Simplifying Personalization: 3 Types of Data to Send Smarter Ecommerce Emails
Have you ever woken up in the middle of the night to get a drink of water — blindly feeling around for a wall and hoping like hell that you don’t step on the dog or slam your foot into the dresser?
I have. And I almost always kick the poor dog.
Now, I might just be abnormally clumsy or prone to insomnia, but those experiences happen more than I’d like to admit. And — stay with me here — they tend to remind me of how I feel when I’m staring at a dataset and trying to make sense of what I’m looking at.
I know I’m in the right general area. I have an idea of what I want to do. And I’m fairly confident that I’m heading in the right direction. But the journey of turning raw data into truly actionable intelligence often feels like trying to get a sip of water at 2 a.m. I might get to where I wanted to go, but it usually comes with a toe stub, an f-bomb, and an awkward stumble down the stairs.
The Plight of the Data-Driven Marketer
Apparently, I’m not the only one.
Here’s a very small collection of research that points to marketers’ struggles to use data in a meaningful way, despite plentiful access to it:
- A 2016 report from Forbes and Oracle Marketing Cloud found that 40% of executives believe their organization’s use of customer data to create new marketing programs is ineffective.
- While 87% of CMOs in a recent EY and Forbes Insights survey acknowledged the role of customer experience, data, and analytics in building “credibility” with consumers, just 37% feel they’re capable of using analytics to tailor communications to consumers.
- A 2016 report on the lag time between data collection and action found that more than 50% of marketers collect data from multiple sources, but struggle to analyze those real-time signals fast enough to make meaningful changes to personalization efforts.
The key takeaway from that research: While most marketers today understand the value of data and analytics, many are wandering in the dark when it comes to deciding which data to focus on and how to most effectively analyze it to fuel more personalized marketing.
Of course, this is a very big problem at a time when consumers demand relevancy and respond to personalization — particularly when it comes to email. One study that analyzed thousands of emails over a three-month period found that simply personalizing an email subject line in the consumer products and services industry led to a 41.9% lift in unique open rates, 49% higher transaction rates, and a 73% increase in revenue per email.
3 Data Points to Send Smarter Emails (Faster)
Now, imagine the impact you could have if you took personalization to another level — automatically segmenting your database by attributes like gender, purchase activity, geography, and on-site behavior (pages visited, items viewed, etc.). And once you had those segments created, imagine that you could automate the process of refining those segments in real-time through progressive profiling — unlocking the door to true 1:1 conversations at scale.
That might sound like fantasyland, but it’s not. It’s Klaviyo. (Sorry, couldn’t resist.)
In all seriousness, this level of granular, dynamic segmentation isn’t as difficult as most marketers think it is. To achieve it, you simply need access to the right data and the right tools. To help with the first, here’s a quick list of three data types ecommerce marketers should be using to send smarter, more relevant emails:
1. Demographic Data: This is basic — but necessary — information, such as someone’s name, age, location, gender, interests, etc., and it’s relatively easy to collect. For instance, location information should be logged automatically in your ESP. The other data points can be collected by explicitly asking for it (having someone fill out a form) or through logical inference (assuming that if someone’s only ever purchased women’s products, they’re probably female).
This data can be used to personalize email subject lines and CTAs, display product recommendations in your emails, and send targeted emails based on specific demographic data.
One word of caution, though: be careful about how much you ask for upfront — particularly if it’s something that’s likely to change. Collecting someone’s birthday when they first purchase something is a great way for you to trigger a special offer without worrying about the data becoming irrelevant. But asking someone what they’re interested in upfront isn’t always going to be useful six months later.
2. Navigational Data: This data tells you everything you need to know about how a particular customer or user engages with your site. What items have they viewed or added to their cart? What devices do they use when they engage with your site and open your emails?
This information can be collected through custom web tracking technology and used to group customers into dynamic segments based on very specific user behaviors. As you collect more information on each customer, those segments will update in real-time and you’ll be able to send the most relevant messaging to the right person at the right time.
For instance, if a customer views the same product five times on your site, you might consider sending them a browse abandonment email to leverage their interest. One great example of this comes from a men’s apparel company called Criquet. After spending several minutes browsing their new hats and shirts, I received this email the next day:
The email captured exactly what I viewed during my visit and that simple reminder actually led to me making a purchase. Another way to approach this is if someone is consistently viewing things that are in the same category — like evening dresses or home decorations — you can use that information to send them additional items in that category that they may have missed.
3. Third-Party Data: This data can be pulled through integrations with the other services you use — Shopify, Zendesk, Salesforce, Stripe, Eventbrite, etc. — and it allows you to acquire a much deeper understanding of who a customer is, how they behave, and which levers you can pull to influence certain actions.
For instance, if your ESP integrates directly with an ecommerce platform like Shopify or Magento, you can easily pull all purchase and checkout data to create unique, dynamically generated one-time use coupon codes. Similarly, if your ESP integrates with help desk software like Zendesk, you can pull in a complete view of who opened tickets (and when), as well as how those issues were resolved. This would allow you to send custom emails to those customers with special offers based on the experience they had.
It’s Time to Stop Stepping on the Dog
Ultimately, those are just a handful of examples of what can be easily accomplished when you have the right data in the right places, and the right tools to make sense of that information.
This leads me to a final point: We marketers don’t have an excuse to stumble in the dark anymore.
We have access to technologies that can help us send smarter, more personalized email flows through progressive profiling and dynamic segmentation. And we’re certainly not lacking data to feed into those tools. As a result, bridging the gap between data collection and meaningful action simply requires that we focus on the information that matters most and the tools that give us the best chance to do something meaningful with it. If you make that marriage happen, the analytics and intelligence floodgates will fly open.
Or, you could just keep stepping on the dog in the middle of the night and praying the f-bombs that fly out don’t wake up your spouse.