Email Marketing Insights

Here's an article I wrote for Figaro Digital:

Big Data. It’s a hot topic and it’s being debated by direct and email marketers alike. Some argue that the concept has been around for years (they aren’t wrong). Others have concerns about big data providing marketers with the mechanisms to flub their results. (They aren’t wrong either). Still others believe it is the solution that drives a more one-to-one experience between the marketer and the customers (and it could).

The big data discussion is becoming a very real business initiative for companies across multiple verticals. This leaves email marketers concerned about the implications this discussion will have on their channel. What will you do with the few quintillion bytes of data generated every day? It’s a good thing email is a dynamic and flexible channel, but that doesn’t mean email marketers must scramble to adapt to this unprecedented paradigm of lots and lots of data today. Do the increasing volume, velocity and variety of data spell doom for seasoned email marketing practices? Not really.

Leveraging data is nothing new – it’s core to the practice of marketing. What the big data initiative means for email marketers is access to the data they have wanted for years but haven’t been able to get their hands on. But be careful to not overthink it. The goal is to be smarter marketers, providing a more relevant and meaningful experience to the subscriber. Don’t lose sight of the fundamentals of email marketing; rather, enhance your capabilities with a newfound wealth of information.

Targeted, meaningful interactions

It is the data, not the intuition, that makes marketing programmes successful. Being relevant is something we have discussed as an industry since its inception. We prefer not to batch and blast; instead, we strive to create unique and optimal engagements with the customer to drive desirable behaviours. And it is the ability to leverage data in order to make informed decisions and drive relevant offers that helps to achieve that reality.

According to the 40/40/20 rule of email marketing: 40 per cent of a programme’s success is determined by getting the right message to the right person (data); 40 per cent of the success is delivering the message at the right time (data, again). The remaining 20 per cent is how it is delivered. The creative element associated with the message – and determining the right creative envelope can also be born out of, yep, you guessed it, data. It is important to keep your focus on turning the dials that are most impactful first. After all, with all this data at your fingertips, it wouldn’t be unusual to find yourself diving down a variety of data-mining rabbit holes.

Start by leveraging the insights you gain to determine what the right message is for your audience. Years back I worked for a loyalty agency, and we always started with the ‘who.’ Understanding an audience or a segment is the first necessary step in determining an offer. If you don’t know who you are trying to appeal to, how can you ever be relevant? Once you have the audience, start looking at historic behaviour. Past offer performance will help determine what to offer (or better yet, what offers to test) to the defined audience. Next, you need to determine the proper timing of the offer. (Is it seasonal? Is there a product or buying cycle to consider?) Finally, but just as important, is identifying creative for delivering this carefully crafted message. Don’t be afraid to step out of the box and have a little fun with it.

Find the offers that drive ROI

Email marketers have been running tests, comparing results and measuring lift and incremental behaviour since before email marketing was a channel. This is a practice which direct mailers really perfected – largely because of the increasing costs to print and mail offers, but whatever the reason it drove significant relevance. With the flexibility of email, these tests are easier and more effective (and leveraged less often) than before. Get back on the testing bandwagon – strive to be a more relevant marketer. The more data consumers generate, the more relevant they expect you to be; don’t lose sight of the importance just because it is cost-effective to send email.

You need to get your testing methodology defined and implemented with the data you have access to today because as more data becomes available (and your ability to analyse it speeds up) you will need to be in the practice of testing as an organisational culture. You should also start looking more closely at your metrics for success. Be prepared to move beyond conversions to bigger concepts like ROI and lifetime value.

Keep striving for greater gains

We’ve come a long way from the days when marketers could say “I waste half my advertising dollars, I just don’t know which half.” Advances in cross-channel tracking and reporting enable email marketers to build detailed reports for follow-up. Still, most of these reports have been limited: either in detail or timescale. For example, a detailed report is given about a specific mailing or programme, but only aggregate-level data is available over a quarter or entire year. This has long been a reality of data storage limitations associated with system performance, and that is one big challenge which the big data effort is addressing. The ability to store, process and analyse mounds of information is making many reporting geeks extremely happy.

What’s exciting for marketers is the promise of a data structure that can store and make available highly detailed information on what emails/campaigns/ promotions users have received, how they’ve responded to those and how that behaviour has changed over a year or longer. How will personas and strategies change when such detailed data over such a long period is available so quickly?

So, the principles of marketing will remain unchanged as big data becomes reality. Data, and how it’s used, remains core to a marketer’s strategy.

One thing may change: analysis. As the sets of data become larger, methods of analysis beyond the experience of most marketers become necessary. I’m talking about statistical modeling and predictive analytics - the types of things quantitative analysts do for a living. Some larger organisations, in parallel with tech changes to accommodate big data, have created teams of quants to service different business units (including marketing) with this type of analysis. Marketers must learn to speak the language and ask the right questions of these people as they become a part of the marketing process.

If you’re experienced in the ways of marketing, big data shouldn’t be something that keeps you up at night with anxiety. Although you may lose sleep thinking about all the opportunities it provides for creating more relevant and effective programmes.

Here's an article I wrote for ClickZ:

As marketers progress rapidly toward the analytics 2.0 horizon brought on by the ability to collect and store more data, one phrase has echoed through my mind: "It's time to get mature about data."

The opportunities and problems brought on by "big data" are nothing new for marketers. We've always had to deal with information on who our customers are and what they do. Ever since we've realized that "half our advertising dollars are wasted," we've been driven to better understand how exactly our marketing spend influences behavior.

And for the past 20 years or so, we've done alright. We've built systems that track bounces, opens, clicks, unsubscribes, and complaints. We've connected that with on-site behavior to understand conversions. We've linked customer data back to the email to drive dynamic personalization. Well done.

But have we begun to understand statistically how our campaigns that don't drive conversions influence behavior? Do we know which factors out of that set of customer data are better to target when personalizing? And, for these two questions and hundreds more, can we justify our answers using data and analysis?

I'm guessing that for many of us, the answer is no.

Time to grow up.

As the realm of measurement moves out of the purely technical and shares importance with the marketing side of the house, are we in marketing prepared to use this awesome capability that we're about to gain?

Here are five things you can do to create a data maturity growth spurt:

1. Ditch the single channel mentality. Email affects behavior, but so do other channels. Recently, a major company's marketing department presented the revenue generated by each digital channel to their financial leaders. When they summed all the channels, they showed $200 million generated, which made the CFO laugh, since the company had only generated $120 million.

Research the methods and new tools available that make it possible to more accurately attribute revenue and understand better how channels influence behavior.

2. Get better metrics. The fundamentals remain important, but other methods of analysis will provide deeper insights into your performance.

For example, consider building cohort analysis into your reporting practice. You'll get a better idea of how customers gained through different marketing mixes perform over a certain period of time, possibly identifying key insights in lifetime value that could not be had attributing value from the most recent purchase only.

3. Understand the stats. Statistical analysis that used to be seen only in the world of insurance and finance are now being leveraged by marketing departments to better understand big data.

It's unrealistic for most marketers to reach the same level of proficiency that these Masters and Ph.D-level quants have. But that doesn't mean a marketer can't learn enough to understand what methods are being used and how to interpret the results. Learn to speak the language of the quants; be able to ask intelligent questions that a quant can work with; and be able to understand the relevance and limitations of the results she presents.

4. Look outside to find success. Research other companies that are using data and new analytical approaches to understand their successes and failures. Attend conferences, ask colleagues, and read plenty. Identify key takeaways for your business, tools you should consider, pitfalls to avoid, and results to expect.

5. Use it. This principle is as true as it ever was: all of this maturity is worth nothing if you don't use it to test new things and improve.

"The man who does not read has no advantage over the man who cannot read." - Mark Twain

Grow up, and join the marketers who are always striving for the next level of data maturity.

Here's an article I wrote for ClickZ:

As the big data trend becomes less of a discussion and more of a real business initiative for more and more companies, many email marketers are concerned about the implications of this brave new world, where a few quintillion bytes of data are generated every day. Must email marketers scramble to adapt to this unprecedented paradigm of lots and lots of data? Do the increasing volume, velocity, and variety of data spell doom for seasoned email marketing practices?

Not really.

Dealing with data is nothing new for marketers - it's core to the practice of marketing. When the rubber hits the road, and big data talk becomes real change in an organization, the email marketer is given an opportunity to better apply the principles she has been applying way before the arrival of lots of varied data moving fast.

Principle 1: More Targeted = More Relevant = Better

As Kara Trivunovic wrote in an earlier ClickZ column, big data might be the current catch phrase du jour, but what it really means for marketers is relevance. Marketers have been aiming for relevance since the early days of direct mail and cross promotions. Email marketers specifically have been leveraging available data to deliver relevant emails to the right person at the right time. Over the last 20 years, we've gathered more data from different sources at increasing frequencies (sound familiar?). Each additional source and increase prompted an adjustment in strategy - for example, dynamic content in emails based on customer profile is near standard today, when a lack of usable data made it near impossible just a decade ago.

A similar shift must occur once the availability and accessibility of data to the email marketer increases. In the past you versioned based on the most recent purchase…how will you version based on the last three purchases? In the past, your post-holiday efforts may have been aimed toward anyone who didn't redeem an offer during the holiday…how will you adjust your strategy where you can build targets for those who haven't purchased in November and December for the last five years?

Principle 2: Find the Offers That Drive the Most ROI

Email marketers have been running tests and comparing results since people were called email marketers. These tests became easier and more effective once the technology allowed faster creation and reporting on the tests.

As structures to deal with big data arrive, marketers will be able to run more complex tests faster, with more versions over longer periods of time. Also, metrics for success may move beyond conversions to bigger concepts like ROI and LTV. The core concept of trying to find which mix works best, however, remains unchanged.

Principle 3: Report and Improve

We've come a long way from the dark ages of the perennial quote, "I waste half my advertising dollars, I just don't know which half." Advances in cross-channel tracking and reporting enable email marketers to build detailed reports for follow-up.

Still, most of these reports have been limited: either in detail or in timescale. For example, a detailed report is given about a specific mailing or program, but only aggregate-level data is available over a quarter or entire year.

What's exciting for marketers is the promise of a data structure that can store and make available highly detailed information on what emails/campaigns/promotions users have received, how they've responded to those, and how that behavior has changed over a year or longer. How will personas and strategies change when such detailed data over such a long period is available so quickly?

So the principles of marketing will remain unchanged as big data becomes reality. Data, and how it's used, remain core to a marketer's strategy.

One thing may change: analysis. As the sets of data become larger, methods of analysis beyond the experience of most marketers become necessary. I'm talking about statistical modeling and predictive analytics…the types of things quants do for a living. Some larger organizations, in parallel with tech changes to accommodate big data, have created teams of quants to service different business units (including marketing) with this type of analysis. Marketers must learn to speak the language and ask the right questions of these people as they become a part of the marketing process.

If you're experienced in the ways of marketing, big data shouldn't be something that keeps you up at night with anxiety. Although you might lose some sleep thinking about all the opportunities it provides for creating more relevant and effective programs.

Here's an article I wrote for iMedia Connection:

"Big data," the buzz phrase of the year, is at once promising and frightening. Email marketers in particular love the promise of super-relevant, lifecycle-sensitive campaigns. Those same email marketers are, in many cases, scared away from actually using "big data" because of the work involved (i.e., hiring quants, investing in data cleanup, etc.).

Good news: Many of the benefits that a truly analytic approach to "big data" provides are available without a radical investment. The key is to quantify what exactly a "big data" process would give you and then replicate that without the actual modeling and intelligence that true "big data" analysis would provide.

What does "big data" actually do for email marketing?

This approach leverages lots of consumer data points to deliver highly targeted offers in a relevant way. For example, a hotel chain might have information from reservation systems, front desks, loyalty programs, and email behavior. It combines this data and runs PhD-level statistical analysis to discover that its customers fit four distinct patterns of staying: some stay only on holidays, some stay every three months for business reasons, etc. Based on these segments, email promotions and lifecycle campaigns are dynamically populated with targeted information.

The summary above is extremely basic, and the methods used can extend much further, but the case study is useful as an example of what is possible.

How to do big data without doing "big data"

The promise of "big data" is unparalleled insight. But many, if not most, email marketers still have "big data"-type insight within reach, but they have failed to implement a strategy to get it.

What profile information do you have on your subscribers? If you are an online retailer, do you know why a specific customer abandoned his or her cart? Why not include a simple one-question survey as part of your abandoned cart program? It's true that some people won't answer, but some will, and then you can respond accordingly.

For example, if shipping cost was a main concern, you can follow up with an email detailing other options, your customer support contact info, and potentially an offer for the next time the person shops if he or she completes this order. If such a survey had six reasons from which people could choose to indicate why they didn't finish their purchases, you've now built out six segments of customers with one email survey. (Note: Those who don't reply might fit into one of the six, or might not, so they are not exactly like a seventh segment.)

You've just built a model for cart sensitivity without building a model. Is it perfect? No. Is it as good as true modeling? Almost certainly not. But it's better than nothing, and it just might be better than what you're doing now.

Another example: A retailer is running an A/B test to see how one offer (50 percent off one item) performs against another offer (buy one get one free). The retailer wishes it also knew which of its clients preferred which offer, not just which one performed better. After running the test, it comes back to those who didn't take the offer and asks if they would rather have had the other offer. The test is over, so those results are unaffected. But now the people who take the opposite offer have indicated a preference for that kind of offer.

Again, this approach isn't perfect, and not nearly as beneficial as a full model would be, but it still has value. The next time there is an offer, the retailer can test its assumptions and see if this strategy results in more revenue from the profiled subscribers. It can also repeat the strategy with further tests to profile more people.

I admit that the methods above are crude. The point is there are already strategies to approach customers with more targeted and relevant information that don't require "big data" expertise. Master that, and then make the investment in "big data."

Here's an article I wrote for ClickZ:

In the world of email marketing, the word "relevance" used to be the catchphrase du jour - followed closely by every email strategist's favorite, "it depends." But these both seem to be losing ground to "Big Data." According to IBM, we create 2.5 quintillion bytes of data each day. That data is generated through myriad places - online footprints, purchase behavior, weather beacons, take your pick. We are tracking everything these days - the challenge becomes making it actionable. The issue with big data is not the acquisition of the data elements - it's the digestion and application of them.

Typically catchphrases are just that - catchphrases. They carry very little weight or meaning to the end goal, but big data is actually a pretty meaty topic that marketers and PhDs alike are trying to digest. It's a reality that marketers, politicians, parents, and yes, email marketers need to consider.

For email marketers, where there is big data there is...relevance. There are many phases to the big data challenge, but as we turn an eye to email, there are some additional elements that should be considered and applied to your approach.

Do Your Own Analysis

Analyzing large data sets and learning anything from it is no small undertaking. It's a very labor-intensive task - but for an email marketer the challenge is oftentimes even bigger. Not because we aren't capable, or that we have more data, but because we're often at the tail end of most analytic efforts. The email team inherits personas and segments - so to rally at the front end to drive it can sometimes appear preposterous to internal counterparts. The recommendation here is to stand firm. There are elements that you can leverage from previous engagement (or lack thereof) with your email program that help to develop very effective lookalike models and predictive modeling that can drive long-term success for your email program.

Watch Out for Hyper-Targeting

Achieving a near 1:1 email communication experience has long been the Holy Grail of the email marketing vertical. With the highly dynamic tools and technical capabilities available today, the ability to communicate in that manner is relatively easy to accomplish - the challenge arises when you want to learn what the engagement means to your business overall. Many times, the success or failure of leveraging big data to drive targeting and segmentation doesn't happen because of your ability to do it - rather your ability to measure it. So if you're going to leverage big data to make your email hyper-relevant, be extra diligent on the reporting front. The takeaway here is to make sure you continue to analyze the data, leveraging the engagement metrics associated with each person you communicate with. It's no longer about a target audience but rather about an individual.

Know What to Say

Once you have found your golden child (or children) among your massive data set, you don't just stop there. It isn't about just finding the right audience; you have to message to them in an effective and efficient way. If the messaging appears too "big brother," then you're going to create a "creep factor" with the recipient. Or if you get the data wrong, you may put the customer off entirely. Regardless, it's imperative that the audience you have identified is getting messaging that actually matters to them - otherwise all the analysis of that big data was largely useless.

Whether big data is on your radar as an analytic effort for your marketing department or your email program specifically or not, you are certainly hearing the conversations about it. We all have the ability to be more prescriptive with the consumer today and oftentimes there is an expectation that you "just know" these things about them. Acting contradictorily to their expectations could be detrimental in the long run. After all, you don't want your recipients closing out your email, asking themselves, "Don't they know me at all?"

Last week, I wrote an article for ClickZ about how to leverage the various forms of customer data collected across your organization to power more relevant marketing campaigns.  You can check out the article below or read it on ClickZ. Let me know if you have any feedback in the comments
 
In 2001, when I was early in my career as an email marketer, I had a meaningful dialogue with a database marketing expert.  This gentleman, let’s call him Ted, shared that there are three basic ways we learn about our customers.  We look, ask or buy.  And they follow that order in terms of importance and value when it comes to the targeted email we send them.
 
Data from Looking
Looking at a customer's behavior across you brand's various touchpoints is the one of the most valuable sources of data you have to estimate future buying interests and patterns and craft targeted email campaigns accordingly. There are numerous ways you can gather this data. For example, you can observe a customer’s browsing behaviors through web site analytics to gauge their interests, or you can monitor responses to your email campaigns using real-time response data gathered from your email marketing technology. You can (and should) also analyze purchase data and recognize any seasonal trends or spikes (birthdays, holidays, etc.).  Behavioral data obtained by 'looking' is very powerful, because you get data on what your customers actually do versus what they say they do, which leads us to the next way: Asking.
 
Data from Asking
Supplementing your behavioral data with preference data obtained by asking your customers about their interests is a smart idea.  That's because for all the strengths of 'looking,' this method also has some weaknesses.  For example, you may see that a customer recently bought some baby clothes, so on the surface, it would seem that following through by sending a targeted email with a series of offers on new baby merchandise would be the right move.  However, it's also quite likely that this was purchased as a gift for someone else. If you had recently asked them about what products they were interested in, and baby/kids were nowhere on the list, you could pair that data with frequency of purchases from that category to make an assessment on what offers to send in the future.  There are a number of ways to 'ask' customers to provide this information, such as registration pages, preference centers or surveys. Of course, you need to gauge how much you ask by the method of collection.  Bombarding a customer with 20 questions at registration might be a little overboard for your target customer, but asking a few key questions and then following through with a survey a few weeks later (after they've bonded with your brand), might be a better approach.
 
Data from Buying
The third option is to purchase customer data. It's fairly easy to buy demographic and psychographic data on your customers from data aggregators like Infogroup or Acxiom. However, there are risks when it comes to sending targeted emails that you craft because of this method. You need to make sure that you fully understand how the data was collected and how recent it is. The last thing you want to do is to introduce bad data into your customer records.  You also need to understand the level of data you can acquire (and use) without freaking out your customers. If they've never told you March 1st is their anniversary, and you send them an offer – it might make them confused or suspicious about your brand. Buying data is the most risky method, so make sure  you fully understand the potential hazards before you proceed.
 
Behavioral Data is King
While all customer data has value and allows the email marketer to be relevant when communicating via targeted email, the most telling data is the behavior data.
 
Let me show you what I mean.  If you’re a wine merchant, you might ask me if I prefer red or white wine – and my quick response would be RED!  However, if you monitored my buying behavior you would notice that I buy WHITE wine two out of three times.  This is just one reason why looking is better than asking. Another reason is that your fill rate by asking may be very low.  An average preference center has a 2% fill rate on any iteratively profiled fields.
 
Knowing that website, targeted email and buying  behavior are the most important attributes on a consumer, it’s no surprise that shopping cart abandonment, purchase confirmation, win-back campaigns and newsletters personalized to the consumers behaviors are clear outperformers.
 
Ted was right in both 2001 and 2011.  Happy Holidays!

Interested in reading more about email marketing best practices? Try giving one of our other blogs a look.

You wake up at 4am - petrified - that tomorrow's email marketing campaign will contain typos, broken links or irrelevant messaging.

Should you be just as worried about your email marketing data security?

News broke this week that Epsilon, an email service provider that distributes more than 40 billion emails annually for 2,500 clients, was the victim of a massive email marketing data security breach. As such, the email marketers working with companies affected by this breach (including Best Buy, Target, TiVo, Capital One and JP Morgan Chase) began the week by sending delicately crafted messages to subscribers apologizing for this violation of trust.

But when the shock and anger fades, how will consumers feel about these brands?

In many cases, unless you work in a highly regulated industry like financial services or pharmaceuticals, data security usually isn’t a top priority in terms of email marketing program investment. However, marketers need to seriously weigh the value of a consumer’s trust in their brand when evaluating their email marketing data security policies…because as we all know, trust is a fragile, challenging and uniquely important thing to earn for an email marketer. In a world already plagued by spam, consumers are all too ready to opt-out of an email program if they feel like it in any way will compromise the sanctity of their inbox.

To help email marketers evaluate their options, StrongMail has published a new whitepaper called "Email Marketing and Data Security." In it, we describe the factors that make your email list vulnerable - and four important guidelines that marketers can apply to protect your subscriber information.

Key topics addressed within this white paper include:

• The importance of launching a risk management initiative
  
• When to handle security audits internally and when to outsource
  
• How security risks can be minimized with alternative deployment models such as an on-premise email solution

After what’s being dubbed as the “Epsilon Valdez” by Reuters, even the Wall Street Journal is questioning whether or not brands should be outsourcing their email marketing to a third party.

I am proud to say that many security-conscious Fortune 2000 brands have chosen to manage their email systems in-house using a commercially supported system provided by StrongMail. What this means is that you can purchase an "ESP in a box," which provides you with the same features and functionality of an outsourced email service provider, complemented with a full range of award-winning agency strategic, creative and production services to ensure that your campaigns continue to perform at their highest possible levels. Our on-premise option eliminates the resource burden usually associated with managing an in-house email marketing system, while enabling the brand to keep their customer data secure behind their firewall.

Email marketing data security, and the protection of your subscriber information, should play just as key of a role in your email marketing success as the relevancy of your campaigns. In other words, if it's 4am and you do not have a security plan in place, you are probably right to wake up in a cold sweat.

If you are ready to talk to StrongMail about email marketing data security, call us at 800-971-0380 or fill out our Contact Us Form.

In my latest MediaPost Email Insider column, I explain how Apple missed an opportunity to leverage all the data they had about my interactions with their brand to serve up relevant information - essentially to recognize and capitalize on my status as one of their more loyal customers. Read the article online or below to find out how they started out strong with their email marketing communications and then failed to follow through with the same level of relevance in future communications.

Why Won't Apple Love Me Back?

MediaPost Email Insider

Ryan Deutsch, Nov 11, 2010 02:30 PM

For those of you who don't know me, I am an Apple Freak. Ever since my first iPod, I have been enthralled with the brand and its products. As an email marketer, I have often used Apple communications as examples of best-in-class email marketing programs. However, today I must admit that there seems to be something lacking in the way my favorite brand communicates with me via email. If I'm truly honest with myself, the latest email programs I have received from Apple suffer from a common problem that plagues business-to-consumer brands. Let me paint the picture....

A few years back, I purchased my first iPhone, an iPhone 3G (I know, why didn't I have a first generation iPhone, but that's a whole other story). Anyway, as you can imagine, I was uber-excited at the time and anxious to dig into the phones features. Like any good Apple customer, I waited in line for the device and then played hooky from work so I could set it up. A few days later, I received my welcome email from Apple. To this day I consider it one of the best triggered-welcome messages I have ever received.

The communication welcomed me to my new phone, provided extensive education opportunities so I could enjoy the myriad of features, introduced me to the app store and some top applications that I should consider purchasing, and so on. We all know that there are some basic building blocks to any welcome program. Welcome emails should connect with the consumer, set expectations about the type of dialogue the consumer can expect via the email channel, confirm opt-in and address housekeeping items like add-to-address book, etc. However, more than anything else, a welcome email should add value to the new relationship between the consumer and the brand. This "value" sets the tone for the inbox-based relationship going forward. Apple did all of this and more.

Like many others, I stood in line a few months ago and purchased an iPhone 4. I repeated the steps described above and once again, a few days post-purchase, I received my iPhone 4 welcome email. As I looked at the message (a similar yet scaled-back version of the original I received with the iPhone 3G), something just did not feel right. The message just did not seem addressed to me; in fact, I immediately realized that this EXACT same message could be sent to every new iPhone 4 owner. I felt slighted. I'm not just any iPhone owner! In fact, I considered myself an Apple evangelist.

Since my iPhone3G purchase, I had stopped by the Apple store and bought a Time Machine, headphones (the expensive wireless Motorola Rokr S9-HD) an iPad (the big one!), another iPhone for my wife, a Mophie Juicepak Air for my 3G -- and the list goes on and on. Yet here I was, being treated as if I'd woken up on the morning of the iPhone 4 release and become just another first-time Apple customer. Slowly, I relaxed and talked myself into believing that Apple has some solid reason for not understanding our relationship. After all, they're Apple. Steve Jobs looks good in those black mock-turtlenecks, and I can forget one minor slip-up. When I forget what kind of cake my wife likes on her birthday each year, I expect her to understand and forgive, right?

I was calm for about 97 seconds. That was when I noticed the stack of iTunes gift cards on the desk. My son had just had a birthday, and we'd bought him an iPod Touch. He also raked in like $50 in iTunes gift cards. I opened iTunes (on my iMac) and took a look at the purchases I had made over the years. Movies, music, books, applications -- I swear, I've spent more on content with iTunes than I have at the Apple Store! I looked back at the iPhone message -- nothing about books, movies, application, etc. Not only had they not acknowledged my value as an Apple customer, they'd completely ignored my ecommerce relationship on iTunes. To Apple, the retail store and iTunes may be separate lines of business, but to me, they are one and the same. In fact, it is the seamless relationship between content on iTunes and the Apple devices that keeps me coming back for more. That's was it I promised myself that one day, I would share the experience via a blog post.... Mission accomplished.

The takeaway is that consumers expect a higher standard today. We expect this higher standard not from every brand or every email communication. There are many emails that I subscribe to that are a necessary element of a rather passive business-to-consumer relationship. I subscribe, I open, but I rarely buy. I am fine with these brands treating me like the number that I am.

But there are a select few brands that are special -- brands that I have committed to through loyalty based on repeat purchases, online advocacy and overall brand engagement. To these brands I should be considered a "Best Customer" and treated accordingly.

I expect these brands to make the effort to analyze data warehouses, ecommerce systems and CRM solutions to create a picture of our relationship and leverage that picture across communications and offers. As an email marketer, I understand that the 360-degree customer view I am referring to is not always easily achieved. However, I have worked on projects with clients that have accomplished this and treated their best customers in a way that inspires advocacy, loyalty and brand engagement.

If you are involved with email marketing, CRM or ecommerce at a large consume- focused brand, take a look at your communications. Think about your best customers. Are you treating the relationship with respect? Apple isn't -- and come on, this is Apple we are talking about. These guys invented the Mac, iPod, iPhone and iPad, so please forgive me if I think it's within their scope of abilities to create customer-focused email marketing programs!

Today's installment of "The 12 Days of Email Marketing Tips" provides a tip on list build and co-marketing in your email programs. Several emailers at the EIS this week talked about their company's strategies for list build through partnerships. They all agree that choosing the right partner makes all the difference in getting more opt-ins from the partner's audience. Many of them had learned this lesson through the trial and error of picking partners that didn't fit their audience and vice versa.

Once finding a partner bird worthy to flock with, the next challenge becomes address and data inclusion. In my experience, this works best when the new partner data gets tempered into the email or customer database over time. This will help you manage deliverability reputation and introduce yourself to this newly opted-in audience over time with the least risk.

Forrester reported earlier this year that list build/attrition is the primary concern of email marketers. Partner opt-in programs are a safe way to add new addresses to your promotional email programs. Just pick the right partner with the right audience and temper that data into your programs.

Tomorrow I'm going to "bling" some gold. Out!

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