt advanced analytics while sitting on mountains of data, due to structural inadequacies.

I firmly believe the Big Data movement should be about

  1. getting rid of the noise, and
  2. providing simple answers to decision-makers.

Bragging about the size and the speed element alone will not bring us to the next level, which is to “humanize” the data. At the end of the day (another cliché that I hate), it is all about supporting the decision-making processes, and the decision-making process is all about ranking different options. So, in the interest of keeping it simple, let’s start by creating an analytical haven where all those rankings become easy, in case you think that the sandbox is too juvenile.

Top 10 Ways to Improve YouTube Video Search Ranking

YouTube recently announced reaching a new milestone of 1 billion unique monthly visitors, or 15 percent of the planet. Those kinds of numbers are why you want your videos to be found organically on YouTube. Today, we turn the subject to inbound video direct marketing and how to attract traffic to your videos on YouTube. You’ll learn about 10 tools

YouTube recently announced reaching a new milestone of 1 billion unique monthly visitors, or 15 percent of the planet. Those kinds of numbers are why you want your videos to be found organically on YouTube. Today, we turn the subject to inbound video direct marketing and how to attract traffic to your videos on YouTube. You’ll learn about 10 tools that can lead to clicks to your landing page and the opportunity for you to convert that video viewer into a paying customer. Using these tools, you can:

  • Drive traffic to your website or landing page
  • Build your mailing list
  • And sell your products or services

(If the video isn’t just above this line, click here to view it.)

There are several factors that will influence how your video will rank on YouTube, or if it will make the “related videos” or “recommended videos” list. Today’s video suggests ten of those factors, such as:

  • How speech-to-text conversion technology means you need to use your keywords in your voice-over script
  • The importance of including your transcript for closed captioning (with broader implications of improving your search ranking)
  • Loading your description with long-form direct marketing copy
  • Encouraging social signals, and more

Today’s list is just the beginning, and in our next blog, we’ll dive even deeper into some of the often overlooked tactics that can help your video rank higher on YouTube.

If you’ve identified tools and techniques that have helped your videos rise to the top in YouTube rankings, please share them below so other direct marketers can learn what’s worked for you.

Why You Should Beware the ‘Quick SEO Copywriting Fix’

The question comes up during almost every conference at which I present: “I hear what you’re saying about writing quality content. But I need immediate results. What’s a quick SEO copywriting fix I can try?”

The question comes up during almost every conference at which I present.

“I hear what you’re saying about writing quality content. But I need immediate results. What’s a quick SEO copywriting fix I can try?”

I understand this mindset. I really do. Now that the recession is easing its iron grip on marketing budgets, companies are trying to make up for lost time. Now, more than ever, forward-thinking businesses have the opportunity to make a huge impact on their search engine rankings. And they’re doing what they can, where they can—as fast as they can.

But here’s the thing. There is no “quick SEO copywriting fix.” There’s no “easy way to get to the top of the search engines” like the spam e-mails promise. You can’t wave a magic algorithmic wand and transform your copy into search engine goodness.

You just have to roll your sleeves, do the hard work and get it done.

Unfortunately, many companies fall prey to this quick fix mentality and do stupid things that mess up their SEO campaigns, branding or both. For instance:

  • Building out stand-alone “SEO pages” geared to pull rankings

  • Hiring $10/post bloggers to write keyphrase-stuffed posts

  • Tweeting incessantly about their products or services without becoming a part of the Twitter community

Although these folks feel like progress is quickly being made (“Woo-hoo, now we have 50, poorly-written posts about legal services”), what they don’t realize is the unforeseen consequences. Poorly written content doesn’t convert. “Stand-alone” pages typically are over-optimized messes that search engines ignore. Splattering your sales message all over Twitter makes your firm look like a spammer.

So, what can you do to start seeing the search results (and conversions) you crave? I am so glad you asked …

1. Evaluate your existing content. Every marketer can leverage its own low-hanging fruit and focus on what specifically matters for its site. For some sites, penning new page titles can drive amazing results. For others, keyphrase editing (adding keyphrases to existing content) may be appropriate. Consider bringing in a consultant for this part of the process. The reason? The consultant doesn’t “own” the copy and can see it with fresh eyes. Because he’s not close to it, he can notice opportunities your marketing department may not.

2. Check your keyphrase research. It’s easy to let your keyphrase research stagnate when you don’t have the time (or funds) to spend on your site. Now that you’re planning a content overhaul, it’s crucial to examine what other keyphrases opportunities you can leverage – especially keyphrases that represent the research phase of the buy cycle. Research tools like WordStream, Keyword Discovery and Wordtracker can help you see what phrases people type into the search box to find products and services like yours.

3. Develop a (workable) content schedule and budget. Rome wasn’t built in a day, and your site won’t be rewritten overnight. Work on your most important pages first, and set up a schedule where you’ll work on a certain number of pages a month. Or, if you know that writing content in-house isn’t your style, hire an experienced SEO copywriter and have him help. Creating content in baby steps is completely OK – and gives you the satisfaction of seeing continued improvement.

It’s tempting to fall prey to the SEO copywriting quick fix. But when you take strategic baby steps and focus on what’s really important to your site’s success, you’ll finally realize the search ranking (and conversions) you crave.

Marketers, Stop Ignoring Your Content Marketing Strategy

As I write this, I’m on the plane heading back from DMA09. While I was moderating the Search Marketing Experience Labs, one common element ran through every site review: When you ignore your SEO content marketing strategy, you’re hobbling your conversions, ignoring your customers and forfeiting your search engine rankings. Here’s why.

As I write this, I’m on the plane heading back from DMA09. While I was moderating the Search Marketing Experience Labs, one common element ran through every site review: When you ignore your SEO content marketing strategy, you’re hobbling your conversions, ignoring your customers and forfeiting your search engine rankings. Here’s why.

Seth Godin had it right when he said, “The best SEO is great content.” A well-written product page can skyrocket your conversions. A fantastic blog post can gain your company new leads and incoming links. The right Twitter tweet can gain not just followers but evangelists for your brand.

It’s really that important.

I’ve been in the SEO industry for 12 years. During that time, I’ve seen companies spend six figures on design, embrace five-figure monthly PPC costs and chase the latest “sexy” online marketing tactic.

Yet unfortunately, these same companies will ignore the foundation of their SEO and conversion success—creating customer personas, developing a keyphrase strategy, and developing useful, keyphrase-rich content that helps prospects across the buy cycle and engages customers.

Instead, the content becomes an afterthought. The one piece—heck, the only piece—of a company’s marketing strategy dedicated to engaging with customers becomes, “Isn’t SEO content supposed to be stuffed with keywords in order for me to get a high ranking?”

And that’s sad.

Think of your SEO content marketing strategy as your online salesperson, enticing your prospects to learn more and communicating with your audience. Your SEO content strategy could encompass many things, including:

  • Product/service pages.
  • Blog posts.
  • Articles, FAQs and white papers.
  • Twitter tweets.

Every word you write is a way to engage, inform and, yes, sell. But most importantly, a content marketing strategy helps you communicate with your prospects on multiple levels.

Fortunately, some companies “get it.” Forbes reported in its 2009 Ad Effectiveness Survey that SEO (and yes, that includes your content play) was the most effective online marketing tactic for generating conversions. Furthermore, Mediaweek reports in its article, “Marketing Must-Have: Original Web Editorial,” how AT&T created more than 100 how-to articles targeted to small business owners. Paul Beck, senior partner and executive director of Ogilvy Worldwide, is quoted as saying, “Having a core content strategy is the secret to engaging an audience.”

And at the end of the day, isn’t engagement what it’s all about? The company that engages, profits. The company that doesn’t—even big-brand companies that dominate the brick-and-mortar world—get left in the dust.

My monthly SEO & Content Marketing Revue posts will show examples of companies who “get it”—and what they’re doing right. I’ll share what’s worked for companies like yours, as well as what to avoid.

Most of all, I’ll share how the right SEO content strategy can gain your company the SEO and conversion “win” you may have been missing up to now.

And I’ll answer your questions (because, yes, you will have questions,) showing you how to leverage the power of strong, customer-centered content.

Stay tuned. This will be fun. Promise.