3 Steps to Complete a Competitive Content Marketing Review

You’ve got to know what’s out there if you’re going to attract the audience you want. So it’s worthwhile to evaluate your content marketing in relation to what’s already out there.

You’ve got to know what’s out there if you’re going to attract the audience you want. The best content in the world won’t gain any traction if someone else said the same thing 15 minutes ago. So it’s worthwhile to evaluate your content marketing in relation to what’s already out there. Here are three steps to completing a competitive content marketing review:

Step 1. It’s Not About Your Competitors’ Content (Yet)

You may be tempted to fire up your browser, do some searches for the terms you want to rank for, and see who and what pops up. That would be a mistake that can lead you down a rabbit hole and far, far away from your own goals.

Begin first by examining your own content and your analytics data to see what content you’ve created that has performed best. This will give you a baseline against which to evaluate the results you find on competitive sites.

Your goal during this content marketing review isn’t to beat everyone in everything – even if that was possible. Your goal is to beat all competitors in the niches you identify as most important to your target audience and in which you have significant expertise or perspective.

Step 2. Review Your Marketing Goals

Next, review your sales, marketing, and product goals to make sure the content you have out in the world is working toward the goals you have today. It’s not uncommon for older content, aimed at other goals, to continue to garner a strong audience. Of course, being off target, these content elements don’t help your bottom line. (Which is another great reason to perform a content marketing review at least annually and prune or edit content that isn’t aligned with your marketing message.)

Step 3. Review Competitors’ Content Marketing

With all of that information in hand, now it’s time to fire up your browser and see what content you are competing with in your chosen niche. Be sure your review includes long-tail keyword phrases as well as broader queries. This should help you get a solid picture of your content strengths and weaknesses from the top of your funnel to the bottom.

You’ll also want to check the products/services that are being marketed by the content you find. It may be that some keyword phrases are more commonly used in other industries or in other ways than you intend. Performing well against those keywords may drive traffic, but it’s unlikely to generate conversions.

To summarize all of the above, your content marketing review should focus on evaluating:

  • Targeting — are you speaking to the right audience?
  • Content — are you addressing your prospects’ primary concerns?
  • Distribution — are you getting content in front of your target audience?

When You Fail, Don’t Blame Data Scientists First — or Models

The first step in analytics should be “formulating a question,” not data-crunching. I can even argue formulating the question is so difficult and critical, that it is the deciding factor dividing analysts into seasoned data scientists and junior number-crunchers.

Last month, I talked about ways marketing automation projects go south (refer to “Why Many Marketing Automation Projects Go South”). This time, let’s be more specific about modeling, which is an essential element in converting mounds of data into actionable solutions to challenges.

Without modeling, all automation efforts would remain at the level of rudimentary rules. And that is one of the fastest routes to automate wrong processes, leading to disappointing results in the name of marketing automation.

Nonetheless, when statistically sound models are employed, users to tend to blame the models first when the results are less than satisfactory. As a consultant, I often get called in when clients suspect the model performance. More often than not, however, I find that the model in question was the only thing that was done correctly in a series of long processes from data manipulation and target setting to model scoring and deployment. I guess it is just easier to blame some black box, but most errors happen before and after modeling.

A model is nothing but an algorithmic expression measuring likelihood of an object resembling — or not resembling — the target. As in, “I don’t know for sure, but that household is very likely to purchase high-end home electronics products,” only based on the information that we get to have. Or on a larger scale, “How many top-line TV sets over 65 inches will we sell during the Christmas shopping season this year?” Again, only based on past sales history, current marcom spending, some campaign results, and a few other factors — like seasonality and virality rate.

These are made-up examples, of course, but I tried to make them as specific and realistic as possible here. Because when people think that a model went wrong, often it is because a wrong question was asked in the first place. Those “dumb” algorithms, unfortunately, only provide answers to specific questions. If a wrong question is presented? The result would seem off, too.

That is why the first step in analytics should be “formulating a question,” not data-crunching. Jumping into a data lake — or any other form of data depository, for that matter — without a clear definition of goals and specific targets is often a shortcut to demise of the initiative itself. Imagine a case where one starts building a house without a blueprint. Just as a house is not a random pile of building materials, a model is not an arbitrary combination raw data.

I can even argue formulating the question is so difficult and critical, that it is the deciding factor dividing analysts into seasoned data scientists and junior number-crunchers. Defining proper problem statements is challenging, because:

  • business goals are often far from perfectly constructed logical statements, and
  • available data are mostly likely incomplete or inadequate for advanced analytics.

Basically, good data players must be able to translate all those wishful marketing goals into mathematical expressions, only using the data handed to them. Such skill is far beyond knowledge in regression models or machine learning.

That is why we must follow these specific steps for data-based solutioning:

data scientists use this roadmap
Credit: Stephen H. Yu
  1. Formulating Questions: Again, this is the most critical step of all. What are the immediate issues and pain points? For what type of marketing functions, and in what context? How will the solution be applied and how will they be used by whom, through what channel? What are the specific domains where the solution is needed? I will share more details on how to ask these questions later in this series, but having a specific set of goals must be the first step. Without proper goal-setting, one can’t even define success criteria against which the results would be measured.
  2. Data Discovery: It is useless to dream up a solution with data that are not even available. So, what is available, and what kind of shape are they in? Check the inventory of transaction history; third-party data, such as demographic and geo-demographic data; campaign history and response data (often not in one place); user interaction data; survey data; marcom spending and budget; product information, etc. Now, dig through everything, but don’t waste time trying to salvage everything, either. Depending on the goal, some data may not even be necessary. Too many projects get stuck right here, not moving forward an inch. The goal isn’t having a perfect data depository — CDP, Data Lake, or whatever — but providing answers to questions posed in Step 1.
  3. Data Transformation: You will find that most data sources are NOT “analytics-ready,” no matter how clean and organized they may seem (there are often NOT well-organized, either). Disparate data sources must be merged and consolidated, inconsistent data must be standardized and categorized, different levels of information must be summarized onto the level of prediction (e.g., product, email, individual, or household levels), and intelligent predictors must be methodically created. Otherwise, the modelers would spend majority of their time fixing and massaging the data. I often call this step creating an “Analytics Sandbox,” where all “necessary” data are in pristine condition, ready for any type of advanced analytics.
  4. Analytics/Model Development: This is where algorithms are created, considering all available data. This is the highlight of this analytics journey, and key to proper marketing automation. Ironically, this is the easiest part to automate, in comparison to previous steps and post-analytics steps. But only if the right questions — and right targets — are clearly defined, and data are ready for this critical step. This is why one shouldn’t just blame the models or modelers when the results aren’t good enough. There is no magic algorithm that can save ill-defined goals and unusable messy data.
  5. Knowledge Share: The models may be built, but the game isn’t over yet. It is one thing to develop algorithms with a few hundred thousand record samples, and it’s quite another to apply them to millions of live data records. There are many things that can go wrong here. Even slight differences in data values, categorization rules, or even missing data ratio will make well-developed models render ineffective. There are good reasons why many vendors charge high prices for model scoring. Once the scoring is done and proven correct, resultant model scores must be shared with all relevant systems, through which decisions are made and campaigns are deployed.
  6. Application of Insights: Just because model scores are available, it doesn’t mean that decision-makers and campaign managers will use them. They may not even know that such things are available to them; or, even if they do, they may not know how to use them. For instance, let’s say that there is a score for “likely to respond to emails with no discount offer” (to weed out habitual bargain-seekers) for millions of individuals. What do those scores mean? The lower the better, or the higher the better? If 10 is the best score, is seven good enough? What if we need to mail to the whole universe? Can we differentiate offers, depending on other model scores — such as, “likely to respond to free-shipping offers”? Do we even have enough creative materials to do something like that? Without proper applications, no amount of mathematical work will seem useful. This is why someone in charge of data and analytics must serve as an “evangelist of analytics,” continually educating and convincing the end-users.
  7. Impact Analysis: Now, one must ask the ultimate question, “Did it work?” And “If it did, what elements worked (and didn’t work)?” Like all scientific approaches, marketing analytics and applications are about small successes and improvements, with continual hypothesizing and learning from past trials and mistakes. I’m sure you remember the age-old term “Closed-loop” marketing. All data and analytics solutions must be seen as continuous efforts, not some one-off thing that you try once or twice and forget about. No solution will just double your revenue overnight; that is more like a wishful thinking than a data-based solution.

As you can see, there are many “before” and “after” steps around modeling and algorithmic solutioning. This is why one should not just blame the data scientist when things don’t work out as expected, and why even casual users must be aware of basic ins and outs of analytics. Users must understand that they should not employ models or solutions outside of their original design specifications, either. There simply is no way to provide answers to illogical questions, now or in the future.

SEO: A Changed and Changing Discipline

SEO should play an important role in the marketing department; however, the death of SEO is frequently decried and its obituary written. This is because its role and fit in the overall marketing mix has changed and evolved. Once viewed as a technology play, organic search is often still considered the province of technicians, and is separated from the strategic marketing effort. Given that search often provides the tip of the spear for getting new business, this separation is a huge mistake. Today, SEO must be aligned with and guided by the overall marketing goals. This alignment can be best achieved when the SEO expert is part of the strategic marketing team.

SEO should play an important role in the marketing department; however, the death of SEO is frequently decried and its obituary written. This is because its role and fit in the overall marketing mix has changed and evolved. Once viewed as a technology play, organic search is often still considered the province of technicians, and is separated from the strategic marketing effort. Given that search often provides the tip of the spear for getting new business, this separation is a huge mistake. Today, SEO must be aligned with and guided by the overall marketing goals. This alignment can be best achieved when the SEO expert is part of the strategic marketing team.

SEO itself has changed. Once upon a time, SEO experts were characterized as techies focused on how to beat each new search engine algorithm change. As they say, that game is over. Google claims to have more than 200 ranking elements in play. No matter how good the SEO expert is, accurately determining all 200 elements and interpreting the valence given to each is in the realm of fantasy. Gone are the cat-and-mouse games. Today, SEO is real roll-up-the-sleeves marketing.

Technical SEO still exists, for a site must be found in the search indexes for it to drive traffic from search. Today, technical SEO experts are expected to identify what is preventing a site from being indexed. It may be as simple as a situation that I encountered where a site had been pushed live from the development environment with a robots.txt file still in place that directed search engines not to index the site. Once this block was removed, the site performed just fine. Most situations are far more complex. These are puzzles that require the SEO expert to review the site’s code and understand the total technical environment in which it runs. Given the complexity and technical depth required to do this, it is tempting to consider the SEO expert a technician, but this is just one area of SEO expertise. Today, some SEO experts do nothing but audit sites and troubleshoot what is creating problems.

Organic SEO experts are often characterized as keyword manipulation specialists. Once upon a time, this was a big part of the SEO toolkit. Today, as Google’s processing technology has shifted from keyword matching to a more sophisticated interpretation of the query and how it relates to the user’s intent, the SEO expert has had to look beyond keyword matching. Because Google no longer provides keyword data in the analytics, the SEO expert has to take a different approach. Searchers still use words in their queries, so keywords are far from gone as part of the discipline. Interpreting page and content relevancy are replacing the more simplistic keyword approaches. The SEO expert has evolved into an expert on online user intent: “What did the user really want to find with that query, and is the site relevant?”

With the explosive growth of social media and the realization that users value the opinions of peers more than marketers, the search engines have added elements to their algorithms that allow them to determine whether one site is more trusted and trustworthy than another. This is a potential game-changer, because bad reputation and negative customer ratings are not just an SEO problem. The SEO expert is expected to understand how to enhance the positive and deemphasize the negative. Poor reputation is a marketing problem.

Gone are the days of the SEO expert as just a technician and a traffic driver. Today’s SEO practitioner should be a valuable part of the total marketing team and a key player in the development of the marketing strategies and tactics that will lead the business to success. Is your SEO expert still waiting for an invitation?

How Big Should Your Campaign Budget Be?

How do you set a budget for a multi-touch, multi-target B-to-B digital campaign like the one Michelle was describing? The short answer is: Spend as much as delivers your threshold level of ROI. But, in B-to-B, it’s not so simple. Large enterprise sales cycles are long, as much as 18 to 24 months, so sales results won’t be available until long after she needs to make campaign decisions

At the ClickZ Live conference in New York, Michelle Killebrew presented an interesting case study of an IBM campaign called “Rethink Business.” It got me thinking (or, should I say, rethinking?) about campaign budgeting. My question is: How do you set a budget for a multi-touch, multi-target B-to-B digital campaign like the one Michelle was describing? The short answer is: Spend as much as delivers your threshold level of ROI. In other words, if Michelle’s campaign is generating qualified leads that convert to sales at a return that pays for themselves, covers her overhead, plus leaves a profit for IBM, she can keep the campaign running until the cows come home. Or until the campaign fatigues, and dips below the required ROI hurdle rate.

But, in B-to-B, it’s not so simple. Large enterprise sales cycles are long, as much as 18 to 24 months, so Michelle’s sales results won’t be available until long after she needs to make campaign decisions. And some of those results may never be known, since the leads are likely being worked by third party channel partners, who are often reluctant to share sales information.

Plus, Michelle said she had other objectives in mind for this campaign other than leads. She wanted to create digital experiences for prospect engagement, and she wanted to demonstrate the use of IBM’s proprietary marketing tools.

So in B-to-B, budgets are often set at a higher level than campaign ROI. Here are five methods that B-to-B marketers may be using to set marketing budgets.

  1. Percentage of last year’s budget. Take last year’s budget and subjectively add or cut, to arrive at a figure for this year’s budget. Can be applied by periods other than a year, like the quarter. Not based on much logic, but in common practice.
  2. Percentage of sales. Calculate a percentage of expected sales in the coming year; 4 percent is common in B-to-B for large, mature companies. Avoid using last year’s sales volume as the basis for this calculation. If last year was a bad year for your company, you won’t have a large enough B-to-B marketing budget to meet your growth goals in the coming year.
  3. Percentage of selling cost. A variation of No. 2, where the denominator is sales salaries and commissions, instead of revenue. You might see percentage levels like 20 percent to 30 percent with this method. It neatly reflects B-to-B marketing’s role as an efficient driver of sales productivity.
  4. Match your competition. In a high-growth, fiercely competitive stage in the product life-cycle, keeping up with your competitors may make sense. If you can find out what they are spending, which may require some clever intelligence-gathering activity.
  5. Zero-based budgeting. In this method, you determine your specific marketing goals, tied directly to business objectives. Then, you figure out what you need to achieve your marketing goals. For example, say it costs $350 to generate a qualified SMB lead that will convert to sales at 20 percent conversion rate. To bring in 3,000 SMB customers in the year, we need $5.2 million budget ($350/.2*3000). Clear and accountable.

Zero-based budgeting is the best way to go, in my view. You have a firm grasp of the numbers, and you are delivering against business objectives. With this approach, you can take you plans anywhere in the organization, and explain what you’re doing in a way that is meaningful to everyone.

A version of this article appeared in Biznology, the digital marketing blog.

Help! I’m Being Stalked by a Bathtub!

As a marketing agency, we’re always recommending different media channels to our clients depending on the product, the target audience demographics, marketing goals, etc. And, like many of you, I thought online retargeting was a clever way of “helping” to remind browsers that since they had been interested in a product/service at one point, they might still be interested in making a purchase from that site, so a little tap on the shoulder seemed like a clever way to stay top of mind. Until it happened to me.

As a marketing agency, we’re always recommending different media channels to our clients depending on the product, the target audience demographics, marketing goals, etc. And, like many of you, I thought online retargeting was a clever way of “helping” to remind browsers that since they had been interested in a product/service at one point, they might still be interested in making a purchase from that site, so a little tap on the shoulder seemed like a clever way to stay top of mind. Until it happened to me.

Retargeting, for those of you who may not know, involves having an advertiser drop a cookie into the consumer’s browser which enables the advertiser to follow that consumer around and display an ad for the advertiser after they’ve left the original site.

The logic is sound, the process is relatively simple, and it seems to make good marketing sense. Before it happened to me, I equated it to shoe shopping. I visit a store and see a pair of shoes I like. I try them on, but since I haven’t really looked in a lot of other shoe stores yet, I decide to put off the purchase until I’ve looked at all my options. But in the back of my head a little voice keeps whispering, “Those black patent kitten heels were perfect—even if they were $100 more than you wanted to spend.” I may or may not go back to that first store to get them but I do think about those shoes for quite a while—and with my luck, I return to the store only to find they are now sold out in my size.

But if I was shopping online and the shoes I liked were at Retailer A, I’m now seeing ads for those shoes no matter where I cruise on the Internet. Yep. Those black patents are now stalking me. Not whispering, but shouting out to “come back!”

However, I must confess that my recent stalking incident was not about shoes at all, but about bathtubs.

My husband and I are remodeling a bathroom, so I’ve spent quite a bit of time searching for the perfect bathtub online. Yesterday I actually placed an expensive bathtub in my shopping cart and proceeded to check out, but at the 11th hour started thinking that maybe my contractor could purchase the same tub for a better price. So I abandoned my cart. And in the process, it seems, launched obsessive tracking behavior that could only be rivaled by a professional stalker.

No matter what site I visited while researching client-related work, bathtubs kept appearing. Some were in the upper right hand corner of the page, so as I scrolled down the page they would disappear from view. Whew!

Others seemed to travel down the page with me … tumbling tub over tub with prices flashing, offers blazing and the lure of a long, hot soak compelling me to glance … nay linger … on the designer tub dangling within the reach of a mouse click.

But since I had no intention of completing the purchase transaction without the nod from my contractor, the ads seemed to get more annoying than helpful as the day went on. At one point, a colleague was looking over my shoulder while we were reviewing some online research. After looking at the page for about five minutes, she pointed to the tub ad and commented, “That tub reminds me—did you finish remodeling your bathroom yet?”

Intellectually I understand why retargeting is so valuable. Statistics show that 95 percent of users leave a site without making a transaction, and the ones retargeted are 70 percent more likely to complete a purchase, so it makes perfect sense to retarget.

However the default setting for most retargeting platforms is 30-90 days, so if you’re planning to include retargeting in your marketing mix, think carefully about cookie duration and ad fatigue. Because right now, my fatigue is only off-set by the dream of a long, hot soak in my new tub—cookie-free.