Monthly Archives: January 2013

Culture is Top Down

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Corporate culture is determined top down. There is no such thing as great corporate culture despite the leaders. Great culture only exists when leaders exhibit the qualities they want to see from their teams. The worst is when managers complain about their own team culture. “I don’t understand why my team is always bitching and complaining!” Touche.

Why is this? It’s because people, for the most part, are coachable and want to succeed (I say “most part” because unfixable assholes do exist and that is a hiring problem). Corporations are organized in a way that incentives upward mobility. So for the most part, employees are looking up and learning what they need to do to go up. If they see leaders politicizing and getting rewarded, they will mimic those behaviors, creating a political culture. If leaders exhibit teamwork and integrity, that will trickle down the organization and create a positive culture.

So if you are a leader of an organization and want to build a good positive culture, first look at yourself and how your fellow leaders are acting. You may be able to fix your corporate-wide culture issue just within the leadership team.

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Office Politics Suck

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Office politics suck. It sucks for the company and it sucks for the employees.

It sucks because it is so resource intensive. It takes time and effort to learn the dynamics of the organization (the rules of the game), play the game, and be good at it. This is time and effort that should have been used for more productive activities.

It sucks because the asset and skill set you build are not transferable. You are making your investment in people and how they perceive you, and people change in organizations. The boss may get transferred or fired or may hire a new favorite. The company you work for may go bankrupt. You and the investment you have made are at the mercy of these changes. Also, that nice title you got without the competency to back it up will bite you in the ass in the future (and everyone below you will be laughing).

So why do many (most?) companies fall into the trap of unproductive, political corporate culture? The answer is simple game theory. The team’s output is maximized when none of the members are playing politics. However, from an individual member perspective, if someone else is playing the game, you are better off playing, even at the expense of hurting the productivity as a team. Once the game starts, you can’t expect the members to take one for the team and get screwed over. As Ice-T once said, with office politics certainly on his mind, “Don’t hate the playa, hate the game”.

If you want to build a productive organization you need to prevent the game from starting by taking these two steps. First, you have to hire the right people. One bad apple can really mess things up, especially if that person is higher ranked. Trustworthiness is more important than experience and knowledge. Second, as a leader of a team, you have to stay disciplined. Management laziness and ignorance creates a breeding ground for misaligned incentives. If you can’t see through the bullshit your reports present you with, you don’t deserve to be overseeing that area. If you see a bad apple, you need to have the discipline to fix the problem.

In The Five Dysfunctions of a Team Patrick Lencioni illustrates a pyramid that summarizes the essential components of a productive team. Not surprisingly, trust is the base of everything. Team members trusting each other that they won’t play politics will ensure a bullshit free (not conflict free) environment, leading to a more productive team.

 

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An Efficient Market for Online Ads

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This is another old post from Tumblr.

In my last post, my argument was essentially: The difference of CPM/CPC/CPA is risk transfer and the fair price can be determined by looking at the mean and standard deviation of the three risks that are being shared between the advertiser and the publisher. The three risks being: click through rate (CTR), conversion rate (CVR), and average order value (AOV).

I was thinking option pricing may work to get to the right price but upon further thinking, this looks more like a job for crystal ball or some statistical simulation tool. So, if you have all the data points, you can plug them in and boom, you should have an idea of fair relative price for the each model. If you are bidding on an impression at a certain price, how much should you be willing to pay in CPA? This equation can be expressed like this:

image

This assumes that whatever bid price you have for an impression is the right price. In a perfectly efficient market where the bid price is always right, this makes sense. But we live in a imperfect world and advertising is especially an inefficient market, so this should be the other way around. Our bid price for an impression should be driven by how much % of sales for this certain product you are willing to give up in order to show an ad in front of this person. So the equation that we are actually trying to solve should be:

image

If I am making 5% gross profit margin on a speaker, I am only willing to give up 0.5% of the price. But if I sell my own brand of super expensive shoes at the GP margin of 70%, I may be willing to pay 10% of sales. So the % of sales you are willing to give up is purely an internal decision driven by the nature of the product and your risk adversity. Let’s say for this one user looking at this blog, I know the likelihood of him clicking on the ad is, the likelihood of him buying something is, and know how much he will spend, plus I know I am willing to pay 10% of sales to get this guy to purchase my product, then I know exactly how much this impression is worth to me and how much I should bid.

The only problem is, we don’t know the exact CTR and CVR and have no idea on their standard deviation. This is where big data comes in. If we can identify this user and look at his past behavior, we can make an intelligent guesstimate of the variables. We may also take context into consideration. If you are looking at a funeral website, you probably won’t be clicking on an ad for a sports car.  But what is the problem we are trying to solve? It’s not just that the more data the better. We need to predict this user’s behavior as accurately as possible.

So the point of this post boils down to this. The holy grail of online advertisement is predicting intrinsic demand.

That means I want to show you an ad with a product that:

  1. You don’t already have
  2. You don’t know you want (because you would be on Google or Amazon searching for that product if you wanted it now)
  3. You really really want

I think in the online advertisement market, we are finally trying to figure out #3. Retargeter do this by saying, “hey you almost bought this product, maybe you would actually buy it if I give you free shipping”. The problem with that is scale. You don’t abandon shopping carts everyday so the amount of ads you can serve with this method is limited or you will be annoyingly repetitive. So, in order to understand what you really really want that you don’t know already, we want to understand who you are and your taste.

Let’s say I sell leather pants (which I don’t and have no plans to). There is a user looking at a blog and I have an opportunity to show an ad.
Here is the user profile:

  1. Male
  2. 30’s
  3. stable job
  4. married with children

I’m quite indifferent about this guy. Not willing to pay much at all.
But what if we also find out he loves metal, Harley Davidson’s, and wears leather jackets? This is a guy I want my ad to be shown to and I am willing to pay for it. So the more specific information I have about this guy and the more match I see with my product, the more I am willing to pay.

The race for the new new thing in online advertisement is, who can predict a user’s taste and match that impression to the advertiser with a product that maximizes his desire. At this point, advertisements should become less of an annoyance and more of a content. Amazon has a whole bunch of information on what you’ve bought before and can infer what you might like. But the cool thing about Pinterest is that it can connect a whole bunch of seemingly random products that you like, or someone similar to your taste likes and create a taste map. This can be used to understand the user but also to understand the product.

With the granularity of decision making reaching the logical extremes of “one impression” to “one product”, it looks like the field is finally set to start the data collection & algorithm battle. There is so much inefficiencies for companies to profit from and it will be interesting to see if giants like Google/Amazon/eBay will figure it out first or a new comer like AppNexus/Pinterest will come out on top. I’m excited to see Pinterest taking a stab at this with their unique asset.

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CPM, CPC, CPA, and the Transfer of Risk

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This is a post I wrote on Tumblr a long time ago (July 2011!) but no one read it.
The point is still valid, so I’m copying it here with slight updates.

I am currently employed in the CPA side of the online advertisement industry, putting me solidly in a minority. There are millions of sites that explain the definition of CPM, CPC, and CPA individually, but not many describe them relative to each other. Just to go over the basics:

  • CPM: Cost Per Mille. Advertiser pays the publisher per 1000 of visitors who the advertisement is shown to. Cost = # of Impressions / 1000 * CPM
  • CPC: Cost Per Click. Advertiser pays the publisher for each click on the advertisement. Cost = # of Clicks * CPC
  • CPA: Cost Per Action. Advertiser pays the publisher for each desired action such as a percentage of sales or a filled out form. Cost = # of Actions * CPA

So how do these different pricing models relate to each other?

  • # of Clicks = # of Impressions * Click Through Rate (CTR)
  • # of Actions = # of Clicks * Conversion Rate (CVR)
  • So, # of Actions = # of Impressions * CTR * CVR

If you are paying $100,000 for a campaign and you get 1,000,000 impressions, CTR = 10% and CVR=10%, what are the CPM, CPC, and CPA?

  • CPM = $100,000 / (1mm / 1000) = $100 (or $0.1 per impression)
  • CPC = $100,000 / (1mm*10%) = $1
  • CPA = $100,000 / (1mm*10%*10%) = $10

This means we can relate the three this way:
CPM/1000 = CPC/CTR = CPA/CTR/CVR

Except, we did not consider one thing, and that is this guy…


Risk.

Both click through rate and conversion rate will have a standard deviation, meaning those numbers are never constant.Sometimes the numbers will be above average and sometimes will fall below average. Even if you know the median CTR and CVR for the publisher, advertiser, and the advertisement, that’s not always going to happen (in fact that will almost never happen). The wider the distribution curve, the more likely the CTR and CAR will diverge from the median, which means higher risk for either the advertiser or the publisher.

Changing the pricing model from CPM to CPC to CPA is the act of transferring risk from the advertiser to the publisher. Let’s take a leap of faith and assume that the advertiser wants to drive sales.

In a CPM model, the advertiser is bearing both the risk in CTR and CVR. From the publisher’s perspective, all you need to do is drive traffic and you’ll get paid. If you decide to run a yamaka ad on a mormon website, you’ll still get paid. The advertiser is bearing all the risk.

In CPC, the advertiser transfers the CTR risk to the publisher. Now that yamaka ad is not going to do too well. The incentive for the publisher is to show advertisements that is relevant to the audience so they can generate clicks.

In CPA, the advertiser transfers not only the CTR risk but also the CVR risk. So even if the publisher is able to generate traffic to the advertiser website, they won’t get paid unless the user actually purchases something or fills out a form.

That is asking a publisher to do a lot. If you think about a percent of sale offer, the publisher is taking more risks than just CTR and CVR. If the user only spends $2 on the website, the publisher will only get a tiny pay. So the publisher is also taking on the risk of the average order value (AOV). In fact, CPA is basically riskfree for the advertiser and it should not even be considered a marketing expense. It is more of a cost of goods sold expense.

So, in order for the publisher to take on more and more risk, the below formula must hold true.
CPM/1000 < CPC/CTR < CPA/CTR/CVR
The publisher must be rewarded with a higher payday with CPA compared to CPC, which in turn will be more expensive than CPM.

Exactly how much more expensive should CPA be? That’s the million (billion?) dollar question. We are valuing risk based on standard deviation which from my knowledge, sounds awfully like an option…

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Hello world!

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Hello world, says my one week old daughter.This is a site to jot down thoughts while (and hopefully after) I am on parental leave.
A little bit about myself:

  • Strategy & operations professional in ad tech
  • Japanese
  • Living in NYC
  • NYU Stern MBA
  • System consultant / engineer in Japan (60~80 hour weeks at 50K, how did I do it for 6 years?)
  • I love technology and business models enabled by them
  • For details of my professional background view my LinkedIn profile
  • Knicks Fan: I still think Melo and Amare can coexist
  • Runner: Ran 2011 NYC marathon at 4:06, will do better this year
  • Home cook: I’m cooking every meal while on leave. A sample of my work is on Pinterest

If you want to contact me, follow me on twitter and DM me!

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Profile (Japanese version)

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プロフィール

  • 海外在住25年(米国21年、英国4年)。
  • 2001年米国の二流大学を三流の成績で卒業(GPA2.8)。専攻は情報システム(Management Information System)。勉強せずに遊び呆けてました。
  • 卒業後日本でシステムコンサルタントとして6年間勤務。
    • 大手企業のシステムの要件定義から開発、テスト、導入まで携わっていました。
    • ちなみにこの期間に努力することを覚えて、今の自分があります。
  • 2007年にNew York University Stern School of Businessに留学。
    • StrategyとCorproate Finance、Marketingを主に勉強。
    • 2009年卒業。一応MBA with Distinction。
  • そのままニューヨークで仕事を見つけて、現在はオンライン広告系企業で経営企画・戦略立案的なことをしています。
  • テクノロジーを利用したクリエイティブなビジネスモデルが好き。
  • 趣味はマラソン、料理、食べ歩き、先週から子育て。
  • 質問などあれば、コメントするか、TwitterでDM送ってください。
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