Customer Equity Accelerator Podcast

Ep. 78 | Learning From Others with Rich Fox


"When you boil it down we are all trying to accomplish the same two things: grow revenue and improve margins." - Rich Fox


This week Rich Fox, VP of Analytics and Data Science at Apex Parks joins Allison Hartsoe in the Accelerator. Apex Parks Group, one of the largest entertainment center companies in the United States, operates amusement parks, water parks, and family entertainment centers. Like so many people who work in data science, Rich has varied background. In this episode he shared what he’s learned from the hospitality industry, ecommerce and more. Rich is a passionate advocate for customer lifetime value and the many ways it can be used within an organization.

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Show Transcript

Allison Hartsoe: 00:01 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. Each 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 learning from other industries and to help me discuss this topic is Rich Fox. Rich is the VP of analytics and data science at Apex Parks, Rich welcome to the show,

Rich Fox: 00:47 Allison. Great to be here today.

Allison Hartsoe: 00:49 Could you start by telling us a little bit about Apex Parks and then perhaps how you landed there?

Rich Fox: 00:54 Sure, so Apex Parks group owns amusement parks, water parks, and family entertainment centers throughout the country. We currently own 16 parks coast to coast. So from California to New York, we have parks in Florida, Texas, the Midwest. And I got involved because, and it's a relatively startup company, it's just close to five years old now. And it was formed to roll up parks in the industry. And a new management team came in, and a new CFO came in, and I had worked with the CFO before, and in this industry, we have a significant amount of data, especially about the customer. So the data side and data analytics and predictive analytics was going to be very important for the overall corporate strategy. So she talked to me about joining the team, and I did about two and a half years ago.

Allison Hartsoe: 01:42 Mmm, that's fantastic. Where were you before Apex Parks?

Rich Fox: 01:46 So before that I was at an e-commerce company called that sells motorcycle parts in San Diego.

Allison Hartsoe: 01:54 There's nothing like e-commerce to sharpen your data science skills. No doubt.

Rich Fox: 01:57 Yeah. Well prior to that I'd spent a lot of time in hospitality, mainly restaurants and some hotels. And when I was in restaurants, we were anxious to get our hands on customer transactional data, and fortunately especially 10 15 years ago that they just didn't have it. So go in the e-commerce I was like a little kid in a toy store.

Allison Hartsoe: 02:18 Well let's use that to start our discussion about learning from outsiders or outside industries. Why should I care about learning from these outside industries? Data science is just blazing with new information and new technologies. Don't we have enough to just think about within our own space? Why is it important to think about other spaces?

Rich Fox: 02:41 Well, I think you can always get great ideas no matter what the industry is. You saw this back in the 90s even Accenture was gathering best practices from various different industries and then sharing them with companies no matter what industry that you were in. And this could have been for accounts payable processes or accounts receivable processes or closing the books in a more efficient and effective way. So I think we can always learn from different industries. And really when you boil it down, everybody is pretty much the same. We all have customers, we all have processes, we all have employees, and we're all trying to accomplish the same two things, and that is grow revenue and improve margins.

Allison Hartsoe: 03:25 And perhaps innovate. I would throw that in there as well as that may be the third leg of the accomplishment piece.

Rich Fox: 03:31 Absolutely.

Allison Hartsoe: 03:32 Right. So are there specific industries that you think are worth paying attention to or worth looking at when it comes to understanding what we do with customer information? Perhaps some industries like B2B might not be as strong, or maybe they are as strong.

Rich Fox: 03:50 Well yeah, B2B is, it has additional challenges, and I'll talk about that in a moment. So looking at other industries to get good insights on how you can work with customer data and approach customer analytics. Probably E-COMMERCE is up there on at the top because as you pointed out, they have such rich data, they have customer data for every single transaction, and probably no one does it better than Amazon. And in addition to that, not only do you have all of the customer transactional data, you know what the customer is looking at in your store, which is your website, and you can do a lot with that data to understand the customer's actions and their preferences and their behaviors. So there's a wealth of information in there so you can make a lot more intelligent offers and promotions to the customer because you understand them so much better.

Rich Fox: 04:45 And then I think after that, it's probably the players like Google and Facebook and Netflix and Linkedin who have done an excellent job gathering a significant amount of data on their customers and what they're doing on their platform and using that in intelligent ways to market in and engage with the customer in a more effective way. So back to the B2B. Yeah, B2B companies that I talked to and that I've consulted with during my consulting days, they have the additional challenge. Let's say that the end consumer is typically not their customer that they're selling to another company that might be turned around and selling to the end consumer. So in a way, they have two customers, and I think the companies that have probably done it well is they look upon it that way, that they've got two sets of customers and they need to understand both of those sets of customers and they need to make them both happy. And the way they acquire data on those two sets of customers is different because typically they don't have that direct relationship with the end consumer.

Allison Hartsoe: 05:50 That makes sense. So in those examples that you mentioned, e-commerce being at the top, obviously Google, Amazon, Netflix, they have significant data, and they use it, like you said, in very intelligent ways, but it seems like they have almost, maybe not 100%, but they have a lot of rich deep data to work with. On the other hand, you have B2B companies where their end consumer isn't the customer, so they may not have as much data. Is there something that people who are sitting in the middle can work with that maybe they don't have 100% of the data, but they have something, and they want to use it to try to maybe leverage best practices from other industries or try to make some sense of what they have?

Rich Fox: 06:33 Yeah, that's a great question. And unfortunately, many companies out there are not as fortunate as the e-commerce companies or the Google and the Facebooks of the world. So I'll use a specific example in the hospitality industry for restaurants. So I advise some restaurants through the San Diego State School of Hospitality and tourism management where I teach in a master's program. And what's interesting, what they have done to try to move towards the rich customer data that other industries have that we've talked about is to take the old idea of the loyalty program but take it to the next level. And there's cloud solutions out there. One of them was called open table, which has actually been doing a very good job in helping restaurant companies acquire that customer data. So open table is just a platform where you can make reservations, but open table is actually discovered that probably their best service is not the ease of making a reservation, but the data that they are collecting on the guests that is making the reservation.

Allison Hartsoe: 07:37 Is that right?

Rich Fox: 07:37 Yeah. So they've actually created some services to help the restaurant understand customer acquisition and customer retention. And they've actually done a really good job at it. And so that has really helped a restaurant company that never really had transactional data at a customer level before now start to have it. And they've actually gone, they've come out with a new platform recently that is totally in the cloud, and they've realized how important this insight is. And they've created dashboards and reports that look at customer acquisition and customer retention, not only for the restaurant company but if they've got multi-units, which many restaurant companies do, they can look at individual unit acquisition and retention metrics.

Allison Hartsoe: 08:21 Oh that's cool. And I bet the restaurants are just eating this up.

Rich Fox: 08:25 Yeah, it's wonderful. Cause, like I said, 10 15 years ago when I was in the restaurant business, we were anxious to get our hands on this type of data that just really didn't exist. And restaurants have had loyalty programs for some time, but unfortunately, a very small percentage of their guests typically sign up for the loyalty program. Even the big players in the industry that have been doing it for some time, they just don't have significant membership in the loyalty program. So then services like open table and there's some other ones out there have done a very genius way of gathering customer data while providing a very useful service. They just make dining easier for all of us.

Allison Hartsoe: 09:07 Now I'm going to guess that for a lot of restaurants, not only are there a small percentage of guests that sign up for the loyalty program, but there's probably a small percentage of guests that come back again and again. So services like open table might be helpful in understanding things like how frequently the guests dine out in general that a restaurant might not be able to see no matter how much data they collected. Is that correct?

Rich Fox: 09:32 Well that is true. And the restaurant industry gets complicated because there's a lot of different types of brands and concepts. So it goes everywhere from fast food, which is called QSR for quick service restaurant. And then you've got fast casual. You've got full service, casual dining, which is the TGI Friday's and Chili's of the world. And then you get into full service, upper end and then fine dining and white tablecloth. And depending on what segment you're in, there are different ways to capture that customer data. So for example, you know, look at what Starbucks is doing with their app and McDonald's also, a friend of mine is in the customer analytics group at McDonald's and they've been doing a lot of interesting work looking at customer data and customer lifetime value and customer segmentation. And the app has helped them significantly. So here, once again, here's some new technology that has come along that's now available with all the mobile devices that we carry, and the apps that these restaurants are coming out with like the Starbucks and McDonald's is providing them very rich customer data.

Allison Hartsoe: 10:37 I remember the story. I think Pete fader tells it, but originally, Starbucks was an example where they had a big gap in customer knowledge because it was basically sitting in the head of the Baristas. But in your example, and what they did as well as they put it into the mobile app and suddenly the customer preferences are clear across the board and can be mined for the data as opposed to just the Barista recognizing, oh, this is my regular Monday morning crowd and here are the things they like.

Rich Fox: 11:06 Yeah, absolutely. And I think what a lot of these companies have done really well, and then you always need to approach it this way, is that when you're rolling out new technology like this, you have to put the customer first, and you have to do it to make their experience better and you're going to make it either faster or easier or more efficient. And that's how you get customers and guests to sign up for this and start to use your mobile app at a much higher rate than the old loyalty programs. And then the customer data is just essentially a byproduct of rolling out new technology that makes your customer and guest life easier. I think another company that does this incredibly well is Disney. The experience at Disney World and Disneyland is so much better than the old days because of the technology that they have rolled out, and it's much easier now to move through the park and to enjoy yourself during the day.

Allison Hartsoe: 12:02 Do you think that's because I remember, I think I was part of the first group, but just by accident, my family and I happened to be down in Disney World in Florida as they were rolling out the bracelets, and I think what's interesting about that technology is at first it felt somewhat invasive. Okay. They're tracking me around the park, but what they're also doing is managing the crowds in the park to create a better experience for folks and then they tie that into those fast pass tickets that they push out. I think it's a really well thought out strategy. Is that what you were alluding to when you talk about the tech they've rolled out?

Rich Fox: 12:37 Absolutely. I think it's genius. So anyone that goes to one of the Disney parks, if you're not familiar with the fast pass, it's the only way to spend a day at the park is utilizing their fast pass program because it reduces the amount of time that you spend waiting in lines, which is probably the thing that people like the least about going to an amusement park and in the old days you had to go to where the ride was physically located in the park. There were machines there where you could get a fast pass. In the old old days, it was almost unlimited how many fast passes you can hold at one time, then Disney started to manage the program, and there was a limit on how many fast passes you could have at any point in time so that they can manage the people better. So you found yourself running around the park to get FastPasses at certain times.

Rich Fox: 13:26 Now, with the app it's really so much easier. The other thing they've done in the app, which is just excellent, is that they've made it very easy to connect people that are in a group. So most people when they go to Disney, they go with their family, but not only with their family, with groups of friends. And of course, you want to ride rides with everyone that's in the group. So they've made it very easy in the app to connect people that are in the group. So not only are you getting a fast pass for yourself, you're getting a fast pass for the entire group. So once again, they've put the customer first, and they've made your experience much better and much easier. And from their perspective now they know who is in the group, what is the size of the group, and in the hospitality business, there is an economic model, and group size is one of the parameters that gets plugged into the economic model. So they now have a better understanding of the group size. How big is the group, all the rides that that group is riding, where have they gone in the park. What is their journey through the park? So it's a real win-win.

Allison Hartsoe: 14:29 You know, I love that you use the word journey through the park because I think that picks up on an interesting concept. We oftentimes talk about online journeys, online customer behavior, and yet this example is all offline. Do you see a strong connection between the way we think about physical movements and the way people are acting and the way people act online and the connection of data science perhaps?

Rich Fox: 14:55 Absolutely. So we're doing that at my company is that, so our parks are what's called cashless, which means that you cannot use money in our parks. You have to purchase a play card, and you put value on the card, and then they do anything. You simply swipe the card on the ride or the game. So we capture everything I guess does in the park based on the swipe. So I know how long they've been in the park. Every ride that they have ridden and every game that they've played and where they've gone. And of course it's all timestamped, so I know what their journey is. So we're working on doing analysis that is similar to website analysis, looking at the journey that a website visitor has gone through a site and time on site and the pages visited and what product pages and where did they land, where did they start, where did they exit, and then are the exit pages common?

Rich Fox: 15:46 So is there an issue there possibly that you need to work on? And we're looking at the same type of approach with the park data to understand not only how the crowds are moving through the park because just like other amusement parks, we are very seasonal. We're very busy during the summertime and during holidays when the kids are out of school and on the weekend. So we want to manage the people as best we can to minimize the length of time people stand in lines. But we also want to understand are there areas of the park where there are bottlenecks? Are People not going in there because either they don't know about that area of the park or maybe they don't find it interesting? And we're doing behavioral segmentation based on the type of activities that individuals do in the park like you would see for website traffic. And then I think the retailers are doing similar approach also, looking at the journey that somebody does through their store and what do they look at and how much time do they spend in front of a different display or a different counter and then have they purchased anything and to understand the journey that a customer does through a brick and mortar store, just like the e-commerce companies do.

Allison Hartsoe: 16:57 Yeah. A while back we had Gary Angel on the show. He's the CEO of digital mortar and also a personal friend of mine. And what they're doing is working with the data that comes through the cameras and the Wifis and trying to understand that physical journey. But Gary Actually wrote the book on measuring the digital world, which is literally what it's called and the strategies and tactics are still what we use to understand behavior across a site, but it can easily be applied to, I think, behavior in the physical world. So that's a great example of the two pieces coming together. We're really all online and offline all the time.

Rich Fox: 17:34 Yeah, I think for retailers and for brick and mortar stores, which once again we're very similar to the restaurants. 10 15 years ago they didn't have this data. And of course, they would love to have the data that e-commerce have, the truly understand the customer and now with this new technology they can. And then they take it a step further is that connecting the online and offline experiences and the customer data provides valuable insight and the total customer experience.

Allison Hartsoe: 18:06 And when you said provides valuable insight, does that mean that it's somewhat predictive?

Rich Fox: 18:10 Oh, I think absolutely. So you know, I remember when Best Buy was going through their turnaround, and one of the things that was in the news is that people were using Best Buy to just what was being called showroom to go in there and look at products and then go buy them online. Well, now you're reading that for certain situations. Actually, the opposite is happening, that people are in stores and thanks to all the mobile devices we carry, they're in a store, and it's so easy to research something online and then possibly purchase it in the store or whatever reason. Maybe the price is better, or you want a maintenance contract, or there's another reason. So I think connecting that online-offline experience provides very valuable insight into the customer behaviors and their desires and their journey. Because I think what a lot of companies are finding out is that a customer does not make a purchase on the first visit to either a website or to a brick and mortar store.

Rich Fox: 19:09 And what is that journey? How many times do they have to go to the website or the store, and what is that order before an actual purchases made? And probably even more so than that as you and I know, our goal here is never to get a customer to make a single purchase. Our goal is to get that customer and convert that customer into a longtime loyal customer with a high lifetime value. So it's like we used to say in the restaurant business, I was in the restaurant business back in 2008 2009 when the recession occurred, and we all did Bogos buy one get one, and we did a great job at driving traffic through the restaurants. We also did a great job at losing money, and it's just simple math. When you cut your price in half, and your costs remain the same, your margins get significantly deteriorated. So it's very easy to get a customer to make a purchase. It's not so easy to get them to come back and to make additional purchases and turn them into a longtime loyal customer and to maintain margins or even better yet to grow margins.

Allison Hartsoe: 20:11 I can imagine that's looked at very closely what you're saying with the margins. Was there ever an idea in the restaurant space to change pricing based on the loyalty of the customer?

Rich Fox: 20:23 Well, it's interesting that you should bring that up. So an old good friend of mine who's been in the restaurant business his entire career, we've always bounced around the idea of dynamic pricing and yield management that you see in like the airlines and the hotels and applying that to an industry like restaurants. So in the restaurant industry, when you look at the business, you break it up into day parts, breakfast, lunch, snack, dinner and late night. And then just like other hospitality industries, different days of the week are have significantly different patterns in demand. So the weekends are much busier than during the week. And then times of the year are very different. So, of course, December restaurants are very busy. January is the slowest months of the year. So we've bounced around the idea of day part pricing and seasonal demand pricing in the restaurant industry as well as looking at it by the loyalty of customers.

Allison Hartsoe: 21:19 And when you were looking at that, was it difficult to roll out? Was it just that the technology wasn't there even though the ideas and the math worked

Rich Fox: 21:28 well? Yeah, the technology wasn't exactly there as well as it needs additional testing. And this is probably where some AB testing from the e-commerce world would be really helpful because even though from an economic perspective and if you do the math and put a pencil to it, it may really make sense. The thing is, is the customer going to accept it, and what is it going to do to the customer experience?

Allison Hartsoe: 21:52 That's one thing that I always feel is missing as we talk about customer lifetime value and we talk about all these different ways to analyze the data. The idea of customer voice is sometimes missing from the equation or not easily connected and yet I think it can be in the examples or in the previous businesses that you've worked in, did you find that because you were physically closer to the customers, you had a better sense of their voice?

Rich Fox: 22:18 I think so. So in the restaurant business, what's very popular is to perform focus groups. So you bring in a set of guests as well as people that have not dined at your concept or brand and to do different testing. So maybe you're working on a new menu, or you're working on some new promotions, or you're working on a new menu design, and you want to understand what resonates the best with your guests as well as what resonates the best with non-guest to try to bring them into your establishment.

Allison Hartsoe: 22:51 And now I always think about that as survey through the mobile app or through the website. You can survey pretty easily although I wonder if maybe we're being surveyed to death a little bit.

Rich Fox: 23:01 Well, I think we have to be really careful about that. So for example, in our business, everyone that's been in our park in a day in any of our parks that we have an email address on, they get a survey the next day asking them about their experience. But we want to be really careful that we don't overdo it. So no one gets an email with a survey a more than once in a four week period.

Allison Hartsoe: 23:26 That makes sense. I know for a while, every time I flew certain airlines, I'd get a survey saying, you know, how did we do? Or I rented a certain car, and it would just hit me again and again and again, and it's like, okay. It's just not that different.

Rich Fox: 23:38 Well, and it's also to, you know, on many of these apps that we all use, you get the pop-up. Do you want to rate the app? I use the Wall Street Journal app, which is a great publication, great source for news, but I get these constant pop up. Do you want to rate the app? And I wished there was a choice where it says, I don't want to be prompted anymore or I want to just rate it once and I'm done.

Allison Hartsoe: 24:00 Yeah, that makes sense. I want to turn our conversation over to a little bit to the manufacturing side, maybe to the B2B side. And one of the things that you mentioned in our previous conversation was the idea of waste and finding what the ideal cost is. Can you explain a little bit about maybe from the restaurant business, this is most applicable, I'm not sure, but can you explain a little bit about how the concept of waste might transfer into data science or online businesses?

Rich Fox: 24:31 So yeah, sure. So in the restaurant business, we look at ideal food costs, which is very similar to the standard costs that manufacturing companies use. So based on what has been sold in a day in a restaurant, it goes through and their software to do this, and it's usually referred to as back of the house software where the kitchen resides, and it calculates what your ideal food costs should have been for that day. And it's based on the recipes that have been imported into the software and in the recipes, certain types of waste are taken into consideration. So when food is cooked, there's shrinkage in there, and depending on what items are being prepped, there is some waste that is built into the recipes. But then there's additional waste when maybe items are overcooked, and they have to be disposed of or, or there are other ways. So then you compare ideal costs to actual costs

Rich Fox: 25:26 and look at that gap. So we call that the gap analysis of ideal food costs. And most brands have a target gap that they want all the restaurants to achieve. And in fact, the restaurants I've been in when we did the annual planning for the food costs and in restaurants, food costs and labor costs are your two largest costs. We planned it based on the variants to gap, and if certain units had a very high gap, we wanted them to manage that down to the target for the company. So I think the same thing with looking at other manufacturing industries that go through and do standard cost approaches and they price out their product or costs out their product to understand all of those different aspects.

Allison Hartsoe: 26:10 I see. Do you think that optimization has an impact on the ability to deliver high-quality customer service, or does it affect the restaurant industry? Would it affect the front of the House as well?

Rich Fox: 26:23 Well, it does because the interesting thing in hospitality, especially in a restaurant, so I've always thought a restaurant is like a retail business and a manufacturing business combined. So in the restaurant business, there's the front of the house in the back of the house. So the front of the house is all guest-facing, and that's where you meet the host. And then once you're at the, you interact with the server, and all of those experiences and the metrics that you would analyze and the data you would capture is very similar to a retail business. But then the back of the house, which is the kitchen, it's like a manufacturing company, you're manufacturing food. Just at the end of the day where a manufacturing company would have work in process and finished goods. There are no work in process and finished goods in a restaurant company because as a good as when the item is finished it goes out to a table immediately.

Rich Fox: 27:14 And then the other key thing about restaurant is what's called speed of service. So, and especially when you look at the day part, so the lunch day part is all about speed because typically people only have a certain amount of time to have lunch at the dinner day part. That's about increasing the check average. So the amount that the people are spending as well as the customer experience. So from a restaurant perspective, not only in the back of the house are you trying to manage costs and you're trying to manage the ideal food cost, but speed is also very important as well as the quality of the product. So especially the larger restaurant companies have taken this to real science in designing the kitchens and the back of the house, and there are engineering firms that work with them in doing the layout of the kitchen.

Allison Hartsoe: 28:02 That makes sense. And I remember watching the movie, the founder where they were like literally laying out the kitchen for the ideal efficiency. It was really fascinating scene. I had never realized how much time matters in the back of the house. But coming back to CLV, I wonder if retailers can think about the front of the house and the back of the house if these two groups and the optimizations and the data and the way that we mine it are not United through the CLV calculations.

Rich Fox: 28:34 Well for the retailers, the back of the house is your inventory management and your supply chain management. And one of the worst things that can happen is that you're not properly matching supply and demand. So an item is in high demand. And if you don't have that in inventory, that's a problem because the customer is not going to be happy. So from a retailer perspective, and I guess from a retail perspective, the other issue is too much supply. And that's where retailers spend a significant amount of time managing inventory and their supply chain to try to match supply with demand as well as they can. And I guess to make an analogy to the restaurant business. You could say waste is oversupply and that leads to significant discounts and that erodes margins. So one of my early mentors in analytics, Dr. Richard Conley used to always say everything in a business is mapped back to the income statement, your P and L statement.

Rich Fox: 29:31 So taking this example about retailers and trying to match supply and demand and you're starting from the customer, and you're trying to improve the customer experience and optimize lifetime value, but you also want to make sure that the inventory that you've got in all the different stores is matching demand because otherwise you're going to be left with a lot of inventory and then you're going to have to discount that to move that inventory. And in many retailers, it's very seasonal as you move through the different seasons of the year and as you do those discounts, it really erodes margins which flows right through to your income statement and then that hurts your overall ROI for the business.

Allison Hartsoe: 30:11 And it also trains your customers to wait for discounts because you've made this mismatch. Now they don't want to buy it full price.

Rich Fox: 30:18 I think that is a thing that many of us have done inadvertently and many companies is trained customers to wait for discounts, and you see that across the board and it's like I tell people, why would you ever give a customer a discount that's willing to pay full price? And today with all the rich data we've got about our customers, we know who always purchases with a discount and who always purchases without a discount and pays full price. And we know those customers that always purchase the new items when they first come out and the customers that are looking for the best deals and only buy things when they go on discount. So I think that's one of the, you could say byproduct benefits of doing all the customer analytics. And the additional insights we've got into our customers now is that we can manage our discounts much better, which not only improves lifetime value but improves the income statement, which goes back to my strong belief that if you really want to improve the overall financial performance of a company is to drive lifetime value because they are so closely connected.

Allison Hartsoe: 31:23 I love that. I'm such an advocate of CLV anyway, but I just love how you pinned all that together. It's fantastic. Thank you. So let's say that I'm convinced and I realized that I should be looking at other industries and trying to pick out pieces of information that would benefit my company. Where should I start? What are the, maybe the areas of low hanging fruit that people should focus on?

Rich Fox: 31:48 I'm a strong believer that you should always start with the company's goals and objectives and gather the business requirements that are going to help the company achieve those and then determine what are the KPIs and key metrics in those business requirements and then determine what data do you need to satisfy those. Now, like I said earlier, all companies are trying to grow revenue and improve margins and to do that most effectively, it's to improve your understanding of the customer and drive lifetime value. So I think you're always looking at what is the effective data that you can gather to understand the customer and to improve the customer experience and to optimize lifetime value. And I think you need to develop a roadmap. So a lot of this analytics that is performed, that is highly effective in companies it takes time to do it right. And there it takes time to build out some of these technologies and some of these databases and data lakes and data warehouses that we all use.

Rich Fox: 32:48 So I think a good roadmap helps you start on that. Now that doesn't mean though that you don't get benefit from it until you complete the roadmap, you start to get benefit from the customer analytics. Actually very early on in the process is what I have found. And I think when you're identifying that roadmap and you're laying it out, you need to look for the early wins and the low hanging fruit and to be able to get those to improve your ROI as well as at the same time building the longterm vision. So I think that all has to be connected.

Allison Hartsoe: 33:20 I think you're absolutely right. And I've heard this again and again in that you can't just make it some holy grail that everybody's after for three years. You have to look for ways that you can put money on the table, have those quick wins that help you move the whole process along.

Rich Fox: 33:36 Yeah, I totally agree. And I think back in the 90s when everyone was building the enterprise data warehouses, and it took months and years to build, and you didn't see any results until it was completed or close to completion. Those days have changed, and today with a lot of technology that is out there and there's a lot of excellent cloud solutions that help you perform a lot of these. So back in the old days, typically everything was on premise. We're building out servers, we all own a lot of hardware and a lot of software. It took time to get up and running when you needed to add additional horsepower to a server. Well, that required additional hardware and all that. Now with all of the cloud solutions that are out there and a lot of excellent companies that provide a lot of targeted solutions in different areas in the customer area and in other areas of the business, it's so much easier to get up and running and see results faster.

Allison Hartsoe: 34:31 I understand that makes sense. So Rich, if people want to reach you, how can they get in touch if they have further questions or they just want to reach out and say hello?

Rich Fox: 34:40 So the best way is through Linkedin, connect with me on Linkedin and just simply send me a message that you listen to the podcast and you'd like to talk about customer analytics. I'd be more than happy to engage with you.

Allison Hartsoe: 34:51 That's very generous of you and I have really enjoyed our conversation. I'm just so happy to have you on the show today. Thank you for joining us.

Rich Fox: 34:59 Well, it's been my pleasure, and I love talking about customer analytics and customer lifetime value.

Allison Hartsoe: 35:05 Remember everyone, as always links to everything we discussed are at, and when you use your data effectively you can build customer equity just like rich has been showing us. This is not magic. It's a very specific journey that you can follow to get results. Thank you for joining today's show. This is your host, Allison Hartsoe, and I have two gifts for you. First, I've written a guide for the customer centric Cmo, which contains some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to, three, one, nine, nine, six, (31996) and after you get that white paper, you'll have the option for the second gift, which is to receive The Signal. Once a month. I put together a list of three to five things I've seen that represent customer equity signal not noise, and believe me, there's a lot of noise out there. Things I include could be smart tools. I've run across, articles I've shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and The Signal. See you next week on the Customer Equity Accelerator.


Key Concepts:  Customer Lifetime Value, Marketing, Digital Data, Customer Centricity, Long-Term Customer Value, Marketing Leaders, Analytics, Creativity, Product Development, Audience Research

Who Should Listen:  CAOs, CCOs, CSOs, CDOs, Digital Marketers, Business Analysts, C-suite professionals, Entrepreneurs, eCommerce, Data Scientists, Analysts, CMOs, Customer Insights Leaders, CX Analysts, Data Services Leaders, Data Insights Leaders, SVPs or VPs of Marketing or Digital Marketing, SVPs or VPs of Customer Success, Customer Advocates, Product Managers, Product Developers

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