Why Personalization Matters in Direct Mail

Many times, companies shy away from complex personalization because they are worried about getting it wrong. Don’t let your fear stop you. Check out these stats from an InfoTrends 2016 study.

personasBefore we really get started, let’s define what personalization in direct mail is. It’s more than just putting the name and address on the piece.

In 2016, even just adding “Dear name” or just a name is no longer the best way to personalize your direct mail. Don’t get me wrong, you still need to have the name, but you can get so much more creative than that. So don’t just stop there, get more personal with offers, images, landing pages and more. Here are three reasons to get personal:

1. Recipients Like It

With all the technology out now, there’s no reason why you can’t personalize your direct mail, so just do it. Recipients have stated in multiple studies that they pay more attention to mail pieces that are personalized. It makes them feel special.

2. Better Targeting

With personalization, you are better able to target your message to each person. Using the information you have collected in your data base, you can create custom offers that appeal to the right people based on purchase history.

3. Better Response

Since your recipients like it and you can better target them, you are going to get a better response rate with personalization.

Many times companies shy away from complex personalization because they are worried about getting it wrong. Don’t let your fear stop you. Check out these stats from an InfoTrends 2016 study.

Survey Question: Does the customization or personalization of a direct mail piece make you more likely to open/read it? 29.2 percent said “Yes, Much more likely” and 55 percent said “Yes, a little more likely.” With numbers like that, you need to be personalizing.

Personalization in 2016 isn’t hard. There’s great software out there to help control the variable data and ensure that the correct information is on each piece. The key component is what you have in your data base. The more information you have, the better the personalization will be.

Top 3 Ways to Personalize Direct Mail

1. Images: The visual component of your direct mail sets the mood and draws the recipient into your message. Getting the images that will appeal most to each person is very important.

2. Offer: Using the data you have about each person to tailor the offer to their needs is very powerful. The right offer will get the response, the wrong one will not.

3. Copy: The copy is the obvious personalization to the recipient. It uses their name and other information from your data. Use the copy wisely to draw interest. Even though they like personalization, too much copy is a problem and they will not read it.

Are you convinced that personalization is the key to better ROI? I am. If you want to start smaller to take some of the fear out of it, you can do that. Just make sure you don’t get stuck in the mode of just using a name and calling that personalization. Build up your personalization with each mailing by incorporating more data and more complex variables. This gradual build up will give you the confidence to keep getting more personal. What ways have you used personalization?

5 Reasons to Add Bing Ads to Your Search Campaign

Put simply, you shouldn’t ignore Bing Ads just because it’s dwarfed by Google AdWords. Microsoft has invested heavily in Bing’s success and those efforts are paying off. Bing Ads offers a viable alternative option for connecting your business with new, potential customers. Here are five reasons why you shouldn’t hesitate to make Bing Ads part of your long-term marketing plan.

bing logoIt’s easy to overlook Bing Ads when planning your online marketing efforts.

Google is the undisputed king of search with more than $67 billion in ad revenue in 2015 — by comparison, Bing finally achieved profitability in the first quarter of this year with just $1 billion in revenue. To describe Bing Ads as Google’s little brother might be too much of a compliment. Search is Google’s world, and Bing is just living in it.

Still, Bing has proven itself as a viable upstart in the search business. In April 2015, Microsoft renegotiated its contract with Yahoo to allow Bing’s ads to appear on 51 percent of Yahoo desktop searches — a nice boon for Bing’s bottom line. Microsoft also sold Bing’s display network and map data assets, streamlining the platform’s approach toward search. And now Microsoft is broadening Bing’s potential by incorporating it in several emerging products and technologies. You’ve heard of Cortana in Windows 10? Yep, that’s powered by Bing.

What does this mean for you, a small business owner?

Put simply, you shouldn’t ignore Bing Ads just because it’s dwarfed by Google AdWords. Microsoft has invested heavily in Bing’s success and those efforts are paying off. Bing Ads offers a viable alternative option for connecting your business with new, potential customers. Here are five reasons why you shouldn’t hesitate to make Bing Ads part of your long-term marketing plan.

1. Bing Ads Are Often Cheaper and More Effective

As you could probably guess, most advertisers turn to Google. AdWords is really your only option for reaching the largest number of consumers with the least amount of effort.

Bing is much smaller than Google in terms of reach and revenue — which also means there are far fewer advertisers on Bing’s search network. And that means less competition for marketers who want a piece of Bing’s action. And less competition means cheaper costs per click — up to 33 percent less, according to some studies.

Not only is Bing usually cheaper, but advertisers also get higher ad positions than they would on Google’s more crowded search network. And higher ad placements usually result in higher click-through rates and conversions! Even though Bing doesn’t reach nearly as many people, these benefits are enough to make Microsoft’s ad platform attractive.

2. Bing Ads Let You Effectively Cut Off Tablets

Google caused a collective groan from PPC marketers by taking away the ability to block traffic from tablets. In Google’s eyes, tablets are the future of home computing and should be treated the same as desktops. For everyone else, tablets are giant smartphones both in how they function and how people use them — and that means lower CTRs and conversions than desktop searches.

Similar to Google, Bing has altered its device targeting options so tablets and smartphones can’t be completely turned off. However, Bing allows for incremental bids to be set on both types of mobile devices. Want to turn off tablets? Simply set your incremental bids on tablet traffic to decrease by a substantial percentage. It’s not a bulletproof way to ensure you’ll block all tablet traffic, but at least you won’t spend much money on the few clicks that slip through.

3. Bing Ads Let You Choose Your Partners

Want to choose between advertising on Google or its search partners? Well, you can’t. Google doesn’t let you choose one or the other. Either way, you’re stuck with Google’s primary network. You also don’t get to see which search partners might be running your ads. This is a problem because, while search partners often provide cheaper clicks, sometimes that traffic drastically underperforms.

Bing, on the other hand, gives you complete control. You can advertise only on Bing and Yahoo, or only with search partners — or you can run your ads on all platforms. Also, if you choose to target search partners, you can run reports to see exactly who those partners are. You can then take the additional step of blocking underperforming partners from running your ads. It’s a fantastic benefit that can make search partner targeting so much more worthwhile. And you can’t get that with AdWords.

4. Bing Ads Give You More Control Over Demographics

AdWords allows plenty of demographic targeting options for the Google Display Network, but demographic targeting isn’t an option for search network advertisers by default. Note that it’s possible to get demographic targeting for Search, but you need to go through a Google rep to get it turned on in your account.

Bing Ads, on the other hand, offers both gender and age targeting options by default. This is handled similarly to device targeting — rather than completely block certain demographics, you can decrease bids to specific demographics to effectively exclude them from your campaigns. These adjustments are made at either the campaign or ad group levels, giving you the ability to split test different ad groups with unique demographic targeting settings.

5. Bing Ads Are More In Tune With Social Extensions

A strong social media following is a strong indication of being an online authority — and that’s why Bing started testing social extensions back in 2014. If your business has a large Twitter following, then Bing’s automated social extensions will display your number of Twitter followers alongside your ad. It’s a meaningful extension that can boost your ad’s credibility and help drive conversions.

AdWords also has social extensions, but only for Google Plus. And who uses Google Plus? It’s no secret that Google has bent over backward pushing its social media platform, but Bing’s social extension provides a much more meaningful and socially relevant benefit.

Want more Google AdWords Tips?  Click here to get the Ultimate AdWords checklist.   

8 Considerations for Planning a Google AdWords Campaign

Ready to make a splash in Google AdWords? If you’re marketing your small business, then you may have first-hand knowledge about the ease of using Google’s ad platform. But don’t be fooled — it takes more than hastily written ad copy and keywords to be successful in AdWords.

TM0810_searchglobe copyReady to make a splash in Google AdWords? If you’re marketing your small business, then you may have first-hand knowledge about the ease of using Google’s ad platform. Anyone with a Google account and a credit card can get ads up and running within minutes. Online marketing can be an intimidating concept, but AdWords distills the creation of ad campaigns into a simple, step-by-step process.

But don’t be fooled — it takes more than hastily written ad copy and keywords to be successful in AdWords. Much like cooking isn’t as simple as throwing food into the oven, creating profitable campaigns in AdWords requires knowing your target audience, analyzing competitors and defining goals for your advertising efforts. Do these things, and your campaigns are far more likely to hit their desired targets. Neglect this pre-launch research, though, and your ads may never flourish.

Here we’ll review eight important steps when planning your Google AdWords campaigns. Whether you’re new to AdWords or have some experience, these easy steps can strengthen your advertisements right out of the gate.

1. Define Who You’re Targeting

Think of your AdWords campaigns as radio stations. If you wanted to attract the most listeners, you wouldn’t play the same music on all of your stations. Some stations would play the current pop hits, while others may play rap, classical or country. Each unique station would resonate better with specific groups of people.

So when creating your campaigns, think carefully about who you’re trying to reach with each one. If you’re marketing a shoe store, do you want your newest campaign to target male or female shoppers? Are you marketing formal shoes or sneakers? Are you trying to appeal locally or attract nationwide online orders? Or perhaps you’re selling to a niche market, like people with unusually large feet? Any information you can gather on your target audience will help you build your campaigns.

2. Find Relevant, High-demand Keywords

Building quality keyword lists is essential for all your campaigns. However, good keywords need to be more than relevant — they also need to be in high demand. In search marketing, demand is measured by how many people are searching for various keywords. Keywords that garner little attention from Web users aren’t going to help your advertising campaigns.

Fortunately, Google makes it easy to find relevant, high-demand keywords. Simply enter your keyword ideas into the AdWords Keyword Suggestion Tool, and Google returns lists of similar keyword terms along with their estimated monthly search volumes and various other metrics. Estimated costs per click are shown, but these figures are often incorrect. Definitely pay attention to the level of competition for each keyword term; keywords with higher levels of competition are being bid on by more AdWords users, which pushes up the required bids for premium ad placements. You’ll maximize your reach and make your budget go further by finding relevant, high-volume keywords with less competition from other advertisers.

3. Make a Focused Sales Pitch

Knowing how to blast your ad to the masses is important, but reach doesn’t matter if your ad isn’t interesting. What exactly are you selling, and why should your campaign’s target audience care? What makes your business or your product special? Are you offering a deal or discount that your customers shouldn’t be without?

Your sales pitch must be short and sweet. Pay-per-click ads don’t leave much room for making your point, which is why it’s so crucial to zero in on one or two selling points for each of your campaigns. Choosing the sales pitches for your various campaigns goes hand-in-hand with knowing your target audiences.

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.

Geo-Targeted Mobile Marketing Is Not a Trend

Video accounts for 50 percent of all mobile data. Experts are predicting that geo-targeted mobile marketing will be the hottest trend for 2014. A large majority of mobile advertising focuses on location, as this is one of the biggest advantages to utilizing this type of marketing. Along with geo-targeting comes a ton of great reasons to include video into your marketing mix if you haven’t yet

Video accounts for 50 percent of all mobile data.

Experts are predicting that geo-targeted mobile marketing will be the hottest trend for 2014. A large majority of mobile advertising focuses on location, as this is one of the biggest advantages to utilizing this type of marketing. Along with geo-targeting comes a ton of great reasons to include video into your marketing mix if you haven’t yet. This week’s blog will give you some insight on some of the benefits of using geo-targeted mobile marketing.

Remember the first day of college when you tried to get to your classes by reading a map? Remember the embarrassment of entering the class late? If you could have had a more efficient way to navigate to your classes, I’m sure you would have used it. If you were an administrative decision maker for the same school, you would find data used by those same lost students helpful in planning the flow of traffic for your next event.

Businesses inside of malls are now offering banner related ads in part of their targeting. Many of these retailers are including a directory to the their searched stores making them easy to find as well as dangling a bunch of carrots in front of their retail noses. You can forget about having to look for the kiosk that shows the “You are here” arrow. Mobile marketing not only helps with ROI, it also helps maintain customer retention by offering easy-to-find sales and store location, the shopping experience just got easier.

Apps like Vine and SnapChat are making a killing on posts with advertising. Businesses like the Gap encourage their visitors to use these types of apps to shout out “cool” finds while visiting their stores. With the idea that you can send and receive information about sales while you’re on the go it seems that this is really going to make some waves at the cash registers.

What good is advertising when you are physically on the move? The cell phone is the perfect solution to people on the go who don’t want to carry a ton of direct mail, newspaper coupons etc., while they shop.

This type of data is collectable and can be monitored, measured and maintained with the help of Google Analytics.

While they have been predicting the use of mobile marketing as the hottest tool next to the Ginsu Knife, this year will prove to be one of the biggest uses of mobile marketing.

Behavioral Targeting Industry Needs Further Delineation

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).
 

I received an interesting press release the other day from ValueClick Media that recapped a recent behavioral targeting panel that took the stage at the Hard Rock Hotel in Chicago.

The panel featured an industry analyst (David Hallerman, senior analyst, eMarketer), a behavioral targeting product expert (Joshua Koran, vice president, targeting and optimization, ValueClick, Inc.), a brand marketer (Julian Chu, Director of Acquisition Marketing, Discover) and an interactive agency executive (Sam Wehrs, Digital Activation Director, Starcom).

What I found most interesting about the release was that fact the group discussed and agreed on the need for delineation between the different approaches to behavioral targeting.

“While it is important to understand the difference between retargeting – which Hallerman referred to as “reactive” – and the more complex models, the panel agreed it is also critical to understand the differences within the more sophisticated group of behavioral targeting approaches, and Joshua Koran shared three designations: “clustering,” “custom business rules” and “predictive attributes,” the release said.

The “clustering” approach assigns each visitor to one and only one segment while the “custom business rules” approach offers marketers the ability to target visitors who have done X events in Y days, with Boolean operators of “and.” “or,” and “not.” Finally, the “predictive attributes” approach automates the assignment of interest categories based on the visitor activities that best correlate with performance; thus, the system is continuously learning to identify multiple interest attributes per visitor.

Another notable takeaway was the need for a focus on the customer experience and the corresponding importance of demonstrating value to customers when serving behaviorally targeted ads.

According to the release Julian Chu offered three questions marketers must address to make behavioral targeting a valuable experience for customers instead of merely serving the ads, which would unavoidably become customer annoyance: How are you going to do it? Where is it going to happen? What is going to happen at that time?

Presented as part of ValueClick Media’s ongoing Media Lounge education event series, this event – The Changing Behavioral Targeting Landscape – as well as the discussion itself underscored the importance of education relative to this increasingly important online advertising technique.

Food for thought!