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?"