[00:00:41] Andrew Michael:  Hey, Babis welcome to the show, 

[00:01:27] Babis Makrynikolas: Andrew excited to be here. 

[00:01:29] Andrew Michael: It's great to have you for the listeners, Babis is the VP of Product and Pricing at Blueground, a flexible apartment rental startup on a mission to make people feel at home, wherever they choose to live.

Babis is also a product management instructor at Product School. And prior to Blueground, he served as a product. Leader across several years. Business units at Amazon. So my first question for you obviously is where do you feel at home? 

[00:01:53] Babis Makrynikolas: Yeah, it's a tough question. I'm originally from Greece.

So definitely, Grace's home, but I've been also living in the U [00:02:00] S for the last 10 years. So I guess Seattle is my new home. So it's it's a little bit of both, I would say. And there are many other places of Like having friends and like feeling like a very warm welcome there. So I would prioritize, often since Seattle, but there are so many other places that like I could really start home.

So quite a few. 

[00:02:17] Andrew Michael: Yeah. I think it's one of those things. Like when you've spent a little bit of time away from like the place where you grew up solely after a while, Like it becomes this mixed identity of like, where is home? And I have this as well. Like I grew up in South Africa and now I've spent more time in Cyprus and I grew up in South Africa, but I also spent like a year almost in Boulder, Colorado.

I spent some time in Copenhagen. Yeah. You get to meet friends and then you almost feel torn at some part. Like you want to go back contribute where you can't. And I really liked the hearing a little bit about Blueground and the mission for it. So maybe you can tell us a little bit about the startup and what's almost a really intrigued by the role of product and pricing, the VP of product and pricing.

Because as a title, it's not really something, you hear you obviously a VP of [00:03:00] product, but you also have pricing in this home. It's just a little bit about Blueground and what your role is. 

[00:03:05] Babis Makrynikolas: Sure. Yeah, I guess I was Blueground we're going to go first furnished and fully equipped apartments. And right now we operate in about 14 cities and we have about 4,000 departments we're growing fast.

And the idea is that you can basically. Moving in a house with just , a piece of luggage and you're ready to go and you can start exploring the city try to do what you're there to do, either a vacation or starting any job, or like going on with your life without setting up a new home, being a hassle.

So that's the idea and we're on a mission to do that across many more cities. Now in terms of of my personal background. It's an interesting intersection and happened Organically wasn't products. And like the last 10 years product has grown a lot. So it's been a, an interesting journey.

And at some point I was finishing my MBA and got an offer from Amazon and the idea was to join one of their teams. And I didn't know which team cause they give you a generic offer. And so there's some sort of matching process [00:04:00] between incoming PMs and teams that have product needs. And so I started this discussion they asked me about my preferences.

I started meeting people in one of the ideas was, I want to do something with data. I really liked the data even before my my Amazon days. So I thought, okay, I'm joining Amazon driven company grade. Like how I double down on data. Of course the answer was, Hey, all our teams are working with data, so that's best way to select a theme.

And I was like, okay, I don't know what those would tell you guys just place me like anywhere really, I'm sure I'll find a good challenge, but they're like, okay, we think our pricing team might have a little more data than your average team. So how about that? Which sounded interesting. So I.

joined the pricing team. And so the way it works at Amazon is that every every group has product managers that leaddifferent initiatives. And so this was where the pricing product intersection happened to, to start and then going through different roles in teams sites. I kept in some of these roles, like this pricing angle, like after e-commerce I moved to Amazon web services and I did pricing for the cloud.

So I got a different cloud perspective, [00:05:00] but at the same time, Sorry, pricing perspective, but at the same time I was doing product. And yeah this title, followed me now with at Blueground around yeah. 

[00:05:07] Andrew Michael: Too merged together. Yeah. And it's interesting as well when you say doing pricing at Amazon it's specifically a product squad and like what would be the main bulk of work that you're working on?

Would you the touch, anything towards billing side of things. It was purely just looking at pricing and packaging and figuring out how the product should be packaged. 

[00:05:26] Babis Makrynikolas: So I think AWS was a combination of things definitely packaging and like the product definition was part of it. , we had a close collaboration with the billing platform, because a lot of challenges were how do we price things?

And also, how do we bill for the things we price, because you have the concept of metering and you try to meet the real time and yeah. August interesting dollars is about someone running so many instances. And how do you estimate how much, what they're running is cost and so on.

So there was this element and there was a space around the new pricing models. How about we give our customers the [00:06:00] ability to buy in bulk, get some discounts or buy products that might have different availability requirements and get them discounted because they're okay with like lower availability, for example, or how about you buy a different instance for how valuable right.

In in, in your region. Lots of variations where we tried. Create win-win solutions between Amazon AWS in this case and customers there was the operational aspect of pricing. So I need to price so many different skews. How does this happen from a product perspective and how.

What systems should they have in place to support this pricing? Upscale? Cause I have thousands or millions of life skills, so it's not like as simple as here's an Excel file with 10 products, let's put a number in each of the 10. And then there's the more interesting aspects of like data analysis where you have the team.

So they define the story economies. Purchasing patterns and like how customers react with different products, which products are more sticky. If you buy this product with other products, would you buy in like how these things interact? So it had like very interesting areas around pricing. 

[00:06:59] Andrew Michael: Yeah, it [00:07:00] sounds super interesting.

Listening to talk through this. I think one of the interesting areas, and I think I'd be interested to hear how this was done at Amazon, but when it comes to pricing and packaging, typically like people think to go test these sort of things. And I think more often than not, most startups just don't have the volume and the capacity to actually effectively do tests on pricing and packaging.

And I'm wondering like at a place like Amazon, we actually do have the volume and the traffic. How do you actually go ahead. Test pricing and packaging. So you mentioned you're trying to think in the AWS example, you mentioned you had a few different pricing models or as a discount and it was packaged deals and things like that.

Are these tests that you're running or like, how are you evaluating the effectiveness of them? 

[00:07:43] Babis Makrynikolas: So I think in most cases , the idea starts., Amazon from the customer. Should we try to deeply understand customer needs?

And what problem are you actually trying to solve? What specific use cases the customer has in the cloud world? This would be what workloads. The customer wants to do support with this new instance, let's say [00:08:00] in e-commerce might be more straight forward because you have products that are out there before Amazon inventing them.

And so you have already some references. And then what you try to do is build the right set of features. To meet your customer's requirements at the right price point. So like the reality is because pricing is quite sensitive. It's harder, especially in the cloud space to actually test it because testing would mean increase, decrease the price, which is not something you can easily do because customers.

At the same time, requires some stability and want to know exactly how much we're going to pay. So it's a weird thing. There's some things you can do launch different prices in different regions, but this is not the perfect experiment because at times, like different regions, he like so or, the commerce space, you can change prices every hour, every day and see how your purchases like also.

Potentially change and see what happens. This is also tricky because you have so many substitutes, so many other products like that might affect the pricing decision. So you have to really control for a bunch of variables. There's really no like very accurate [00:09:00] pricing testing. So there's a combination of some data points that directionally point you somewhere.

But a lot of it is, I'm understanding my customer, they use cases and I feel that this is a good price point I've done. I know 20, 30, 50, a hundred interviews. And that continues. Talk to customers and get feedback, which makes me more confident in about, the price points.

And obviously you, then you see the, after the fact like they actually sold so much, was that more or less what they expected expectations made me about and their pricing because I filled out and fine tune as well. 

[00:09:28] Andrew Michael: Yeah. That makes a lot of sense. And it's interesting just hearing you talk through it, because I think.

It's almost, you have the opposite challenge then at Amazon, where you have so many customers that it becomes restrictive place to actually test things, because they're just so much more volume and people to uncover and see what's going on. And I think that's always a concern when people do pricing tests is like, oh, what if at one person sees one pricing, another one sees another price, like a, is that not bad for us?

And how do we solve that? And I could see that definitely being a big problem at Amazon with the scale that there is I'm interested in though as well. So you [00:10:00] mentioned. Like you've done your research, you've done your interviews and you understand what a good price point is. And I think this is well typically when we do pricing and packaging research, it's done with panel studies and trying to understand that, like what price point would it be too much?

What price point would it be? Like a good deal with prices, two loads over. So are you using any methodology it's like the vendor list and, or pricing sensitivity? Like how do you go about on these interviews? What is your methodology and process to figure out what is that initial price point you want to be positioning the product at?

[00:10:30] Babis Makrynikolas: So again it depends on the domain, I think like for if you think about the commerce. And lots of it could just be like, what are the other references, right? E-commerce like by and large is commoditized. And so in a way you have to be competitive. So it doesn't matter what you think the right price point is.

If the market thinks the right price, 0.2% lower, probably you'll never sell it because people can easily check prices across. And there are so many aggregators and tool where you can be informed about other prices. With Blueground, for example, is a little different because you have a specific assets and [00:11:00] there's.

There's some competition, but there's no exactly similar assets. So there is again, probably a price point that like you can you can assume is it's good, but like you have some buffers to play. And you can, but I think the main idea is like, how do you get these intelligence from the market and continuously adjust your prices?

In headings like eCommerce, where you can, like customers are familiar with the idea that prices change daily or hourly or whatever, you can fine tune your price points based on the data that the market gives you. And so if, for example, you have stock for a hundred items and you can get more of those.

It's a very different problem than like you have stock for a thousand items and you can replenish like every hour. And so if the market tells you that this is a low price point and your strategies allows us for the foreseeable because, you have essentially unlimited supply. You might go for a lower price point.

If you only have a hundred of those. And these are like, I don't know, a limited edition, and you want a bit more, you might go highly higher. Unfortunately, there are more complications, for example, like you have to think of customer experience. Like in most of these settings, you're also [00:12:00] building.

A more long-term relationship with your customers on the purely transactional. I want to buy you, we just today. And want to never see you again. And so you cannot price Scouts. You can, you want to build like this relationship, but like everyone leaves their conversation and their transaction feeling like a winner and feeling they lot more value than they gave us.

So you have to also like they can book out, NPS or whatever, your customer satisfaction metric is and feel that you gave good value. 

[00:12:25] Andrew Michael: Yeah, I agree as well. Like I think it's not just thinking about the short term sale, but more like the longterm repeaters in the e-commerce space, like repeat sales and SaaS, like retaining customers.

I think like qualitatively for me, a good measure. I think always is . Like looking at the number of customers actually complained about price and the number of like reasons like for churn specifically, like when it comes to talking about sauces, like if you don't have a reasonable amount of people complaining about price like maybe 15, 20%, you're more than often the probably too cheap.

And you should think about raising prices and then the opposite, if you're too high, from [00:13:00] existing customers, you get a little bit of a sense of price point itself there. But then also like the idea as well of like, when we think about pricing and packaging, asking your customers what a good price point is not necessarily a good place to start because they're already your customers.

So they're essentially you're selling to the market, not to existing customers. So it's important to understand these panels and these research. Talk to us a little bit about AWS side then, and putting together those packages. What did you use interviews look like speaking to your customers?

So you identified okay. A use case that went the way they wanted to use their all right. The infrastructure. What would a typical interview look like? With a customer, trying to understand how to package and then what the process would look like. What would you be asking? 

[00:13:43] Babis Makrynikolas: Sure. Yeah. The typical would be talking to like actual users of the product.

So usually it would be a DevOps team would be the engineering team of one of your customers and try and understand their use cases. Try to understand exactly what they're trying to do. What pain points they have and maybe. What they're trying to w what they're using [00:14:00] right now, this shows the same problem, and then understand if you can offer something much better.

Cause if I was, many pencils, like different combinations of central instances, which have hundreds of combinations. And so there's something out that would like probably they're probably already using right. Or they've build their own custom solution. So what you're trying to see is What is their solution, like how we could potentially even improve that.

What's what's interesting is to like, really understand the requirements are they trying to do something that has, I dunno, like a very heavy computer use case, or is it like, is memory there? The channels is availability, the channels, what are they really driving for? And then design the right product to solve their problem.

The price point obviously matters, right? Comes at the second point you try to get to the pricing recommendation and do the exercise of okay what is this product? How do we position it? And how much would it cost in terms of the packaging options we usually offer?

Like all these packaging options, meaning you can, if you want to buy in balance, there's a discount if you want. I don't know if you're okay with Laura availability, there's also a discount. So then it's up to the user. [00:15:00] Navigate these selection of options and decide what the best trade off between price and value.

They they get to select the right item. If someone, for example, can commit to using the same type of server for three years, there's a good discount because Amazon knows that whatever several they have in their data warehouse data, and there will be utilized for two years, and that has some value for Amazon and they're happy to pass some of that discount to the customer.

And so 

[00:15:24] Andrew Michael: on. Okay. So it sounds like very much as well at Amazon. It's cost-based pricing is an important component in terms of like how you price your product. I'm interested now as well, a little bit at Blueground though, where you mentioned this, there's nothing really comparable on the markets to what you're doing right now.

So you're pretty much trying to figure out and set up what the right price point is for the product. How are you doing that now? Blueground is all obviously not having the access of resources and introducing something fairly new as a concept. What is the pricing and packaging work look like there?

[00:15:59] Babis Makrynikolas: [00:16:00] Sure. When I'm thinking there's something, there's nothing like comparable. Exactly the same asset. They're definitely like other options. There are some competitors out there and there's some choices our customers have as alternatives. For example, you can argue that if someone wants to go to New York for two months, Blueground doesn't exist in no competitor that does something similar exists.

They can stay in a hotel. So there's a price for that. Or they can another, no rent a house and turn the seats. There is a cost for that and you can approximate it. So there is some idea of what, how would someone solve the same problem? Because essentially, it's like a job to be done kind of idea.

You all felt like that solution on the job, which is, I want to, I don't know, find that accommodation in New York kind of thing. So there. Some comps and you can find the for example, we were going to go through a BNB and see like how much a furnished apartment costs.

It might not be one to one, but you get an idea of how much someone, could potentially pay for another solution. Adjust based on how better or worse your solution might be. So you might say, okay, I feel I'm more premium. How [00:17:00] much premium hi people, the number, but are, 20% more premium.

Okay. Maybe that's a good trade-off and a lot of it again is like understanding like your demand and supply versus the market's demand and supply. Get signals for the market. So a lot of what we do is really identify these signals where the market is telling us like, okay we have more demand for your products and supply in the market.

And so there's no beginning to increase prices and be unpleasant decrease prices to manage our revenue goals with low ground, we tried to revenue optimize it's a little different than other cases where you have a unlimited supply. We don't have unlimited supply and also having bacon supply.

The function's a little 

[00:17:36] Andrew Michael: different there. Yeah, absolutely. It's it sounds very much what Blueground is selling to me is convenience. And in the sense as well because you could essentially do these three Airbnb and other places, but just to understand as well, is the concept with Blueground.

Monthly it month to month or quarterly or six month it's more longer term stays, but you would just want the convenience of finding a familiar place. Or do you have a [00:18:00] subscription with Blueground where you can access places in different locations? What is that? 

[00:18:04] Babis Makrynikolas: So we do a customer can select their duration of stay.

Right now we offer 30 plus stays. You have to stay for at least a month, but you can select if you want to stay for a couple months or for a couple of years. And we accommodate both the main difference from like an Airbnb solutions. This is a fully managed experience where.

Operates the the property attorney pernicious. So there is, there's a standard, I guess there's some buyer, but like we always exceed in terms of convenience in terms of the amenities we offer in terms of the furnishing and in terms of the equipment in the apartment as versus, an Airbnb host with where there is no standardization, Great horse out there, but it's a little bit of a like you have to search a little more to find, what meets your needs.

And we recently launched another model, which is you essentially commit for, six months or a year. And you can move around the apartment. So especially now with digital nomads and this remote work becoming more [00:19:00] mainstream, this is something interesting that a segment of our customers are looking up.

And so we designed the product to meet these needs. So you can spend three months in New York and then you can go to lunch and then you can go to Dubai and, you have a master lease that covers that. Depending on your specific moves, you can praise your leads and then you can take another listen and other city it's an interesting concept.

[00:19:21] Andrew Michael: Cool. I like that. So you have one subscription in a sense, and then you can stay in multiple places and just move around and get a familiar feel as well. Like you say, knowing that one company is running everything and that's nice. So next question I had then as well. Trying to compare.

And maybe the question is what's been your biggest challenge then going from place like Amazon having I'm sure like resources left and right. Having access to a ton of data and then moving to place. I blew ground now fast-growing startup. But just not nearly, I think, as established in other ways as Amazon.

So what's been like one of your bigger challenges adapting to this environment. 

[00:19:58] Babis Makrynikolas: It's a good question. [00:20:00] Part of it is obviously Amazon, operating for, I don't know, like 27, 28 years now has established some data processes and some operational excellence practices and has all of that.

Experience of running running teams and running product teams, engineering teams, data teams, stuff it's hard to match anywhere else, only at Blueground. And so there are some resources which are very well-established and you join the, a group. Have this central hub of analytics.

That being said, Amazon is also growing organically and growing in a very much distributed way. And so every new theme is also giving their own thoughts on how they want to grow. And it's not all problem central. Even a timezone you have to fight for your data and have discussions about like how you get more data that you might don't have.

How'd you get access to something that you might not be logging properly. So there is a little bit of both. You have a great infrastructure, like as a base, but like every new product, has its own data needs. And so it takes time to [00:21:00] build a rubric. We'll see, we're starting at a much earlier point where we try to establish this framework while growing and while building teams.

And so we were behind, like compared to Amazon, but. We like, I think, having enough data for for our needs and building as we go, I think there is a lot of it is like building like centralized things, but also in bed. Data analysts or data scientists, or data related roles within teams to also get this data engine running.

There is, there's a little bit of a challenge there, but the way we're working on it 

[00:21:33] Andrew Michael: and how are you structuring? Then, so do you have a business intelligence team or data analytics team? What is the model that they operate under? And 

[00:21:41] Babis Makrynikolas: we do. So we have a business intelligence team, which is a centralized team that owns reporting KPIs across the company.

On. Our own company departments can work with the BI team to get their dashboards up and running. We're using some open source tools internally, which can help any member of the company to [00:22:00] get access to data without really having a technical knowledge. For example, you don't need to be able to write SQL to get simple answers.

Your question was like, okay, how many of my customers do XYZ? Obviously, for more sophisticated questions, you, you might need some help, but we try, with this open source tools to be able to answer other no 50, 70, 80% of questions and unlock the themes to really leverage data.

And I think lately we've been experimented with like models where we also embed data analyst or business analyst. I can different teams for example in the pricing team most of the members of the team are business analysts. And so they can definitely rather than run the SQL and pull data and are more familiar with these processes.

There is a little bit of both, starting centralized and then like a business analyst here and there. But I think it's also key to do the third thing, which is. Everyone, even if the title is not formerly business analyst, be able to know what data we have or how to request new data or self serve themselves to use data, like up to a point so that you really empower the whole [00:23:00] organization to use data.

And not just like the few that I don't know, everybody's techniques. I know how to do it. 

[00:23:05] Andrew Michael: Yeah. And like empowering the team with and making sure your, the data literacy is up there, I think is really, it's a great round. It's not a, it's a really impactful way. Like you really want to be using your analysts and your more technical team members to be tackling deeper problems and doing more intense research, not just answering questions that pretty much anybody can with a little bit of basic knowledge pull out themselves.

Exactly. 

[00:23:28] Babis Makrynikolas: Yeah. Like how many books had it yesterday? This would have a desperate for that. It's not like a question that you're under 75 days, hours a day. 

[00:23:34] Andrew Michael: And answer the same question. That was something that Hotjar was sending up to the business intelligence team. We always optimized for the local the global maximum.

So when we thought about the next project we weren't answering, like we actually, in the very beginning, we said, do not come to us for new requests, because if we start answering those now, like we're never gonna get to building the things we want to do. So we just started like picking off the biggest problems, building dashboards, building self-serve [00:24:00] analytics, and then educating the team along the way.

And I think that really put the team in a good position to be able to do some really interesting work then, and not be bombarded just by the same questions over and over. And then slowly as you said, getting to that embedded model now where they're working directly with product squads and individual challenges and But setting the foundations of things really important and educating the team on how to self-serve and democratize analytics is definitely the way to go.

But yeah. As soon as well, Tom is getting shorts. I want to ask you a question. I ask every guest to joins the show. Let's imagine a hypothetical scenario now that you joined a new company churn and retention is not doing good at this company. And the CEO comes to you and says, Hey, Babi's we really need to turn things around.

We only have 30 days to make an impact. You're in charge. You need to make a dent on the number. What are you going to do? The trick is you're not going to tell me that I'm going to go speak to customers, understand the biggest pain points and problem, and start doing that. And cause that's what everybody says.

You're going to pick something that you've either used yourself in the past or [00:25:00] seen work in the past to help increase retention. And you just blindly run with that. 

[00:25:06] Babis Makrynikolas: Okay. That's a, that's an interesting one. Sorry, I'm laughing because I, it's tricky to get into the executional mode.

We have understanding the problem deeply. I'm forcing myself to do it. Like I want to caveat that with the fact that I would probably try some of this other problem, but it's to take it to, so I guess I. Interpret their question is like what's your assessment of turn a root cause without knowing anything, but also without having the ability to investigate further 

[00:25:30] Andrew Michael: what's something you've seen, that's been effective in the past.

With reducing churn, 

[00:25:36] Babis Makrynikolas: I think a little bit of a. Company specific, for I need to probably understand the context, understand, this is might not be allowed. So I would do something like, improving customer experience. And this is a very wide range of things you can do.

But I think, in principle, the more value you give it. The lesson various customers have to, are willing or incentivize to leave your service. And so I would probably [00:26:00] go with something like improve my product in a material way to give customers more value.

Improve my customer support or, find other ways where I can add value to my customers. And this is a little bit different dependent, so we can talk about details, but like the idea would be increased. The value I provide to my users, bottom line. 

[00:26:17] Andrew Michael: Cool. So next question I have for you then is what's one thing that you know today about churn and retention that you wish you knew when you got started with you.

[00:26:27] Babis Makrynikolas: I don't know part of my, my, my challenge, like answering this specialist. I think I'm not thinking about certain additional daily basis. I think. Maybe repeat this customer, which is the, the inverse, but like somehow, it's I don't think of that as a, on my day to day my KPIs are not around turn attention.

Yeah. 

[00:26:48] Andrew Michael: Even like, when you say repeat customers and things like that from a Blueground perspective now would you say like retention is not a main driver?

Is it like repeat purchases? What is the main focus then? What are the [00:27:00] main, 

[00:27:02] Babis Makrynikolas: this is you could be a repeat customer, but it's not a given. It's not, if you think about it, like we're not selling a subscription, we're not selling like Compute, for example, like AWS.

Essentially if you're running your website, you need to purchase every day or every month. Cause otherwise, your head will be down or you've been, you have to go to the competitor. So there are like a few occasions where you need to use Blueground. Today like starting a new job.

 We try to increase that. And so w we had good success with some of our customers, for example, becoming more longterm customers, so Blueground but a lot of it, and I think there is that for example, is where we are talking about adding more buyers, right? So you want to have such a good solution, but like some of your customers will decide to stay with you.

There are cases like mine where I just put the hat. It was so blue round is like outside of my like the company, like I would use in Seattle because I own right now. But any, if I go to Greece, for a couple of months, I'll use them, but maybe then I'll use them two years later.

So it's a little bit of a tricky concept because you don't have a very specific usage [00:28:00] pattern. Like you might have another businesses, I dunno, like the clouds you clearly have like customers that like repeats month over month. And if you lose someone, it means that they decided most likely to do something else it's like with Blueground.

It's not exactly the case, like Airbnb or other types of companies. Like it's a little more transactional. Or an ad hoc type of of use and you might have them and even Airbnb, you might go on vacation twice a year and we'll ground, you might starting a job every three years. You'll see a repeat pattern over a long period of time, but it's a little harder to measure compared to other companies where this repeat usage patterns are like more standard.

[00:28:30] Andrew Michael: That's interesting in its own rights, I think. And that's something I think when we think about building habits that. People can't really build a habit out of your product because of the lifecycle and the frequency of use. This is something as well it's really good from Nir Eyal's book called hooked.

We talks about like how to build habit forming products, and really one of those key things is around the frequency of usage. if you don't have a frequency of usage if I remember correctly, it's been awhile since I read the book, but like of less than 30 days. So you need to make sure that People [00:29:00] have a trigger to use your product within 30 days, to be able to like effectively build a habit around that.

The one antidote I like on this is that although like a business like Blueground is not something that you frequently use. There's a good story. In newscasts thing from Zillow the property company, they also had a similar challenge in the sense that. You don't buy a house every day. So once you buy a house, it's yes.

I bought my house. I'm not going to do it another six months from now. It's probably five years down the line. I'm going to things. So what they were trying to figure out was I, okay. Like how can we still stay top of mind and how can we encourage those repeat purchases and retain those customers longterm?

And what they ended up doubling. Down on was a couple of things. One was content and the other was a home. Pricing calculator and effectively what they would do is like you would sign up for the service. Once you bought the house with Zillow and every like month or six months, whatever the frequency was, they would send you an update and say Hey, Babi is like your price.

You bought it at this much. Your current valuation of the house in your area would goes something like [00:30:00] this. So that's something. It's interesting for the buyer because they can say, okay, wow. Like I bought this and it's increased 20% in value over the last two, three years. And then also I'm still remembering Zillow next time I want to go and do that repeat purchase.

So just thinking through that in the context of a business like Blueground or some other places where you're looking for these repeat purchases, but the frequency is not entirely there. I find very fascinating. Is probably more longterm plan something. That's, you've got a thousand other things you're trying to tackle now at Blueground, but it's definitely thinking about like retention and how do you keep customers when the frequency is not.

Totally. 

[00:30:37] Babis Makrynikolas: Yeah, it is. It's a very tricky one, I would say. Yeah, it's hard to have the balance that I think Zillow and Redfin and this several companies also now are getting into the the business of actually taking over the sale of your house. And actually they give you a cash offer. So having this data, I think plays really well on other business models they're inventing which is a good monetization strategy for them having you, around.

And [00:31:00] like knowing that like the moment you are thinking of selling your house, most likely you're like. That's the ones that we say, if you don't want to appropriately list your house. So that's, I think that's a, that's an interesting example for sure.

[00:31:11] Andrew Michael: Super interesting. How they've used their leverage is that data.

And then the LTV of your typical customer just goes through the roof then, like you're actually not only helping them find the house, but selling the house and buying in front of them and in commission all around. I love that. Cool. Maybe is there any final thoughts you want to leave the listeners with today and you think they should be aware of like from your work or how can they keep up to speed and connect.

[00:31:32] Babis Makrynikolas: Yeah. Anyone can connect with me on LinkedIn. I also publish a blog whenever I have time. I've been not very consistent, I have to say, but like it's, dot com. So I, I write about most about product and pricing. Either way they can follow me. I'm also on Twitter, again, not super active, but I try to engage every now and then.

Yeah, feel free to reach out a hundred. Thank you so much. All the questions. Really interesting discussions there. 

[00:31:56] Andrew Michael: Nice. Yeah. We'll definitely link in the show notes. So if you want to [00:32:00] check it out and subscribe to whenever Babi's writes something like, go ahead and do it and yeah.

Thanks so much for joining. It's been great chatting today. We should be going forward. 

[00:32:09] Babis Makrynikolas: Likewise. Thank you. 

[00:32:10] Andrew Michael: Thanks.

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