Vice President, Marketing Services
As vice president of marketing services at email marketing and cross-channel marketing solutions firm StrongView, Katrina Conn focuses on helping clients improve their email and broader cross-channel marketing efforts. Conn recently spoke with eMarketer’s Lauren Fisher about the growing use of data for a more real-time, contextual approach to message relevancy.
eMarketer: What are some of the main factors and influences affecting email marketers today?
Katrina Conn: One of the biggest things we’re thinking about right now is the use of data. Email marketing needs to move toward a much more contextual state, and marketers need to think beyond just lifecycle marketing.
What do we know about the consumer and their current need? From there, you begin to identify those contributing data attributes that inform the current state of disposition: things like time of day, day of week, whether they’re at work, commuting, which devices they’re engaging with, things like that.
So when we’re thinking about that contextual data, we’re breaking it down into three different components: profile data, interaction data and external data.
Profile data includes demographic and psychographic-type information. It can also include purchase history, permission levels and those types of things.
Interaction data is anything related to channel engagement, activities, location or device type. The third leg, external data, is a little bit harder to capture in real time, but it involves paying attention to things like what might be going on with the economy, weather and the stock market, among other things.
The thinking is that by combining all of this data, we can go beyond the linear process of just lifecycle marketing. Lifecycle marketing is just one component of someone’s contextual state. There’s more to a consumer than just their lifecycle segmentation: whether they’re in a renewal stage or they are a lapsed customer who we want to reengage.
There is a real-time component to that person, and it’s here that being able to integrate all those disparate data types becomes important, though doing so is not without challenge.
eMarketer: What are some of the challenges you see?
Conn: When you’re combining disparate data sets, there’s a level of latency with traditional CRMs. We’ve all experienced the situation in which we’ve bought something and then we get an offer for the same product because they weren’t able to update their profile about us [quickly]. It’s still very common to see a disconnect between web analytics tracking ecommerce purchases and email systems, and that’s a challenge.
The second problem is stitching that addressable consumer across all of these channels. It’s hard to do that. People have really rich data, they just don’t know how to put it together and interpret it so that it can be actionable.
The third challenge is organizational structures. At most companies, they are developed for independent channel responsibility with various providers serving as channel execution experts. You have your mobile partner, your social partner or your email vendor. The reality is, most providers today can execute across the channels, but the organizations themselves haven’t caught up with how to maximize those efficiencies within their own structure.
eMarketer: What mobile-specific trends are you seeing?
Conn: For many, mobile is still this overwhelming elephant in the room, and it’s an enigma. Mobile means so many different things. It could mean a mobile website, SMS, responsive design—those are all components of the broader mobile category.
I also think companies are still behind in understanding that consumers are mobile. Therefore, regardless of what you do with mobile, you have to be able to present your content in the most readable, mobile-friendly, optimized format possible. But it’s hard to quantify the missed opportunities and to know what portion of the mobile audience is just ignoring messages because they can’t read them.
I think if people understand the opportunity cost, it would be a no-brainer, and mobile-first would be more of the standard approach.
eMarketer: What is an example of how you might use those three main data types above to more effectively address a customer via email?
Conn: For one of our clothing retailer clients, we did some analysis to understand whether their customers preferred to shop in-store or online.
There are specific behaviors that could contribute to one or the other, but you may also have a customer who does a little bit of everything—they’ll open an email, go in-store, order online and even sign up for a loyalty program via SMS. They might buy online but return products in-store. When you have customers like that, it can be very difficult to determine how to group them, unless you model it out.
Say the retailer has a weekday promotion where they want to drive in-store traffic for specific merchandise. We can assume that individuals who buy in-store have higher order values because they are more apt to purchase larger-ticket items that they can try on and make sure they fit, which they can’t do online.
Now, if we recognize that our customer is an in-store shopper because she likes the in-store experience for those larger-ticket items, and there just so happens to be a blizzard outside, the likelihood of her stepping in-store is not high, even though that’s the customer’s main preference.
So instead of trying to drive her in-store with a promotion during that blizzard, maybe we send her the same email promotion with dynamic content that highlights things like purses, watches or other items that she would be more likely to buy online because she doesn’t have to try them on.
Even though we’re missing the larger opportunity with this customer, the retailer can still try and capture some revenue even though that person might have been identified as an in-store shopper. When bad weather is occurring, why not optimize the content to promote things they might buy online so the customer can shop from the safety of their own home?