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 everybody. Today's show is about cultivating customers. And help me discuss this topic is Elea Mcdonald's Feit. Elea is the assistant marketing professor at Drexel University's Lebow College of business. Elea, welcome to the show.
Elea Feit: [00:48] It's great to be here.
Allison Hartsoe: [00:50] So now you have a really great background around marketing and marketing research. Could you start by just telling us a little bit about your background, and how you got into the topic of cultivating customers?
Elea Feit: [01:05] Sure. So, um, I actually grew up in the market research industry. I spent about eight years at General Motors where I worked in market research for new product design. Um, and after that, um, there were just problems that were sort of sitting on my desk that I wanted to explore more deeply. So I got a Ph.D. in marketing, became a marketing professor. Um, I'm actually an expert in conjoint analysis. If any of your listeners are, are interested in conjoint. I've written a lot about that. Um, and as a professor, I'm now tasked with kind of two things. First, I'm supposed to create new knowledge, which for me means developing and rigorously testing new marketing analytics methods. Um, second, I'm supposed to disseminate that knowledge, which means everything from this podcast to I've written a book on hour for marketing analytics, to I teach undergrads how to set up ad campaigns on Instagram. That's kind of the scope of the types of things that I do.
Allison Hartsoe: [02:08] That's really broad, and that's good for us.
Elea Feit: [02:10] Yeah. And I'm, I'm really excited to talk about cultivating customers. So for me, these things always start with a business decision. So whenever I get a chance to talk to a marketing professional, I asked them what decisions do they have to make, especially which decisions really bother them, where they feel like I don't have the data I need or the information I need to make the decision. They feel like I'm just guessing. Yeah, that's every day. And I feel like analytics should always start with the decision and build out the information that you need to make that particular decision. And it just turns out that cultivating your existing customers is a topic that frequently comes up, and it comes up in kind of funny ways. So, some people will ask like we send a ton of emails, and I don't know if I'm sending enough and I'm not sure who I should be sending them to. Or a similar question about catalogs or outbound phone calls or kind of all the ways that we have to communicate with our customers. How do I plan and manage that? And it's a very tactical question, but one that brings us back to thinking about customers as individuals. We shouldn't just be thinking about should I communicate on email or catalog, but thank you. Thinking about how does this customer want to communicate with me? And analytics allows us to do that at scale to really customize experiences to customers at a broad scale.
Allison Hartsoe: [03:36] I love that you said that. That was one of my 2019 predictions is could we please stop talking channel channel channel and start talking customer, customer, customer as it happens all the time. Uh, I love that you said that. Then, you know, perhaps it's really obvious, but why should I care about cultivating customers? Is there, and maybe a right way and a wrong way to do it?
Elea Feit: [03:58] Yeah, I think a lot of kind of the sexy things to do in marketing have to do with acquiring new customers. So think about like a Superbowl ad, which was maybe one of the coolest things you could do as a marketer. It's about reaching out to people you've never met and trying to bring them into your organization as a customer. And so that's kind of cool, but it's actually easier to cultivate the customers you already have. So with the rise of customer relationship management systems, a lot of marketing organizations have very good records on all of their interactions with their customers. So you have transactions, but you might also have inbound phone calls where the customer was asking a question or an email that you sent them to promote a new offering, their visit to a website to seek information. And it turns out that that data is great for predicting CLV and identifying high-value customers.
Elea Feit: [04:52] I know you've had some previous guests on the podcast who are really focused on predicting future CLV. The thing is that CLV isn't static. As marketers, we can change the future value of a customer but altering how we interact with her. In some level that's kind of an obvious thing. Think about it in the extreme. If I have a massive service failure with a customer, um, so a customer walks into my coffee shop, and I pour hot coffee all over her, um, I can take that customer's future value from $1,000 to zero in just a moment. And so I really think marketers should be thinking more on the positive end of that of how do my interactions with a customer change her future CLV. How can I keep it in and reach that customer, and not think of CLV as a static thing, but as a dynamic thing that I manage through my relationship with the customer?
Allison Hartsoe: [05:46] Are you thinking more that customers are, are more finicky than perhaps we allow or we see when we're looking at CLV
Elea Feit: [05:53] I think that's right. And not to say that customers are finicky, but that customers respond to how we interact with them. So for instance, if you take the basic business decision, we talked about before, which is how often should I send emails to my customers? Well, if you sent an email every single day to a customer, you are likely to get that customer to unsubscribe. And now your opportunity to cultivate them. You've, you've essentially turned it off on yourself, and you've decreased that customer's future CLV by over interacting with her essentially. And so we should be thinking about, uh, how our interactions build a relationship and make the customer more likely to purchase from us in the future.
Allison Hartsoe: [06:39] So the trick is knowing how to figure out where your marketing actions affect the customer behavior.
Elea Feit: [06:44] Yes, exactly. And that gets back to the analytics piece of this. So if you have good records on what marketing touches each customer receives, and how they responded, so that responding could be opening an email or it could be buying something.
Elea Feit: [07:00] You can actually start to identify what I would call persuadable customers. So let me step back and define some terminology. So I'm going to say marketing touch for any time you interact with the customer. So it could be an inbound call, an outbound call, it could be an email, just an interaction between you and the customer. And I think we should really stop thinking about channels and start thinking about those touches. Cause that's how the customer experiences as a sequence of interactions with you. And she maybe cares about what channel those are and maybe not. We need to look at it, the data to figure that out. But a marketing touches just a way we interact with the customer. And a persuadable customer is a customer who reacts positively to um, uh, marketing touch. So you're going to find that you have some customers who are extremely loyal, um, buy a lot from you.
Elea Feit: [07:56] High-value future value customers, but totally unpersuadable, um, nothing that you do changes that. They just love you, and they're going to keep loving you. You'll have other customers who are kind of in the middle. They, they like you, but the more you touch them, the more they respond. And maybe that's through a particular channel, but the more you email them, the more they buy from you. Uh, and so the trick is to go back and your data and look at how you have interacted with a customer in the past and say something as simple as here's all the weeks where I sent an email to this customer and all the weeks where I didn't, and add a customer level. How is she responding to the emails? And you'll find like maybe when I look at databases, often about half of the customers are nonresponsive on a particular channel. And so you can just stop those touches and start investing in other ways to communicate with that particular customer. So you can actually come up with kind of a responsiveness score for each customer. So you'll have some customers who respond a lot and some customers who don't respond at all.
Allison Hartsoe: [09:05] Okay. So now I'm going to bet that this approach is only applicable to certain types of companies that have a, a purchase cadence that might allow them to nurture that, um, persuadable customer the more frequent cadence.
Elea Feit: [09:21] Absolutely. So, um, as I mentioned before, I grew up in the car industry, and the unfortunate thing about cars, and maybe houses is that people only buy once, or infrequently long enough. You know, the time in between cars is seven years now in between new car purchases. So the chances that you can even track the customer for that long to figure out if they come back and buy a second car are pretty low. So this is more something for retailers who have a kind of ongoing purchasing relationship with a customer or grocery stores. Um, discount retailers, high-end retailers, restaurants, service providers, anyone who has that ongoing series of marketing touches that could lead to, um, more transactions for each customer.
Allison Hartsoe: [10:09] Now, I have seen one exception to this, and that's in the healthcare space where a company might have, you know, while you're attached to a certain company, they see a strong cadence of activity, and then you go dark on them for a while, and then you might come back, and they see another cadence of activity because they're dealing with your entire lifespan. I always thought that was an interesting application where they were filling in the gaps in between extrapolating from other customers.
Elea Feit: [10:36] Yeah, absolutely. So there's actually some interesting work by Eric Bradlow at Wharton looking at what he calls r f m c. So RFM is recency, frequency, monetary value, which are kind of summary statistics that describe how good a customer is. And he added to that, C for clumpiness, uh, and it, he's actually done some work that shows that those customers who are clumpy, who, where their transactions tend to bunch up in a particular period of time actually have higher future CLV. Um, because you might see them nothing for a while. Uh, but then they're gonna have a clump of transactions with you in the future.
Allison Hartsoe: [11:20] That might be an interesting future guest.
Elea Feit: [11:22] Yeah, potentially.
Allison Hartsoe: [11:24] Good, good. Okay. So now you have actually written a paper about this topic. Could we dig into some examples about the study that you did and how that worked?
Elea Feit: [11:35] Yes. This is actually a paper I wrote with that same Eric Bradlow, and a postdoc at Wharton named Daniel's Santosache, who's now a professor at Ohio State University. And we were working with a firm that offers customers a curated selection of products that change frequently. So think like restoration hardware or pottery barn. They have some base products, but mostly it's fashion products that are changing out very regularly. And so for a business like that, if you don't tell customers about those new products, they may not purchase. You know, it's really the spark of discovering something new that makes customers in a business like that purchased. So this company uses catalogs and emails that feature beautiful pictures of the products, and there are niche brands. They don't do any mass media. So their basically their way of communicating with customers, they're marketing touches our catalogs and emails.
Elea Feit: [12:32] And the question, the sort of base question they had when they came to us was a, should we be sending catalogs at all? And they were looking at it from a very channel perspective that catalogs are expensive and emails are cheap. So let's just turn off the catalogs. And we took a look at their CRM data and using a fancy statistical model called hierarchical bays. We were able to score customers based on how they respond to catalogs and emails. And so we took 5,000 of their sort of good customers, active customers. We had about a year and a half of their catalog campaigns, which were monthly. And what we did was we looked at how customers respond to those catalogs, assuming that you know, the catalog effect is the highest right after they get the catalog and then it trails off. They've probably thrown the catalog away by a month later. And so we actually came up with a score for each customer on two things.
Elea Feit: [13:33] And this is the interesting part, the two things are how much did they buy when we don't send them a catalog, or kind of a baseline purchase propensity, and how much did they buy when we do send them a catalog. There was also a score for email, so how much did they buy when we send them an email? And the really interesting thing is that those things are not related to each other. So I'll oftentimes, we think that customers who purchased a lot are going to be most responsive to our, our marketing touches. We have this sort of mental model that those customers that buy a lot are the ones that are loyal. They have a strong relationship and feeling towards the brand, and, when we communicate with them, they'll be highly, highly responsive to that. It turns out that they're unrelated. They're not negatively related. It's just there are all kinds of people.
Elea Feit: [14:24] There are people who buy a lot and don't respond to catalogs and emails. There are people who buy a little and respond a lot, and those are the ones you want to market too. So you might miss them if you were just thinking about monetary value, you might miss those low-value customers who have, who are highly persuadable and we could push them into being high-value customers by communicating with them more. We actually benchmarked our approach to scoring customers to typical RFM scoring, and we could increase sales by 10% by actually communicating not with the people who are high value, but the people who are persuadable.
Allison Hartsoe: [15:00] So would you say that um, someone who's persuadable as essentially like a, like a hand raiser?
Elea Feit: [15:06] Hand raisers one way to think of it, but I think of it more as a person who is responding to us and to our communication. I think of it more like a relationship. We're trying to build relationships between a brand and a customer on a one and one level, and so if you were trying to build a relationship in the real world with you know, a potential future spouse, you would see how they responded to things. Oh, I took you out to dinner. You said, thank you. I bought you a gift. You seem to really like it. We should be doing the same thing as marketers. We should be looking back at the data. Obviously, we're going to use analytics to do this at scale, but looking back at the data and saying, how responsive are you as a customer, and investing our marketing touches, which are costly. We can't communicate every day with every customer, but really budgeting those marketing touches to the customers who are going to respond to it based on how we've seen them respond in the past.
Allison Hartsoe: [16:05] I love that analogy of the real world relationship building, whether it's a date or a spouse. I think that works really well because there's a lot of sensitivity to the response of the other person in those early relationship building days. But then there's also this sense of like when does the relationship start to turn? When does the customer or you know, this, the future spouse perhaps on the other side start to be like, Eh, I'm not so sure. When do we start to enter the realm of too much communication and too much information, and somebody kind of dropping back? Is this model sensitive to that so that you can know when you're over marketing to someone?
Elea Feit: [16:47] The particular doesn't include that, but uh, that is something that you definitely want to start building into your models of marketing responsiveness that overcommunicating can be just as bad as under-communicating.
Allison Hartsoe: [17:00] Okay. And then based on the way that you set up this experiment, is it fair to say that we know it's not a self-fulfilling, um, system because you've got control and you've got a baseline against the group. So it's not just that I send people information and they responded, and I saw an uptake in purchases. It's the control that holds that separate and makes the clarity come through.
Elea Feit: [17:24] Yeah, you're raising a very good point. I guess you read the paper cause I hadn't brought that up yet, but this data set that we had had basically the firm was, every time they ran a catalog campaign, they would take about five or 10% of the customers that they thought they should target. They would hold out and not send the catalog too. And so that gives us a really clean read on how good would these customers have been in this month if we hadn't communicated with them because those are customers that we didn't communicate with. Um, so having those control groups kind of built into your CRM system is a really good idea and allows us to do much better analytics.
Allison Hartsoe: [18:04] Yes. And I think that's always hard for companies. Did the company volunteer this or did you have to convince them, hey, we're going to really need a control group here?
Elea Feit: [18:14] Interestingly, the company, I wish I could say who they were, but I can't, um, because I'd like to give them credit. But they had this ongoing hold out system and uh, they were analyzing it kind of one campaign control versus treatment for one campaign. And then the next campaign, and what we did was we really added along stringing those together so that we can look at the individual customer level and say in some months she randomly didn't get a catalog. How does she respond in those months versus the months where we did send her a catalog and be able to make that within customer comparison was what we really added.
Allison Hartsoe: [18:50] I see. I see. So they were doing testing control on like the creative, did more people open or did this creative work or did this message work and you took them to the customer level? Exactly. Yeah. Yeah, that makes sense. Okay. So this is a fascinating piece of research, but it's not the same as what we might think of in a buy till you die model. Can you talk a little bit about that aspect?
Elea Feit: [19:13] Yeah. So the buy till you die models traditionally don't allow for customers to respond to marketing. And what I'm talking about, which I'd sort of broadly categorize as marketing response models, they take into account marketing, but they ignore the future possibility of dropout. And so one of the real sorts of frontiers in this area of research is to put those two things together. So we have models that have attrition where customers become not that attached to our brand anymore along with marketing touches and starting to understand how marketing touches can prevent that dropout.
Allison Hartsoe: [19:53] Got It. Any other examples you want to share?
Elea Feit: [19:55] Yeah. Another example I wanted to highlight is a company called blue labs that does experiments with potential voters. I guess you can guess what their party affiliation is from their name. They also work with commercial clients, but mostly in the off years. So, they're pretty busy with political stuff, um, in the, on years. And what they do is they'll actually take all of their ways of connecting with a future voter, and they'll, so that could be a canvas or knocking on the door, a postcard, um, email, all the different ways that they could communicate with a customer. And they actually create an experiment where they survey customers before and after to ask them, would you be likely to vote and which candidate would you be most likely to vote for? So you get us, the customer gets a survey call, who would, are you likely to vote and which candidate would you vote for?
Elea Feit: [20:51] Then the customer is randomly assigned to get some set of these marketing touches. So say someone knocks on her door, promotes the candidate. Then later they get a phone call that the customer doesn't realize is related that asks again, who are you likely to vote for? Are you going to vote? And they actually look at the uplift between those two events. The reason they have to do this survey is that voting only happens once. So they have to kind of make pseudo voting before and after the test so that they can see how the customer is responding. Did she change her intentions about voting or who to vote for? And then what they do is they build a model that relates that uplift to other features that they can tell about the customers, which helps them identify what types of customers respond well to which types of marketing. And that allows them when they meet a new customer, for instance, they have learned that customers who have a bumper sticker for the candidate or a yard sign are actually not that persuadable. They're already stuck on the candidate that they're interested in. And so you probably shouldn't waste your time canvassing, uh, one of those households because if they've got the bumper sticker, they're already committed. So this idea of using testing to figure out which channels work and actually watching how customers respond to this is I think a really important frontier for marketers.
Allison Hartsoe: [22:17] Well, and it gets right into what you were saying about persuadable customers. You know, if I've got the bumper sticker in the front yard, then I'm dug in, I'm a high value or maybe a high potential voter of a particular candidate. But by making that public, I've committed to that. And so I don't think I've ever seen somebody who put a yard sign out for one candidate and then put a yard sign out for a different candidate that was from a wholly different party.
Elea Feit: [22:42] Right. And so actually some campaigns are building CRM systems to keep track of that data. So if the canvasser walked by the house and saw the bumper sticker or the yard sign, they put it into an app, and that goes into a CRM system.
Allison Hartsoe: [22:55] Oh no that's scary. It's, it's a data collection at its finest.
Elea Feit: [23:02] Well it is a public statement, right? It isn't. It is that you posted to your Facebook, you put the sign in the yard.
Allison Hartsoe: [23:10] Yeah, yeah. I can see how that becomes predictive or helps them use their resources better in the same way that we want to cultivate customers. We want to really understand how to best use those marketing resources. And, and I think that is just like you said at the very beginning, it's a really important business question. I only have so many levers I can pull, and I have an opportunity costs related to time, and how many people I have. What makes sense to do first, second, third and you know along those lines, let's say that I'm convinced, and I want to use this strategy to cultivate my customers. What should I do first?
Elea Feit: [23:48] Well I just want to look back to one other thing that I like about the blue legs labs example, which is just to point out again that the potential voter who has the yard sign for your candidate you might not want to invest in. Even though there are good and loyal customer as evidenced by their yard sign, we don't necessarily want to waste resources with them because they're not changeable based on how we communicate with them.
Allison Hartsoe: [24:13] Not Changeable either way. Like they're already dug into your party, so don't waste resources or they'd already dug into the other party.
Elea Feit: [24:21] Yeah. Or maybe take the next step and try to convert them into being a canvasser for donor. But you wanted to sort of talk about kind of next steps that someone who is excited about this idea can take. Yeah, sure. So the first thing is to start collecting data on your customers as individuals. So you want a database where each row in the database is a customer, and you have a record of here's all of the touch points we've had across all the channels. You want to get those out of separate channel silos and into the same system. So we can take a three 60 look at here's this customer, all the ways we're communicating with her and all the ways she's responding back in kind. So if you're a coffee shop or a convenience store, this may mean setting up a loyalty program so that you can track customers from one transaction to another. If you're a retailer, you're going to need you to develop database systems to track transactions to the same customer using a name, address match. But really getting that 360 view of the customer in a consolidate system that you can manage access is a huge effort. But one that will pay off big time.
Allison Hartsoe: [25:33] Now when you talk about customers, a lot of businesses that have this transactional data, Russell, with the issue of household versus individual, uh, for example, I go down to buy a pizza, and or maybe I placed the order and my husband uses his credit card, and then the next week I use my credit card. How important is it to understand that household level versus the individual level?
Elea Feit: [25:58] Really important, and this is also where we haven't talked very much about demographics. I'm not that keen on database of pens or collecting demographic data because of this household problems. So a lot of fashion retailers report that their customers are largely male and middle age because their dad. First credit card that actually makes the transaction is dad. I see. And so this is why I think it's important to take each customer as they come and look at how that customer is interacting with you. Because just because they fall into a particular demographic bucket doesn't mean that they are or are not going to respond to emails. I really advocate to follow the data. Would I make my database at the household level or the customer level depends on the kind of business and kind of where I think the relationship is? So with a pizza shop, I would want to do it at the household level as best I can because that is kind of the relationship between pizza and the household.
Elea Feit: [27:00] Not between an individual, but if it's a fashion company, I might focus more on the individual because that is sort of where the relationship resides.
Allison Hartsoe: [27:09] Yeah, that's good advice.
Elea Feit: [27:10] But the more important thing is once you have that data to follow the data. So at the most basic level, just look at how much each customer transacts in periods when she is touched with some marketing communication versus not touched with that marketing communication, does she buy and weeks where she got an email and does she buy more than weeks when she didn't get the email? And you can do predictive modeling beyond that. But that's the basic concept is to just look at that past data and identify how customers are responding to your marketing.
Allison Hartsoe: [27:43] Which necessitates the control.
Elea Feit: [27:45] Yes, absolutely. So that's the third thing is you need to start running some experiments where you withhold that marketing communication from some customers, and you want to have it so that when you look back at an individual customer record, you have periods where she is hit and periods where she is not hit with marketing touches so that you can compare those at the customer level, not just in the in a particular, you know I did a Christmas campaign and I held out 10%, and so I can say that my Christmas campaign is working but also so that I can look back at this customer and all of my interactions with her, and I have some times where she got catalogs and sometimes where she didn't get catalog.
Allison Hartsoe: [28:24] Perfect. This makes a lot of sense Ellie. Thank you. Now if people want to ask you more questions about your paper or about your book, how can they get in touch with you?
Elea Feit: [28:33] Sure. You can follow me on Twitter, and my Twitter handle is e l e a f e i t. That's my first name and my last name. You can private message me on Twitter. I usually respond. If you're interested in the book, I have this book with Chris Chapman, which is a how-to guide for marketing analytics using the r statistical language and that's available on Amazon, and the second edition will be out this summer. So if you really want to get hands-on and learn how to do this stuff by typing code, that would be the book for you.
Allison Hartsoe: [29:04] Very nice. I love that you've got a second edition coming out already. That's not a small feat, but you know, good for you. Fantastic.
Elea Feit: [29:11] Yeah. When you do statistical languages, they evolve very quickly right now because the data science world is just booming, and so you have to keep up with the software.
Allison Hartsoe: [29:21] Yes, no kidding. Well, as always, everything that we discussed is at ambition data.com/podcast. Ellie, thank you so much for joining today. I really like the things that you say. You encourage us to think beyond, you know, just kind of straight forward CLV into the broader picture of the persuadable customer, the customer that you want to cultivate and the areas that marketing can really be of influence and I think that's incredibly important, right? We don't want to just constantly be beating people up with messages. We really want to form that relationship, and that seems to be the key to the output of your research.
Elea Feit: [29:58] Yeah, absolutely. It was a pleasure to talk about it.
Allison Hartsoe: [30:01] Good. Remember everyone, when you use your data effectively, just like Elie did, you can build customer equity. It is not magic. It's just a very specific journey that you can follow to get results.
Allison Hartsoe: [30:15] 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