Ned Hayes [00:00:01] Welcome to SparkPlug, where we talk to smart people working at the intersection of business and technology. Brought to you by SnowShoe making mobile location smarter. SparkPlug is happy to welcome Zabe Agha, founder and chief executive officer at Metrical. Metrical is the leading provider of predictive shopper engagement solutions that increase online creation and conversion and improve customer loyalty. Their A.I. based software enables retailers to drive increased revenue by optimizing a digital funnel and predict customer behavior, engage customers in real time and enhance their experience. So I’m hoping that Zabe can tell us what all of that means. Welcome, Zabe.

Zabe Agha [00:00:46] Thank you very much, and thanks so much for having me. 

Ashley Coates [00:00:49] Thanks so much for being here, Zain. We start by telling our listeners a little bit about yourself and how you got to where you are today. 

Zabe Agha [00:00:57] Yeah, the story about where things started actually starts in Pakistan. So a long time ago, in a country far, far away, my parents were immigrants to the United States and we I came to the U.S. when I was seven years old. We ended up in California initially and pretty much a typical immigrant story. My father lost a very promising career in the government over there. My mother was actually one of the most renowned artists in Pakistan at the time, so she was giving up that career primarily with the hopes of coming to the U.S. and having a better future for their family and their children. And, you know, knock wood, we have done quite well in the sense that, you know, both of their kids, my sister and I both went to great schools. We’ve had an amazing experience in the workforce and in entrepreneurship. And you know, here we are about 20, almost 30 years later in the United States. I’m now in Boston, typically I’m out on the West Coast, my dad lives here, my mother passed away about 10 years ago and but she was an amazing influence in terms of entrepreneurship for me. When we first came to the U.S., they were doing typical things, my dad was going to school. My mom initially was actually retouching photographs for the small little brush, pre days of Photoshop just to sort of update of photo prints so that they would look good for graduations and weddings and things of that type. And then later on, when we got our first Mac, which my dad and I one at a Mac convention or Mac Expo, we got a copy of all this freehand, which was prior to Adobe and a Mac two and super excited about this machine that I’d never seen before, but just very excited about the fact that we had a computer. I was going to plan to use it for games, but my mom basically figured out how to use it for graphic design. And so she actually started a company specifically around graphic design and how to take the idea of just doing very simple images, getting them printed out and then doing presentations for people. And that’s really engrained in, me this idea of entrepreneurship. And, you know, cut forward, a few years later, I decided I wanted to focus on computers as my future. I did an undergrad in computer science at Ohio State ended up in Silicon Valley, working for for a whole bunch of startups during the dot com boom and then met my wife at a very early age as well. And she’s been an amazing part of my journey as an entrepreneur and sort of like that support that everybody needs, especially when you constantly think about quitting on a maybe weekly basis. And after having done startups, I decided to start my first business, which was Missing Link Technology Partners. It was a software consulting firm focused really on software development that did really well and got us to go live in Europe for a few years. We worked for ING banks, so we lived in Belgium and then in Greece. I sold that company while we were in Greece on vacation and I met somebody and we started another company there called Greece101.com, which was basically Yelp for Greece that did well. We exited that company, came back to the United States a few years later, primarily to sort of settle down and start a family. And we ended up in Massachusetts initially, and I was working for a company that basically managed the Neumann power grid. My wife was doing her doctorate. We ended up in California when she finished there, I started working for Autodesk and to make an already long story a little bit shorter. That was really where I first started getting exposed to e-commerce. So Autodesk was a company where I was helping the team to basically think about how to sell a product online, as had traditionally been a company that had sold through a channel. And so what we were really focused on, I was like, How do you go from bare bones of saying, Look, we want to sell a product to actually doing it and then actually selling it. And so e-commerce was where I basically started at Autodesk and got my experience in the customer experience space. But then also sort of learned all of the things that sort of surround ecommerce, everything from systems to actually get an e-commerce site up and running. How do you actually manage teams from a product user experience design point of view? How do you actually measure AB tests? How do you figure out what works, what doesn’t work? And that was sort of like the incubation period for me to really sort of understand where there was a lot of potential problems in e-commerce that we could solve with the tools that we had at our disposal, but we weren’t really able to always focus on those problems, and that was the genesis of Metrical, where I realized that we could take the problem of not understanding our customer and finding a way of being able to engage with them and leveraging data to be able to do something better. 

Ashley Coates [00:05:30] That’s incredible, so it sounds like that entrepreneurial spirit really ran deep in your family. 

Zabe Agha [00:05:36] Yeah. And I think, you know, as a result, I’ve started multiple companies and I have a spreadsheet I maintain of other future companies to start as well. So hopefully I’ll get a chance to get to some of those as well. 

Ashley Coates [00:05:48] Oh, great. 

Zabe Agha [00:05:49] I’ve been thinking about how to run my business. I’ve been realizing that ecommerce is a very tricky space. You know, it’s an area where a lot of companies have a lot of data, but they haven’t actually done anything with it. And at the same time, there’s a lot of innovation that’s happening. And so one of the things that’s really particularly interesting to me is how we bring those two together.

Ned Hayes [00:06:09] Was that the genesis of Metrical was that kind of where where it came from for you? 

Zabe Agha [00:06:14] Yeah. So after leaving Autodesk, the thing that I realized was that there was this fundamental problem of not understanding the customer. And so the initial version of Metrical, which I call Metrical 1.0, was really the customer experience space. It was focused on how do you better understand from the customer’s point of view, what is it that they’re not getting. So in an e-commerce experience? When somebody gets to a product that they’re looking at over and over again, why aren’t they adding it to their cart? Or if somebody gets to the point where they actually create a cart but they don’t purchase, which is a huge problem, obviously in retail. Why isn’t that person actually pulling the trigger? And so the first part of Metrical was really focused on understanding that pain point in the form of micro surveys to basically ask the customers, like Why aren’t you moving forward with your customer journey? And that led to this realization that all this data that we heard about not only what the shopper or the customer was telling us, plus all of this other data we had about which campaign they were coming from, which products were they looking at, which carts or what was the size of their carts, what was in their hearts. All of that combined was a really amazing dataset that we could do something more with, and that led to Metrical 2.0, which is our current iteration. And that is this combination of data about the customer and everything that they’re doing. That basically lets us find a way of being able to let retailers see into the future. We have this amazing dataset of over 4.5 Billion points of unified data about the customer journey from everything from the campaign that brought them to a site all the way through checkout. And we have it for multiple retailers across multiple verticals that most companies don’t have know. Unless you’re like the Amazons of the world, you don’t have access to that kind of data. And so if you’re a large retailer, by the way, like a Best Buy or somebody, you have that data, but you only have it for Best Buy. You know, we have it for a whole bunch of different verticals. So that allows us to be able to look across industries, verticals, price points, marketing campaigns and see what is it that really can matter to a shopper. 

Ned Hayes [00:08:28] I was struck by the phrase that you use that they can see into the future. So this is almost kind of a time travel device that you can take modeled information that can inform what future behavior will look like. Is that accurate? 

Zabe Agha [00:08:41] That’s right. And we take that information and we use it in a way that allows us to be able to help the retailer figure out what’s the best way to engage a shopper, but at the same time are very sensitive to what’s happening in the world with regard to privacy. So one of the things that’s actually unique about us is that we are really focused on PII. None of the data that we digest is allows us to be able to identify the customer. So everything that we have is anonymous in terms of what somebody is doing. We have an ID, unique ID that we generate for that person. And what is it that they’re doing when they’re traversing a site? But we can never point to an individual and actually say that we know who the individual is. 

Ashley Coates [00:09:22] How will the predictive algorithms change our future of retail? Do you see behaviors changing? 

Zabe Agha [00:09:29] Well, we already see behaviors changing in the way that we’ve seen companies like Amazon react, and we’ve all read those stories about that person that started getting information about pregnancy related advertising or products because Amazon figured it out before that person may have told anybody. So I think that’s already a part of our lives. What I think is really interesting is the ability for brands and retailers to not necessarily predict exactly what it is that the customer may want in the future. But what is it that we can predict about what they need right now? I think there’s two parts to this equation. Companies like Amazon will obviously want to understand the future because they’ll want to know what products to sell you. But from our point of view, you know, when we’re working with this huge group of retailers that are trying to just sort of keep their head above water or are trying to stay relevant in this world of digital and Amazon, they’re really focused on how do they sort of close that sale now? So for. Our customers and Metrical predicting the future is a way for us to be able to help the brands and retailers react now. And so I think that in the context of e-commerce, in the context of all of this data, brands need to find a way of being able to leverage information in such a way where they can provide a better experience to the customer, which means giving them what the customer wants when they want it. And I think for the most part, even in the world of e-commerce, things have been rather staid, the source said. Hey, here’s our website. It’s basically a digital version of our store. You come and do what you want to do, buy what you want to buy and then leave. And you today a retailer isn’t really stuck. Once somebody leaves, you don’t have much of an option to be able to engage that person. So we’ve all gotten those email reminders that, hey, you forgot something in your shopping cart. But the reality is that for most retailers, they don’t have your email address. You only get that email address if at some point you were a customer or you visit their site and you’re willing to give them your email address. But 70-80% of people that visit an e-commerce site, they are not identifiable from e-commerce from an email point of view. And then the other thing that retailers can do and something that we’ve all seen and just of completely ignore now is retargeting. So these ads that follow you all over the web and what we find is that, you know, they are a commodity from a retailer’s point of view. They don’t have an amazing click through rate. And if somebody does click on them, the conversion rates are amazingly low. So, you know, retailers are often throwing good money after bad because you came to they basically had an advertising campaign to get you to come to the website, you came to the website and you bounced. And as soon as you bounced that ROI, the advertiser had spent with Instagram, Facebook, Google, that’s that money has sort of burnt, didn’t do anything. And now they’re going to throw the good money after bad in the form of retargeting. They’re saying, Hey, we spent a dollar to get you to come. You didn’t do anything. So now let me spend another 20 cents to try to get you to come back. Right. And that has got a very low effectiveness. So in terms of predicting what we think we can do is help retailers understand how to spend that dollar better. How do you actually make that dollar more effective? And so for us, it’s less about predicting what you may want to markets to the customer in the future. But what is it that you can actually do to engage them better? So for us, it’s all about predicting engagement. 

Ned Hayes [00:12:57] Well, I want to come back to that retargeting question because one thing that we’ve seen with facial recognition or with Facebook understanding of user information is that users take active steps to avoid or to even block it. You know, we’ve seen people use makeup to block facial recognition. We’ve seen people do things that are kind of violating the terms of service in order to get around sharing information with with Facebook and with other people. Do you think that we’ll see a consumer backlash on on being able to understand predictive behavior and consumer reaction to that?

Zabe Agha [00:13:39] To some extent, I think we’re already seeing it. You know, companies like Apple have already done the entire IDFA, which basically is taking a stance that’s saying that, you know, for Apple browsers and Apple products, we’re not going to let companies like Facebook or Google track you. I think a lot of companies that have announced earnings in the last few weeks have actually been hit really hard as a result because they’ve shown that their numbers were really dependent on Apple being able to identify who those people were. So there’s already that happening. I think it’s a two sided coin because in a sense, you know, companies like TikTok have shown us that people are willing to completely give away their privacy if they can get a laugh. But on the other hand, there’s this knowledge that we live in this world where we’re worried either about a surveillance state or we’re worried about companies having our data and using it in ways that we don’t want to. And I don’t know if we are going to find that balance soon. And I think that in terms of predictive behaviors, it’ll probably be a question of, you know, what are things that people are willing to let companies predict about them? And that will obviously then differ by who those people are or maybe certain classes of people that are really interested in commerce are OK with that. But then other groups of people that are more security conscious and don’t want to be tracked, you know, are not going to be open to that role. 

Ned Hayes [00:15:14] Yeah, could could you actually take these next questions? 

Ashley Coates [00:15:17] Yeah, absolutely. We understand that J.C. Penney partnered with Metrical this year to help drive their e-commerce transformation and improve their customer experience. Can you share more with us about what problem you were solving for them? 

Zabe Agha [00:15:34] Yeah. So we initially started off working with J.C. Penney is primarily to help them understand how to leverage their data in a way that allow them to be able to do what retailers are really focused on. One is increase revenue, but the mechanism to do that, obviously is by increasing conversion rates. And so like a lot of brands and like a lot of retailers, the issue that J.C. Penney had was specifically around taking their cart abandonment rates and lowering it. So fundamentally, for most retailers, what that means is that you’ve got a large group of people that get to the point where they go to the website, they browse, they find a product or products that they’re interested in. They have a high level of purchase intent and the spend in the sense that they look at those products, they spend some time figuring out which ones they want and then they add that item to their cart, so they’ve actually invested something to get to that point. What you see is that over 80% of those people basically end up leaving, right and then go back to that earlier issue of what we were talking about of either email reminders or retargeting as the mechanism to try to get them to come back. And what J.C. Penney has wanted to do was to find out whether there was a better or a different way. And one of the things that’s really interesting about Penney’s is that they’ve been around for a long time and they’ve had some financial issues over the last couple of years, but they’re actually one of the most innovative companies that we’ve worked with. They’re amazingly nimble compared to some other well-known brands that we work with. They go from idea to execution in under a week, whereas other companies take three to five weeks. We’ve found a really great partnership with them, not only in the sense of the service that we provide, but also because they’re extremely willing to be experimental. And for Penney’s reducing cart abandonment was our number one goal and what we ended up finding was that by leveraging their data, we were able to not only identify and predict those people that were coming to the site and building carts and they were going to abandon. But we were able to execute a series of tests that allowed us to be able to identify mechanisms that we could use to engage with them. And that ranged from everything from discount, which is what J.C. Penney is known for, obviously a discount retailer to doing things that actually didn’t give away margin. So we were finding that by engaging with potentially abandoning shoppers at the right time and just reminding them that, hey, you’re above that free shipping threshold. Don’t forget you qualify for free shipping, or don’t forget you qualified for 10 percent off your order that we were able to actually drive substantial increases in conversion rates. The flip side of that is that we are able to not only engage those people that are likely to abandon, but more importantly, we don’t engage the people that aren’t willing to abandon. And so if somebody is coming to the site and they’ve got a high purchase intent, they’re looking to get rid of the checkout and we think that they are going to move forward with their checkout process. We don’t want to interrupt that customer journey. We don’t want to get in their way. We don’t want to offer them a discount because we don’t actually want to bite into margin unnecessarily if that person was actually going to move forward with their purchase anyway. And so for J.C. Penney, as they see, it’s were this two sided benefit, which is one, you know, let’s convert the people that they want to convert because they’re going to abandon. But also, let’s not give away margin unnecessarily to people that we’re going to purchase anyway. And so that relationship is but it’s gone from our part of image solution to our cart creation solution where we’ve actually moved up in the funnel as well. And now we have a solution that looks at people that are coming to the site that come in from advertising or organically get to a point where the search for specific product. But then they’re going to bounce from the sites and we’re able to predict that bounce. So what we can do is actually say, Hey, this person’s about to leave, you spend a dollar to get this person to come to the website and now you’re about to lose that dollar. What can we do to actually engage that person and get them to stay on the site? And we found that we can get over 40 percent of people to actually create a cart. We reduced cart abandonment by over 18 percent. And we find that for return on ad spend, we see over a double digit increase in terms of how much advertising dollars that we’re spending before and how much they’re getting as a result of Metropol engaging them in the short term. 

Ned Hayes [00:19:53] So it’s great to hear that you see J.C. Penney as innovating. Do you feel like your work can really help J.C. Penney to survive that, the changing waters of retail and to thrive? 

Zabe Agha [00:20:09] Yeah, you know, I think there’s a little bit of a misconception about J.C. Penney as well because we sort of. And I think when we think of any brand, we sort of lumping a brand together. I don’t know if you’ve been following in the media recently, there’s been a lot of articles about Macy’s.com business being spun off separately from the brick and mortar business, somebody I think recently mentioned that for Neiman as well. I think the reality is that for a lot of these larger brands that we know, there is a two sided or two versions of operations for that company, there’s companies that run the brick and mortar. And then there’s a company part of that company that end up running the e-commerce. And of course, there’s an omni channel connection between those two, and there’s people that sort of operate on both sides of that bridge. But, you know, for the most part, we think that companies like J.C. Penney, as we know, but in fact, their e-commerce is thriving, is doing really, really well. The brick and mortar side is a little bit more challenging. Obviously, you know, COVID as a headwind, it’s been an issue for everybody. Simon Properties recently acquired J.C. Penney, and I think that they realized that J.C. Penney is an anchor store for them to actually get people to come in to the malls. The bigger question really is like what’s going to happen with brick and mortar over the next few years? And how will the state of brick and mortar change with regards to people actually leaving their house and buying things? Amazon is actually now looking into actually buying retail space to potentially actually get people to come in. They’ve already have their four star stores that you can actually walk into. I don’t know if you guys have any of those in Portland where they’re selling their top four star products randomly, and you can sort of walk in and check those out. So I think brick and mortar really has a future. I think right now it’s a little bit of an uncertain future. But I think companies like J.C. Penney that have been around long for a long time are definitely doing well in the e-commerce space with some help for companies like Simon will navigate the brick and mortar space quite well. 

Ashley Coates [00:21:56] In a recent article about the partnerships you were discussing at all that you were discussing all the different technological choices that retailers face and the disruption taking place in in the detail. So let me start that again. You were discussing the disruption taking place in digital and all the different technological choices facing retailers. Can you speak more about all of those different choices and that challenge to retailers? And did you base any of those as you working with J.C. Penney? 

Zabe Agha [00:22:31] Yeah, I think, you know, technology is evolving so fast right now that for a lot of companies trying to figure out how to be able to navigate that path is complex. I think, you know, it’s complex for all of us as well. I still haven’t figured out Snapchat. I’ve downloaded at least three times and I still don’t know what to do with it. 

Ned Hayes [00:22:50] And me too, I do not get Snapchat. 

Speaker 4 [00:22:53] Yeah, it’s so popular, though, and I’m like, Well, what? What is it about Snapchat? And as soon as I get into it, I have no idea or it’s even happening. I think that’s probably more a sign of where I am in terms of my age curve than anything else. But I think for large brands, you know, I think data is their Snapchat moment where they’re trying to figure out that as technology is changing and evolving, how do they actually take it and start leveraging it in a way where they can actually find it useful? And so fundamentally, for e-commerce brands, the core problems that have existed in terms of people visiting, browsing, abandoning those were not really new problems. They’ve just sort of moved from brick and mortar to online. They’re different in terms of their intensity. They’re different in terms of their scope, but they’re fundamentally the same thing. And retailers don’t really have a robust solution for those typically sort of fall back on what we were talking about earlier in terms of, you know, for table stakes of email reminders and retargeting. And those functions aren’t really about intelligent or you just sort of have to do them because they’re what’s available. So they’re not using their data to the best of their ability. And our focus is really on helping retailers find a way of being able to cutting through that noise so that they can apply technology to that data in a way that will enhance the customer lifetime value in a way that will provide an unlock for the retailers to be able to say, OK, there’s a way for us to be able to take all this data we have about people that come to our site, what they look at, which campaigns they come from, or price ranges of items. We’re looking at which items are adding to their carts, which items are they purchasing, which ones are they abandoning, which categories are interesting to the customer? Which categories are not interesting to the customer? And then finding ways to be able to use that in real time and engage the customer. And for the most part, there’s so much out there in terms of like chat as a mass Facebook messaging, you know, UTM codes for marketing campaigns. Where does a retailer focus? You basically have to cover all of that. And so where we’re really focused on finding a way of being able to help the retailer find that point where they can have the most impact. Every retailer sort of has to do everything that they can. What we’re trying to do is we’re saying we’re going to give you a magnifying glass. We’re going to help you look at your data and find that particular portion of your data where you can actually drive the highest ROI. 

Ned Hayes [00:25:20] Right, right. Metrics matter. Data matters. And I I read up on this and I understand that after deploying predictive A.I., J.C. Penney saw a I think it’s 40 percent increase in new card creation and an 18 percent reduction in card abandonment. That’s pretty impressive. 

Zabe Agha [00:25:38] Yeah, it is impressive. And I think we’ve seen that same sort, those same metrics across other retailers as well. It’s a lot of it comes down to culture and just sort of accepting that, look, you know, we’ve already got a data science team, but that the science team has got 100 problems to solve. And here’s somebody that’s sort of helping us inspect the data that we have and find a new way of being able to tackle the problem of data. How do we open up the kimono a little bit and share what it is that we have in a way that can actually drive more value? 

Ashley Coates [00:26:13] That’s fantastic. Also earlier this year, your company announced the Metrical Abandonment Survey Shopify, its first exit based survey. Can you share more about what Insights Survey provides? 

Zabe Agha [00:26:27] Yeah, so that comes from the Metrical 1.0 idea where we initially started in the world of CX. One of the things that we wanted to revive was the idea of better understanding the shopper. So we’d gone from understanding visitors websites and what it is that they were doing and why they were doing it. And so what we realized was that we could leverage that same survey capability in the form of a one click micro survey that can be displayed anywhere on the site to let retailers basically query somebody and say, Why are you doing what it is that you’re doing? So two very simple examples. One is somebody that comes and looks at the same product over and over and over again, but never added to their cart. Why is that person doing that? And what is it that we can gain or learn from them about? Why are they constantly visiting but they’re not moving forward? Are they concerned about the price or the concern about the shipping? Are they concerned about the size of the color they want not available? I don’t think retailers and brands actually know that they sort of take a guess at OK, well, this product gets a lot of page views, but what’s the actual reason that that item isn’t being added to the cart? The other cases where somebody has items to their cart and then leaves? But why are they leaving? Are they not ready to buy because they’re worried about the cost of shipping. They don’t know about whether it’s secure enough. What we found was that the top three reasons for shoppers to not move forward with their purchase when they added an item to their cart was one they weren’t ready to buy. There was some sort of a factor with regards to price or product feature like color or size that was limiting factor. The second one was the shipping cost was just too high. So for a lot of companies, shipping is a barrier where they realized that there was a shopper realizes that I’m paying x dollars for the product, but then I got to pay a significant amount to actually get it. And that in this world of one day shipping and two day shipping with Amazon and Amazon Prime, that equation has change for a shopper. And so in many cases, people jump straight to Amazon to see if they can just get the same product, but then cut out their shipping costs. And then the third product is actually related directly. The third item is related to the previous one, which is that they found a better price elsewhere, so they’ll often just do comparison shopping. We all live in a world where we’ve got multiple tabs open in our browsers, so you could be shopping in one tab and just open another tab and just say, OK, well, can I get a better price? And so many people are just saying that, look, there’s no reason I need to buy it from you. I can get the same thing from somebody else at a better price. 

Ashley Coates [00:29:04] How has the completion rate been for that survey? 

[00:29:07] Our typical completion rates around three and a half percent, which is not bad for a one click survey. The thing that actually makes our data more interesting and some of the stuff that we only share with our customers is that we’re able to tie the reason that somebody is abandoning to their entire clickstream history. So when somebody says that I found a better price elsewhere, we know which campaign actually brought them to the website, which products that they’re talking about, which products that they actually add to their cart and for which products do they find a better price elsewhere versus which products were shipping a concern? And so the retailer can then take that data and allow them to be able to think about what qualifies for better shipping rates or what needs to change about that product so that it’s more marketable to the shopper. 

Ned Hayes [00:29:56] So you’ve spoken about how businesses are using this data. Do you feel like this will end up in a better consumer shopping experience? 

Zabe Agha [00:30:07] I think the consumer really just wants what they want at the end of the day. What they’re really interested in is getting their products. They are interested in getting a high quality product at the price that they want in the timeframe that they want. That’s the fundamental idea of a consumer. What we think that we can help retailers do, and I think that AI and predictive in general will help brands do is find a way of being able to deliver on that promise as best as they can. So there’ll be two, there’s two, basically two groups of companies or will be two groups of companies that leverage data to be able to get the consumer the way that the consumer wants it and those that basically will continue to do things the way that they’ve always done things, which is just the traditional shopper comes, browses, buys, gets. I think that companies that leverage their data will be able to find a way of being able to get that consumer their product faster and get into the hands faster, fundamentally is what I mean by that and also get at the price point that the customer wants, rather than having to worry about doing shopping across multiple sites and finding the best deal because that takes time for the consumer and they may not necessarily want that. 

Ashley Coates [00:31:20] And I understand that the micro survey can be displayed anywhere. Can you share some examples of alternative placements? 

Zabe Agha [00:31:26] Yeah. So one can be where somebody is putting something in their cart and they’re exiting. So that’s something that you want to obviously engage with them if you can. That person’s going to leave anyway, and there’s a very good probability that they’re going to leave then. Can you get some information from them, at least before they go? The second one is on a product detail page, so somebody like I said earlier, visits that same product over and over again. How can you detect that? OK, this is the third time somebody visited this product in the last three days. Now’s the time to be able to engage them and ask them, You know, what’s keeping you from actually buying this product? 

Ned Hayes [00:32:04] So surveys information gathering techniques, all of those can often be overlooked by people. People are used to just clicking past, just like you talked about retargeting information, people ignore it. So is Metrical able to change or influence that behavior at all or just clicking past or ignoring it? 

Zabe Agha [00:32:24] You know, at the end of the day, the content that’s presented to a shopper needs to be compelling enough for the shopper to engage with it. So one of the things that we try to do when we’re working with brands is that any engagement that we have in terms of other lightbox content that we present on a page needs to not be disruptive. It needs to be something that is consistent with the design, style and feeling of the site so that the shopper feels that they’re not getting served something by some third party all of a sudden, and that the experience that they’re having is closely integrated with what they would expect on that site. There’s a lot of companies we don’t work with, particularly because they sort of fall in a certain class where they have a certain customer experience. So, for example, luxury isn’t a big focus for us because when you’re buying a luxury product, you have a very specific experience that you’re expecting. And the way that Metrical engages with the shopper in terms of identifying that you are potentially going to abandon engaging you on the site isn’t something that necessarily is very appealing from a luxury point of view. But we think that there’s a massive market outside of luxury. So that content needs to be something that the shopper thinks. OK, this is interesting enough that I want to actually engage with it. We track actual Metrical engagement rates so that, you know, when Metrical is available to a shopper on the site, how likely are they to engage? And that is that engagement positive or negative in the form of they dismissed it and they don’t care or they liked it and they move forward. And we find that our engagement rates to begin with a really, really high, they’re mid mid double digits. And then we say, OK, what percentage of these people actually close and say, we don’t want to move forward versus those that say, yes, this is interesting and we do want to move forward. And we find that the engagement rate of people that are actually wanting to take up the information that’s being presented by the brand with the retailer is higher than the people that dismiss it. 

Ashley Coates [00:34:25] So what’s on the horizon at Metrical, what of the next few years hold for your company? 

Zabe Agha [00:34:32] So for us, I think that we have initially started off in the heart of advent space and then we moved up funnel to the bounce stage where people kind of PDP’s and the balance. We know that there’s lots of other areas where a lot of money is being spent by retailers and brands, but there’s a high rate of either attrition or bounce. I was reading an article yesterday that came out that said that in the United States, once somebody does a search and they go to the search results page, $300 billion in advertising is lost by people that basically leave after doing a search. So this is on an e-tailers website. So what is it that can be done there? So we’re starting to think about different points of the funnel and the different points of the customer journey where we think that we can help retailers engage shoppers to not only provide a better experience to the shopper and get them what they’re looking for, but also help the retailer save money on the advertising side and make money on the sales side. 

Ned Hayes [00:35:34] Well, we talked about kind of the near term of what’s going to happen for Metrical and your customers. I’d love to kind of jump into the future a bit. I don’t know if you’re familiar with Isaac Asimov’s Foundation trilogy or not. 

Zabe Agha [00:35:48] I’m actually reading Prelude to Foundation right now and watching the amazing Apple Plus Series that’s going on. Have you seen it? 

Ned Hayes [00:35:57] Well, no, no. I haven’t seen it yet, but I’ve read it. And the interesting thing about that book I’m gonna geek out in science fiction for a second here is that Harry Seldon, who was a main character in there, feels that he can predict the future right? And he believes he can see 30,000 years into humanity’s future. And so, you know, predictable behavior, understanding patterns, that’s what it’s all about. So tell me about the future of Metrical out 10 20 years. Where is the future of predicting behavior going? You think it’s the foundation future or a different future? 

Zabe Agha [00:36:29] I don’t think it’s a foundation future, I don’t know if we’re getting to a point where Empire will be concerned about our predictions. But that’s also a reference to sort of like the main antagonist in the story series. But for us, I think our goal is to continuously help brands and retailers think about the best way to be able to drive revenue and engagement for them and a better customer experience for the shopper on the site. I do think that we will have much more of a broad impact in that rather than just being one or two points in the customer journey. It’ll be maybe nine or 10 different points. I also think that there’s a potential where a lot of this information that we have across different retailers comes to bear in such a way where we can actually help retailers understand what things matter for, what customer at what time of day with what maybe the weather’s going to be next week and what engagement makes sense that sort of gets into that sort of foundational kind of future. But right now, I think a lot of that data is hard to get to and finding a way of being able to leverage it is difficult. But I think that the more and more data that becomes available, the sources of data that are out there as they become more and more available, that’ll help us to be able to provide a higher level of accuracy and provide a better experience. But I don’t think we’ll be at a point where we’ll be asked to leave the planet because we’re too heretical with our ideas. 

Ned Hayes [00:37:56] No, that’s great. 

Ashley Coates [00:37:58] That’s good to hear. So, yeah, one last question for you, Zabe. Also, looking into the future, what would you like to be remembered for? What do you hope is your personal and professional legacy? 

Zabe Agha [00:38:11] That’s a good question. Last year, I didn’t show this before, but last year I got cancer and it was a relatively late stage disease and very eye-opening also had it in the middle of COVID, which was sort of a, you know, just a bizarre time to go through that experience. And I think my take away from that entire experience was other than having the most amazing support from my wife, kids and family was that, you know, life is really short. And for the most part, wondering and thinking about what’s going to happen in the future is basically a fool’s errand because there’s too many variables at play. And unlike the, you know, the large data sets that we play with in our predictive AI, the data set of life is just so huge that predicting anything is a fool’s errand. And for the most part, I really like running our business. I enjoy my family. I love my quality of life. I’m really focused on sort of maintaining and managing healthy called quality of life, having a very wonderful relationship with my partner and ensuring that my children have a great upbringing and hopefully a very sound and enjoyable future. And I think that that would be my legacy. You, I’m not. I don’t care if there’s ever a statue. I actually hope there’s not a statue, but I’m really wanting my children, my friends and my family to have the best possible lives that they can and also have an amazing understanding of the current moment and realizing that, you know, living now is more important than living in the future. And I think that if anything, what I would want to be remembered for is just to be remembered as the person that just focused on what was happening there and then not worried about what was going to happen tomorrow. 

Ned Hayes [00:39:58] Thank you for the fantastic conversation today Zabe. I appreciated the engagement and the discussion, and I look forward to Metrical predicting the future in some regard. 

Zabe Agha [00:40:07] Thank you. I appreciate that Ned. And Ashley, thanks so much for setting up this podcast. I really, really enjoyed having a conversation with you. 

Ashley Coates [00:40:17] You too, thanks so much for being here. 

Ned Hayes [00:40:19] Thanks for listening today to the SparkPlug podcast and brought to you by SnowShoes. Snow.sh For smarter mobile location. SparkPlug is a wholly owned property in SnowShoe all content. Copyright 2021 SparkPlug Media.