Q&A with Dilshan Kathriarachchi, chief technology officer at EQ Works
Age has never been a limiting factor for Dilshan Kathriarachchi. He has assembled his life and career around emerging mobile technology, specifically when it comes to marketing.
By the age of 15, he became an instrumental part of the roll-out of Sri Lanka’s first e-commerce platform in conjunction with the country’s Commercial Bank.
By age 27, he had already launched a brand that has become a leader in Canadian mobile technology. Now, at the ripe-old age of 31, Kathriarachchi is the chief technology officer of EQ Works, where he uses mobile location data and insights to pioneer some of the marketing industry’s most unique capabilities.
Q: Where is mobile marketing, specifically direct marketing, now as opposed to where it was 10 years ago?
A: Much more accurate. We have so much data that we didn’t have 10 years ago and I think a lot of that has to do with how widespread different devices have become. Ten years ago we just had desktop devices and we had the traditional channels, so that has changed completely. People don’t really engage in traditional channels like they used to. Instead, they are entirely focused on mobile devices, connected T.V. and interacting with passive variables. I believe that your ability to market directly to someone, even on mobile, is entirely dependent on what data you have on that person. The amount of data we have on an individual customer has grown exponentially. Data is driving direct marketing right now.
Q: So what’s the best way to access that data? If that data is all over the place, how would a marketer access it?
A: I think we need to figure out a way to distill all the different data sources into a single profile, whether it’s a prospective customer persona, whether it’s a an already-known customer profile or someone that’s talking about your brand on social channels. Increasingly, what we are seeing is that there are advanced brands that have invested independently in these channels so they have very strong products. While this gives a good understanding or emerging understanding of what a particular consumer looks like online, there is no way to cross-pollinate those data sets to create silos within organizations.
So that’s a big part of a problem I see often. My team is able to source data from both internal resources and publicly available information. And finally, and most excitingly, we have found a very public and very successful data set that has emerged within the last four years, or so—location data with mobile. Many apps or mobile services are location dependent. Social networking has been using location very successfully. So naturally, location has become a very important feature of mobile. This has created a huge invisible layer that spans every single user, every single location, every single industry.
We are now tapping into that location data as a layer that we can add onto existing information. Once we compound these different data points together into a single profile we can enable direct marketing, whether it is personalized messages to engage users or unique and niche messages to a niche audience much more successfully than anything we have seen before.
Q: If this is all new, how can other industry leaders attempting mobile direct marketing refine their strategies?
A: First of all, mobile doesn’t have cookies, the same way we have on desktop. So a lot of the mobile stack struggles there, because a lot of the existing technologies essentially fly in from Mars, look at a user and ultimately depend on those cookies to have historical data.
So then what happened was you had mobile-specific technologies that emerged independently and then there were a lot of existing technologies that were able to do things on the web. With that distinction, some marketers were very good at the mobile site, but didn’t have that ability to replicate that success on other devices and other experiences. Or, they had a really strong base within the traditional cookie world and couldn’t recreate that success in mobile.
What my development team at EQ has done is to create what we call a cross-device profile, so we can map a person’s mobile phone, their tablet, their laptop or desktop into a single unified profile. That allows for a single, unified strategy.
Usually where things really start to fall apart is when you know how to convert someone really well on Facebook, you know how to convert someone really well when you send them an email, but you don’t know how to convert them through display. You know, you have a very successful display strategy that works on computers, on desktops, on browsers but you don’t know how to replicate those displays on in-app experiences. And then what that does is create a glass ceiling for the brand.
Q: So what’s the next step for direct mobile marketing?
A: So now, my team is analyzing all the location data available. Initially, location data was solely used by drawing these things called geo-fences. So we take a point of interest that consumers find interesting and we will map out the boundary of that location.
Q: Based on their mobile devices?
A: Based on the geography of that location. So it could be a store, and you would map out the boundary of that store. Now if you have an idea of my location, you can tailor messaging, or call to actions for your product within the vicinity of that store. It would be amazing, extremely successful, right?
However, the reality is that very few of those people would fall into a category that indicates they would be interested in that product. The non-digital equivalent of that strategy would be a brand that is trying to grow itself purely taking up shelf space, all shelf space, in a retail environment.
That is often insufficient, right? What you need to be able to know is where does that consumer live, work, play? Where do they consume content? Then, reach them outside of the store so you can track them into the store. What my team does now is, we take that as a single and not necessarily a rule of delivering messaging.
We can look at the store and say, who has been into this store? What does that tell us? Then we attribute that to that user’s profile so that we can—three days later, a week later—still place value on the fact that they went into that store.
Let’s take a location like Starbucks. The fact that a consumer went into a Starbucks is probably a lot more valuable than the fact that this user is physically at a Starbucks right now. Because the amount of users you reach using that methodology is very limited compared to what can be reached, if we think about reaching the entire Starbucks audience .
So audience versus proximity is kind of one thing that’s coming up down the pipe. The other aspect that is coming down the pipe is now that we understand what kinds of locations people are interacting with, we want to match the facts together to create a complete user profile.
If we can see you at a university, for a certain number of hours a day, we know that you are probably a student, right? The fact that you are shopping at Whole Foods can tell us that you are probably in a higher income bracket in a spectrum of household income. And then the fact that we see you at a GO station can tell us that you’re probably a commuter. Compound those facts and we have the behaviour profile of a probably a higher income household student who is probably commuting to university.
Those sorts of insights are built on top of atomic segments to create much more powerful personae and insights than ever before. Marketers who would have initially started buying that audience while they were directly in a specific location are now transitioning to location-based behaviour data that is far more powerful and direct to consumer.
This article originally appeared in the March 2017 issue of Direct Marketing.