The Economics of a Timeshare


I hate timeshare salespeople
I was in Cabo recently for a vacation. Our plan was to just sit by the beach and not do much. The problem was, the resort we stayed in was building a new timeshare complex near by and had those pesky salespeople in the lobby. “Timeshare salespeople” has a similar ring to “root canal appointment” and “IRS audit” – not something you want to deal with on your beach vacation.
Long story short, we decided to go on the tour & hear their pitch to get them off our back, get $300 in food credit, and to satisfy my curiosity. I was mainly curious about the economics of a timeshare and why these heavily incentivized, aggressive sales approach was rational for the seller.

The economics
The model room they showed us in the construction zone was beautiful. It was modern, had a nice view of the beach, and had great amenities. There are three ways a seller can make money off of this unit. They can sell it as a condo, rent it out as a hotel, or sell it as a timeshare.
Even in Cabo, a 2BR condo like the one we saw probably costs around $1M. Obviously they are showing us the best one and the actual room we would get is likely much less desirable, but for the sake of argument, let’s assume the market value of the unit as a condo is $1M. For the seller, a condo gets them instant revenue & cashflow, which allows them to pay off the cost of construction quickly and move on to the next project.
If this were a hotel room, they can easily charge $700 a night, and assuming 60% utilization the unit would generate about $150K annually. The problem with the hotel business model is that it is a high initial investment business that takes years to generate a profit. It will take 6-7 years of continuous management and maintenance of the unit to generate as much revenue as a condo, but in 20 years time, they would generate $3M. The beauty of the hotel business model is the upside and high margin they can generate after the initial cost is recouped.
The price of a timeshare essentially came down to $50K for the right to use the unit for a week every year for the next 25 years (so basically $2K/week). The great thing about a timeshare from a seller’s perspective is that they can sell the same unit 50 times over, which  means they can generate $2.5M. The buyer would finance the $50K by borrowing from somewhere and pay the seller upfront. For the seller, they are able to combine the financial upside of a hotel and the instant cash generation of a condo. No wonder the salespeople are so aggressive. The timeshare model generates more revenue, gets them cash up front, and minimizes the risk & cost of having to manage utilization.

From the consumer perspective
So, why would anyone pay for a timeshare when the consumer is the one baring the financial risk and is getting tied into a long term commitment? Well if you take at least one week of vacation every year (most people do) and really really like Cabo or wherever your timeshare is, then the $2K a year will get you a $700/night hotel room for 5 nights at your favorite location. That’s not a terrible deal. Timeshares are also part of a network of other timeshares, so if you want to use your week of vacation somewhere else, you likely can find a place in the network. Even though they are selling the units as a timeshare, as a buyer, you are basically buying into a lifelong vacation club membership.

The problem is the risk and misaligned incentives. Why would a timeshare maintain the properties at a pristine level if that is not going to generate any additional revenue? What happens if the timeshare company goes bankrupt? What if a hurricane hits and destroys the building? Who is responsible to pay for the repair and are they incentivized to fix the problem? When you are on vacation and you have a salesperson telling you to sign a contract right then & there, are you really in the right mindset to make a $50K commitment?

For me the answer is clear: Thanks for the food credit, but no thanks. Now if you could stop bothering me, there is a chair on the beach with my name on it.

Winter is Coming (to Ad Tech)


This past couple of weeks have been full of great news in ad tech. The Trade Desk went public and immediately saw their stock price pop 60%. AppNexus raised a small round, presumably setting themselves up for an IPO. Krux was acquired for $650-750M by Salesforce. HookLogic was acquired by Criteo for $250M.

Is the future of ad tech rosy, or is this the sunshine before a long winter?

Well, as a proud owner of RocketFuel stock (bought at $25, now hovering around $2.5, do the math) I am looking at this as glass-is-half-empty. Don’t get me wrong, I love ad tech. Tech IPO is a good thing and these companies have built real, tangible businesses that can withstand the scrutiny of the SEC and the public market (I’m looking at you Uber and Airbnb). The problem is, beyond the wall(ed garden), a massive disruptive force is rising up, looking to swallow up the whole industry. Facebook.

We all know Game of Thrones is a thinly veiled metaphor for the ad tech industry. We are all fighting over market share, going after each other’s advertisers and their budgets. New houses emerge, alliances are made, innovative weapons are developed, all to claim the throne, which Google is sitting on. House Salesforce has acquired Krux as they go to war against House Oracle and House Adobe. House Criteo has acquired HookLogic to strengthen their arsenal. House Amazon has fucking dragons.

When Bezos had long hair

Meanwhile beyond the wall, Facebook has evolved into a completely different creature. They were initially happy staying on the other side of the wall, but the Audience Network (where FB allows advertisers to buy non-FB mobile ad inventory using FB data) is the breach of the wall. They are coming and no one standing between them and the advertiser is safe.

Zuck delivering his sales pitch

Zuck delivering his sales pitch to advertisers

How formidable is Facebook?

Let’s look at the numbers. Let’s see… in Q2 2016 FB had:

  • 1.7B MAU
  • $6.2B in revenue
  • $2B in Net Income

First of all, MAU and quarterly financials in Billions is ridiculous. They also have $5B in cash, so if they wanted to, they could just buy Criteo ($2.1B) and The Trade Desk ($1B) and AppNexus (prob $1B) and MediaMath (prob $1B) without borrowing money. Not that they would, but they could. Facebook’s market cap ($370B) is bigger than Salesforce ($48B), Oracle ($159B), and Adobe ($54B) combined, plus you can throw in IBM ($110B) for good measure.

Let’s look at FB from a capability standpoint. At a high level, points of differentiation in ad tech are:

  • inventory (eyeballs = scale)
  • data (what you know about the eyeballs)
  • algorithm (to optimize ROI)
  • access to ad budget

The four capabilities reside in different parts of the ad tech ecosystem today. Publishers own the inventory and SSPs consolidate them. Data sits in DMPs and the DSPs provide the algorithm + connects the dots with the SSPs & DMPs. Agencies have access to the budget. Most players in the Lumascape does one of the four. Some players can check off multiple boxes, for an example Appnexus could be considered a SSP + DSP and they also have direct advertiser relationships.

What is scary about FB is that they do all four and they do it 10x better.

  • Inventory: 1.7B MAU is a good start + nothing stopping FB from expanding the Audience Network to more inventory
  • Data: This one is obvious. FB knows more about their users than anyone. (although Amazon does have purchase data = the aforementioned dragons).
  • Algorithm: Army of top data scientists working with massive, proprietary data is a winning formula
  • Access to budget: FB has improved the Ad Account and Business Manager to make it easier for advertisers to manage campaigns themselves. The number of advertisers taking FB ads in-house is only going to increase, chipping away the agency’s business.

To top it all off, because FB can directly connect the advertiser budget to an impression without the slew of vendors taking a margin, FB can do all this at a lower price. Traditionally, a dollar an advertiser had spent would become 50 cents by the time it reached the publisher as the money change hands throughout the Lumascape, whereas in the FB ecosystem, the dollar is essentially pure margin.

What’s Next

Digital ads will continue to grow and eat away TV and other marketing budget, so ad tech will not go away overnight. But while ad tech players are busy fighting each other (like the houses in Westoros), a formidable force has started it’s march south. FB will continue to invest in new inventories through partnerships and acquisitions like Instagram, and will further their self service agenda. There will be further consolidation in the Lumascape to connect the ad dollars to eyeballs more efficiently (as in lower margins for everyone). A lot of small players will get crushed. The time to join forces with rivals to beat the common, great threat has passed. Every house must now answer – why do they deserve to survive when winter is here? What value are they adding that Facebook does not / will not?


How the Unicorn Bubble will Pop


I flew into JFK, saw that the taxi line was really long, called a Lyft, and was on my way 2 minutes later. And I paid $10 less than I would have, had I chosen to wait in line for 30 min, ride in a hygienically questionable car driven by an uber aggressive maniac yelling into his phone, with the radio blasting in an unrecognizable language. Instead I was in a quiet, clean Camry driven by a polite person, with a free bottle of water. Unicorns are changing the world, making incumbent business models obsolete by improving the service by 10x while saving people money AND helping people earn a living. There is no doubt these guys are improving the overall wellbeing of Americans and in the long run it will be very hard for anyone to stop them. But Uber should not be valued at $50B valuable when you adjust that potential for regulatory and liquidity risk.

Are we in a bubble?
Yes, I think we are in a bubble. Nothing like the dot com bubble or the financial crisis of 2008, but a modest bubble in a more localized area of our economy; the tech private IPO market. The term private IPO is already an oxymoron (come on, private initial public offer?) which is a strong signal that the end is near. Investors are throwing billions of dollars to companies with audacious business models & goals without properly adjusting valuation for risk.

No Exit in Sight
Some of the unicorns are too big to be bought out, too risky to go public, and too expensive to operate to turn a profit in the short term. Who’s going to pony up $50B to buy Uber? $24B to buy Airbnb? $11B to buy Pinterest? Well, I guess the last one is not completely unrealistic, but the point is, this is not exactly a seller’s market at this price range.
Which brings us to the IPO as an exit option. How do you know you are operating a risky business? How about: When a leader gets arrested for merely doing his job. An IPO is a tradeoff of accessing large amount of capital in exchange for transparency and scrutiny. You can’t really take a company public operating in a regulatory gray area. You would also need a financial plan to justify a high valuation. Kind of hard when you thought you had a million contractors and overnight you may be stuck with a million employees with benefits. Adding to this, it is increasingly hard to get to cashflow positive in Silicon Valley nowadays when an entry level engineer will cost you $150K (with benefits) and you are competing against Google, FB, and Apple for talent.
Another way to return $$ to investors is to pay out dividends. LOL. Sure, that will happen in our life time.

What will trigger the bubble to pop?
Private companies are by definition less transparent and less liquid than public companies. Because valuation of these companies don’t get adjusted in real time like the public market, we see valuation jump up round to round, making it a bumpy uphill graph that looks like stairs. That is, until you exit and join your post IPO buddies, or you run out of cash and have to raise another round at (gasp) a lower valuation.
Is Uber worth $50B? I don’t think it is, not today after you adjust for regulatory and liquidity risk. Two things can happen when Uber runs out of cash and has to raise another round. The existing investors plus additional suckers driven by the fear of missing out doubles down at a higher valuation and extends the bubble period or (I think the more likely scenario is) Uber raises a round at a lower valuation, which triggers the bubble to pop.
What is interesting about this bubble pop is that it will be a series of smaller pops spreading over months or even years. Uber will cause the most significant splash and will affect the public market.  Then we will wait a few months until the next unicorn not ready for an IPO has to raise a round at a lower valuation, and then another. Each time this happens, the asset column on an investor’s balance sheet shrinks, investors and entrepreneurs become more risk averse, and startup fueled innovation slows.

So who’s getting screwed?
Investors, investors of investors, founders and employees will feel the most direct hit since they own these less valuable assets. Let’s think about what the ripple effect will be.

Early round startups: lose
There will be less funding to go around to the new new things. The apparent decrease in upside will make entrepreneurs more risk averse and it will be harder to convince early employees to leave a big company to join a small startup. VC’s will also have a tougher time raising funding.

Publicly traded tech companies: lose then win
A lot of focus will be put on righting the unicorns, which would include added financial discipline and perhaps rounds of firings and key employees leaving. I would think stock prices of tech companies will go down with the ripple effect but Google, FB, Apple, etc. are companies generating real profits with less regulatory risks and full liquidity. They had to overpay for engineers because startups were luring them with equity upside but disappointed rockstars will start leaving unicorns and tech giants will pick them up immediately. These are rare game changing talents that could show up in bunches. In the long run the tech giants will get stronger from this.

What’s next?
I think this series of events is just a hiccup on the hype cycle. A lot of unicorns are building businesses that are disrupting dysfunctional industries, creating jobs, and generally improving the world. Uber may not be worth $50B today but they just need to tackle the issues they are facing one by one and in a few years they could get there (or more). The cause of this round of bubble is less about the businesses faking value and more about investors making undisciplined financial decisions; a private company valuation inflation if you will. Unicorns are supposed to be hard to find. A billion dollar valuation represents more than just market size and potential but the ability to execute on and operationalize the opportunity. Some unicorns will lose their status after this is all over but that does not mean they are worthless. Some will figure things out and will make a dent in the universe.


Measuring Digital Advertisement Performance


Impressions, viewable impressions, engagements, clicks, conversions, site sales, new customers, lead generation, brand recognition, etc. etc. There are so many ways to measure the performance of your digital advertisement campaigns. In an effort to differentiate their offerings and communicate their value to their clients, online marketing vendors have created a cluster of performance metrics. I will take a stab at making some sense of this complicated world of KPI’s.

What is the ultimate goal of a marketer?

The answer to this question, as is most things in life, is “it depends”. Some marketers may be focused on getting new customers to sign up and some may be tasked with achieving volume even at the expense of margin. Some marketers are looking for stringent ROI and some want to increase brand awareness. The one common theme among marketers is that they want to influence their audience. Whether the ad triggers a consumer to click through and buy a product on the spot, or the ad enters the consumer’s subconscious and two years later ends up in a purchase, what is important is that the ad is causing some (positive) change to the consumer’s psyche.

All measurements are flawed

Yes, all of them. Take for example, the device you are using to read this. Have you ever seen an ad for the product or the brand? Did the ad have any effect on you that caused you to ultimately purchase the device? Your likely answer is yes I have seen an ad for my phone or tablet or PC or the brand but no, I can’t tell you whether it had an effect on me. This is because our purchase decision is nonlinear and often illogical, and you just can’t quantify influence. However, in aggregate, marketing works and advertisement works. After all, you need to be aware of the existence of Apple, the iPad, and the iPhone to buy them, and it seems like a bunch of people are purchasing the products. This means, as responsible adults, we have to take a crack at measuring influence in order to figure out a way to influence consumers effectively and efficiently.

Measuring Influence

Impressions, clicks, conversions, etc. These are all proxies for influence, and they are all flawed. The graphic below describes the upside and downside of these metrics.

False Positives

Let’s take a look at our good friend Impressions. Yes we can easily count the number of impressions an ad delivered. However, 56% of online banner ads are never seen (according to Onscroll). So 56% of impressions have exactly 0% chance of influencing the user.

This is where the question of viewability comes into play. Unless the impression is viewable, it serves zero purpose for a marketer. Well great, we should only be looking at viewable impressions then. OK but most viewable impressions didn’t do jack to the consumer’s brain. It is way more likely that the consumer did not even notice the ad because she was busy consuming the content which was why she was there in the first place. When was the last time you saw an ad that actually made an impression on you (pun not intended)?

So measuring our campaigns by impressions is flawed for two reasons. 1, there are a bunch of impressions that are not viewable (which can be solved for) and 2, there are a bunch of impressions that are not influencing the consumer. By casting a wide net, we end up with a measurement that is full of false positive signals.

False Negatives

The flip side of the false positive issue is the false negative issue. Let’s look at clicks. Have you ever accidentally clicked on ad? Sure. Does it happen often? Not that often. So if you clicked on an ad, you were likely influenced by it. Something has happened in your brain.  However, when we look at ads, even if they influence us, it does not mean we will click on them. The more likely scenario is, you see an ad, register it in your brain, and move on with life.

Engagement is a measurement that alleviates some issues with click without solving the bigger issue. The definition of what constitutes an engagement is up for debate and I’m not here to settle any debate, but I look at engagement in two layers.

One is the mouse over. You point your mouse over an ad for a couple of seconds and we assume your eyes followed, so not only was this ad viewable, it was viewed. Have you ever unintentionally left your mouse over an ad? Yes, and it happens more often than an accidental click. When you look at an ad, do you put your mouse on it? Um, maybe? Nah. This measurement has both false positive and false negative issues.

The second layer of engagement is when a user plays around with the ad unit. Let’s call this “interaction” to make the distinction from the mouse over. Some ads are dynamic with buttons to scroll through different products, or zoom in features, or some interactive functions. I would never click on an ad, but I may play around with an ad just to see. The experience is not as disruptive as a click through because the user does not get sent to another page when she may or may not be done consuming the contents of the current page. These interactions are definitely influencing the consumer, even though the user is not clicking through. This measurement solves for the false positive issues, but will never cover all influential touch points, so the false negative issue remains.

In (Cheesy) Summary

Some metrics are definitively better than others, like viewable impression is a better proxy for influence than regular impression, but there are no end all be all metric. Influence is merely a concept and cannot be measured quantitatively, at least not precisely. If I can be poetic and cheesy for a second, influence is kinda like love. You know it’s there but how do you measure it? There are proxies to quantify love like the number of text messages sent, your heart rate when in front of the one, or the size of the rock on a ring, but none are accurate. The goal of measuring influence is not to be accurate. What is more important is to be actionable, because at the end of the day, the marketer’s goal is not to measure, but is to influence.


CPM Pricing in a Post-Last Click World


“How much should one bid on a specific impression?” is a question I have been trying to answer for years and have shared my thoughts on this blog. Today, we are approaching CPM bid price as lazy financiers approach asset valuation; assume other similar transactions were done rationally and base our pricing on their results. Well what if everyone else had made the same assumption? Are we sure the market value of an impression is equal to its intrinsic value?

CPM Pricing in the Last Click World

In the last click world, life was relatively simple. As a merchant, I know my margins and I can tell you how much % of revenue I am willing to part with for a sale. Given that information, I can back into a CPM price I am willing to pay, using best guesses for click through rate (CTR), conversion rate (CVR), average order value (AOV), and adjusting for risk (or how confident you are in your guesses). More details in this post.  To summarize in one formula, the concept looks like this:

CPM=ƒ(μCTR*μCVR*μAOV*% Sales, σCTR, σCVR, σAOV)

We have always used eCPM as the common denominator to compare performances of different pricing models, which ignored two important things; the quality of the impressions, and the value of risk transfer. The formula takes these two key points into account. Better quality of impression can be defined as an impression with a higher mean CTR and/or CVR and/or AOV, and you should be willing to pay more. Higher risk for the advertiser can be defined as a higher standard deviation for CTR and/or CVR and/or AOV and you should be willing to pay less.

Then Came Attribution

The last click model is linear but consumer journey is not. You can’t look at each marketing channel & campaigns in a vacuum, since that is not how people experience your brand and end up becoming a customer. The concept of attribution makes total sense. The issue is, in reality it is impossible to attribute a sale “accurately”. Not even the consumer will be able to tell you why she ended up buying that item on that day.

For all we know, the customer bought the item because she had a bad day, had one too many glasses of wine, and saw a handbag she liked displayed in the storefront on the way home. So we should attribute 30% of the sale to her ex-boyfriend, 40% to the bar (20% to bartender, 20% to the guy buying her drinks), 20% to the store, and of course 10% to Google because she searched for the product name to get to the purchase page.

By trying to illustrate why accurate attribution is impossible, I also just illustrated why last click is pretty much always wrong. In the above case, all these things contributed to the sale but Google just happened to be conveniently located as the last click hub of anyone knowing what she wants to buy. Where is the value in that as a marketing channel? Why would Google deserve 100% of the credit? (hint: it shouldn’t)

So we know last click is (almost) always wrong and attribution is never accurate. That’s why a whole slew of attribution companies are out there touting their attribution logic to be the most accurate. “Black box” guys hire PhD’s and crunch numbers based on consumer touch points and conversions and other data points (“Can’t explain exactly why it’s accurate but trust me, I’m a doctor”) and some are more open, incorporating the advertisers requests (“Yea, we have no idea either. Let’s prove your boss was right all along”).

No matter how imperfect attribution is, it is reality for digital marketing today because never accurate sure beats always wrong. So what does attribution mean to our little formula? Obviously, the formula needs to be adjusted but just how?

Influence is the New Click

When we review the formula, we notice that two things happen outside of the advertiser’s domain; the impression and the click. CVR and AOV happen on the advertiser side and the advertiser obviously has control over the % of Sales to be paid. In the post last click world, what we care about is “influence”. If accurate attribution was available, we would be measuring how much of the influence did this particular ad unit have to this consumer’s purchase. Clicks used to be the go to proxy for influence because it required user action and intent. But what about video ads? Interactive ads that don’t need you to click? Audio ads? A really well made static display ad that captivates you? There are so many things that can happen between an impression and a click. Click as a measurement of influence is flawed and by association, CTR is also flawed. What matters is the conversion rate between who the ad campaign reached and who visited the advertiser site. This is measuring unique users, so a user coming back to the site via retargeted ad would count as one visitor. Let’s call this conversion rate RUR for Reach to Unique Ratio.

The reason why we should look at RUR from a unique user perspective is because the consumer journey does not end when the user visits the site. It ends when the user converts or the marketer gives up. So when a user visits a product page, leaves to do additional research, the retargeting ad that brought her back should not take whole credit of the sale. The brand touch points that brought her to the site in the first place should also be credited. There’s another variable that we have to consider. How many touch points it takes for the user to become a unique visitor to the site (TP as in touch point, not toilet paper).

The New Formula

Given all these thoughts, I can finally put together the theoretical CPM pricing formula taking attribution into consideration.

CPM=ƒ(μRUR/μ#TP *μCVR*μAOV*% Sales,
σRUR, σ#TP, σCVR, σAOV)

Let me try to explain this verbally. The CPM you should be willing to bid on a given impression has to do with the average % of users you reach who turn out to be a unique visitor to your site, how many touch points it takes for you to get this user to come visit your site, and once visited, what the conversion rate, AOV are, and of course, how much of the revenue you are willing to give up. Oh, and don’t forget to adjust for risk.

You may have noticed the definition of CVR actually changed slightly in this formula because we are allowing for the eventual return of this user outside of the current session, so CVR is actually “eventual CVR”.

Ah, Crap

I hope you didn’t notice I took a major short cut. Because I just did and I want to go to bed but promised myself I would finish this post today. Attribution. Yes it is still haunting me. The assumption I made in the formula is that every impression is equal and I can use the average number of touch points to get to the CPM price. This means, if we average 4 touch points before a user visits the site or we give up, I am attributing 25% of the credit to each of the touch points.  If the whole attribution movement taught us anything, it taught us that each touch point is unique in how influential it will be. Let’s call that variable % Infl. By definition, the % infl will add up to 100% when the user visits the site or the marketer gives up.

CPM TP1→N =ƒ(μRUR * %Infl TP1→N *μCVR*μAOV*% Sales,
σRUR, σ%Infl, σCVR, σAOV)
where sum (% Infl TP1→N) = 100%

I don’t even know the mathematically correct way to denote this but the spirit is there. I think I actually used an Excel formula near the end, but whatever, I had 3 hours of sleep last night. When you are bidding on an impression, you have to know where in the consumer journey this person is and what state of mind she is in, and what touch points she has had with your brand. Given all that and with the magic of big data, and your future intent to buy this consumer’s impression, you buying this next impression will have a certain effect on this person’s psyche and hopefully purchase behavior in the future. When the % Infl adds up to 100%, theoretically the user has RUR% chance of visiting the site, CVR% chance of becoming a customer, and spending $AOV.

In Conclusion, So Many Freaking Caveats

Well at least I got to some kind of formula, no matter how weird it looks. There are a boat load of caveats and three come to mind immediately.

First, as I mentioned above, there are so much predictive algorithm that needs to happen to get to the RUR and % Infl variables. But this is the nature of being able to bid on the most granular level possible to mankind, the impression. Now most bidding currently happens on the segment level and my thought process assumes personal level, so maybe this is the kind of thing we need to think about in the future.

Another caveat, not sure if you caught this, is the question of incrementality. I went through all this trouble to calculate how much this impression is worth, but in reality, the advertiser already has some organic traffic that converts at a certain rate. Shouldn’t the advertiser spend only on the difference of the effect? There is a lot of argument that can be made for both sides but maybe some kind of discount needs to happen. Also remember way earlier in this post I mentioned that the quality of the impression not only increases CTR but potentially also CVR and AOV.

One final caveat. Won’t advertisers care about the life time value of the customer instead of the one sale? Yes, most certainly and I think the formula can be adjusted to take that into consideration. At some point in the near future, I’ll explore this idea.

Man, the future of digital marketing is full of formulas and quantitative intellectual reasoning. There has got to be someone better at this than me. I failed calculus 18 years ago (I was a terrible student back then)!


Turning Excess Supply into a Business


It’s been a while since I last wrote a post. Meanwhile, my daughter who was a week old when I wrote my first post and was a tiny creature will be turning one tomorrow and has become a havoc wreaking human being.

Today I wanted to talk about finding business opportunities. No, I’ve never started a company and am yet to make my millions, but there are certain ways to look at the world to find your chance. Entrepreneurs don’t necessary start their businesses through these perspectives but not everyone is über passionate about that one thing in life either. I believe passion is necessary for an entrepreneur to succeed, but that passion does not have to be the subject of your business. There are successful people who own waste management companies, who may not be passionate about garbage.

Anyways, Excess Supply is today’s theme. If you look at some successful companies, they leverage existing assets that are underutilized.

For example:

  • Say I own a vacation home in Hawaii I use 3 months a year (25% utilization), there is Homeaway or Airbnb.
  • If I’m traveling, my car will be sitting in an airport parking lot for the next week, costing me money. Well there is FlightCar.
  • Speaking of cars, why own a car when you won’t drive it more than 2 hours a day? Zipcar.
  • What if I’m a bike messenger and I’m not always busy during the day? Postmates.
  • I run a website with a bunch of remnant inventory. That’s where DSP/SSP/RTB comes in.
  • You get the point.

    The good thing about excess supply is that it’s sunk cost for the owner. If I’ve already bought a car and it’s going to sit at the airport parking lot, I might as well rent it because making a couple of extra bucks is better than paying for a week parking. And “might as well” comes cheap. Why are listings on Airbnb cheaper than hotel rooms despite being bigger with wifi and a fully functioning kitchen? Because the renter is visiting friends in Boston for a week but she still owes the full month rent, so she might as well rent it for cheap than not rent it at all. The renter is better off, the visitor is better off saving money on hotels, and there is still money left for Airbnb to be in business.

    Every asset not utilized is an opportunity. Every parked car is underutilized. Every empty space (office buildings are completely empty at night and a lot of homes are empty during the day…). Restaurant kitchens in closed hours (maybe you can rent them to run your business from 2am?). Food delivery guys in non-peak hours (what else can they deliver?). If you go to the movies on a weekday morning, there are 3 people in a theater that sits 200, can’t they rent the space out to someone?

    Next time you walk around town, look for these underutilized assets because there maybe a business opportunity there. And any of the owners will gladly let you utilize the assets because, hey, they might as well.


    The Intrinsic Value of an Impression


    In the finance world, there are roughly two ways of calculating the value of an asset; the market approach and the cash flow approach. You can valuate company X by comparing it to a similar company Y, by taking the ratio between the market value and operating profit or EBITDA or other financial measurements. Alternatively, you can come up with the best guess estimate of future cash flow from the company and use a discount rate to calculate the net present value. Now, calculating future cash flows is painful and calculating the discount rate can be even more of a pain the ass, whereas the multiple approach is much quicker because it makes one key assumption: the market value of the comparison asset is correct. The flaw with the market approach is that when the comparison asset is not valuated appropriately you are never going to get your valuation right. When the market is wrong everyone is wrong.

    So being this is (sometimes) a blog about ad tech, why am I talking about this? Because I wanted to pose a question: When you are making your bid for an impression, what are you basing it on? Are we assuming that whatever the market is willing to pay for the impression is the “right” price? Are we bidding the current price plus a penny for an impression we want? You can already guess where I am going with this. What if we are pricing everything wrong? What if everyone is overpaying for those impressions?

    Conceptually the cash flow method is simple. We should be valuating the impression based on how much the impression is expected to bring us in terms of revenue (or profit). Say you have a $100 product you have an ad for and you are willing to spend $10 to get someone to purchase it. If you knew for certain that a person has a 10% chance of buying your product by showing your ad, you should be willing to spend $1 on that impression. Now, as discussed in my older post An Efficient Market for Online Ads there are a few variables at play here:

    • Click Through Rate (CTR)
    • Conversion Rate (CVR)
    • Average Order Value (AOV): the average value of the basket
    • % of Sales I am willing to spend for a conversion

    How much I am willing to spend on an impression can be expressed as: CPM = CTR*CVR*AOV*%Sales. These variables all have different variances meaning some variables are more predictable than others and how confident I am about the variances will affect how much I am willing to pay. The % of sales I am willing to spend is an internal decision that depends on my cost structure, capital structure, and appetite for risk. Given all this, the formula looks like this:image

    The above assumes last click attribution which is being really under fire these days (for good reason). I will share my thoughts on attribution on CPM pricing in another post, but the point here is, you should not assume whatever price the market is bidding for an impression is the price you should be willing to pay. Theoretically there is a “right” price you should be willing to bid for every impression, given the characteristics of the impression and your situation. Whether you act based on the market value or intrinsic value of that impression is up to you.


    My War Story: Worst Project Ever


    People in Japan work long hours. Especially those in the IT sector. I spent 6 years working 60-80 hour weeks and was involved in some crazy projects. This is a first hand account on the worst project I have ever seen or heard of.

    I was probably 25 or so and it was the first project I was assigned to as a leader. I was to manage a couple of programmers (who were both in their 40’s, which is completely unrelated, but one of them used to program with punched cards. PUNCHED CARDS!) to develop a data migration tool. Yes, this project required 3 programmers full time about 6 months to develop a tool just for data migration. This was because we were a small part of a $10mm+ ERP project for one of Japan’s biggest companies to be unnamed, and the implementation was led by a public system integrator also to be unnamed. The data migration tool development was complicated but contained. However, the ERP implementation project was just insanely out of control. I got to sit court side for the fireworks.

    The lesson here is, never bite off more than you can chew. The main implementation partner did not have the skills or experience necessary for such a large scale ERP project. What ensued was your classic scope creep story that brought seemingly endless new use cases. With the client being so huge, they were basically customizing SAP to a level that the system was not designed to accommodate, but no one had the balls to say no. There were more than 200 developers coding furiously what seemed like 24/7 and people were getting worn out. Most developers were working 7days and putting in 100+ hours week in and week out and trying to handle an endless string of requirements change. The whole project room had a stench of guys not showering in a week and the atmosphere was literally that of a death march. There was no redemption or silver lining. We were bound to fail and there was nothing anyone can do about it. But for some strange reason, everyone worked their asses off and refused to see the reality that we were doomed.

    For me the most interesting part was seeing how people snapped under this extreme stress. The overall project manager developed ulcers and had to pass stones with his urine, which apparently was excruciatingly painful. But he was better off than some people. I actually saw ambulances come in to the office on two different occasions. Once in an afternoon on a Wednesday where a guy was getting carried off sitting on a stretcher, staring at blank space seemingly lost the will to move. The other one was on a Sunday afternoon. This guy must have not left the office or taken a shower in days. Two paramedics held each of his arms and he had wet his pants and was drooling and screaming incomprehensible words while he got dragged away. This guy was in such a primal state of being that he was barely functioning as a human being.

    More than a year later the project was halted with nothing to show for. The contract guaranteed deliverables, which meant the client didn’t have to pay a penny and the main implementation partner bared all cost. For the 200+ people involved, not a single person was better off (except arguably for my team. We got paid, were relatively unharmed, and I got to see some things you don’t see everyday).

    This was my war story. Anyone else got some interesting war stories? Let’s trade war stories.


    Horrible Bosses: The Uber Micro Manager


    I finished up a management training recently, and that got me thinking about my past bosses. I’ve worked for quite a few managers, some good, some bad, and two distinctively horrible. One was your classic horrible boss. He was incompetent, always looking for opportunities to steal other people’s credit, and a general scumbag. The other one was a more rare and interesting case. He was extremely logical to the point of being robotic and his issue was definitely not incompetence. He was the ultimate micro manager and had to control 100% of your effort, down to your thought process. I have to say working for him was the turning point of my career.

    I was 25 years old and had 3 years of experience as an IT consultant at the company. I had moved back to Japan for this job and was getting comfortable with the culture and gaining confidence at work. When I was assigned to the project, my colleagues had warned me about the project manager. In the five years that he had worked there, he had sent numerous young consultants to therapy and a lot of them would never come back. He had a reputation of being too tough but he would get challenging projects done and the company valued that.

    The first three months of the six months project was tough but bearable. It was a three person team and I would get yelled at sometimes but it wasn’t anything I couldn’t tough it out. After three month, the other member of the team had rolled off the project and from there, it was a living hell. There was not a day that went by without getting yelled at. Every powerpoint slide, every excel sheet, every word I mouthed, every output I created was absolute shit. Everything I did was illogical and wrong and everything I had ever done in my life was worthless. If this was just an asshole boss, I could have shrugged it off and bad mouthed him behind his back over beer to feel better. What really sucked about working for him was that he was always logical and every feedback he had was backed with facts. He had notes of my actions and words that he would pull out as evidence and he created an environment that forced me to face the fact that I was wrong, illogical, stubborn, immature, incompetent, and useless.

    A lot of times when he asked me a simple question, something like “what happened to that meeting material?” and it would take me a split second to answer because some thoughts crossed my mind unconsciously. Before I could answer, I was getting yelled at again for the thoughts that he somehow knew was crossing my mind.He would be able to verbalize my thoughts better than I could. “When I asked you this, your initial thought was X and then you thought Y. Those thoughts are inefficient and worthless.”

    After getting tired of giving me the same feedback over and over, he made a list of things he wanted me to be always aware of. Every morning I come into the work, I would spend the first 10 minutes reciting the list to him. It went something like: “I will listen to what you say. I will make sure I write down every instructions given to me. Before I ask for your review I will confirm that the output achieves the objective.” and so on. When I did something wrong during the day, which inevitably happened multiple times every day, I would have to go back to the list and recite again.

    The most intense part of the project was only for three months, but it was the longest and toughest three months ever. I became the first person to go through a two person project with him and not end up in therapy, although I have to say I was pretty damn close. I definitely came out of the project stronger and a lot more humble (that’s what happens when every single non fact based confidence is shattered). Could I ever work with him again? Hell no! However, I do have a weird appreciation for what he had done, I guess it’s similar to Stockholm syndrome or maybe just the result of the brainwashing. After the project I did grow as a consultant and became a lot more disciplined. I also learned how not to manage a team and the importance of treating your team with dignity.

    In my defense, I am a smart and logical professional (I swear!) and even back then I was a competent consultant. I consider this period as boot camp and I needed a little ass kicking because let’s face it, I was a spoiled brat. However, I believe extreme stress is not always the fastest way to grow or succeed. I don’t need to beat him as an individual (and I don’t think I can) but I hope to be a better manager and a more effective leader than him. I look up to him in many ways but at the same time, he is my anti role model as a manager.


    Next Step for Ad Tech: Product Data Innovation


    We’ve reached the most granular level in online advertisement targeting technology: an SKU to an impression. So where will the next innovation come from? I’m thinking the next big step in ad tech will be on the product side, helping advertisers pick which SKU to show a particular impression, at a specific bid price.

    An impression is more than just the age and gender of the person. It is the person at the exact time and place. It includes the demographic, psychographic, and geographic information, the taste, mood, and everything about who the person is, adjusted for the context of what she is looking at. We probably can’t combine all those data for a given impression yet, but we’ve made a lot of progress in painting a picture of this person at the exact state in time.

    What about the SKU side? When an impression shows up on a website, how much should an advertiser bid on this impression and more importantly, which product should he show?
    I believe an impression has a fair intrinsic value that is a function of the predicted click through rate, conversion rate, the average order value, and how much % of sales the advertiser is willing to pay, adjusted for risk. In a formula, this looks like this:

    I’ve outlined my thought process in a previous entry, found here.

    My question is, of all the advertisers that are bidding on impressions, how many are looking at the product side on a data driven way? By data driven, I don’t mean “this product is designed for single urban males who like cars”. I mean “analyzing all the transaction history involving this product, the calculated CTR & CVR of this impression is X & Y, with standard deviation of Z”. The point is, advertisers are choosing which product to show without the rigorous past purchase analysis that should point them to the optimal SKU to show at the optimal bidding price. If you sell hundreds or thousands or even millions of SKUs how can you be sure that the ad you decide to serve is the best one in your product portfolio? Also if you are not amazon or Walmart do you have enough data to really make an intelligent decision?

    The next innovation in ad tech will effectively collect these purchase data across merchants and transform it in a way that is useful to advertisers. With information asymmetry out of the way, advertisers will bid on impressions based on predicted CTR, CVR, and AOV. So what will win an impression when multiple merchants are selling identical products? It is the % of sales the advertiser is willing to give up thus ensuring the publisher will get the maximum $ for the impression. The user who will see the ad will see a calculated, optimized ad, which should match his profile so well that the ad will be less of a distraction and more of a content. Of course the advertiser who was willing to pay the most will get to show the ad, so we have a win-win-win situation between the user, publisher, and the advertiser. Moreover the product side innovation should drive automation even further. Ultimately, a user showing up to a website will trigger a process that will sift through millions of products across advertisers to find the optimal one. That sounds like a more efficient market to me.