Happy Holidays! We’re taking the day off, however, if you’re up to your ears in eggnog, football or family, then kick back with your favorite beverage and take a break to view one of these webinars. ~ The SparkPost Team

Learn how to create individualized experiences at every digital touch point in our Personalization 2.0 webinar with Forrester. Learn how to get ready for CASL enforcements round 2 in our ‘What CASL Means for Email Marketers’ webinar with the CRTC. Learn how to go beyond demographic data with your analytics to truly personalize your customer’s experience in our Contextual Marketing 101 webinar.
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At one of the most anticipated sessions of our annual Insight user conference, SparkPost CMO Steve Dille was joined by a panel of SparkPost customers—all savvy email marketers—to discuss how they are using big data, transactional and triggered email, and SparkPost to reimagine how email drives the entire lifecycle of individual customer engagement with businesses like Zillow, Etsy, and CareerBuilder. Here are some highlights from the discussion of “A Segment of One.”

A Segment of One

Data-Driven Email Marketing and User Engagement

Right out of the gate, it was clear that these data-driven marketers approach email a little differently than more traditional, campaign-focused organizations might. CareerBuilder’s Scott Burdsall noted that his team thinks about email from the perspective of the customer’s inbox. That point of view is crucial, he said, because customers don’t react to discrete marketing programs in isolation from one another; rather, their experience is the sum of all messages he or she receives. For his business, email is the primary source of user traffic, and it’s driven by job seekers wanting to find the right job, right now. By combining information about job openings, individual user interaction patterns, and third-party economic and other data, the company seeks to get that right message in front of the right customer at the right time.

Zillow’s Tara Clark agreed that email is her most important driver of bringing people back and driving reengagement with the site. And, like CareerBuilder, Zillow relies heavily upon a combination of proprietary and third-party data to trigger messages at key points in the home-buying lifecycle. She also sees significant opportunity in identifying what data indicators reveal when a potential seller is about to become active on the site and to use that information to develop a seller-specific messaging strategy.

At Etsy, email marketer Matt Sperling looks at site and transaction behaviors, email engagement patterns, as well as interactions with mobile app push messages to guide his strategy. One challenge for his business is the global nature of the Etsy marketplace—data signals and contextual cues aren’t necessarily the same in every part of the world.

Personalization or Segmentation?

All of this data-driven email led SparkPost’s Steve Dille to ask if one-to-one personalization was necessary, or if more classic segmentation was enough to be successful. At CareerBuilder, Scott Burdsall noted that one-to-one is the nature of the job-seeking business, but that broader segmentation can be an effective approximation for identifying factors like message frequency and content that might be relevant when a user is no longer an active job seeker, for example. Zillow’s Tara Clark pointed out that the very best email programs have room for all types of content: one-to-one, segmented, and bulk marketing, and Matt Sperling of Etsy observed that using segmentation as a means of testing is also an important building block for true one-to-one personalization.

Do You Need Big Data?

As all three marketers noted: data helps! Big data science can lead to unexpected learnings. But, they also reminded us that even the “small data” of listening to individual customers and knowing your audience can be a very good beginning of that road.

What Metrics Are Important?

Although these three businesses are in very different markets, they each reflect variations on a marketplace business model. That commonality leads to some shared metrics for measuring the success of their email programs: revenue, of course, but also crucially bringing buyers and sellers to the table; by driving buyer/user engagement, they create demand for the seller or professional side of the transaction. As such, key metrics for all three include engagement, frequency, conversion on various steps in the lifecycle, and so on—and which in turn help to tune the email cadence, content, and calls to action. (By the way, that virtuous cycle of data, email, and user engagement is something that stands out in most of the successful data-driven marketing examples we’ve seen repeatedly among our customers.)

Challenges, and Looking Ahead

Etsy’s Matt Sperling observed that getting message frequency right is one of the most challenging things for marketers, and that identifying what signals the right frequency for each member has a very large impact on the lifetime value of that customer. And, as both Zillow’s Tara Clark and CareerBuilder’s Scott Burdsall noted, there can be points in the customer lifecycle where the metrics “go dark.” Getting explicit as well as inferred insight into those moments is a key challenge going forward for their businesses, and data-driven marketers in general.

It’s no wonder the ballroom at the conference hotel was jam-packed for this Insight session. The audience was treated to an eye-opening discussion that showed just how far the state of the art has evolved from old-school, bulk email marketing. Plus, it’s clear that the sophistication and respect for their customers’ needs these experts conveyed set a high bar for marketers in every industry.

Over the next few weeks, I’ll be digging deeper into the intersection of data-driven marketing and email. Until then, what’s worked (and what hasn’t) for your business when it comes to data-driven marketing? I’d like to hear from you.




Personalization at Every Digital Touchpoint

FjordI’m a day from starting my holiday. My wife and I are heading to Norway to explore fjords, learn about Viking history, and soak in an abundance of high-latitude sunlight. At the same time, my experience over the past few days of packing for my trip has made me recognize just how many perishable moments exist in the space of 24 hours.

What’s a “perishable moment?” Let me explain.

Most every year we take a big trip somewhere. We travel light, lugging our belongings on our back. We don’t book but the first few nights of wherever we stay, to avoid being “trapped” in some lackluster tourist oubliette. Nonetheless, every year, I seem to find some new piece or another of gear that promises to make traveling easier, or more efficient.

The other day, I went and picked up a bunch of travel supplies at my sporting good store:

  • 2x ultra light packing cube systems
  • 2x double combo cable locks
  • 2x regular combo locks
  • 1x ultra light mesh stuff-sack
  • 1 GoPro wireless remote
  • 1 Multi-tool (to replace the one that went missing in my black hole of a home office)

This list isn’t extensive but given that these six items are all travel-related, one might infer that I’m taking a trip. Now, if I were in the shoes of the email marketing manager at that sporting goods store, I’d want to know what kind of products my customers are buying, from which categories, and at what frequency. If I could pull together data from these sources, I might discover a perishable moment that warranted an email. So, let’s see what a mockup of what my purchase and email history in the store’s marketing database might look like:

Lens Perishable moments chart

From a superficial glance at this table, one might assume that I like to rock climb and that I get cold easily. What this doesn’t show is that I returned all of the jackets because they didn’t fit. With a closer look, perhaps one could make the inference that my purchases for the jackets were the result of a 25% off coupon—I bought them all at once and tried them on at home intending to reap the 25% off coupon on the one that fit the best. As it turns out, none were for me.

But if we step back and examine the entire list of items, their purchase source, the fact that I didn’t open emails about cycling, but that I purchased climbing and travel gear… well, then the assumption I’d make is that I’m heading on a trip, possibly to a cold place, to do some outdoor climbing. Maybe to a foreign destination, as I might not buy luggage locks otherwise. One more assumption I could make is that the trip is coming up quickly: luggage locks are not something you buy a month in advance; they’re something you discover you need a few days before you leave because you’ve lost the key to your lock or you simply misplaced the lock.

So, that scenario is painted from a number of assumptions, but they’re all based on empirical data. And that’s reason enough to see that I have an opportunity to entreat my customer with an opportunistic marketing message with a subject line along the lines of the following:

  • “Boarding will begin shortly, do you have everything you need?”
  • “25% off in-store any last minute items before your big trip!”
  • “The right gear for the right rock!”

The idea is that I’m making an educated guess that the customer is traveling somewhere, they might be rock climbing, and the trip is probably starting in the next 72 hours.

Perishable moments are opportunities derived from the careful analysis of cross-channel data to make the right assumptions or inferences, and then by testing the results with a series of targeted emails. For a perishable moment to be uncovered you have to have access to data, lots of it, and from multiple sources. Another dimension that’s missing from the simple table above is the customer’s web browsing history; plugging that in would’ve uncovered the fact that I shopped for deals on the outlet site, looked at warm hats, mittens, umbrellas, and rain jackets. The pattern of purchases, the emails I opened—they all infer that I’m taking a trip somewhere possible cold to do some climbing. (In truth, the gear was for climbing in Tahoe not Norway, but I am taking a trip, and I do rock climb, so the assumption is mostly right and would still yield, at the very least, an open.

Perishable moments don’t have to be this complex. They can simply be assumptions about the patterns of our lives, and the fact that we all have routines. The average person goes to work between 7–8 AM. Maybe he or she is riding a train, or stuck in a car, or on a bus commuting to the office. These moments are great opportunities to connect the commute to something simple, an “eye-opening deal,” a “wake up to savings,” something that brings together the routine and the opportunity.

Perishable moments are just that—fleeting. To capitalize on this concordance of place, product, price, and promotion, you’d need to ensure that your messages arrive in the recipient’s inbox in a timely manner. Fortunately, users of SparkPost know that our industry-leading deliverability and throughput mean that latency is, frankly, a non-issue. And SparkPost’s powerful templating API lets marketers create dynamic offers that leverage a wealth of behavioral data and programmatic rules for data and assembly within the body of the message.

Perishable moments like those reflected in the story of my upcoming trip to Norway may be brief, but with the right data and deliverability, they can be very rewarding opportunities for successful marketing.


15984246967_da3cb52c76_zIn the spirit of the title, and because I’m at the Marketing Sherpa Email Summit in rainy Las Vegas, this will be a short post. Ok so reach in your pocket and pull out your mobile phone. Now unlock it and tell me if email is one of the apps on your 1st (home) screen. I bet it is. If the average adult smart phone user has around 80 some odd apps, then email is always on the home screen.

Great, now that we settled that, let’s get onto the meat of this—the perishable moment. Earlier today I saw a great study from Localytics; if you don’t know them, and you have an app, then you should check out what they can do for your mobile initiatives. Turns out that as the snows on the East Coast have fallen, app usage has gone through the roof. This shouldn’t come as a great surprise—quite the contrary it’s completely understandable given that mobile devices are filling in the moments of boredom we experience during the day. The more snow that falls, the more people are stuck at home watching the bleak winter and occupying their time by checking the weather, shopping, checking out photos, checking the weather to see if its safe to go outside, looking at more photos, back to the weather, then maybe some news or reading books, and then back to the weather. See a trend?


So I’m just going to throw this out there: snow days are perishable moments—they represent a distinct moment in time when a specific, regionally isolated event, creates a unique segment of users that should be marketed to differently than everyone else. What, if anything did you do during the snow-pocalypse of 2015? Did you promote snowblowers and warm booties instead of sending the routine 20% off discount that some retailers send with frustrating regularity to everyone, every week, sometimes 3x a week? If you did, then bravo! If you didn’t, then consider yourself on notice.

Perishable moments require data, a sense of creativity, the ability to quickly execute and a measure of ‘carpe diem!’ Email marketers should take a page out of the play books of the mobile crowd and realize that their recipients are more fluid than a defined segment based on age, zip code and gender. Recipients can move in and out of segments quickly based on not just the weather, but recent clicks, opens, browsing history, web activity, purchases or events that transpired the day that someone signed up. These are all defining criteria for micro-segmentation that begins to feel like a personalized approach. Think small, be light on your feet and take advantage of the moment by ensuring you can turn a snow day into a captive audience day.