Ep. 127 | Getting To Know Your Customers with Moira McKenna
This week Moira McKenna, VP of Business Development and Customer Success at data onboarding company Throtle.io, joins Allison Hartsoe in the Accelerator. Moira and Allison talk about the data that the technology you use everyday collects, the hidden expense of understanding customer data, humanizing data, and how brands can start leveraging their data as a useful asset.
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Allison Hartsoe: 00:00 This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue-generating customers, then this is the show for you. I’m your host, Allison Hartsoe, CEO of Ambition Data. Every other week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let’s go! Welcome everyone. Today’s show is about getting to know your customers, and to help me discuss this topic is Moira McKenna. Moira is the VP of business development and customer service at Throtle, which is a data onboarding company that enables brands to reach individual customers online. Moira, welcome to the show.
Moira McKenna: 00:50 Thanks for having me, Allison.
Allison Hartsoe: 00:52 So could you start by telling us a little bit about what is a data onboarding company? I’m not sure everyone understands that.
Moira McKenna: 00:58 Sure. So kind of insured throttle and other data on boarders in this space really pride themselves in making data actionable for their clients, either their own first-party customer data or working with other trusted data providers in the space in order to look at things like audience segmentation, in order to activate media campaigns, measure ROI of campaigns from a post-analysis basis. And really just try to understand the data that clients have permission them to use and use it to reach those clients better and make better business decisions for outcomes that make sense generally for their C-suite.
Allison Hartsoe: 01:37 So always the question with data is most people, if I asked them, they think that every device in their house and their phone is listening to them and every camera is tracking them. And so as a result, there are very high expectations from customers who think that if a company has my data, they should be using it correctly. What is the actual state of customer data within those companies today, do you think?
Moira McKenna: 02:03 So you can what we refer to as the creep factor, and yeah, there’s definitely some creep factor involved when you’re talking about things like my phone’s listening to me, and then I saw an ad on Instagram or is Alexa always on? And they should know these things or contrarily people don’t recognize that when they sometimes make a trade-off for ease of use of an app of the program, even sometimes like to get a newsletter that or coupon that might be an interest to them that they’re making a trade-off and that there is some data collection happening.
Moira McKenna: 02:33 From a creep factor perspective, I don’t think it’s nearly as prevalent as people think, but there are definitely companies looking at you, Amazon and Facebook and Google that has some inherent things that default in their technologies and their apps that would, it asks you. Do you want your location data on at all times or only when using the app, when it asks you, do you want to leave your microphone on or have allow camera access? That’s changing the flow of data. And though it’s convenient, I think it comes with some questions consumers have to ask about who I’m permissioning this to and how they may use it. In general, if you move outside the tech realm, um, and you look towards retailers, folks in the travel space, they have a ton of data. They have transaction data. They have loyalty card data, all things were at some point in time, there was probably a trade-off with the consumer to get it.
Moira McKenna: 03:23 What we find in our business is they have a tremendous amount of data, but quite frankly, none of it talks to each other, and they don’t know how to use it. So, therefore, yes, they do have a lot of consumer data, but no, it’s not being used a lot of times in probably the most efficient way possible to really talk to and recognize and know their consumers in a way that somebody who is really clued into the data space and understands the data on themselves, it’s floating in the world would assume. So it’s too full for what I, it sounds a little pejorative, but for informed consumers about data and that trade-off, they would expect to be treated a certain way. I think by clients and have their data used in ways that enhance their user experience, enhance offers personalization for those that are a little less understanding of this environment and all the ways in which when you check a little box, because you want to enter a contest, what that means. It’s a little scary that their data is floating out there. And I think they misunderstand how it can negatively impact them. And that the threat though real, is not nearly as crazy as people I think would like to lead certain populations to do it.
Allison Hartsoe: 04:29 Yeah. I think most of the companies that I have had experience with, they definitely collect data, but the point you made about the data doesn’t talk to each other, and it’s not being used effectively as a very salient point. So even though somebody may be collecting your data, the fact that they can personalize for you or they can make a good offer for you is just not there for most companies. And so the question I wonder is why is that? What is it? That’s the missing piece that these companies have all this stuff, but they’re not able to get it to go.
Moira McKenna: 05:04 Yes, so I think there’s a couple of things at play there, right? If you look at businesses that have been around for a long time and we’ll look at retail and travel in particular, obviously there’s significant data at places like from the financial world credit world, healthcare world pull different set of privacy guidelines, things of that nature. So I’m going to set that off to the side and talk more about consumer-based data. So if you look at a retailer, your local grocer, for instance, for decades, they’ve been collecting telephone numbers and home address numbers for their loyalty program. So that when there’s a deal on buy 10 Campbell soup cans get one free or whatever it is, they can offer that out to their best customers. And historically, that was just it. You were a loyalty shopper, and that’s where the beginning and end came. So from the East coast.
Moira McKenna: 05:49 So we have some ShopRites out here. So they have giant Eagle. Yeah. Or like, yeah, like the Kroger’s of the world. So they have my name and address. And in my case, they still have my phone number from my parents’ house, where I haven’t lived in about 20 years, but I can’t figure out how to change that. So that’s like one of those examples, plug it in. And historically, I’d get $5 off a Turkey at Thanksgiving and spend a bunch of money. But now those folks at least have recognized the value of understanding what’s in a cart, right? So if you take it one step further, and now you’re getting your coupons after you check out and you’re like, that’s really strange. I just bought Campbell soup, and they’re giving me a coupon for progressive soup. That’s not by chance. So that data in say the grocery retail space, people have understood what they can do with it and how they can monetize it from a CPG perspective, that data gets tied to loyalty card data is really important to understand what else is in the basket and to create programs within shopping environments to hopefully influence a purchase.
Moira McKenna: 06:48 So that’s a place where you’re like, okay, this makes sense. But then you look in, in a place where there’s like travel, especially the hotel industry where there’s been so much consolidation. And like you might have had a loyalty card with one company and with consolidation and acquisition. Now it’s a shared umbrella that has 40 different brands. And you would think because you had a loyalty membership with one that data would now spread across this whole new portfolio. That’s in the same family. You’ve been a loyalty member for years, and it just doesn’t happen. And as for me, prior to COVID, who is a frequent business and leisure travel, I would see this all the time. I keep my loyalty with historically two sets of hotel chains in one, in particular, had gone through a big acquisition.
Moira McKenna: 07:34 And it’s just like, I don’t know how many times I’ve been asked if my address is still the same when I go to check-in and it’s still wrong from years and years ago, it frustrates me because they should get this right. I stay with them 50 nights out of the year. We spend a lot of money with them. They have my transaction data, they have my loyalty card data, or they ask me if I want to sign up for their credit card, which I already carry. And I used to pay for the room, like from an informed consumer, like they should be getting that right. So in certain areas, the trade-off is there. I think clients can see, or customers can see it. And they see a payoff, and other places, all they see as frustration. And shouldn’t you be able to get this right? So it’s the job of folks like us at Throtle to help them look at those data silos, look at the data they have available to them, distill it down to a common link. In our case, a persistent ID that allows them to maybe recognize more in one silo data that is also more on another silo of the data that historically they would be able to pull those things together cause maybe I used a different email address.
Allison Hartsoe: 08:35 Okay, Throtle is a fairly new company, and there have been companies that notoriously Axiom slash live ramp and many others out there who have these gigantic database providers who have been trying to provide this information for so long. Why can’t I just contract with one of those providers and fix this problem? Why throttle, why now?
Moira McKenna: 08:59 One just given a somewhat new entrance into the space. So we’d been a standalone company since 2016 with decades worth of data and experience behind us from our C-suite on down. And what you find with some of these larger companies is that sometimes the cost is just too fast. We’re getting it. So most organizations publicly traded, and that’s one hurdle. So there’s a lot of clients that need this, and the pricing is just too insane. And so they need to have an alternative. It’s not to say that’s our focus. We have fortune ten clients, but we have the ability to be much more flexible than some of these larger organizations. Secondly, for large organizations, such as some of the ones you named, this structure of how data comes into their environment, it gets processed, and it gets spit back out in a lot of ways has to be templatized because that’s just how they grow. If everything is custom, nothing is nearly as efficient as it should be. Our focus has always been on meeting client’s data needs, where they are, which means, like, let’s talk about your data flow. What do you have currently? What are you doing with it? How do we architect a solution outside of just pure data onboarding? So from a true identity resolution standpoint, that works with what you have today and where you want to go tomorrow rather than forcing clients to conform their practice to what we offer.
Allison Hartsoe: 10:19 I want to pick up on that for just a second because it’s really important. I see this a lot of time with tools, and there’s this rigid structure. And what basically, it has to happen is the burden becomes the company’s burden to take in that data and customize it and figure out how to shoehorn it into what is needed. And this is a very time-consuming process. It’s a hidden expense that you never see coming until your team is like, how do I deal with this? How do we get value out of this? And then a year or two later, people are saying, why did we sign a seven-figure contract when no one is using this? Because it’s like trying to shoehorn information through. And I have seen that firsthand. It is hidden, and it is important.
Moira McKenna: 11:04 And it’s why it’s really important for us to spend a lot of time with clients on the front end of contracting, to really understand the architecture that they’re working in and how we can help that given what they’re working with to make the data more useful to them in a more timely fashion so that they can get into the thing they need, which are those insights, be it with their own customers or their own business practice or media activation on behalf of their clients to move products, influence consumers, whatever their end goal is. We try to take on that from the front end so that we can move to contract and move to the outcomes of client, whats to seize as soon as possible and not have them run up against what you’ve experienced. The other thing too and like you mentioned a cost, and it’s a, I’d say a three-fold costs, one, there’s the cost of the contract two, there’s the cost of time because you didn’t understand necessarily what you were getting into.
Moira McKenna: 11:57 So you thought you might be able to build a product using this in two quarters, and it’s taken a year. So that’s been a time suck. That’s had another impact on your business. And then there’s just the resource strain where some of this data stuff is really complicated. And if you don’t have the staff that can understand it and deals with it, then you have a resource challenge as well, because you either have to go and hire to deal with it. Or you spend a lot of time trying to educate in order to make it work the way you think it needs to or should.
Allison Hartsoe: 12:27 And these are not particularly interesting engineering tasks, either. This is more like grunt work you’re trying to bring through so that you can get value. So I think it’s important to call out that it’s not just engineering resources. It’s things that your engineers don’t really want to be doing in the first place.
Moira McKenna: 12:43 Right? Exactly. But they’re the ones that has to do it.
Allison Hartsoe: 12:46 Exactly. Thank you very much. But I think there’s more to it too in the old databases. One of the things I used to see, or I question a lot is the freshness of the data and the quality of the data. When that information comes through, you don’t always have a good sense until you start using it as to how good it is.
Moira McKenna: 13:06 Right. And it’s really hard to qualify, right? And we see that in our business in one day that we always offer to clients early stage of conversation is data testing so that they can look at the data and decide if what they see is useful first of all. Is it what they expected and perhaps at least measured against an existing provider or others they may be evaluating. And we see it as often, given our size and our competitive set is the data is the King.
Moira McKenna: 13:34 So we win deals when we turn over data, and people can actually have all the tools in front of them to go and make the case for why all the other things matter, the ability to be flexible. And in the data architecture, the ability to be flexible in the pricing model, if the data is not good, and there’s no story that works for a company with the data, then the rest of it almost doesn’t matter. So for us at Throtle, you asked why throttle, well, two pieces of our business or core business practice is transparency and accuracy and both our data practice and our business practice, which I’d also say that some of the larger competitors out there are not known for being transparent. Like they’re not known for being flexible. And it’s that transparency, especially given the interesting year that 2020 has been for businesses among other things, that transparency really matters in data and pricing when people are looking at their P and L’s and trying to find cost savings to keep staff and to keep businesses open and to hopefully grow and kind of get through these strange times and to hopefully what is a growth mode for them fueled by better data and better understanding of data.
Allison Hartsoe: 14:41 At ambition data, we know using your customer data to drive cost-effective growth isn’t a walk in the park. It’s more like slogging through a pitch-black forest, tripping over roots and falling in the mud. To eliminate your path, visit ambitiondata.com/solutions and learn how our customer prediction engine can unleash your data’s potential. And now, back to the show.
Allison Hartsoe: 15:05 Let’s talk a little bit more about transparency, and sometimes I think about transparency in terms of how the data is collected, but you were talking about transparency in terms of pricing. Talk a little bit more about what transparency means in this context.
Moira McKenna: 15:19 Yeah. So for us, it’s two-fold. It’s one transparency in the actual data that we’re working with sourcing, and it invariably passing back to clients for them to make the decisions they want. So we talked about this with clients all the time. They sometimes feel as though they get the data back, or they get the data story from their incumbent data providers that make the data provider look good, but it doesn’t necessarily enable them to make the decisions they need. So at Throtle, for us, it’s a key differentiator market that when we’re talking about data transparency, we’re showing our clients exactly how and who we match to in their dataset and how we derived at those match rates we did, or when we’re bringing silos of data together, what we found in your data and how you can see correlations that you might not have recognized.
Moira McKenna: 16:09 So we work with a retail partner, for instance, that did, I just don’t understand why it’s so many first time purchasers. I would suspect that our e-comm channel, in particular, would have a lot of repeats. And when we took him back and showed them the actual data, it’s like, well, they do, but you’re incentivizing them to sign up a new email for a coupon. So you do actually know these consumers, you know, on one or two different email addresses. And so they’re not actually first-time customers, and that’s why your numbers skewed the way you wouldn’t expect it to be. You were incentivizing people to use two, three, four different emails to get 20% off of a fairly large purchase. So being able to turn that back and show them like, Hey, this is Moira. And you know her at Gmail and at Yahoo and at AOL, she’s still one person.
Moira McKenna: 16:53 So you need to talk to her that way. And then, your business outcomes can get adjusted accordingly. The other piece for us then is transparency and pricing is we don’t believe in gotcha contracts. So when we talk about pricing with clients, we always talk about them. Let’s talk about like your current state, your short-term state and your ideal state and what you think that looks like so that we can start with what makes sense for your business now, and then write in what that incremental cost will be. Should these things change in your business, and you grow, or you scale this product more than you think so that when you hit those tiers or those benchmarks, you know exactly what your cost is going to be. And you’re not going to get a big bill from Throtle. And it’s like, where did this thing come from?
Moira McKenna: 17:33 I didn’t expect it. So it allows the client then to also then make the business decision that if I take on this other client, that’s going to have this data need, what’s my cost going to be if throttle comes in and helps me with that. And so it allows clients to decide how they’re going to grow there, and again, just enabling as much information as possible to make the best decisions possible for their business and not feel like they are being forced into making decisions under duress that might have some significant consequences, six months or a year down the road when pricing goes through it.
Allison Hartsoe: 18:06 So it strikes me though that there’s also an element of humanity that comes into the mix when your data is tight when you know who you’re talking to. So take your coupon example for one place to start. If I’m reaching out to people and I’m making calculations about what kind of offers go to which people if my identifier is the email address, I’m suddenly kind of stretching across different offers to the same people in a very weird way, but I think it could even get worse because to me, humanity is a little bit like a conversation with a person I’m trying to make an intelligent conversation and have a back and forth. But if I don’t really understand the data, then I might actually worsen the relationship with my customer. Are there examples that you knew of where somebody is making a bad mistake because they don’t have a good idea about the data?
Moira McKenna: 18:59 Yeah. I know I’ll give you like kind of one we see and try to address with our clients. And then one kind of personal experience that I’ve had that just kind of ticked me off back to how this conversation started is that, you know, you suspect or expect that clients that are companies that have a lot of your data would treat you a certain way and when they don’t like I take offense to it. So, and you asked too about the question of how fresh is your data. And one example that we see and talk to clients about and try to caution it. So our data set is really robust, right? It’s not limited to email. And for us, our core competency is understanding individual identity. And that’s like a true person in a real place that lives in a house that can get mail from a mailbox. Email has a telephone number, potentially has a smart TV and understanding kind of the intersections of all of that and how they lead together back to an individual.
Allison Hartsoe: 19:46 The person in a household, not a household with people.
Moira McKenna: 19:49 Yeah, exactly. Like individuals that exist in the real world, somewhat of a novel concept in the data world. But our philosophy on data is that we start at the individual, and our data set is rooted in offline data. So again, first name, last name, postal address person in the real world. And people move, they change jobs like so in our B2B data set like things change, and coastal address has changed. Email addresses has shift. People change phone numbers, there’s movement in the data. And the only way to capture that is to constantly bring in data sources for us. We republish our graph monthly so that we can capture those moves and data. In one piece of this, and I don’t mean to go kind of grotesque, but it’s understanding things like deceased records. And we talk, especially with retail clients that are doing direct mail campaigns, where they’re trying to potentially send things to actual addresses and people.
Moira McKenna: 20:41 And if they haven’t cleaned their data set and done some robust hygiene or had us do robust hygiene on it, I just always think of like, what happens if you send a mailing for an offer to a house where somebody just passed away.
Allison Hartsoe: 20:53 Oh, especially now.
Moira McKenna: 20:55 Especially now, that’s a really easy mistake to avoid. And whoever opens that mailbox and maybe in a certain state, you just ruined that relationship with that household. Would that person be easy to do? And it’s one thing if it’s like next week, it’s another, if it’s two years later, like you still haven’t gotten this right. Two years later, and that’s just kind of laziness and data. And that’s one of the things that we kind of work with our clients as part of our hygiene process to do things like remove deceased records. So we can add a little bit of that humanity.
Moira McKenna: 21:26 And then for my personal example, I am not a paid spokesperson for stitch fix, but I hate to shop. So I was an early adopter to stitch fix. I still look forward to my boxes, but one time in particular to the same mailbox and address that I get years worth of stitch fix is delivered to there’s this beautiful kind of embossed envelope one day in my mailbox. And it was this kind of letter from the CEO telling me about how great the stitch fix community is and how I should consider joining. And it’s just this, they just know their consumers so well. And it was this whole fo CEO letter about how well they knew their consumers. And then it ended by offering me a chance to try out a membership. And that really frustrated me as a consumer, I so much so that I went and figured out my lifetime value.
Moira McKenna: 22:14 And if when I think over a couple of years, it was close to six or $7,000 and it came to the same mailbox with the same address and the same person as they send two years with a box or two. So I took that as a real big miss. And like, he starts to think about like, if you couldn’t get that right, you send things here once a month, and you couldn’t get this right. Like, what else are you able to get? Right. So, and that gave me a really bad taste in my mouth, so much so that I wanted to show it to some of my data friends. It’s like one of the easiest things in the world to fix. They know who you are, and they shouldn’t have gotten that wrong. And yet they did. So you talk about humanity and everyone’s, it’s like numbers, crunchers, and math. We had talked about math men versus admin at madman. I’m an English major with anthropology, second degree. And I taught high school English for a while. I come at data thinking about things like a more humanistic approach, and the numbers are what they are, but there is this ability, I think, to be empathetic as a brand to your consumer base if you actually understand and utilize the data well.
Allison Hartsoe: 23:14 So would you say that that is foundational as a company is trying to push forward, and they’re getting a lot of pressure to personalize and to meet greet customer experiences and everything else. Are they missing the boat a little bit by not foundationally cleaning the data or really understanding who their customers are in the first place?
Moira McKenna: 23:33 Yeah. It’s always how we start with clients. Like what data do you have? How long have you had it? What have you tried to do with it? What would you want to do with it? Again, it’s always goes back to data testing, like show us some of the data. So we start showing you what we find in it. Again, going back to like old school, like grocers and retailers as a kid, I was not asking. I still do it now. I love contests. I will forever enter a contest. I like to win contests. If there’s a contest, it’s an ongoing joke in my household and my parents that I’m going to enter it. I’m going to win. I haven’t hit the lottery yet. So still waiting for that one. But we’re back to like, as a kid, I would be like, my mom would have shopping and like, there’d be a table where you could fill out postcards to enter to win something.
Moira McKenna: 24:13 Well, those were all schemes for data collection before everybody moved to computers. And you think of data that I, as a kid 25 years ago, filled out on a card and Sears to enter some kind of contest to win my dad some tools for father’s day. Somebody went in, keep that into a system. And until standardization, really a data started to move in terms of dropdown and formatting. It’s just some random person keying in hundreds and thousands of these contests’ green cards. And like, if you think about data with a lot of brands is that’s the basis of their data set. And they built from there over decades in time. There are really varying degrees of sophistication. So the first question always has to be, what do you have? What have you tried to do with it? How long have you had it? And can we take a look at it because anything else stems from that basic principle? I think if you look at DTC companies, they might be consumer brands. They might be CPG brands, but at the end of the day, they are foundationally data and tech companies. And so they’ve got some of the favorites out because they were able to start by putting these systems and processes in place. But the big brands that have been around for a long time to your large retailers, travel hospitality, you name it, they’ve been collecting data, but it probably needs some help.
Allison Hartsoe: 25:32 So along those lines as to how a very interesting reference the other day, and it was about Harris, the casino, and they were talking, the point was from bankruptcy in the bankruptcy proceedings, there was an allegation made that one of, of Harris assets hadn’t been correctly accounted for. And the asset was a 17-year-old customer database of 45 million people in the loyalty program. And I thought that was really interesting because they wanted it to be valued at a billion dollars worth more than the real estate or any other assets. And so I think it’s interesting that one, just the sheer fact that the data exists can increase a valuation for a company that is obviously important, but it really has value. But also, to what you just said, nobody was thinking about the quality of that data. It was just that, Oh, they’ve collected some data.
Moira McKenna: 26:30 Right, Exactly. And I’m trying to like get upset. I can send you the reference, but I saw a similar article about the trouble that the airlines are all in. Right. But they all have these frequent flyer programs. And what is the value of that in terms of trying to figure out what the stimulus packages may be and like, what’s the leverage? So, yeah, again, like it’s crazy that it’s a billion-dollar asset plus, but also, how good is it? And who’s the person that’s making that determination where like, yeah, on volume, it might look like it should be a billion dollars, but on quality, it’s not that. It is It’s kind of crazy that that’s a thing. I mean, it really is. It is an asset when used right. Data is the ultimate asset for business when stagnant or used incorrectly, it can also be a death note.
Allison Hartsoe: 27:15 I love that all about you said data could be the ultimate asset for business and from a company that loves to use data for businesses to help them really understand the way they should be thinking about customers and talking about customers. I’m so aligned with that. So let’s say that I’m a company. I have some customer information. I’m not sure how good it is. What should I do first? How should I get started on my data as an asset adventure?
Moira McKenna: 27:39 Yeah. So I think that the first part is just doing the analysis of what you have in getting a baseline as a business of what’s been collected over time by different potential silos and businesses. Because if you think about it too, like loyalty, we’ll use that again. First, probably someone along the line that was like in charge of creating a loyalty program, and they built a loyalty program in a silo without necessarily forethought that down the road. It should be able to talk to other silos of data within an organization. So I think first, the first step is identifying what is available to you. And then I think what we run into with clients is the question of like, well, what do you want to do with the data? And they’re like, we want to use it, which okay, cool. But I think it’s really thinking through challenges that you might identify in your business or opportunities that you might identify in your business, where it is feasible.
Moira McKenna: 28:31 Just even at the first pass to think that if we get the data, right, it may help us do X, Y, or Z. Now that might change with time. But I think there needs to be some thought given to what the potential could be or what you, as a business decision-maker and or organization, would like an outcome to be that’s actually actionable. And then once you at least kind of have that idea of like what you think you have and where do you think you might like to go? Then I think it’s engaging with folks like us here at Throtle, where we start talking about looking at that data and seeing what’s in it and seeing how we can help potentially bring silos together, help round out single customer view. When you have different lines of business that historically haven’t spoken to each other, it’s identifying may be trends in the data, especially if you’re looking at transaction data where those that may be purchasing your product might not actually align with those folks that you’ve been targeting in your ad campaigns.
Moira McKenna: 29:30 So there’s a misalignment that the data can tell that story. So we spend a tremendous amount of time white boarding and use casing with clients when it’s most helpful when they can at least bring to us. Here’s what I think I have. And here’s where I think I’d like to go. And then we can start having those kind of crawl, walk, run conversations that if we do this in the short term, this is what the outcome can be. And then, building on that, I think it’s complicated. I think people overcomplicate it, I should say. And think that they’re going to jump all in and solve all their data problems out of the gate. And you just need a win. And which allows even others in a big organization, you need somebody who’s willing to take the first chance. What we found with really big organizations is once you get somebody who’s willing to give it a try and they’ve proven that it works.
Moira McKenna: 30:20 It’s amazing how many other lines of businesses start coming through your inbox? Because there’s already now an internal advocate, but also they’re starting to see how a robust data solution in one line of business, in particular, can help others as well. So we don’t need to solve all the problems of the data universe in the first go, but identifying a simple use case out of the gate that can give people a win and prove that it’s a worthy task that maybe over the course of a year or 18 months, it builds to that huge angle, but it gets there one step at a time.
Allison Hartsoe: 30:54 You know, I think this is where in that second element that you were talking about, about what do you want to do with the data? This is an area where we play pretty heavily, and we always talk about, well, if you calculate the customer lifetime value, I’m not saying that that is the be-all and end-all of everything, but at least it gives you an opportunity cost to say if I want this direction, what would the potential there? If I went that direction, what would the potential there? And then you can start to kind of say, okay, against the kind of levers I pull, what would I get out of this system? Which is why I think the granularity of the throttle database to be around the customer is so powerful as opposed to being around a household, or other data sets don’t always cut by household or other things. But I think that power really stems from coming at the customer level. And thank you for having such a great data set that we can all be thrilled about using. Now, if people wanted to reach out to you, obviously, they could go to throttle.io, which is the website, and that website is. Do you want to call out the spelling of that and how they could get in touch?
Moira McKenna: 31:59 Yeah, sure, we are just a direct email at hello@throttle, and that’s T H R O T L E.io. Uh, only one t, not two t’s. Otherwise, I think it comes up, uh, some auto engine company, or you can reach out to me directly at MMcKenna@throttle.io.
Allison Hartsoe: 32:15 Excellent. Everything we discussed today will be at ambitiondata.com/podcast. Moira, thank you for joining us today. It’s great to hear your perspective on data and what’s really driving quality data under the covers, as well as, you know, ways that businesses can benefit from it.
Moira McKenna: 32:30 Great, thanks, Allison. I really look forward to speaking with you again soon.
Allison Hartsoe: 32:34 Remember, everyone, when you use your data effectively, you can build customer equity. It is not magic. It’s just A specific journey that you can follow to get results. See you next time on the customer equity accelerator.