Data Deep Dive: The Art of Targeting

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

Even if you own a sniper rifle (and I’m not judging), if you aim at the wrong place, you will never hit the target. Obvious, right? But that happens all the time in the world of marketing, even when advanced analytics and predictive modeling techniques are routinely employed. How is that possible? Well, the marketing world is not like an Army shooting range where the silhouette of the target is conveniently hung at the predetermined location, but it is more like the “Twilight Zone,” where things are not what they seem. Marketers who failed to hit the real target often blame the guns, which in this case are targeting tools, such as models and segmentations. But let me ask, was the target properly defined in the first place?

In my previous columns, I talked about the importance of predictive analytics in modern marketing (refer to “Why Model?”) for various reasons, such as targeting accuracy, consistency, deeper use of data, and most importantly in the age of Big Data, concise nature of model scores where tons of data are packed into ready-for-use formats. Now, even the marketers who bought into these ideas often make mistakes by relinquishing the important duty of target definition solely to analysts and statisticians, who do not necessarily possess the power to read the marketers’ minds. Targeting is often called “half-art and half-science.” And it should be looked at from multiple angles, starting with the marketer’s point of view. Therefore, even marketers who are slightly (or, in many cases, severely) allergic to mathematics should come one step closer to the world of analytics and modeling. Don’t be too scared, as I am not asking you to be a rifle designer or sniper here; I am only talking about hanging the target in the right place so that others can shoot at it.

Let us start by reviewing what statistical models are: A model is a mathematical expression of “differences” between dichotomous groups; which, in marketing, are often referred to as “targets” and “non-targets.” Let’s say a marketer wants to target “high-value customers.” To build a model to describe such targets, we also need to define “non-high-value customers,” as well. In marketing, popular targets are often expressed as “repeat buyers,” “responders to certain campaigns,” “big-time spenders,” “long-term, high-value customers,” “troubled customers,” etc. for specific products and channels. Now, for all those targets, we also need to define “bizarro” or “anti-” versions of them. One may think that they are just the “remainders” of the target. But, unfortunately, it is not that simple; the definition of the whole universe should be set first to even bring up the concept of the remainders. In many cases, defining “non-buyers” is much more difficult than defining “buyers,” because lack of purchase information does not guarantee that the individual in question is indeed a non-buyer. Maybe the data collection was never complete. Maybe he used a different channel to respond. Maybe his wife bought the item for him. Maybe you don’t have access to the entire pool of names that represent the “universe.”

Remember T, C, & M
That is why we need to examine the following three elements carefully when discussing statistical models with marketers who are not necessarily statisticians:

  1. Target,
  2. Comparison Universe, and
  3. Methodology.

I call them “TCM” in short, so that I don’t leave out any element in exploratory conversations. Defining proper target is the obvious first step. Defining and obtaining data for the comparison universe is equally important, but it could be challenging. But without it, you’d have nothing against which you compare the target. Again, a model is an algorithm that expresses differences between two non-overlapping groups. So, yes, you need both Superman and Bizarro-Superman (who always seems more elusive than his counterpart). And that one important variable that differentiates the target and non-target is called “Dependent Variable” in modeling.

The third element in our discussion is the methodology. I am sure you may have heard of terms like logistic regression, stepwise regression, neural net, decision trees, CHAID analysis, genetic algorithm, etc., etc. Here is my advice to marketers and end-users:

  • State your goals and usages cases clearly, and let the analyst pick proper methodology that suites your goals.
  • Don’t be a bad patient who walks into a doctor’s office demanding a specific prescription before the doctor even examines you.

Besides, for all intents and purposes, the methodology itself matters the least in comparison with an erroneously defined target and the comparison universes. Differences in methodologies are often measured in fractions. A combination of a wrong target and wrong universe definition ends up as a shotgun, if not an artillery barrage. That doesn’t sound so precise, does it? We should be talking about a sniper rifle here.

Clear Goals Leading to Definitions of Target and Comparison
So, let’s roll up our sleeves and dig deeper into defining targets. Allow me to use an example, as you will be able to picture the process better that way. Let’s just say that, for general marketing purposes, you want to build a model targeting “frequent flyers.” One may ask for business or for pleasure, but let’s just say that such data are hard to obtain at this moment. (Finding the “reasons” is always much more difficult than counting the number of transactions.) And it was collectively decided that it would be just beneficial to know who is more likely to be a frequent flyer, in general. Such knowledge could be very useful for many applications, not just for the travel industry, but for other affiliated services, such as credit cards or publications. Plus, analytics is about making the best of what you’ve got, not waiting for some perfect datasets.

Now, here is the first challenge:

  • When it comes to flying, how frequent is frequent enough for you? Five times a year, 10 times, 20 times or even more?
  • Over how many years?
  • Would you consider actual miles traveled, or just number of issued tickets?
  • How large are the audiences in those brackets?

If you decided that five times a year is a not-so-big or not-so-small target (yes, sizes do matter) that also fits the goal of the model (you don’t want to target only super-elites, as they could be too rare or too distinct, almost like outliers), to whom are they going to be compared? Everyone who flew less than five times last year? How about people who didn’t fly at all last year?

Actually, one option is to compare people who flew more than five times against people who didn’t fly at all last year, but wouldn’t that model be too much like a plain “flyer” model? Or, will that option provide more vivid distinction among the general population? Or, one analyst may raise her hand and say “to hell with all these breaks and let’s just build a model using the number of times flown last year as the continuous target.” The crazy part is this: None of these options are right or wrong, but each combination of target and comparison will certainly yield very different-looking models.

Then what should a marketer do in a situation like this? Again, clearly state the goal and what is more important to you. If this is for general travel-related merchandizing, then the goal should be more about distinguishing more likely frequent flyers out of the general population; therefore, comparing five-plus flyers against non-flyers—ignoring the one-to-four-time flyers—makes sense. If this project is for an airline to target potential gold or platinum members, using people who don’t even fly as comparison makes little or no sense. Of course, in a situation like this, the analyst in charge (or data scientist, the way we refer to them these days), must come halfway and prescribe exactly what target and comparison definitions would be most effective for that particular user. That requires lots of preliminary data exploration, and it is not all science, but half art.

Now, if I may provide a shortcut in defining the comparison universe, just draw the representable sample from “the pool of names that are eligible for your marketing efforts.” The key word is “eligible” here. For example, many businesses operate within certain areas with certain restrictions or predetermined targeting criteria. It would make no sense to use the U.S. population sample for models for supermarket chains, telecommunications, or utility companies with designated footprints. If the business in question is selling female apparel items, first eliminate the male population from the comparison universe (but I’d leave “unknown” genders in the mix, so that the model can work its magic in that shady ground). You must remember, however, that all this means you need different models when you change the prospecting universe, even if the target definition remains unchanged. Because the model algorithm is the expression of the difference between T and C, you need a new model if you swap out the C part, even if you left the T alone.

Multiple Targets
Sometimes it gets twisted the other way around, where the comparison universe is relatively stable (i.e., your prospecting universe is stable) but there could be multiple targets (i.e., multiple Ts, like T1, T2, etc.) in your customer base.

Let me elaborate with a real-life example. A while back, we were helping a company that sells expensive auto accessories for luxury cars. The client, following his intuition, casually told us that he only cares for big spenders whose average order sizes are more than $300. Now, the trouble with this statement is that:

  1. Such a universe could be too small to be used effectively as a target for models, and
  2. High spenders do not tend to purchase often, so we may end up leaving out the majority of the potential target buyers in the whole process.

This is exactly why some type of customer profiling must precede the actual target definition. A series of simple distribution reports clearly revealed that this particular client was dealing with a dual-universe situation, where the first group (or segment) is made of infrequent, but high-dollar spenders whose average orders were even greater than $300, and the second group is made of very frequent buyers whose average order sizes are well below the $100 mark. If we had ignored this finding, or worse, neglected to run preliminary reports and just relying on our client’s wishful thinking, we would have created a “phantom” target, which is just an average of these dual universes. A model designed for such a phantom target will yield phantom results. The solution? If you find two distinct targets (as in T1 and T2), just bite the bullet and develop two separate models (T1 vs. C and T2 vs. C).

Multi-step Approach
There are still other reasons why you may need multiple models. Let’s talk about the case of “target within a target.” Some may relate this idea to a “drill-down” concept, and it can be very useful when the prospecting universe is very large, and the marketer is trying to reach only the top 1 percent (which can be still very large, if the pool contains hundreds of millions of people). Correctly finding the top 5 percent in any universe is difficult enough. So what I suggest in this case is to build two models in sequence to get to the “Best of the Best” in a stepwise fashion.

  • The first model would be more like an “elimination” model, where obviously not-so-desirable prospects would be removed from the process, and
  • The second-step model would be designed to go after the best prospects among survivors of the first step.

Again, models are expressions of differences between targets and non-targets, so if the first model eliminated the bottom 80 percent to 90 percent of the universe and leaves the rest as the new comparison universe, you need a separate model—for sure. And lots of interesting things happen at the later stage, where new variables start to show up in algorithms or important variables in the first step lose steam in later steps. While a bit cumbersome during deployment, the multi-step approach ensures precision targeting, much like a sniper rifle at close range.

I also suggest this type of multi-step process when clients are attempting to use the result of segmentation analysis as a selection tool. Segmentation techniques are useful as descriptive analytics. But as a targeting tool, they are just too much like a shotgun approach. It is one thing to describe groups of people such as “young working mothers,” “up-and-coming,” and “empty-nesters with big savings” and use them as references when carving out messages tailored toward them. But it is quite another to target such large groups as if the population within a particular segment is completely homogeneous in terms of susceptibility to specific offers or products. Surely, the difference between a Mercedes buyer and a Lexus buyer ain’t income and age, which may have been the main differentiator for segmentation. So, in the interest of maintaining a common theme throughout the marketing campaigns, I’d say such segments are good first steps. But for further precision targeting, you may need a model or two within each segment, depending on the size, channel to be employed and nature of offers.

Another case where the multi-step approach is useful is when the marketing and sales processes are naturally broken down into multiple steps. For typical B-to-B marketing, one may start the campaign by mass mailing or email (I’d say that step also requires modeling). And when responses start coming in, the sales team can take over and start contacting responders through more personal channels to close the deal. Such sales efforts are obviously very time-consuming, so we may build a “value” model measuring the potential value of the mail or email responders and start contacting them in a hierarchical order. Again, as the available pool of prospects gets smaller and smaller, the nature of targeting changes as well, requiring different types of models.

This type of funnel approach is also very useful in online marketing, as the natural steps involved in email or banner marketing go through lifecycles, such as blasting, delivery, impression, clickthrough, browsing, shopping, investigation, shopping basket, checkout (Yeah! Conversion!) and repeat purchases. Obviously, not all steps require aggressive or precision targeting. But I’d say, at the minimum, initial blast, clickthrough and conversion should be looked at separately. For any lifetime value analysis, yes, the repeat purchase is a key step; which, unfortunately, is often neglected by many marketers and data collectors.

Inversely Related Targets
More complex cases are when some of these multiple response and conversion steps are “inversely” related. For example, many responders to invitation-to-apply type credit card offers are often people with not-so-great credit. Well, if one has a good credit score, would all these credit card companies have left them alone? So, in a case like that, it becomes very tricky to find good responders who are also credit-worthy in the vast pool of a prospect universe.

I wouldn’t go as far as saying that it is like finding a needle in a haystack, but it is certainly not easy. Now, I’ve met folks who go after the likely responders with potential to be approved as a single target. It really is a philosophical difference, but I much prefer building two separate models in a situation like this:

  • One model designed to measure responsiveness, and
  • Another to measure likelihood to be approved.

The major benefit for having separate models is that each model will be able employ different types and sources of data variables. A more practical benefit for the users is that the marketers will be able to pick and choose what is more important to them at the time of campaign execution. They will obviously go to the top corner bracket, where both scores are high (i.e., potential responders who are likely to be approved). But as they dial the selection down, they will be able to test responsiveness and credit-worthiness separately.

Mixing Multiple Model Scores
Even when multiple models are developed with completely different intentions, mixing them up will produce very interesting results. Imagine you have access to scores for “High-Value Customer Model” and “Attrition Model.” If you cross these scores in a simple 2×2 matrix, you can easily create a useful segment in one corner called “Valuable Vulnerable” (a term that my mentor created a long time ago). Yes, one score is predicting who is likely to drop your service, but who cares if that customer shows little or no value to your business? Take care of the valuable customers first.

This type of mixing and matching becomes really interesting if you have lots of pre-developed models. During my tenure at a large data compiling company, we built more than 120 models for all kinds of consumer characteristics for general use. I remember the real fun began when we started mixing multiple models, like combining a “NASCAR Fan” model with a “College Football Fan” model; a “Leaning Conservative” model with an “NRA Donor” model; an “Organic Food” one with a “Cook for Fun” model or a “Wine Enthusiast” model; a “Foreign Vacation” model with a “Luxury Hotel” model or a “Cruise” model; a “Safety and Security Conscious” model or a “Home Improvement” model with a “Homeowner” model, etc., etc.

You see, no one is one dimensional, and we proved it with mathematics.

No One is One-dimensional
Obviously, these examples are just excerpts from a long playbook for the art of targeting. My intention is to emphasize that marketers must consider target, comparison and methodologies separately; and a combination of these three elements yields the most fitting solutions for each challenge, way beyond what some popular toolsets or new statistical methodologies presented in some technical conferences can acomplish. In fact, when the marketers are able to define the target in a logical fashion with help from trained analysts and data scientists, the effectiveness of modeling and subsequent marketing campaigns increase dramatically. Creating and maintaining an analytics department or hiring an outsourcing analytics vendor aren’t enough.

One may be concerned about the idea of building multiple models so casually, but let me remind you that it is the reality in which we already reside, anyway. I am saying this, as I’ve seen too many marketers who try to fix everything with just one hammer, and the results weren’t ideal—to say the least.

It is a shame that we still treat people with one-dimensional tools, such segmentations and clusters, in this age of ubiquitous and abundant data. Nobody is one-dimensional, and we must embrace that reality sooner than later. That calls for rapid model development and deployment, using everything that we’ve got.

Arguing about how difficult it is to build one or two more models here and there is so last century.

Marketing Data: Do I Own My Own Name?

I’ve always been uncomfortable with the position taken by some privacy advocates that each of us owns our own information—and thus has some form of property rights derived from this information—and that marketers shouldn’t have use of that information without first having permission and providing compensation

I’ve always been uncomfortable with the position taken by some privacy advocates that each of us owns our own information—and thus has some form of property rights derived from this information—and that marketers shouldn’t have use of that information without first having permission and providing compensation. To this, I say—hey OK, but let’s be pragmatic.

Certainly, if I’m a celebrity, where my name and likeness has commercial value, perhaps as an endorsement, such an “ownership” rationale is a valid one.

But in the exchange of customer data for marketing purposes, this argument lacks merit, in my opinion. The value of my name on a mailing list, for example—mail, email, telephone, otherwise—has nothing to do with “my” name being on the list or, for that matter, “your” name being on that same list. (Even when we are both see ourselves as celebrities.)

Rather, the value of both our names being on the same list is by knowing the shared attribute that placed us both there—alongside the thousands of others on that list. In the world of response lists, it’s the sweat equity of the business where you and I both chose to become a customer that deserves the compensation in any data transaction, as it alone built the list by building a business where you and I both chose to become customers.

Yes, that marketer must provide notice, choice, security, sensitivity, marketing data for marketing use only, and perform other ethical obligations that are part of the self-regulatory process that have governed this business for nearly 50 years—recognizing that customer data is our most important asset, and that consumer trust and acceptance serves as the foundation of the data-driven marketing field. Privacy policies, preference centers, in-house suppressions and DMAchoice collectively serve the consumer empowerment process by enabling transparency and control in this data exchange.

In the world of compiled lists, where third parties assemble observed data for marketing purposes, again there is the sweat equity of the entities assembling and analyzing that data to “create” or “discover” the shared attributes of that data. Knowing these attributes is where the combined data derive their value. Marketers deploy activity based on these attributes to generate commerce. While the relationship between individuals and these third parties may be indirect, we still have the same ethical codes and opt-out tools governing the process. Recently, in the case of Acxiom, we’ve seen such a data compiler working to establish a direct relationship with consumers, providing individuals with the ability to inspect the data the company holds and to suggest corrections—as if the firm were a (highly regulated) credit bureau. (It is not.)

The fact that my name—Chet Dalzell—is on both response and compiled lists, to me, doesn’t entitle me to anything except to expect and demand that these movers of data act as responsible stewards of this information using well established ethics and self-regulatory methods. (Granted, in the US, there are legal requirements that must be met in such sensitive areas as credit, personal finance, health and children’s data.)

This flow of data, as the Direct Marketing Association most recently reaffirmed, generates huge social and economic value—and, in my view, my own participation as a customer in the marketplace is my agreement to allow such data exchange to happen. In fact, were it not for such flows, I might never have been provided an opportunity to become a customer in the first place. Benefits to consumers accumulate, while harm is nowhere part of the marketing ecosystem—other than to protect from identity theft and fraud. I find it fascinating some would-be regulators fail to grasp this truth.

That’s why inflexible government regulations—and opt-in-only regimes—and technology strictures that interfere with my interaction with brands are so troublesome. Such restrictions may claim to be about privacy; more often than not, they’re really motivated by political grand-standing, anti-competitive business models, and the forced building of new data siloes that do nothing to advance consumer protection—and potentially ruin data-driven marketing.

Yes, I own my name—and by choosing to be a customer of your brand, so do you own your customer list. Of course, I am the ultimate regulator in this process. For whim or reason, I can choose to take my business elsewhere.

Now, what about my Twitter, Facebook, Google and Yahoo! profiles?

Will There Be a ‘Snowden Effect’ on Marketing Data?

I didn’t even want to write this headline or blog post, given the fault-filled linkages some people make between marketing and something completely different from marketing. But it never seems to fail: Whenever some big news event captures the media’s attention, politicians’ attention surely follows. And when it has to do with consumer privacy, the results for the private sector—and use of marketing information in particular—are rarely favorable

I didn’t even want to write this headline or blog post, given the fault-filled linkages some people make between marketing and something completely different from marketing.

But it never seems to fail: Whenever some big news event captures the media’s attention, politicians’ attention surely follows. And when it has to do with consumer privacy, the results for the private sector—and use of marketing information in particular—are rarely favorable. This is true even when the responsible use of marketing data has NOTHING to do with the scenarios presented in the news.

U.S. legislative history is strewn with such evidence, linking (erroneously) marketing with some sensational occurrence other than marketing. Here are just three of them:

  • An actress is murdered in Los Angeles (1989). It turns out the murderer hired a private investigator to get her address from the state motor vehicle department, and then stalked and killed her. A bevy of state and federal anti-stalking laws are passed—but Congress passes an additional one, the Driver’s Privacy Protection Act (1994). Would you believe, state motor vehicle registration and license data is curtailed for marketing purposes (data that had been worth millions to the states, never mind losing the beneficial impact to automotive and insurance marketers and consumers), even though such data had nothing to do with the crime?
  • A child is kidnapped and killed, again in California (1993). A grieving father goes on a publicity rampage against presence of children in marketing databases—even though the horrible crime had nothing to with marketing, and even with state law enforcement officials testifying in public hearings following the crime that perpetrators of crimes against children most often stalk their victims physically (from an era prior to social media). Nonetheless, California and national media go after compilers of marketing data related to children. The stage is set later that decade for new privacy restrictions for children’s marketing data online.
  • Judge Robert Bork is nominated by President Reagan for the U.S. Supreme Court (1987). An enterprising reporter manages to publish a list of video titles rented by the nominee (all of them benign, by the way). A concerned Congress—no doubt thinking of its members’ own video rental history—passes the Video Privacy Protection Act (1988), shutting down marketing access to video titles from customer rentals/purchases.

And this summer, we have the National Security Administration revelations from Edward Snowden regarding public surveillance of U.S. citizens in the name of anti-terrorism. Now, we can only guess on what potential debilitating effects may be ahead for marketers, but you can bet some politicians or regulators are drumming beats for a response.

Privacy law in America should be about protecting individual liberty from abuse of information by the public sector—and leave the private sector alone, except in cases where there are demonstrable or probable harms from data misuse or errors. Such is the case with personal financial, credit and health data, for example, where the U.S. government wisely has taken a sector, pragmatic approach.

But Snowden’s government surveillance revelations could very well have a “chilling” effect on more broad marketing data collection and use, too. Politicians, in the name of protecting consumer privacy, may very well rush to curb data-driven marketing activity, rather than tackling the much-harder and real culprit, that is, spying on innocent Americans (and government acquiescence of such activity).

Concurrent to the NSA revelations, the Federal Trade Commission increasingly is vocal on “data brokers” and marketing activity—and trying to link data collection for marketing purposes to non-marketing purposes. Yet, it is dishonest, disingenuous and spurious to do so—and doing so fans fear and hypotheticals, instead of rational thought. There is no relationship between responsible data collection for marketing purposes—which only delivers benefits to the economy, and tax revenue, too—and data used for insurance and premiums, hiring purposes, and certainly the federal government’s activities to monitor internet and telecommunications in order to profile or detect would-be terrorists.

Marketers—for 40 years—have operated under a successful self-regulation code of notice, consumer choice, security and enforcement—and central to this is the use of marketing data for marketing purposes only. That’s as true online as offline. Where would we be without consumer trust in this process?

It may be very appropriate here to legislate what government may access—and how they may access—when it comes to personally identifiable information for surveillance or anti-terrorist purposes. But don’t even utter the word “marketing” in the same sentence. Let marketers continue with self-regulation: We offer consumers notice and opt-out, we focus strictly on marketing purposes only—and everyone benefits in the process.

What Social Sites Should YOU Be Using?

Most people know about mega-popular social sites such as Facebook, Twitter and LinkedIn. However, I get a lot of questions about other, underutilized sites that are on the tipping point of mass popularity—specifically, how these sites can be leveraged for marketing purposes.

Most people know about mega-popular social sites such as Facebook, Twitter and LinkedIn. However, I get a lot of questions about other, underutilized sites that are on the tipping point of mass popularity—specifically, how these sites can be leveraged for marketing purposes.

But before I go into that, I’d like to clarify the differences between various “social”-type sites:

Social bookmarking, news and tagging are sites like Digg, StumbleUpon, Reddit, Delicious and Pinterest. These websites allow users to “bookmark” things they like—content, images, videos, websites—and allow others in the community to see what’s been bookmarked and “follow,” if they wish. This is the epitome of viral marketing and community interaction. When groups of people are like-minded, it’s fun and easy to share feedback of things of common interest. For business purposes, it’s also a strong way to bond with your audience through content, news and images that are synergistic and leverage those interests for increased website traffic and more.

Social networking sites are communities like Facebook, Twitter, LinkedIn and Google Plus. It’s a way for groups of people to meet and stay in touch with each other, for personal and professional purposes. People can friend, follow or fan someone based on affiliation or interest. Another new site is Quora.com, which is a social question and answer site. Users can view by category and post questions or answers on virtually any business-related topic.

Social media refers to sites like Youtube, Flicker or Tumblr, where groups of users share media content such as video, audio or pictures (photos). There’s also new sites like Spotify.com, which are social music sharing sites, where users can listen to mp3 files themselves, as well as with friends, via Facebook.

The following are some social sites that you may want to include in your online marketing mix as well as some other tactical tidbits:

  • Pinterest.com is a social community where users “pin” (think of a bulletin board) things that they like. Quite simply, it’s a virtual pin board. Users can re-pin (which promotes viral marketing) or follow someone with the same interest. Pinterest is a fun site because it focuses on the visual element. You can leverage your keyword-rich content when you add your descriptive text to your “pin.” In addition, Pinterest asks for your URL, which will be a back-link to that webpage. This will encourage search engine marketing, branding and webpage traffic. Pinterest uses graphics, images (pics) and video pictures. And that’s what will grab community members’ attention, along with well-written descriptive text.

Important Tip! For marketing purposes, you can use Pinterest to promote your business or websites related to your business, such as landing pages, squeeze pages, product pages and more. What’s important to know is that if your website, or the webpages you’re thinking of pinning are flash (dynamic) webpages, you will be unable to “pin” it, as there’s no static images on a flash page for Pinterest to “grab” for posting.

So if you’re thinking about using testing Pinterest in your social marketing plan, make sure to pick websites or modify your own webpages to be graphic-, image- or video-rich. Also, like any marketing tactics you’re testing, make sure it’s in sync with your overall marketing plan and target audience.

If you’re target audience is an older crowd, then this may not be the best website, or channel, to reach them.

  • Quora.com is a great online resource community of questions and answers. If you want to reinforce yourself as an expert, you can search questions related to your area of expertise and post responses that are useful, valuable and actionable. If you have a legitimate question about any topic, you can post by category and view replies from others who may be versed in that field. Quora is a great way to create visibility for yourself. As well, it allows you to upload relevant back-links which encourage website traffic and linkbuilding.

Important Tip! It’s important to keep a steady presence on Quora. Stick to your areas of expertise (categories and topics). Make sure you have a keyword rich descriptive bio about yourself and include back-links to relevant websites. As with most all search, social and content marketing strategies—relevance and usefulness is key. All of these things help with credibility and branding. In addition, Quora’s pages are indexed by search engines and do appear in organic search engine results pages (SERPs). That, in and of itself, can expand your reach and visibility, which can lead to increased website traffic, which can then be parlayed into leads or sales.

  • Digg.com.com is one of my favorite content bookmarking sites. You can upload content “snippets” or news nuggets. The site will also pull in any images and well as back-links appearing on the same page as your content. Content can be given a “category,” so that the right readers will find it. The more popular your content (number of “digs”), the more people in the community it gets exposed to. Viral marketing and traffic generation (to the source website in the “digg”) are typical outcomes from this website. Reddit.com is a similar site, which allows users to upload a content excerpts (article, video, picture) and link to the full version. This is a great site to increase your market visibility and extend reach. It’s also a powerful platform to drive website traffic.

Important Tip! Use content that is “UVA”—useful, valuable and actionable, something newsworthy and/or interesting to your target reader. It’s very important to have a strong, eye-catching or persuasive headline that people in the community will want to read. There’s so much background noise on Digg that you want your content/headline to jump out at the reader. Also, include a back-link in the body copy you are uploading. This will help with branding, link-building and traffic generation. With Reddit, your content excerpt space is limited, so make sure to pick content that will not only resonate with the target audience, but also screams out to the reader to “click here” to read more. Then link to your full article, which should be posted on an inside page of your website.

  • Google+. Google Plus is Google’s attempt at social networking. It’s not as popular … yet … as behemoth Facebook (900 million users as of April 2012), but it’s got “teeth,” at around 90 million users. And because it’s Google, there’s some great search-friendly benefits built right in. For example, it’s indexed by Google, so your messages can get found faster. This helps with search engine visibility and website traffic.

Important Tip! For business purposes, you can share relevant information and personalize your “social” circles; thereby, targeting your message better for each group. It’s easy to share and rank (a combination of Digg and Facebook) content such as posts and messages. And there’s also a variety of sharing options like content, video, photos (similar to Pinterest, Flickr and YouTube).

With social marketing, it’s a matter of matching the content type to the most synergistic platform and audience. Social marketing may not be for every business. But I believe it’s certainly worth a strategic test. Just remember an old copywriting rule of thumb, which is “know your audience.” If you know who your target reader (prospect) is, then you can craft enticing messages and pick social platforms where those prospects are likely to congregate.

Most any social marketing site can be leveraged for marketing and business purposes. But make sure to keep your messages fun, entertaining, engaging and interactive. Because, after all, that’s what the “social” in “social marketing” is all about.