Make Me an Offer — But Set My Expectations

What’s the ideal offer expiration date? Any good direct marketer knows that you have to test and learn what works for your brand, but in the early days of direct mail the rule of thumb was six to eight weeks (long enough for the recipient to receive the offer in the mail, write a check and mail it back).

What’s the ideal offer expiration date? Any good direct marketer knows that you have to test and learn what works for your brand, but in the early days of direct mail the rule of thumb was six to eight weeks (long enough for the recipient to receive the offer in the mail, write a check and mail it back).

But now that brands can communicate with customers instantly via email, text, Instagram and Facebook, offer windows can be shortened to a hours. And, when positioned appropriately, can drive a quick hit of revenue.

But here’s a case of what NOT to do …

On Friday, September 4, I received an email offer from Travelocity to click the link which would reveal how much I’d save (with the promise that it would range from 10 percent — 75 percent off on hotels). Given that I travel a lot, and I often book with Travelocity, it was an offer worth my click time. Plus, the button was kind of fun with a “Surprise Me” action message.

Naturally I was disappointed when I learned I had only earned 10 percent off and with another click had deleted the email message from my desktop and my memory bank.

But two days later, on Sunday, September 6 at 6:50 pm, I received another Travelocity email. This time the subject line was “Don’t Forget to Click. Reveal. Redeem.”

Given that it was a long weekend I didn’t check my emails until Monday, Sept 7 and, since I had completely forgotten about the earlier Travelocity email (since my inbox is filled with hundreds of email exchanges a day), I clicked the link in this email too. Only this time I got the message “Sorry! The Coupon is no longer valid” with a little clock icon reinforcing that time had run out.

My first reaction was that somebody at Travelocity had screwed up. Surely any email offer was going to last more than a day or two.

First, I found the original email offer in my deleted folder and it told me the offer expired on September 7. But instead of telling me I only had a few days or 72 hours, the email just gave me a calendar date — which, at the time, seemed like the distant future.

The September 6 email also noted that the offer expired on September 7 … but it should have said “24 hours” which would have given it the sense of urgency it deserved.

Instead, this Travelocity customer had a negative experience with the brand — and all over a potential 10 percent savings.

The point is, it’s critical that you think carefully about your offers, their activation windows and how you position it in your communication. Travelocity could have created a lot more interest and excitement if their original subject line had said “72 hour sale” in it … and their follow up email had “Final 24 hours of our sale.”

Motivating your target to act is one of the many challenges facing marketers today, so if you’re going to include an offer, make sure you give it the urgency it deserves.

Not All Databases Are Created Equal

Not all databases are created equal. No kidding. That is like saying that not all cars are the same, or not all buildings are the same. But somehow, “judging” databases isn’t so easy. First off, there is no tangible “tire” that you can kick when evaluating databases or data sources. Actually, kicking the tire is quite useless, even when you are inspecting an automobile. Can you really gauge the car’s handling, balance, fuel efficiency, comfort, speed, capacity or reliability based on how it feels when you kick “one” of the tires? I can guarantee that your toes will hurt if you kick it hard enough, and even then you won’t be able to tell the tire pressure within 20 psi. If you really want to evaluate an automobile, you will have to sign some papers and take it out for a spin (well, more than one spin, but you know what I mean). Then, how do we take a database out for a spin? That’s when the tool sets come into play.

Not all databases are created equal. No kidding. That is like saying that not all cars are the same, or not all buildings are the same. But somehow, “judging” databases isn’t so easy. First off, there is no tangible “tire” that you can kick when evaluating databases or data sources. Actually, kicking the tire is quite useless, even when you are inspecting an automobile. Can you really gauge the car’s handling, balance, fuel efficiency, comfort, speed, capacity or reliability based on how it feels when you kick “one” of the tires? I can guarantee that your toes will hurt if you kick it hard enough, and even then you won’t be able to tell the tire pressure within 20 psi. If you really want to evaluate an automobile, you will have to sign some papers and take it out for a spin (well, more than one spin, but you know what I mean). Then, how do we take a database out for a spin? That’s when the tool sets come into play.

However, even when the database in question is attached to analytical, visualization, CRM or drill-down tools, it is not so easy to evaluate it completely, as such practice reveals only a few aspects of a database, hardly all of them. That is because such tools are like window treatments of a building, through which you may look into the database. Imagine a building inspector inspecting a building without ever entering it. Would you respect the opinion of the inspector who just parks his car outside the building, looks into the building through one or two windows, and says, “Hey, we’re good to go”? No way, no sir. No one should judge a book by its cover.

In the age of the Big Data (you should know by now that I am not too fond of that word), everything digitized is considered data. And data reside in databases. And databases are supposed be designed to serve specific purposes, just like buildings and cars are. Although many modern databases are just mindless piles of accumulated data, granted that the database design is decent and functional, we can still imagine many different types of databases depending on the purposes and their contents.

Now, most of the Big Data discussions these days are about the platform, environment, or tool sets. I’m sure you heard or read enough about those, so let me boldly skip all that and their related techie words, such as Hadoop, MongoDB, Pig, Python, MapReduce, Java, SQL, PHP, C++, SAS or anything related to that elusive “cloud.” Instead, allow me to show you the way to evaluate databases—or data sources—from a business point of view.

For businesspeople and decision-makers, it is not about NoSQL vs. RDB; it is just about the usefulness of the data. And the usefulness comes from the overall content and database management practices, not just platforms, tool sets and buzzwords. Yes, tool sets are important, but concert-goers do not care much about the types and brands of musical instruments that are being used; they just care if the music is entertaining or not. Would you be impressed with a mediocre guitarist just because he uses the same brand of guitar that his guitar hero uses? Nope. Likewise, the usefulness of a database is not about the tool sets.

In my past column, titled “Big Data Must Get Smaller,” I explained that there are three major types of data, with which marketers can holistically describe their target audience: (1) Descriptive Data, (2) Transaction/Behavioral Data, and (3) Attitudinal Data. In short, if you have access to all three dimensions of the data spectrum, you will have a more complete portrait of customers and prospects. Because I already went through that subject in-depth, let me just say that such types of data are not the basis of database evaluation here, though the contents should be on top of the checklist to meet business objectives.

In addition, throughout this series, I have been repeatedly emphasizing that the database and analytics management philosophy must originate from business goals. Basically, the business objective must dictate the course for analytics, and databases must be designed and optimized to support such analytical activities. Decision-makers—and all involved parties, for that matter—suffer a great deal when that hierarchy is reversed. And unfortunately, that is the case in many organizations today. Therefore, let me emphasize that the evaluation criteria that I am about to introduce here are all about usefulness for decision-making processes and supporting analytical activities, including predictive analytics.

Let’s start digging into key evaluation criteria for databases. This list would be quite useful when examining internal and external data sources. Even databases managed by professional compilers can be examined through these criteria. The checklist could also be applicable to investors who are about to acquire a company with data assets (as in, “Kick the tire before you buy it.”).

1. Depth
Let’s start with the most obvious one. What kind of information is stored and maintained in the database? What are the dominant data variables in the database, and what is so unique about them? Variety of information matters for sure, and uniqueness is often related to specific business purposes for which databases are designed and created, along the lines of business data, international data, specific types of behavioral data like mobile data, categorical purchase data, lifestyle data, survey data, movement data, etc. Then again, mindless compilation of random data may not be useful for any business, regardless of the size.

Generally, data dictionaries (lack of it is a sure sign of trouble) reveal the depth of the database, but we need to dig deeper, as transaction and behavioral data are much more potent predictors and harder to manage in comparison to demographic and firmographic data, which are very much commoditized already. Likewise, Lifestyle variables that are derived from surveys that may have been conducted a long time ago are far less valuable than actual purchase history data, as what people say they do and what they actually do are two completely different things. (For more details on the types of data, refer to the second half of “Big Data Must Get Smaller.”)

Innovative ideas should not be overlooked, as data packaging is often very important in the age of information overflow. If someone or some company transformed many data points into user-friendly formats using modeling or other statistical techniques (imagine pre-developed categorical models targeting a variety of human behaviors, or pre-packaged segmentation or clustering tools), such effort deserves extra points, for sure. As I emphasized numerous times in this series, data must be refined to provide answers to decision-makers. That is why the sheer size of the database isn’t so impressive, and the depth of the database is not just about the length of the variable list and the number of bytes that go along with it. So, data collectors, impress us—because we’ve seen a lot.

2. Width
No matter how deep the information goes, if the coverage is not wide enough, the database becomes useless. Imagine well-organized, buyer-level POS (Point of Service) data coming from actual stores in “real-time” (though I am sick of this word, as it is also overused). The data go down to SKU-level details and payment methods. Now imagine that the data in question are collected in only two stores—one in Michigan, and the other in Delaware. This, by the way, is not a completely made -p story, and I faced similar cases in the past. Needless to say, we had to make many assumptions that we didn’t want to make in order to make the data useful, somehow. And I must say that it was far from ideal.

Even in the age when data are collected everywhere by every device, no dataset is ever complete (refer to “Missing Data Can Be Meaningful“). The limitations are everywhere. It could be about brand, business footprint, consumer privacy, data ownership, collection methods, technical limitations, distribution of collection devices, and the list goes on. Yes, Apple Pay is making a big splash in the news these days. But would you believe that the data collected only through Apple iPhone can really show the overall consumer trend in the country? Maybe in the future, but not yet. If you can pick only one credit card type to analyze, such as American Express for example, would you think that the result of the study is free from any bias? No siree. We can easily assume that such analysis would skew toward the more affluent population. I am not saying that such analyses are useless. And in fact, they can be quite useful if we understand the limitations of data collection and the nature of the bias. But the point is that the coverage matters.

Further, even within multisource databases in the market, the coverage should be examined variable by variable, simply because some data points are really difficult to obtain even by professional data compilers. For example, any information that crosses between the business and the consumer world is sparsely populated in many cases, and the “occupation” variable remains mostly blank or unknown on the consumer side. Similarly, any data related to young children is difficult or even forbidden to collect, so a seemingly simple variable, such as “number of children,” is left unknown for many households. Automobile data used to be abundant on a household level in the past, but a series of laws made sure that the access to such data is forbidden for many users. Again, don’t be impressed with the existence of some variables in the data menu, but look into it to see “how much” is available.

3. Accuracy
In any scientific analysis, a “false positive” is a dangerous enemy. In fact, they are worse than not having the information at all. Many folks just assume that any data coming out a computer is accurate (as in, “Hey, the computer says so!”). But data are not completely free from human errors.

Sheer accuracy of information is hard to measure, especially when the data sources are unique and rare. And the errors can happen in any stage, from data collection to imputation. If there are other known sources, comparing data from multiple sources is one way to ensure accuracy. Watching out for fluctuations in distributions of important variables from update to update is another good practice.

Nonetheless, the overall quality of the data is not just up to the person or department who manages the database. Yes, in this business, the last person who touches the data is responsible for all the mistakes that were made to it up to that point. However, when the garbage goes in, the garbage comes out. So, when there are errors, everyone who touched the database at any point must share in the burden of guilt.

Recently, I was part of a project that involved data collected from retail stores. We ran all kinds of reports and tallies to check the data, and edited many data values out when we encountered obvious errors. The funniest one that I saw was the first name “Asian” and the last name “Tourist.” As an openly Asian-American person, I was semi-glad that they didn’t put in “Oriental Tourist” (though I still can’t figure out who decided that word is for objects, but not people). We also found names like “No info” or “Not given.” Heck, I saw in the news that this refugee from Afghanistan (he was a translator for the U.S. troops) obtained a new first name as he was granted an entry visa, “Fnu.” That would be short for “First Name Unknown” as the first name in his new passport. Welcome to America, Fnu. Compared to that, “Andolini” becoming “Corleone” on Ellis Island is almost cute.

Data entry errors are everywhere. When I used to deal with data files from banks, I found that many last names were “Ira.” Well, it turned out that it wasn’t really the customers’ last names, but they all happened to have opened “IRA” accounts. Similarly, movie phone numbers like 777-555-1234 are very common. And fictitious names, such as “Mickey Mouse,” or profanities that are not fit to print are abundant, as well. At least fake email addresses can be tested and eliminated easily, and erroneous addresses can be corrected by time-tested routines, too. So, yes, maintaining a clean database is not so easy when people freely enter whatever they feel like. But it is not an impossible task, either.

We can also train employees regarding data entry principles, to a certain degree. (As in, “Do not enter your own email address,” “Do not use bad words,” etc.). But what about user-generated data? Search and kill is the only way to do it, and the job would never end. And the meta-table for fictitious names would grow longer and longer. Maybe we should just add “Thor” and “Sponge Bob” to that Mickey Mouse list, while we’re at it. Yet, dealing with this type of “text” data is the easy part. If the database manager in charge is not lazy, and if there is a bit of a budget allowed for data hygiene routines, one can avoid sending emails to “Dear Asian Tourist.”

Numeric errors are much harder to catch, as numbers do not look wrong to human eyes. That is when comparison to other known sources becomes important. If such examination is not possible on a granular level, then median value and distribution curves should be checked against historical transaction data or known public data sources, such as U.S. Census Data in the case of demographic information.

When it’s about the companies’ own data, follow your instincts and get rid of data that look too good or too bad to be true. We all can afford to lose a few records in our databases, and there is nothing wrong with deleting the “outliers” with extreme values. Erroneous names, like “No Information,” may be attached to a seven-figure lifetime spending sum, and you know that can’t be right.

The main takeaways are: (1) Never trust the data just because someone bothered to store them in computers, and (2) Constantly look for bad data in reports and listings, at times using old-fashioned eye-balling methods. Computers do not know what is “bad,” until we specifically tell them what bad data are. So, don’t give up, and keep at it. And if it’s about someone else’s data, insist on data tallies and data hygiene stats.

4. Recency
Outdated data are really bad for prediction or analysis, and that is a different kind of badness. Many call it a “Data Atrophy” issue, as no matter how fresh and accurate a data point may be today, it will surely deteriorate over time. Yes, data have a finite shelf-life, too. Let’s say that you obtained a piece of information called “Golf Interest” on an individual level. That information could be coming from a survey conducted a long time ago, or some golf equipment purchase data from a while ago. In any case, someone who is attached to that flag may have stopped shopping for new golf equipment, as he doesn’t play much anymore. Without a proper database update and a constant feed of fresh data, irrelevant data will continue to drive our decisions.

The crazy thing is that, the harder it is to obtain certain types of data—such as transaction or behavioral data—the faster they will deteriorate. By nature, transaction or behavioral data are time-sensitive. That is why it is important to install time parameters in databases for behavioral data. If someone purchased a new golf driver, when did he do that? Surely, having bought a golf driver in 2009 (“Hey, time for a new driver!”) is different from having purchased it last May.

So-called “Hot Line Names” literally cease to be hot after two to three months, or in some cases much sooner. The evaporation period maybe different for different product types, as one may stay longer in the market for an automobile than for a new printer. Part of the job of a data scientist is to defer the expiration date of data, finding leads or prospects who are still “warm,” or even “lukewarm,” with available valid data. But no matter how much statistical work goes into making the data “look” fresh, eventually the models will cease to be effective.

For decision-makers who do not make real-time decisions, a real-time database update could be an expensive solution. But the databases must be updated constantly (I mean daily, weekly, monthly or even quarterly). Otherwise, someone will eventually end up making a wrong decision based on outdated data.

5. Consistency
No matter how much effort goes into keeping the database fresh, not all data variables will be updated or filled in consistently. And that is the reality. The interesting thing is that, especially when using them for advanced analytics, we can still provide decent predictions if the data are consistent. It may sound crazy, but even not-so-accurate-data can be used in predictive analytics, if they are “consistently” wrong. Modeling is developing an algorithm that differentiates targets and non-targets, and if the descriptive variables are “consistently” off (or outdated, like census data from five years ago) on both sides, the model can still perform.

Conversely, if there is a huge influx of a new type of data, or any drastic change in data collection or in a business model that supports such data collection, all bets are off. We may end up predicting such changes in business models or in methodologies, not the differences in consumer behavior. And that is one of the worst kinds of errors in the predictive business.

Last month, I talked about dealing with missing data (refer to “Missing Data Can Be Meaningful“), and I mentioned that data can be inferred via various statistical techniques. And such data imputation is OK, as long as it returns consistent values. I have seen so many so-called professionals messing up popular models, like “Household Income,” from update to update. If the inferred values jump dramatically due to changes in the source data, there is no amount of effort that can save the targeting models that employed such variables, short of re-developing them.

That is why a time-series comparison of important variables in databases is so important. Any changes of more than 5 percent in distribution of variables when compared to the previous update should be investigated immediately. If you are dealing with external data vendors, insist on having a distribution report of key variables for every update. Consistency of data is more important in predictive analytics than sheer accuracy of data.

6. Connectivity
As I mentioned earlier, there are many types of data. And the predictive power of data multiplies as different types of data get to be used together. For instance, demographic data, which is quite commoditized, still plays an important role in predictive modeling, even when dominant predictors are behavioral data. It is partly because no one dataset is complete, and because different types of data play different roles in algorithms.

The trouble is that many modern datasets do not share any common matching keys. On the demographic side, we can easily imagine using PII (Personally Identifiable Information), such as name, address, phone number or email address for matching. Now, if we want to add some transaction data to the mix, we would need some match “key” (or a magic decoder ring) by which we can link it to the base records. Unfortunately, many modern databases completely lack PII, right from the data collection stage. The result is that such a data source would remain in a silo. It is not like all is lost in such a situation, as they can still be used for trend analysis. But to employ multisource data for one-to-one targeting, we really need to establish the connection among various data worlds.

Even if the connection cannot be made to household, individual or email levels, I would not give up entirely, as we can still target based on IP addresses, which may lead us to some geographic denominations, such as ZIP codes. I’d take ZIP-level targeting anytime over no targeting at all, even though there are many analytical and summarization steps required for that (more on that subject in future articles).

Not having PII or any hard matchkey is not a complete deal-breaker, but the maneuvering space for analysts and marketers decreases significantly without it. That is why the existence of PII, or even ZIP codes, is the first thing that I check when looking into a new data source. I would like to free them from isolation.

7. Delivery Mechanisms
Users judge databases based on visualization or reporting tool sets that are attached to the database. As I mentioned earlier, that is like judging the entire building based just on the window treatments. But for many users, that is the reality. After all, how would a casual user without programming or statistical background would even “see” the data? Through tool sets, of course.

But that is the only one end of it. There are so many types of platforms and devices, and the data must flow through them all. The important point is that data is useless if it is not in the hands of decision-makers through the device of their choice, at the right time. Such flow can be actualized via API feed, FTP, or good, old-fashioned batch installments, and no database should stay too far away from the decision-makers. In my earlier column, I emphasized that data players must be good at (1) Collection, (2) Refinement, and (3) Delivery (refer to “Big Data is Like Mining Gold for a Watch—Gold Can’t Tell Time“). Delivering the answers to inquirers properly closes one iteration of information flow. And they must continue to flow to the users.

8. User-Friendliness
Even when state-of-the-art (I apologize for using this cliché) visualization, reporting or drill-down tool sets are attached to the database, if the data variables are too complicated or not intuitive, users will get frustrated and eventually move away from it. If that happens after pouring a sick amount of money into any data initiative, that would be a shame. But it happens all the time. In fact, I am not going to name names here, but I saw some ridiculously hard to understand data dictionary from a major data broker in the U.S.; it looked like the data layout was designed for robots by the robots. Please. Data scientists must try to humanize the data.

This whole Big Data movement has a momentum now, and in the interest of not killing it, data players must make every aspect of this data business easy for the users, not harder. Simpler data fields, intuitive variable names, meaningful value sets, pre-packaged variables in forms of answers, and completeness of a data dictionary are not too much to ask after the hard work of developing and maintaining the database.

This is why I insist that data scientists and professionals must be businesspeople first. The developers should never forget that end-users are not trained data experts. And guess what? Even professional analysts would appreciate intuitive variable sets and complete data dictionaries. So, pretty please, with sugar on top, make things easy and simple.

9. Cost
I saved this important item for last for a good reason. Yes, the dollar sign is a very important factor in all business decisions, but it should not be the sole deciding factor when it comes to databases. That means CFOs should not dictate the decisions regarding data or databases without considering the input from CMOs, CTOs, CIOs or CDOs who should be, in turn, concerned about all the other criteria listed in this article.

Playing with the data costs money. And, at times, a lot of money. When you add up all the costs for hardware, software, platforms, tool sets, maintenance and, most importantly, the man-hours for database development and maintenance, the sum becomes very large very fast, even in the age of the open-source environment and cloud computing. That is why many companies outsource the database work to share the financial burden of having to create infrastructures. But even in that case, the quality of the database should be evaluated based on all criteria, not just the price tag. In other words, don’t just pick the lowest bidder and hope to God that it will be alright.

When you purchase external data, you can also apply these evaluation criteria. A test-match job with a data vendor will reveal lots of details that are listed here; and metrics, such as match rate and variable fill-rate, along with complete the data dictionary should be carefully examined. In short, what good is lower unit price per 1,000 records, if the match rate is horrendous and even matched data are filled with missing or sub-par inferred values? Also consider that, once you commit to an external vendor and start building models and analytical framework around their its, it becomes very difficult to switch vendors later on.

When shopping for external data, consider the following when it comes to pricing options:

  • Number of variables to be acquired: Don’t just go for the full option. Pick the ones that you need (involve analysts), unless you get a fantastic deal for an all-inclusive option. Generally, most vendors provide multiple-packaging options.
  • Number of records: Processed vs. Matched. Some vendors charge based on “processed” records, not just matched records. Depending on the match rate, it can make a big difference in total cost.
  • Installment/update frequency: Real-time, weekly, monthly, quarterly, etc. Think carefully about how often you would need to refresh “demographic” data, which doesn’t change as rapidly as transaction data, and how big the incremental universe would be for each update. Obviously, a real-time API feed can be costly.
  • Delivery method: API vs. Batch Delivery, for example. Price, as well as the data menu, change quite a bit based on the delivery options.
  • Availability of a full-licensing option: When the internal database becomes really big, full installment becomes a good option. But you would need internal capability for a match and append process that involves “soft-match,” using similar names and addresses (imagine good-old name and address merge routines). It becomes a bit of commitment as the match and append becomes a part of the internal database update process.

Business First
Evaluating a database is a project in itself, and these nine evaluation criteria will be a good guideline. Depending on the businesses, of course, more conditions could be added to the list. And that is the final point that I did not even include in the list: That the database (or all data, for that matter) should be useful to meet the business goals.

I have been saying that “Big Data Must Get Smaller,” and this whole Big Data movement should be about (1) Cutting down on the noise, and (2) Providing answers to decision-makers. If the data sources in question do not serve the business goals, cut them out of the plan, or cut loose the vendor if they are from external sources. It would be an easy decision if you “know” that the database in question is filled with dirty, sporadic and outdated data that cost lots of money to maintain.

But if that database is needed for your business to grow, clean it, update it, expand it and restructure it to harness better answers from it. Just like the way you’d maintain your cherished automobile to get more mileage out of it. Not all databases are created equal for sure, and some are definitely more equal than others. You just have to open your eyes to see the differences.

6 Factors to Align Direct Marketing Channels With Your Customers

Studies abound about which channels consumers prefer for receiving direct marketing messages. Some studies say consumers prefer direct mail. Others say it’s email. Then, there is the growing use of personalized web experience, social media, text messaging and other forms of messaging. The proliferation of devices and channels seems to be

Studies abound about which channels consumers prefer for receiving direct marketing messages. Some studies say consumers prefer direct mail. Others say it’s email. Then, there is the growing use of personalized web experience, social media, text messaging, and other forms of messaging. The proliferation of devices and channels seems to be unending.

In reality, your customers and prospects will demonstrate to you which channel they prefer, based on their actions. That’s what makes direct marketing what it is. But we are going to offer five qualitative factors, and one bottom line quantitative factor, to internally evaluate and align your message delivery strategy and channel with your customer and prospect’s preferences.

Qualitative factors for customer preference can include:

  1. Pure-play Sales Marketing vs. Content
    As customers and prospects are presented with marketing messages, do they view it as pure-play marketing (i.e., they see through it as your attempt to sell something), or as information and content that will be helpful to them? For example, publishers have succeeded for years when their messaging felt more like helpful information than a pitch to sell a subscription.
  2. Time Sensitivity
    Clearly an email can feel more time sensitive than direct mail, yet, experienced direct mail copywriters have for years been able to convey urgency in copy. But for your customers and prospects, other channels can be perceived as more time sensitive. Email, social media, telesales and even texting are channels that may feel most urgent.
  3. Shelf Life
    Email can vanish in a click. Direct mail can disappear in the trash bin (although it can be fished out of the trash). Higher production value catalogs and direct mail may be held onto longer than down-and-dirty printed packages. And higher production values (such as colors, textures, folds, tip-ons, stickers, die-cuts,and the visual impact of an 11×17 fold-out brochure) are impossible to convey in an email.
  4. How Did They Get My Name?
    Customers probably won’t be as concerned about privacy, but prospects can be much more sensitive. This can be especially the case if your offer touches on information such as health of personal finances. The trust factor is huge in prospects taking an action to pursue learning more about you, or making a purchase decision.
  5. How Do I Know You?
    Prospecting via email can be challenging to get opens and clicks. Run the numbers first (see our post on how to run the numbers). Direct mail for prospecting is getting more and more costly. Social media followers opt-in when they see you on various platforms or are referred to you by a friend. But consider that consumers often identify with social media as a personal platform, not necessarily as a place, to interact with marketing organizations. Better: Your prospect initiates the contact with you, and thus, become a lead. How do you do that? Content marketing, using those other online channels, can be a game-changer for you.

Quantitative Factors: As for quantitative factors you can use to align direct marketing to the media, there is really only one set of numbers to evaluate: Sales and cost per order (or per thousand). As an internal metric, when you evaluate your sales and cost per thousand, you can identify the ultimate metric to assess how your marketing messaging aligns with results.

Bottom line: Be aware of the studies that claim to have answers about which media channel customers prefer. But consider that you know your product better than anyone, you know the channel (or channels) that work for you, and you know your numbers. In a time when we’re awash in devices, channels, and choices, balance how you use each one so you’re aligned with how to drive cost-efficient sales.

Why SMS Will Be Your Mobile Workhorse and 5 Ideas to Get You Started

We’ve talked about the importance of a mobile-friendly Web presence and mobile-optimized email for your small business. But there is one mobile tool that your small business should be leveraging that will be a key puzzle piece to the success of your mobile strategy. Some might argue that SMS is the most effective mobile channel that exists, when it comes to ROI.

We’ve talked about the importance of a mobile-friendly Web presence and mobile-optimized email for your small business. But there is one mobile tool that your small business should be leveraging that will be a key puzzle piece to the success of your mobile strategy.

Some might argue that SMS is the most effective mobile channel that exists, when it comes to ROI.

There is a reason it continues to be the workhorse within the mobile strategies of brands like Coca-Cola, Macy’s, Victoria’s Secret, Target, jcpenney and many more.

5 reasons SMS will be the workhorse in your mobile strategy.

Instant Deliverability: SMS messages offer one of the most immediate marketing channels for businesses. More than 97 percent of messages are read within four minutes of receipt. So if you have a message that is time sensitive, there is no better way to connect with your customer.

Everyone’s Reachable: Nearly 100 percent of the handsets on the market can send and receive text messages. I don’t care that we’ve surpassed 50 percent smartphone penetration in the USA. I don’t care that that will continue to grow. You’re missing out on 40 percent to 50 percent of your audience right now by catering to smartphone-only customers.

Just because my 65-year-old dad has an iPhone now doesn’t mean he will use it the way I do. But you know what … he sure sends a whole lot more text messages to me.

Highest-Possible Visibility: Remember how I said that 97 percent of SMS messages are read within four minutes? Well, that means that 97 percent of your SMS messages are being read—period. When was the last time your email open rate was over 90 percent? I’ll let you figure that one out on your own.

Now I’m not saying “Stop using email.” Email is super powerful and has its place. But SMS offers you a new, quick, high-converting way to connect with your customers that no channel can match.

Highly Targeted: Because buying lists is a no-no when it comes to SMS, you have to build your database of loyal customers. Being a permission-based marketing vehicle, your customers have to opt in to receiving these messages from you.

Yes, that means they essentially raised their hands and said, “I’d like you to connect with me on my most personal device.” The next best thing in my mind is if your customer invites you over for dinner. Mmmmm …

Cost Effective, Considering the Return: For all you marketing folk, this means Return on Investment (ROI).

SMS is way more affordable than you think. Many of you still spend a good part of your budget on direct mail. Again, it has its place in your marketing mix. But look at some of the costs associated with direct mail: You have postage, shipping, mailing lists, printing, packaging/fulfillment etc.

Direct mail depends on your volume. But, at the end of the day, you could be spending 20 cents to more than a dollar per piece. SMS could cost you pennies per message.

As a small business, a Yellow Pages ad could cost you up to $4,000 per year. Yes, people (especially older demographics) do still reach for their Yellow Pages when they need a business in a hurry, but it offers little to no engagement or tracking.

Depending on the size of your small businesses, incorporating SMS into your monthly budget could run you $25 to a few hundred bucks a month. The level of return will far outweigh your older, traditional media vehicles.

OK, so you’re sold on adding mobile to your marketing mix. Congratulations, it was a wise decision, trust me.

Here are 5 ideas for you to get started with SMS this year.

Mobilize Your Loyalty Program: Begin building your list of mobile numbers and send timely, relevant messaging to your customers. This can include special mobile-only offers, promotion opportunities, sales, new product or service offerings.

The more you can personalize these messages, the better. Many of you may already have some sort of loyalty program in place. I’m not asking you to do something totally new. Just add SMS as a component of the loyalty program to bring loyal customers back with relevant, high-value messaging.

Mobilize Your Coupons: Target, jcpenney and Bed Bath & Beyond are great examples of this. Each and every week, these businesses send mobile coupons to their mobile databases. It’s fast, cost effective and convenient for the customers who prefer to receive these offers to their phones. They just bring their phones to the store and redeem their mobile coupons at the point of purchase.

Eliminate No-Shows: Does your businesses depend on filling appointment slots? Doctors, Lawyers, Salons, etc. rely on filling appointments, but what happens when your customer misses an appointment?

Let me guess, you don’t charge for no-shows? Some estimates state that missed appointments for a single physician can be as much as $150,000 in lost revenue and additional labor costs. Multi-physician offices are even more drastic, estimating no-shows in a single year resulting in losses of over $1 million.

So how can SMS eliminate no-shows?

Why not send an appointment reminder via SMS within an hour or two prior to the appointment? Include a number for those who have to cancel. Better yet, let them reply to the message so that it updates your appointment software.

Oh no, someone canceled! Send out a message to your database to fill that last-minute appointment.

If you’re a salon, restaurant or massage therapist, you can send a message to your customer SMS list offering a savings opportunity to the one that fills that appointment slot.

Add SMS and stop losing money due to no-shows.

Engage Customers With Giveaways: Sweepstakes and giveaways have been great ways to build your SMS list in the early stages.

Offer up one big prize and let your customers text in to enter. Give away a monthly prize and give customers a reason to stay on your list.

Not only do sweepstakes entice customers to opt-in, but everyone loves winning prizes. Is giving away one or two free services a month worth generating hundreds of new opt-ins to communicate with moving forward?

Learn About Your Customers With Polls and Surveys: Did one of your loyal customers just purchase from you? A quick SMS message could let them provide valuable feedback on their experience.

SMS is a two-way interactive tool that lets customers provide feedback just by replying to your messages.

Are you thinking about releasing a new product or service? Are you a restaurant and looking to add a new menu item? Poll your audience to get their feedback to help make smarter decisions.

Bonus point: Tie a sweepstakes to your survey and award a lucky customer with a prize of some sort to encourage participation.

Now it’s on you.

These are just a few ways you could quickly begin to incorporate SMS marketing into your business. It’s important to remember that SMS without a strategy or goal will lead to poor results.

Make sure you understand why you’re adding SMS and determine measurements for success to continually optimize your efforts.

The trick is to not re-invent the wheel. You should look to mobilize initiatives you already have in place.

You don’t need to create a separate marketing initiative. You’re already doing what you need to do. Now mobilize it.

Email Marketing to Acquire High Quality Facebook Fans

How much are Facebook fans worth? The answer depends on the quality of the relationship between fan and brand. There is a low entry threshold to become a fan—all it takes is a click or two. When Facebook is the only connection, financial support is unlikely. The best and most valuable Facebook fans are the ones who actively support your business or organization across channels. They are the ones that will respond to promotions and share real experiences with their friends.

How much are Facebook fans worth? The answer depends on the quality of the relationship between fan and brand. There is a low entry threshold to become a fan—all it takes is a click or two. When Facebook is the only connection, financial support is unlikely. The best and most valuable Facebook fans are the ones who actively support your business or organization across channels. They are the ones that will respond to promotions and share real experiences with their friends.

Encouraging people who subscribe to your emails to join your social networks is a best practice because it significantly improves the quality of your fan base. The process is more challenging than it used to be because Facebook eliminated the option for custom landing pages. It can still be done, but there are a few issues with the experience. The email from Belk Department Stores (the first picture in the media player at right) provides a good example.

There are several components that make this a good email for motivating people to cross channels. They are the same items that make all emails more successful at generating a response.

  • The email includes a specific call to action with a reward for connecting via Facebook.
  • There are multiple opportunities to click and connect via Facebook and other channels.
  • The primary promotion is the focus while secondary options are available.
  • The offer is time sensitive.
  • There are clickable links for shopping categories.
  • A web link is available if the email images aren’t available.
  • Unsubscribe, preferences, and privacy links offer control to the recipient.
  • Alternate text for images to encourage people to download images or visit webpage

Three days after sending this email, 16,708 new fans have joined Belk’s network and 34,465 coupons were claimed. How could this be if “liking” the brand is required to claim the coupon? Remember the issues mentioned earlier?

The ability to gate the coupon disappeared when Facebook eliminated custom landing pages. It is technically impossible to require someone to like the page before receiving the coupon. This means that the coupon is available to anyone who visits the page and explains why more coupons were claimed than fans acquired.

If an email increases fans and sales, it is successful even when the two aren’t codependent. The loss of the custom landing page requires good communication on how to access the coupon. Clicking the link in the email takes the recipient to Belk’s Facebook timeline. Scrolling down is required to see the offer. Obviously people are finding it because thousands have claimed the coupon. The unanswered question is how many more would have been claimed if the offer were more obvious?

What if the Belk Rewards tab was temporarily replaced with a 20 percent off offer so it appeared above the fold?

The functionality of the Belk coupon promotion is provided by Facebook. When someone clicks “Get Offer” an email is sent with the offer code. Whether you choose to use Facebook’s advertising products or do it yourself, here are some tips for making it successful:

  • Follow the best practices used in the example email.
  • Tell people how to claim to coupon in the email.
  • Put information about the promotion above the fold so people see it when they land on the page.
  • Include the expiration date on the Facebook post to increase the sense of urgency.
  • Test different strategies and measure everything.

Measuring the results for fan acquisition is a challenge because there is limited data available. Email metrics are much easier to acquire. If you have good benchmarks you can gather enough information to gain insight to the results from fans and Facebook activity.

There is a tendency in social media to acquire quantity over quality. When the focus is the number of fans instead of the relationship, the return is minimal. The best strategy is to encourage top customers to cross channels and join your networks. They will share your information with friends and family. This introduces your company to the people most likely to support your business.