Customer Equity Accelerator Podcast

Ep. 106 | Secret Customer Scores with Wharton’s Sarah Toms


"Companies cannot just flick a switch and assume everything this consumer did with different brands is immediately applicable to mine." - Sarah Toms


Sarah Toms, executive director at Wharton Interactive joins Allison Hartsoe in the Accelerator to discuss a New York Times story about finding your Secret Customer Score. Are these scores a good thing or bad? How do they affect customer-centric thinking and the data behind it? Sarah explores the intersection between company ethics and 3rd party consumer data and comes out with a winning conclusion for all.   

Please help us spread the word about building your business’ customer equity through effective customer analytics. Rate and review the podcast on Apple Podcast, Stitcher, Google Play, Alexa’s TuneIn, iHeartRadio or Spotify. And do tell us what you think by writing Allison at or Thanks for listening! Tell a friend! See the full transcriptView all episodes.

Podcast links - I Got Access to My Secret Consumer Score. Now You Can Get Yours, Too

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Show Transcript

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 a story that appeared in the New York times regarding secret customer scores, and to help me discuss this topic is the wonderful and awesome Sarah Toms. Sarah is the executive director at Wharton interactive and also one of the authors of the customer-centricity playbook. Sarah, welcome to the show.

Sarah Toms (00:50): Hi Allison. Great to be here again.

Allison Hartsoe (00:51): Yeah, it's great to have you back. Thank you. Can you give people just a very brief understanding of your role at Wharton interactive?

Sarah Toms (00:59): Absolutely. I have co-founded a team at the Wharton School, where we are building other ways to teach folks how to learn. So we are building out hyper-realistic games and platforms and other ways to democratize the educational landscape. So I truly am a curious games expert, and with that, I'm able to build amazing partnerships with folks like professor Pete Fader on the customer-centricity side, professor Ethan Moloch on the entrepreneurship side.

Allison Hartsoe (01:26): And I can tell you firsthand. These are really cool games. Really fun. So thank you for all the work you do there. So we're going to talk about this secret customer score article. So this appeared in the New York times November 4th, and it was from a journalist in Cashmere Hill. I'm going to read the first paragraph, just kind of set the tone for what the article is about, and I'll link to the story in the show notes. But he says as consumers, we all have quote, secret scores, hidden ratings that determine how long each of us waits on hold when calling a business, whether we can return items at a store and what types of service we receive. A low score sends you to the back of the queue, high scores get you elite treatment. That's the leading paragraph. He then goes on to imply throughout the story that this is a bad thing. And we've often said the reverse with customer centricity, which is that this kind of segmentation helps companies honor and respect customers appropriately. How should we think about this?

Sarah Toms (02:24): So I have some serious concerns with the approach of offerings like this where it may seem like it is truly being customer-centric, but what they're doing actually under the hood is really clumping customers together and not really understanding the very special relationship that customer has with that brand and how that's going to drive customer lifetime value. So when you were looking at how our consumer performs across many different brands, then you start to aggregate that information in a central repository and say this is what this customer looks like. That's a problem from the standpoint of customer-centricity. So I do actually have some problems with the approach and what is shared in this article with that respect.

Allison Hartsoe (03:06): So now you're talking specifically about the companies that he mentioned, not about him or his opinion, but he calls out five companies in the article, sift Zedick Global, Retail equation, Riskified and Kustomer with a K. and these are the companies that you're saying are pulling and aggregating things perhaps incorrectly.

Sarah Toms (03:27): Exactly. And how it is described in the article. I would say it is holding that information incorrectly. And really my example would be if I am providing one or two-star reviews in one place, let's say on with respect to a certain set of products, how does that impact my relationship with let's say Airbnb or with something else? So that's where I'm really concerned, and I think that companies need to build this expertise and it needs to be very specific to their offerings and their brand. And that's really more of what customer centricity would be about. And it also needs to be in relation to value and not necessarily the types of risks that this article starts to go into.

Allison Hartsoe (04:09): So when we talk about that, it should be specific to value. What should companies be thinking about when they look at the value of the customer base? What are some elements of value?

Sarah Toms (04:19): Elements of values. So base, base, base. Looking at what CLV is looking at, it is looking at frequency. So what's the potential, how frequent is this customer going to come and purchase from me, and at what level to what is the size of those purchases with respect to dollar value, and how long is that horizon potentially gonna last. So that really is the three dimensions that CLV is looking at. Now, something that Pete and I talk about in our book the customer-centricity playbook because we also say there may be other aspects of value that you want to measure in your CLV calculation. For example, referral is very good part of this. So if you have a customer who is referring and helping you to get other customers like them, that might be something you want to bring into that calculation. When you're looking at not for profit, our folks providing their time as with respect to giving you free time from a volunteerism perspective. So does that figure into CLV for you, et cetera. And then also are they out and providing reviews that are very favorable about your product and helping to be kind of your big advocates out there in the social media space for example. That might also be something you want to weigh in on and bring into your CLV calculation.

Allison Hartsoe (05:31): I completely agree with that, and I think it's really interesting because in the article he's pressing really hard on the concept of transparency and basically that consumer should understand how decisions are made and this always reminds me of FICO and FICO scores. FICO scores always seemed like a mystery to me. I'm sure there's some formula somewhere that we can unpack, but it's like, Oh, that's my FICO score. I don't really unpack that too much. Is what we're saying with customer centricity and customer lifetime value a little bit more like saying we're putting more valuable pieces into the formula, and it has greater benefit to the companies and the customers, but should the customers understand how that formula works?

Sarah Toms (06:15): Sure. We put a lot of value with respect to transparency and marketplaces, especially with respect to that relationship, us as the consumer versus who are providing products and services to us. Where it makes sense absolutely having that transparency is good, but I would also push back on consumers and say, does it always really truly matter to you? So what we say from a customer-centric standpoint is we're not talking about treating your less valuable customers poorly. We're just talking about not over-investing in them and giving them more than they actually need or want. And so when you think about something as simple as customer service, customer service is ensuring that you do not dip below a certain level that will make your customers want to lead any customer, high value, medium, or low value. And so something like a customer score, if my customer score is high or low with respect to a company, it should not mean that I'm getting terrible service. It should not mean that I am being treated poorly, but if I am a really good customer of a particular provider, I should get better, offering better service. There should be less friction with respect to, Hey Sarah, we see that you're really liking this suite of things that we're doing. Are you aware of these other things that we're doing? Let's get you into a more premium status.

Allison Hartsoe (07:32): Should a high-value customer be able to delegate those benefits to another customer? And the example I'm thinking of specifically and every now and then I get very sick of the news. So I pull up stories from the good news network, and there was a story about a man who had a very nice seat in first class. There was a woman in the back who was older, very uncomfortable, and he switched seats with her just kind of on a whim. So this would be an example of someone who has a premium that they're passing to someone else.

Sarah Toms (08:02): Sure. Again, does this really factor into customer-centricity? I mean, it makes for a really wonderful, lovely story and kudos to that gentleman, but why not? Now from the standpoint and the, and we see this with Amazon smile for example, where you're able to come through a different portal and provide some of that relationship, some of the goodness within that relationship that you have with the provider and do good in the world with it. Absolutely. That should be a part of it.

Allison Hartsoe (08:29): I always liked that theme. We can do good in the world. It's not just about data and not transparent data. So back to our question about should customers understand how decisions are made? I think oftentimes when it is obvious, like one of the stories I think I heard years ago, was about a guy he was applying for an e-commerce position at a company. And what he did is he bought a product online. He returned it in the store, and he basically went through the customer process to test the systems and see how well connected they were and to see how they treated him. And what's interesting about that is once you understand how things operate, like if I cart something and I don't buy right away, are they going to send me a discount coupon then people start to hack the system. So my question for transparency is sure, we want it to be good, but does that leave us wide open to customers just hacking the system and to get the best offers, the best deals, whatever they can get.

Sarah Toms (09:25): So this really raises a key point of customer-centricity, which is for your best customers, you don't need to necessarily provide discounts. They're not in this relationship to have cheaper parts. They're not looking for a discount of what you're providing. And in fact the opposite. They're willing to invest more to engage with you deeper, and is understanding those customers that are willing to go to the next level with you on this journey and how do you just bell it that relationship so you can deepen it, you can broaden it, and you can also make it more valuable to you. And then with respect to those customers who are all naturally going to try to game the system, absolutely putting safeguards in place so that you are not open to those types of threats but recognizing that you are competing with others. So maybe getting them in the door and this is part of that acquisition cost process.

Sarah Toms (10:16): Getting them in the door to just finish that shopping cart and get it over to actually purchasing. If the discount is palpable if it's something you can take on board, why not? Right? It shouldn't be that big of a deal to you. So again, I do want to harp on the fact that there is a responsibility on both sides here, both the customer and those who are offering the services and the products. We have this ability to retain and track all this amazing data about our customers and come to conclusions based on those behaviors that show up within the data. And we do have a responsibility to be both fair and ethical about our use of that. Right. And so that is something that is still, it doesn't matter about, we're looking at this through a customer-centric lens, data privacy, security and the ethics that come with it. There's still that responsibility. And so with all of this, it's really finding where we feel comfortable. It's finding that comfortable ground between the customer and the brand and how we want to utilize the data and where we feel comfortable in that space.

Allison Hartsoe (11:19): So for different types of customers, there may be different rules. So if you have a younger customer base, there might be things that they're comfortable with that are different than an older customer base.

Sarah Toms (11:28): Exactly. And then also depending on what products you're offering, I mean we all know this very well in the education space where we have to be very careful about for example, for roof, so how we are storing and tracking and utilizing data about our student learners and are we actually using that data. Especially if you're an education technology, and you're selling these products to schools and universities. Are you actually utilizing that data to make the learning better ultimately?

Allison Hartsoe (11:55): That makes a lot of sense, and I really appreciate you calling out the discount point because I can probably count on one hand the number of companies who are thinking about loyalty programs in terms of emotional loyalty or what you said was they're willing to invest more to engage deeper, which to me sounds like emotional loyalty. It's very rare, and the idea that they can do something different is valuable to point out.

Sarah Toms (12:20): Yeah. Pete and I have a framework in our book, which speaks to loyalty specifically. And when we're looking at the tactics and strategies we implement in our company, when we're thinking about retaining customers and also developing them, we actually place loyalty programs really in that space where it is about developing your existing customers who are in more of that low tier, lower value. So just the traditional, and a great example that I like to give is, for example, Starbucks. You've got two customers; they both drink three cups of coffee a day. You've got one customer that's buying all three cups from Starbucks, and you have a second customer who's buying one of the cups of coffee from Starbucks and to they're sharing that they're spreading that share of wallet around. Is the loyalty program gonna make the first customer like a Starbucks loyalty program gonna make the first customer more loyal and better and higher value? Or is it going to serve to improve the value of that second customer? And we all know what the answer is. It's obvious. It's really developing and attracting that second customer who isn't naturally high value for in this example of Starbucks.

Allison Hartsoe (13:22): Yeah. An excellent example. So let's come back to the New York times story for a minute. I want to ask about synthetic data. There have been some interesting use cases wherein the financial space there are companies that are providing synthetic data, which basically imitates the real data and I find this to be at a very thin shield, but perhaps it is a privacy shield that could be used to help companies understand this beautiful segmentation without walking into all the landmines around these still to be tested privacy laws. Is synthetic data something we should look to.

Sarah Toms (13:59): So synthetic data reminds me of simulations and models that we build as well. So I see it as being a tool in the analytics tool belt, but shouldn't necessarily be the hammer where everything's a nail. That would just be my one concern. It can be a good test of hypotheses. So where I really like to start with companies when they're thinking about customer centricity is start small. Don't try to boil the ocean and thinking about those first questions you're going to ask of your data and how you're going to test success and failure. And so using synthetic data may be a great way to start.

Allison Hartsoe (14:34): To test an answer and to see if that holds water. And maybe you could even use it to decide whether you wanted to retain data in the first place.

Sarah Toms (14:41): Exactly. I mean creating the infrastructure to start tracking and then analyzing the data can be incredibly expensive. So to your point there, Allison, fantastic way to just start dipping your toe in, and running just experiments before you actually start to scale up an idea or a project around the analytics you want to do with your customers.

Allison Hartsoe (15:01): Okay, very good. So in the New York Times story, he implies that a lot of people should be scared that third party companies are analyzing consumer data that may not be shielded. And that we have the California privacy law to thank for the ability to get it. And he basically goes through and says, here are five companies that are using data that you did not authorize them to use and here's how to get hold of what they have on you. Should people be scared? Should we be concerned about these companies that are third party don't use the data correctly, might take companies down the wrong path?

Sarah Toms (15:35): I think that there needs to be a concern not just from the consumer side but also from the company side. I think that we need to be smart about assessing these offerings with respect to data and how much water they hold. Even from a legal standpoint, we need to make sure that this information is being adopted and sold and used correctly if this does start to be the new normal.

Allison Hartsoe (16:00): Excellent. Is there anything else you want to point out in the article? Pros, cons?

Sarah Toms (16:04): I guess coming back to the beginning of the conversation, I really and truly just want to highlight the risks with companies thinking that they can purchase information about a consumer and immediately just flick a switch and say, okay, everything that this consumer did in their relationships with these other brands is immediately applicable to how they're going to interact with mine. It is absolutely false. That is not the way we work in the world as far as consumers go. So that is my number one concern with looking at these hidden consumer scores that are being developed, and I do have deep concerns that these are going to possibly become a standard in the customer analytics space that are just going to be a garbage, quite frankly, just something that gets sold to company, it gets adopted, gets looked at as being the standard. And again, we're just going to be in the same space as we are with trying to aggregate information about customers and thinking that an average score is also truly representative of what the individual customer is doing, thinking, behaving, et cetera with respect to the brand.

Allison Hartsoe (17:06): And in a way, it sounds like a lazy solution. Oh, I don't have the resources, and I don't really know what to do with the data internally, so I'll just buy it from this third party company, and they'll tell me. So it's a company not forming a point of view on their customer base and on their data. Would you agree?

Sarah Toms (17:21): I completely agree, and I think that that's another thing is when he and I and especially as we have customers come to executive education, and we're running our simulation, or we're having conversations about our book and customer-centricity, there is definitely this feeling of what is the CLV calculation or what is the way to implement customer centricity and folks are looking for shortcuts, and there is not a shortcut solution. This is a complete change in thinking, and it's a complete shift in the way that you do business, and the way that you work and think about your customers and that includes how you tag and track them and analyze them from an analytics standpoint, and I know you know this very well, Allison, from what you and your team are doing as well.

Allison Hartsoe (18:05): Exactly, yeah. We spend a lot of time looking at that. You know, as humans, we like heuristics, right? We like shortcuts, and I think it's just a matter of time before enough people rustle with it, and they're like, Oh wow, that short cut was a long cut.

Sarah Toms (18:18): Yeah, and we look for patterns like we're designed as that's our nature is we just think that, Oh, well we're seeing this, so obviously there must be a correlation to that, and the problem is that that's actually the opposite of what we're talking about in customer-centricity. It's looking at the heterogeneity across the landscape, and then it's recognizing and celebrating that heterogeneity and then it's the next step is, okay, now what are the best tactics? What are the best approaches that are going to serve us the best as a company and actually serve your customers best as well, ultimately?

Allison Hartsoe (18:51): Excellent. Well, maybe Sarah, they will call you up for the New York times to do a great rebuttal of that story, and we can get more of the right-thinking out there. Meanwhile, if people want to reach you, how can they get in touch?

Sarah Toms (19:04): Great question. So great ways to get in touch with our LinkedIn, Sarahtoms. Also on Twitter, Sarahetoms, and also happy to have folks come to

Allison Hartsoe (19:17): Excellent. As always, links to everything we discussed are at, including the link to this story. Sarah, thank you so much for joining us today to talk about this really interesting topic and the perspectives that are getting bloated out there from very respectable sources. I really appreciate you coming in

Sarah Toms (19:36): Such a pleasure, Allison. Anytime.

Allison Hartsoe (19:38): Remember, when you use your data effectively, you can build customer equity. This is not magic. It's just a very specific journey that you can follow to get results. See you next time 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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