- What We Do
- How We Do It
Caroline McGuckian is a trained boxer who throws right hooks at the heavy bag in the gym she co-owns to work off stress. She’s a Londoner now, but was raised in Ballycastle, Northern Ireland, a village on the North Antrim Coast that was re-introduced to the world as the Iron Islands in Game of Thrones. As a young woman, she couldn’t wait to escape. As a successful entrepreneur, she loves to get back for a change of pace.
She married her childhood sweetheart and the pair are globetrotters who have visited Cambodia, Japan, Indonesia, pretty much all of Europe and chunks of the Middle East and South America. They found being London-bound during the pandemic tough, but now that travel is becoming a thing again, next on their list are Africa and Canada.
McGuckian is also the CEO of Meshh, which provides world-class spatial-analytics solutions and was recently acquired by Limelight Platform. Meshh measures engagement and interaction in physical spaces for event organizers, venues and brands. And then helps them deliver richer, more immersive experiences.
She sat down to discuss what Meshh brings to retail, how the two companies work hand in glove and more.
Three or four years ago, when I told people what I did they ran away screaming. They were like, “Oh, my God, you're a terrible person.” Or they didn't have a clue what I was talking about. I may as well have been speaking Swedish.
But in the six months before COVID, people were starting to come to us asking if we could help them with a measurement / ROI problem. So instead of me saying, “Oh, by the way, here's what we can do, here's how we can help” the tables were slightly turning, and people were understanding that this data wasn't the preserve of a digital world. That, increasingly, we were able to look at behaviors and start to put models and assessments and richer understanding behind real life.
Then the world went to crap. As a result, and rightly so, the subsequent measurement was around COVID health and safety, employing cameras and sensors and measuring capacity, but not with a commercial understanding—it felt highly inappropriate to commercialize anything. And I agreed with that, so Meshh went dark, because it wasn’t okay to be talking about how to sell more things when people's well-being was at stake. Our technology wasn't designed for medical purposes so it wasn't a path I was comfortable pursuing.
What's happened now is that the pace is accelerating. I think that [COVID’s] window of time has helped everyone understand the capabilities of the technology. Everyone is much more familiar with the principles and benefits of the measurement of movement. So long as you are transparent and legal, obviously! So now businesses are starting to work out how they can apply it.
Most retailers struggle with repeat visits. There are lots of technologies that can count, but what they can't do is know if a customer was here last week or differentiate between customer interests. It’s relatively simple in terms of what we do, but it's relatively important if you're a retailer, because one person visiting 10 times is not the same as 10 people visiting once.
All retailers know how many sales they make. But what they rarely know is how many sales out of how many opportunities — what size is the pie?
If you had 1,000 visits and sold 100 widgets, did 500 people make two visits? If you only had 500 visits that's a 20% conversion rate rather than 10%. Could you have doubled your sales with 1,000 unique visitors?
What do those unknowns look like? How do you contextualize it? That's the type of thing Meshh does for retail. We get quite granular. Which element within your store is keeping people’s attention? Where is it located? Where's the customer drawn to first? Are people task based? Or are they browsing? What does that mean? What can you do about it?
So, we try to identify the balance between richer engagement browsing patterns and transactional, mission-based retail.
Meshh is at the top of the data funnel.
Meshh is able to identify certain operating systems. Let’s say Jack and Jill are at the mall shopping and they see a BMW kiosk. They know they need a new car, but they haven't really started the process yet. They walk by and our sensors pick up their smartphones.
A week later, they're back, and BMW is still there. But now, they've got more time and decide to stop. So, we know that there are two devices and a BMW. And we know they were there the Tuesday before. There are also two models, but Jack and Jill only engage with the SUV. Meshh recognises that.
Then they are approached by a salesperson to enter a draw and email list. A couple months later, they’ve received a few emails and decide to go to the dealership. In parallel, we know their devices are at the dealer, and we know those same devices were at the mall two to three months beforehand. And we know which model the device holders were engaged with.
When first-party data capture is required. For example, when Jack and Jill supplied their email address, then the data gets much more granular.
These people are in the dealership. They submitted some information broadly speaking, but didn't follow through until now. Once in the store, they input their email address, which connects them back to that draw, and start configuring a car.
So, Jack and Jill are standing at the SUV, looking at a blue one, adding features and pricing out lease options. Limelight is then tracking those choices and adding that data to a dashboard in real time.
All told, our data tells us where the devices were first exposed to a product and if the device owners were engaged, and then Limelight kicks in with nuance and demographics. That information will then get passed to the retailer and put into their CRM system for someone to follow up.
So far, the only conversation they've had is entering a competition with the person in the mall. We can still start to put them into categories and give them attributes.
Automotive retailers have years of very, very enhanced data. They have sophisticated user profiles of their car buyers. So, the Meshh data and the Limelight data help populate those, really.
So, the BMW rep doesn't talk to them about a red sports car. She knows what they’re interested in and where they are in their journey. And she knows it'll probably take another six to eight weeks before they buy. So, she then has her journey to follow.
Full funnel metrics, connecting passive and active data that’s captured in a single view. What Meshh and Limelight do is provide and enhance profiles so that brands have a better idea of who the customer is, where they've been on their journey, what stage they're at and what their likelihood of being engaged in purchasing is. And then brands know how to behave and take that forward.