Email Marketing Insights

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."