It’s right there, glowing on the horizon: the promise of delivering behaviorally triggered messages to consumers — contextually aware engagement that’s tailored and relevant to each and every individual.
Yet, as we’ve discussed in a previous post, 70 percent of brands have admitted they’ve failed to personalize email messages at all, let alone used behavioral data for triggering.
In fact, according to a 2014 study by Razorfish:
A whopping 76 percent of marketers haven’t yet used behavioral data for segmentation analysis and targeting in any channel, let alone email.
Only 38 percent of those interviewed are able to target a new customer versus a returning customer.
Just 13 percent actually deliver segmented experiences and measure the results.
As I’ve written before, these figures are absolutely not because marketers are dismissive of personalization and triggered responses. The issue lies in the difficulty of Big Data implementation across their organizations.
Customer data is siloed in different configurations across various arms of an enterprise — it isn’t rich or accurate enough in the first place, or the infrastructural commitment for Big Data adoption is too daunting.
Since Big Data implementation hasn’t yet been achieved within their companies, some marketers may default to thinking it’s extremely tough to execute contextually-aware, behaviorally-triggered marketing.
But that’s not true. If you’re an email marketer, you’ve already got tools in hand that will help you develop a personalized email program. And they may actually include the behavioral data that’s critical to making personalization happen.
Context And Behavior: They’re Scalable Definitions
Before we go much further, let’s take a look at some of the terms we’ve been throwing around (and probably taking for granted). Because to have contextually-aware, behaviorally-driven marketing, we’ve got to decide what “context” and “behavior” mean.
The usual Big Data-driven definition of “contextually aware marketing” is that it’s sensitive to the individual consumer’s location, the engagement platform they’re using, the time, their profile, and even other factors like weather. It’s responding not just to who the customer is, but where that person happens to be and what he or she is doing.
The “behaviors” used for targeting, re-targeting and predictive modeling typically are supposed to encompass past purchases, browsing history at a website, and maybe data obtained from third parties, like social media platforms or app providers.
Without Big Data integration, though, these specific definitions obviously aren’t actionable for a marketer.
But if you’re an email marketer, feel free to re-scale your definition of “context” and “behavior” to match up with what you’re able to accomplish with the data and resources you’ve got on hand.
You’ll find you’re very able to execute contextually driven, behaviorally triggered marketing — personalized and individually targeted Big Data-type marketing, in other words. Just without the Big Data.
How’s That Work?
There’s no secret sauce involved. It’s just about understanding the assets you’ve already got in front of you.
I’ve had the benefit of being able to talk to a good many email marketers who’ve taken the approach laid out below to inject personalization and triggering into their campaigns, getting a head start down the path toward Big Data before having to implement Hadoop or hire a raft of Big Data scientists:
Step 1: Capturing Context
As an example, let’s look at an employment/job listings website. If a user opts in for emails, we can pretty much extrapolate the context he or she has entered: that of a job seeker.
The more information they provide, now or later, the more narrowly you can define that context and have a better understanding of their status. Are they between jobs? Looking to jump ship? In what market?
Knowing their general or specific context hands us our chance to engage with them in the first place. Emails to our newfound subscribers can be segmented and timed according to contexts/segments, but this is only the first step toward a much sharper understanding of each user, based on their behaviors within those emails.
Step 2: Extracting Behaviors From Email Metadata
As email marketers, we’re uniquely able to pull data that’s utterly “behavioral” and already resides in our particular silo, so there’s no need to scrape it from other departments: It’s the email metadata we can harvest from each email we drop to our subscribers — in this case, those eager job seekers.
One simple example: We can track which of our new job updates our subscriber has opened and how, among all the jobs listed, he only clicked on “Senior Payroll Accountant” positions located in the northwest suburbs of Chicago. That tells us he’s hoping to figure out PERS (Public Employees Retirement System) deductions and gross-to-nets for a new employer in that area, so we can adjust subsequent message content accordingly.
If a subscriber scarcely ever opens our emails — or is opening every single one — it might give us insight about how truly focused he or she is on their job search. Especially if that behavior matches up with our knowledge of the category or with consumer research.
A marketer can also uncover subscriber interests using basic email forking: If a sporting goods chain’s initial email offers a choice between team sports and individual sports, and the subscriber clicks team sports, the next triggered email can ask him or her to pick either baseball or football, and so on.
To stick with the sports examples, we’ll cite the NFL, which does a superb job of analyzing subscriber behaviors. Should your clicks and opens indicate fandom for a particular team, your next newsletter will amp up the content level devoted to that squad.
Another way to leverage metadata? To test subject lines, a marketer can try different subject lines, then audit opens and clicks using the metadata, allowing them to determine the most effective subject line. That’s data you should be able to access right now, yet you may not be using it to your advantage.
If you’re not able to obtain email metadata, take a hard look at changing your email provider — because that data belongs to you, and you’ve got every right to access and use it whenever and however you want.
Step 3: Refining, Compiling, Personalizing
Whenever they’re opening emails, making specific clicks or following particular conversion paths, each consumer is handing you data to help refine, personalize and trigger the next slate of messages.
Plus, it’s fresh data, captured in very nearly real time, so you’re on top of what their present needs and wants are, and now you have a good handle on the context they’re acting within.
Mind you, you’ve done this without ever having had to truck in data from your company’s CRM d-base (if there is one) or from the website, sales department or third parties. Instead, you’ve begun to build a viable and detailed profile of every subscriber, a profile that may even be usable for predictive analytics.
Step 4: Building A Bridge To Big(ger) Data
Once you’ve begun to leverage email metadata, a perfect next step would be to aggregate it with whatever data you’re acquiring when those subscribers visit your website.
That shouldn’t be too big a stride (especially if you deploy a marketing automation platform), one you can easily justify because it presents you with the chance to personalize both their email messaging and their automated user experience at the website, particularly if they’re clicking through straight from the inbox.
Between email and the Web, you’ve now graduated to personalized marketing engagement across your two most valuable channels. Good job!
A Step, Not A Leap
Utilizing email marketing to build contextually-sensitive, behaviorally-triggered campaigns without having to meet the challenges of Big Data implementation is a present-day reality for some pretty shrewd marketers I’ve met, and it’s a profitable step toward reaping all the rewards of personalization: higher open rates, more click-throughs and conversions and increased purchases.
Is it a substitute for true Big Data implementation? In the macro, I’d say no, because Big Data can be a giant leap forward, with benefits that reverberate massively across an entire enterprise.
But for companies where that leap may still be a few turns up the road, or where they’re looking for hard evidence of Big Data’s promise, email marketing can help them realize the bottom-line advantages of personalized consumer engagement — today.
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