Category Archives: Startups

Nov 10 2014
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Online Media, Startups

MediaMath Acquires Rare Crowds And Its Founder, Eric Picard

By Zach Rodgers (Originally published on AdExchanger, November 10th, 2014)

MediaMath has snapped up Rare Crowds, a small, 2-year-old startup founded by ad tech trailblazer Eric Picard, AdExchanger has learned.

Under the all-stock transaction, Picard will join MediaMath as VP of strategic partnerships as the media-buying platform builds out products around private marketplaces and “automated guaranteed” inventory (i.e., direct site buys).

The deal has the markings of an acqui-hire. Picard, whose title at MediaMath will be VP of strategic partnerships, is the only exec from Rare Crowds’ small team going over to MediaMath. Co-founder and CTO Scott Tomlin will consult with MediaMath through the transfer of Rare Crowds’ technology.

A well-known figure in the ad tech space, Picard founded Bluestreak, an early ad server and rich media platform. Later he was an architect of Microsoft’s ad platform strategy, and he also held a senior product role at TRAFFIQ before that company exited ad tech and repositioned as an agency.

His responsibilities at MediaMath will include oversight of the company’s relationship with Akamai, from which it acquired Advertising Decision Sciences – along with its pixel-free ad targeting technology – in January 2013.

“MediaMath is making an investment in moving beyond real-time bidding, and taking RTB technologies really far forward into other parts of the ecosystem,” Picard told AdExchanger. “The company is investing in the rest of the media plan that is not currently accessible.”

Rare Crowds was initially focused on helping publishers better package their inventory in a programmatic selling environment, but in the last year pivoted to the buy side.

Here’s how Picard described the Rare Crowds value proposition in an AdExchanger interview two years ago:

“The whole industry has been very focused on prediction. We have to predict how much inventory we are going to have so we can sell it in advance, when you are talking about premium inventory. In RTB, all of the systems that have been developed really allow you to target much better and do not have to worry about prediction.

We’re finding this hypertargeted inventory that has more than four attributes, that’s what we define as a ‘rare crowd,’ all the way out to 12 or 15 or 20 attributes. If it exists, we’ll find it.”

Rare Crowds was backed by angel investors including Hulu’s SVP for ad sales, Peter Naylor, Interactive Advertising Bureau founder Rich LeFurgy and Mediasmith CEO Dave Smith.

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Nov 04 2014
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Ad Ecosystem, Ad Technology, Economics, Marketers, Media Agencies, Online Media, Predictions, Publishers, Startups

The Digital Advertising Industry Needs An Open Ecosystem

By Eric Picard (Originally published on AdExchanger Tuesday, November 4th, 2014)

Thanks to amazing new offerings from Facebook, Google, Amazon and others on deeply connected identity and tracking solutions, we are seeing two major developments. For the first time, connected identities across entire populations are available for targeting, tracking, reporting and analytics. But these identity pools exist within walled gardens, siloed to just one provider.

From a tactical and strategic point of view, I completely understand why companies create these walled-garden identity solutions. And to some extent, they will open their walls – metaphorically allowing outside vendors and partners to enter through checkpoints, accompanied by security and wearing clearly labeled badges. Nobody can fault a company like Facebook or Google for being careful about allowing entrée to their walled gardens. The potential for a PR backlash is significant, and that could cause the overall value of their offering to decline. So yes – it’s good to be cautious.

But it does create a significant issue for every publisher outside the top five or so because their first-party data pool is limited to the activity on their own site or apps. They don’t get access to cross-site activity, nor do they have a way to compete with the efforts of the biggest players on their own. It will be hard for publishers – even the large ones – to resist the momentum that will build to plug into these walled gardens, forcing publishers to effectively commoditize themselves in exchange for access to identity, targeting and analytics data.

I’ve long been a proponent of open approaches in the ad-tech space, including open source, open architecture or open APIs. I also am a big fan of well-considered and coordinated industry or consortium efforts. I believe that efforts like OpenRTB, which is pushing for an open API standard for real-time bidding, will be key to helping the industry grow.

Open efforts like this help ensure that the biggest players don’t create huge competitive moats like we saw with paid search, where Google AdWords’ creative, functionality and APIs became the effective industry standard. As a result, any time Google makes any change, all other paid search players must immediately copy Google because of its massive dominance in this area.

Even the biggest players should support these open initiatives because regardless of any disproportionate boost one or two players may get, we’re in a massive growth phase and an open approach has proven a better way to expand industries and sectors. Building significant traction is easier with scale – and by pooling scale, the whole space has the opportunity to accelerate growth.

That said, it’s highly unlikely that Google and Facebook will take a completely open approach on their key initiatives. For one, they have enough scale to catalyze efforts and markets on their own. But more importantly, it’s not in their self-interest to be open. Remaining closed gives them opportunity to maintain control and position in the market while marginalizing smaller players in the ecosystem.

I predict that we will see more industry consortiums created around areas like identity, directly in response to the very large walled gardens that are being built now. It’s really the only way that everyone else in the industry can protect against commodification and ensure a level playing field.

Jan 18 2014
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Ad Ecosystem, Ad Technology, Online Media, Predictions, Startups

How (and why) emerging media should plan for scale

By Eric Picard (Originally Published on iMedia – January 18, 2014)

People in emerging media spaces frequently ask me how they can get advertising into their content experiences or how they can use their technology to create value for advertising technology companies. Recently someone asked me about using bitcoin in advertising. In the past, I’ve spent hours working with clients who have hired me to help them figure out advertising models for their new emerging media products, despite my telling them early on that it’s unlikely that there’s a “there, there” related to their situation due to scale.

This is apparently hard for people to wrap their heads around, so let’s talk about this specific issue — the issue of scale in advertising. At its heart, the issue of scale is possibly the biggest and most fundamental issue in advertising — and it is frequently misunderstood. Here are my three rules of scale in advertising:

  • Advertisers need to be able to spend relatively significant budgets efficiently at a low cost per impression.
  • Advertising campaigns need to be able to reach relatively large audiences without significant complexity in managing them.
  • Return on advertising spend (ROAS) needs to be able to be calculated in some form (including, in many cases, very simple key performance indicators).

Let’s talk first about the difference between marketing and advertising. I’ll give you my definitions, as the dictionaries don’t do justice to the concepts:

Marketing is about communication; it is a commercial message to a potential or existing customer, and increasingly it is a two-way conversation with potential or existing customers. Marketing includes one-on-one conversations between employees and prospects, mail and email communications, advertising, public relations, and more.

Advertising is about reaching the largest possible audience, with the best available message, as effectively, inexpensively, and efficiently as possible, generally through distribution over a large media channel. Advertising is a subset of marketing, but it has unique properties and rules that one needs to be aware of in order to apply it as a revenue source.

The most important concept that defines advertising as opposed to marketing is scale of reach at a reasonable cost. Advertising generally requires that a very large audience can be reached at a low cost per impression. Not only must the cost to reach the audience be relatively low, but the cost to manage the buying of the advertising media must also be relatively low. In addition, the ROAS must be somewhat measurable. That said, ROAS is a fairly squishy way of discussing a variable and varied set of metrics that are generally constructed on a per-advertiser — or even per-campaign — basis to gain an understanding of results.

At this point, a few of you are probably getting ready to argue with me about some of the things I just said. The likely argument revolves around some high-CPM inventory that is bought by some advertisers for some campaigns at a very high rate. And while this does happen, my points above still hold true. The cost of the inventory is relative based on the goals of the campaign and an analysis of its results.

For instance, some inventory that is highly targeted or highly effective can sell for a high CPM, but it can still meet the ROAS goals of the campaign. This can be due to high performance or a relatively rare target audience (perhaps extremely high income or very niche interests, such as pilots, airplane owners, or sky divers). It can also be due to a highly competitive media set (e.g., auto-intenders or people who manage investments).

ROAS is a superset of all the various means of calculating performance because ROAS can be based on brand metrics as well as performance metrics. It can be as laser-focused as a tightly bound formula including cost per acquisition (CPA) and the margin on the product that the “A” drove. Or it can be as broad as understanding that for every dollar of advertising spent (using some kind of analysis that could be sophisticated or simple) gross sales increased by some amount.

The ROAS calculations can also be derivative. For instance, there may be a very clearly understood metric that has very clearly understood value that can be used as the primary goal of a campaign. For instance, in the automotive space, the value of a test drive is very clearly understood; most car companies know exactly what the conversion rate is between test drives and purchases of their cars. It’s common to use test drives as a campaign goal, which is not really the goal of the advertiser, but it is fairly measurable and clearly understood in secondary value in sales.

For those trying to roll out a new (or emerging) advertising medium — one that is based on new content models, new distribution models, or new devices or technologies — this concept of scale is critical. Until a media type can provide enough reach to be of value, it’s hard to use advertising as a mechanism to fund it. That number varies based on the makeup of the audience using the media.

For instance, if a new hand-held device for hedge-fund managers were launching, the audience size needed to be ad supported would be much lower than a hand-held device for the homeless. For a mixed-audience scenario, one that’s by nature more affluent (since most emerging media scenarios tend to appeal to early adopters, who tend to be affluent), the magic number seems to be at least in the hundreds of thousands, but it can range into the millions.

The more information available about the audience that is adopting the emerging media, the more likely early ad funding is to occur. This audience data must be collected up front. The task cannot be left until later. If it is, there likely won’t be a “later.”

 

Jan 25 2013
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Ad Ecosystem, Ad Technology, Online Media, Startups

Can That Startup Stand Alone, Or Is It Just A Feature?

by Eric Picard (Originally published in AdExchanger 1/24/13)

One of the most common conversations I have on a regular basis about startups, especially in the ad technology space is whether a company has legs. Will they be able to grow large and “go it alone,” or are they really just a feature of a bigger system or platform that hasn’t been added yet? This line of thinking is super important for investors to understand, and a key part of the decision-making process for entrepreneurs as they begin building a company.

Why does this matter?

Investors all have their own set of criteria for how they evaluate investments. Angels and Micro-VCs frequently are willing to invest in companies that are built for a relatively short term flip. They ask, “Can the company build a robust set of features that attract a set of customers in the short term, run at a low burn rate (or even quickly become profitable), integrate with one or more existing platforms, and then get bought within one to three years for a decent return?” In this case, building a company that’s a feature is completely viable and acceptable.

This approach is great if the startup can either bootstrap (not take any money) or just pull in a small amount of investment capital and get profitable (or exit) quickly. For angels and micro-VCs, this kind of investment is great because they can get fantastic returns on a short investment horizon, and sometimes it gets them stock in a larger high flying tech company at a great multiple. Sometimes these will be companies at Series B or C rounds that they couldn’t get into, sometimes they’re large publicly traded companies that the investor gets stock in at a significant market discount.

If the startup is going to need significant capital, it must be able to build a large business that has significant revenues and long sustainable growth. It needs to be able to stand alone for three to six years, and during that time build a large company with an opportunity to exit for greater than $100M – and have a credible story for why they could be worth more than $1B and exit via either an IPO or a significant acquisition.

There have been several examples in the past few years of companies that have been funded as if they were a full standalone company that could build massive revenue and exit big. They’ve taken tens of millions of dollars in funding, and need to either IPO or exit for greater than $100M to be seen as a win by their investors. In these cases, the investors couldn’t properly evaluate if the startup was really a feature or a standalone. So it’s important to have a way to evaluate this in order to avoid making those mistakes – both as an investor and an entrepreneur.

Question 1: Will Google or another big company just add this functionality to its existing product?

Many big companies with significant platform investments will constantly extend their product set over time. Big companies (Google, Microsoft, Yahoo, Amazon, AOL) and the big ad-tech specific companies (AppNexus, Rubicon, Pubmatic, MediaMath, etc.) have large engineering teams, and the reality is that it’s most efficient for large engineering teams to work on large, complex and technically prohibitively problems. They tend to add smaller features on a much slower cycle. That doesn’t mean they won’t add them – it just means they have bigger problems to solve that are higher priority – especially around scale, stability, and redundancy. But eventually they’re going to need to add those features, and they’ll do it either through acquisition or committing resources to build the features. At that point, they’ll be doing what’s known as a “build/buy analysis” to determine where to invest the capital to get the product to their customers. The analysis is going to look something like this:

  1. How many man-hours of development will this take to build?
  2. What’s the cost to develop it in-house, both the actual cost of development and the opportunity cost in both time to market, and the other features that the team won’t be able to work on during the development process?
  3. The answer is likely to come out between $6 and $30 million – with a rare outside answer landing around $50 to $80 million, depending on the complexity of the feature.
  4. That means that for most “feature” companies, exits will be in those ranges, with perhaps a 10 to 20% premium applied. The likelihood of a premium relates directly to how much revenue the company brings with it, minus any costs of both operating the company and integration costs.

This means that startups need to think through a bunch of things if they’re building for acquisition. They should be cheap to buy – ideally in the first range we just discussed. They should be easy to integrate. They should have as few redundant systems and features as possible. They should be architected for scale – to handle the kinds of load that one of those big companies will need to transfer onto their systems post acquisition. But they should be cost effective operationally.

One very smart thing “feature” startups should do as early in their life cycle as possible is integrate with as many of those companies as possible. Thus when that build/buy analysis is done by the acquiring company, the startup is already integrated, is top of mind with the acquirer, and already is familiar with how to develop against their APIs. And in many cases the developers within the startup will already be known by at least some of the developers within the larger company.

Another big question that will come up during due diligence by an acquiring company is whether the startup has any redundant systems that will need to be decommissioned or moved into internal data centers. This is more important than many would realize – especially when it comes to datacenter redundancy. Very few startups have the kind of security and cost efficiencies that a Google, Microsoft or Amazon have in house. So if they’ve invested in their own infrastructure, they’re going to need to move this out of their datacenter and into the bigger company’s infrastructure. Datacenter contracts should be crafted to facilitate this – and hardware is probably just a loss. Building in the cloud can solve this in some cases, but in others, might cause problems of their own – e.g. any real time systems built in the cloud are highly unlikely to scale to cover the needs of a big company. Architecting for scale, even if the startup doesn’t scale up right away, is a critical consideration.

Question 2: How do we define a “standalone” business, and what justifies the higher acquisition costs?

First and foremost consideration in this case is revenue. Companies with deep customer relationships and lots of revenue can both run standalone, and/or be acquired for a large amount of money at significant multiples. Think of a few companies that have been acquired on that scale: AdMeld built a real and significant business that had deep customer relationships, real revenue, and was acquired by Google for a significant premium at a high price. Same goes for Invite Media and DoubleClick, both bought by Google; aQuantive, bought by Microsoft; and Right Media, bought by Yahoo.

All of these companies built significant businesses, with large numbers of customers, decent margins, and were not bought for their technology. In each of those cases, the core technology of each company was completely re-architected and redeveloped by the acquirer (in aQuantive’s case, a little less so).

So when starting a company, and when evaluating that company for investment, one must consider how much revenue, and how many customer relationships, the company can effectively build over a three to six year period. If the answer to both questions is “lots,” and a credible case can be made – then consideration for this kind of investment (both in time/effort from the entrepreneur and cash from the investors) is justified.

Other considerations for whether the business will stand the test of time:

How “sticky” will those relationships be? Ad serving is the penultimate example of a sticky ad technology. It’s so complex and cost prohibitive to switch out your ad server – either for publishers or agencies – that it almost never happens. That’s one reason ad servers with lots of traction were bought for such huge premiums, even though there was significant price compression over time. If the technology is sticky because of integrations into either infrastructure or business processes, it’s a good candidate to be a standalone.

Does the company sell a product, service or commodity that has transfer-pricing pressure?

Or, does the company either create the product it sells from scratch or process the product of another company in a way that adds significant value? Ideally in the latter scenario it would do this in a way that is difficult to replicate – for example, by analyzing big data with some unique and differentiated methods in order to add intelligence and value to what it sells.

Transfer pricing pressure is critical to understand: The best current example of transfer pricing pressure is music. Any business that has started in the music space has been doomed to either incredibly poor margins or instability. This is because the music companies can simply increase the costs of the final good being sold (songs or albums) any time they please. As soon as one of the companies selling music digitally begins to get profitable, the music industry can throttle their margins by increasing price. In the advertising space this is similar to a problem early ad networks had – the ad rep firms. Because the ad rep firms didn’t own the inventory they sold, and re-sold the product of the publisher directly with no additional value, they were doomed to low multiple exits and low valuations if they managed to get large enough to IPO.

A recent example of transformation in a category that shows how some of these standalone companies have become more resilient is the data companies. They started out with relatively undifferentiated mechanisms to create targeting segments that led to the creation of ad networks, then were integrated with data marketplaces on the exchanges, and now have transformed themselves into Data Management Platforms. Lotame is a great example of a company that has made this transition – but many others have as well.

By applying this type of analysis to opportunities to create a company or invest in a company, entrepreneurs and investors can make smart decisions about what to build, and what to invest in.

Oct 25 2012
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Ad Ecosystem, Ad Technology, Economics, Media Agencies, Online Media, Predictions, Publishers, Startups

How Do Companies Make Any Money in Digital?

(By Eric Picard, Originally Published in AdExchanger 10/25/12)

In 2007 I wrote a paper that analyzed the lack of investment from 2001 to 2006 in the basic infrastructure of ad technology.  The dot-com bubble burst had a chilling effect on investment in the ad tech space, and as an industry we focused for about six years on short term gains and short term arbitrage opportunities.

This period saw the rise of ad networks and was all about extracting any value possible out of existing infrastructure, systems, and inventory.  So all the “remnant” inventory in the space, the stuff the publisher’s in-house sales force couldn’t sell, got liquidated at cheap prices.  And those companies with the willingness to take risk and the smarts to invest in technology to increase the value of remnant got off the ground and succeeded in higher efficiency buying and selling, and lived off the margins they created.

But we lost an entire cycle of innovation that could have driven publisher revenue higher on premium inventory – which is required for digital to matter for media companies. There’s been lots of discussion about the drop from dollars to dimes (and more recently to pennies) for traditional media publishers. And while the Wall Street Journal and New York Times might be able to keep a pay-wall intact for digital subscriptions, very few other publications have managed it.

In 2006 the ad tech ecosystem needed a massive influx of investment in order for digital to flourish from a publisher perspective.  These were my observations and predictions at the time:

  • Fragmentation was driving power from the seller to the buyer. Like so:
  • A lack of automation meant cost of sales for publishers, and cost of media buying management for agencies, were vastly higher in digital (greater than 10x what those things cost for traditional on both the buy and sell side).
  • Prices were stagnated in the digital space because of an over-focus on direct response advertisers, and the inability of the space to attract offline brand dollars.
  • Market inefficiency had created a huge arbitrage opportunity for third parties to scrape away a large percentage of revenue from publishers. Where there is revenue, investment will follow.
  • There was a need for targeting and optimization that existing players were not investing in, because the infrastructure that would empower it to take off didn’t exist yet.
  • Significant investment would soon come from venture capital sources that would kick start new innovation in the space, starting with infrastructure and moving to optimization and data, to drive brand dollars online.

Six years later, this is where we are. I did predict pretty successfully what would happen, but what I didn’t predict was how long it would take – nor that the last item having to do with brand dollars would require six  years. This is mainly because I expected that new technology companies would step up to bat across the entirety of what I was describing.  Given that the most upside is on brand dollars, I expected entrepreneurs and investors to focus efforts there.  But that hasn’t been the case.

So what’s the most important thing that has happened in the last six years?

The entire infrastructure of the ad industry has been re-architected, and redeployed.  The critical change is that the infrastructure is now open across the entire “stack” of technologies, and pretty much every major platform is open and extensible. This means that new companies can innovate on specific problems without having to build out their own copy of the stack.  They can build the pieces they care about, the pieces that add specific value and utility for specific purposes – e.g. New Monetization Models for Publishers and Brand Advertisers, New Ad Formats, New Ad Inventory Types, New Impression Standards, New Innovation across Mobile, Video and Social, and so on.

So who will make money in this space, how will they make it, and how much will they make?

I’ve spent a huge portion of my career analyzing the flow of dollars through the ecosystem. Recently I updated an older slide that shows (it’s fairly complex) how dollars verses impressions flow.

The important thing to take away from this slide is that inventory owners are where the dollars pool, whether the inventory owner is a publisher or an inventory aggregator of some kind.  Agencies have traditionally been a pass-through for revenue, pulling off anywhere from 2 to 12% on the media side (the trend has been lower, not higher), and on average 8 to 10% on the creative side depending on scale of the project.  Media agencies are not missing the point here, and have begun to experiment with media aggregation models, which is really what the trading desks are – an adaptation of the ad network model to the new technology stack and from a media agency point of view.

The piece of this conversation that’s relevant to ad tech companies is that so far in the history of this industry, ad technology companies don’t take a large percentage of spend.  In traditional media, the grand-daddy is Donovan Data Systems (now part of Media Ocean), and historically they have taken less than 1% of media spend for offline media systems. In the online space, we’ve seen a greater percentage of spend accrue to ad tech – ad serving systems for instance take anywhere from 2 to 5% of media spend.

So how do ad tech companies make money today and going forward? It’s a key question for pure transactional systems or other pure technology like ad servers, yield management systems, analytics companies, billing platforms, workflow systems, targeting systems, data management platforms, content distribution networks, and content management systems.

There’s only so much money that publishers and advertisers will allow to be skimmed off by companies supplying technology to the ecosystem. In traditional media, publishers have kept their vendors weak – driving them way down in price and percentage of spend they can pull off. This is clearly the case in the television space, where ad management systems are a tiny fraction of spend – much less than 1%.

In the online space, this has been less the case, where a technology vendor can drive significantly more value than in the offline space. But still it’s unlikely that any more than 10% of total media spend will be accepted by the marketplace, for pure technology licensing.

This means that for pure-play ad tech companies with a straightforward technology license model – whether it’s a fixed fee, volume-based pricing, or a  percentage of spend – the only way to get big is to touch a large piece of the overall spend. That means scaled business models that reach a large percentage of ad impressions.  It also means that ultimately there will only be a few winners in the space.

But that’s not bad news. It’s just reality.  And it’s not the only game in town. Many technology companies have both a pure-technology model, and some kind of marketplace model where they participate in the ecosystem as an inventory owner. And it’s here that lots of revenue can be brought into a technology company’s wheelhouse. But its important to be very clear about the difference between top-line media spend verses ‘real’ revenue. Most hybrid companies – think Google for AdSense, or other ad networks – report media spend for their marketplaces as revenue, rather than the revenue they keep. This is an acceptable accounting practice, but isn’t a very good way to value or understand the value of the companies in question. So ‘real revenue’ is always the important number for investors to keep in mind when evaluating companies in this space.

Many ad technology companies will unlock unique value that they will be the first to understand. These technology companies can capitalize on this knowledge by hybridizing into an inventory owner role as well as pure technology – and these are the companies that will break loose bigger opportunities. Google is a great example of a company that runs across the ecosystem – as are Yahoo, Microsoft and AOL.  But some of the next generation companies also play these hybrid roles, and the newest generation will create even greater opportunities.

Oct 05 2012
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Ad Ecosystem, Ad Technology, Economics, Media Agencies, Online Media, Predictions, Publishers, Startups

5 Important Memes for the Digital Advertising Industry

Oct 05 2012
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Ad Ecosystem, Ad Serving, Ad Technology, Economics, Online Media, Predictions, Startups

Entering the Fourth Wave of Ad Technology

By Eric Picard (Originally published on AdExchanger.com, 9/18/2012)

Ad tech is a fascinating and constantly evolving space.  We’ve seen several ‘waves’ of evolution in ad tech over the years, and I believe we’re just about to enter another.  The cycles of investment and innovation are clearly linked, and we can trace this all back to the late 90’s when the first companies entered the advertising technology space.

Wave 1

The early days were about the basics – we needed ways to function as a scalable industry, ways to reach users more effectively, systems to sell ads at scale, systems to buy ads at scale, analytics systems, targeting systems, and rich media advertising technology.

There was lots of investment and hard work in building out these 1.0 version systems in the space. Then the dot-com bubble imploded in 2001, and a lot of companies went out of business.  Investment in the core infrastructure ground to a halt for years. The price of inventory dropped so far and so fast that it took several years before investment in infrastructure could be justified.

We saw this wave last from 1996 through 2001 or 2002 – and during that dot-com meltdown, we saw massive consolidation of companies who were all competing for a piece of a pie that dramatically shrank.  But this consolidation was inevitable, since venture firms generally invest on a five to ten year cycle of return – meaning that they want companies to exit within an ideally 8 year window (or less).

Wave 2

The second wave was really about two things: Paid Search and what I think of as the “rise of the ad networks.”  Paid search is a phenomenon most of us understand pretty well, but the ad network phase of the market – really from 2001 to 2007 was really about arbitrage and remnant ad monetization.  Someone realized that since we had electronic access to all this inventory, we could create a ‘waterfall’ of inventory from the primary sales source to secondary sources, and eventually a ‘daisy-chain’ of sources that created massive new problems of its own.  But the genie was out of the bottle, and this massive supply of inventory that isn’t sold in any other industry was loosed.

It’s actually a little sad to me, because as an industry we flooded the market with super cheap remnant inventory that has caused many problems. But that massive over-supply of inventory did allow the third wave of ad tech innovation to get catalyzed.

Wave 3

Most people believe that the third wave was around ad exchanges, real-time buying and selling, data companies, and what I like to call programmatic buying and selling systems. But those were really just side effects. The third wave was really about building out the next generation infrastructure of advertising. Platforms like AppNexus and Right Media are not just exchanges; they’re fundamentally the next generation infrastructure for the industry.  Even the legacy infrastructure of the space got dramatic architectural overhauls in this period – culminated by the most critical system in our space (DoubleClick for Publishers) getting a massive Google-sponsored overhaul that among other thing opened up the system via extensive APIs so that other technology companies could plug in.

Across the board, this new infrastructure has allowed the myriad ad tech companies to have something to plug into.  This current world of data and real-time transactions is now pretty mature, and it’s extending across media types.  Significant financial investments have been made in the third wave – and most of the money spent in the space has been used to duplicate functionality – rather than innovate significantly on top of what’s been built.  Some call these “Me too” investments in companies that are following earlier startups and refining the model recursively.  That makes a lot of sense, because generally it’s the first group of companies and the third group of companies in a ‘wave’ that get traction. But it leads to a lot of redundancy in the market that is bound to be corrected.

This wave lasted from about 2005 to 2011, when new investments began to be centered on the precepts that happened in Wave 3 – which really was a transition toward ad exchanges (then RTB) and big data.

That’s the same pattern we’ve seen over and over, so I’m confident of where the industry stands today and that we’re starting to enter a new phase. This third major ad tech wave was faster than the first, but a lot of that’s because the pace of technology adoption has sped up significantly with web services and APIs becoming a standard way of operating.

Wave 4

This new wave of innovation we’re entering is really about taking advantage of the changes that have now propagated across the industry. For the first time you can build an ad tech company without having to create every component in the ‘stack’ yourself. Startups can make use of all the other systems out there, access them via APIs, truly execute in the cloud, and build a real company without massive  infrastructure costs.  That’s an amazing thing to participate in, and it wasn’t feasible even 3 years ago.

So we’ll continue to see more of what’s happened in the third wave – with infrastructure investments for those companies that got traction, but that’s really just a continuation of those third wave tech investments, which go through a defined lifecycle of seed, early, then growth stage investments.  Increasingly we’ll see new tech companies sit across the now established wave 3 infrastructure and really take advantage of it.

Another part of what happened in Wave 3 was beyond infrastructure – it involved the scaled investment in big data.  There have been massive investments in big data, which will continue as those investments move into the growth phase. But what’s then needed is companies that focus on what to do with all that data – how to leverage the results that the data miners have exposed.

Wave 4 will really change the economics of advertising significantly – it won’t just be about increasing yield on remnant from $0.20 to $0.50. We’ll see new ad formats that take advantage of multi-modal use (cross device, cross scenario, dynamic creatives that inject richer experiences as well as information), and we’ll see new definitions of ad inventory, including new ad products, packages and bundles.

So I see the next five years as a period where a new herd of ad tech companies will step in and supercharge the space. All this infrastructure investment has been necessary, because the original ad tech platforms were built the wrong way to take advantage of modern programming methodologies.  Now with modern platforms available pretty ubiquitously, we can start focusing on how to change the economics by taking advantage of that investment.

I also think we’re going to see massive consolidation of the third wave companies. Most of the redundancies in the market will be cleaned up.  Today we have many competitors fighting over pieces of the space that can’t support the number of companies in competition – and this is clearly obvious to anyone studying the Lumascape charts.

Unfortunately some of the earlier players who now have customer traction are finding that their technology investments are functionally flawed – they were too early and built out architectures that don’t take advantage of the newer ways of developing software. So we’ll see some of these early players with revenue acquiring smaller newer players to take advantage of their newer more efficient and effective architectures.

Companies doing due diligence on acquisitions need to be really aware of this – that buying the leader in a specific space that’s been around since 2008 may mean that to really grow that business they’ll need to buy a smaller competitor too – and transition customers to the newer platform.

For the investment community it’s also very important to understand that while Wave 3 companies that survive the oncoming consolidation will be very big companies with very high revenues, it is by nature that these infrastructure heavy investments will have lower margins and high volume (low) pricing to hit their high revenues. They still will operate on technology/software revenue margins – over 80% gross margins are the standard that tech companies run after. But the Wave 3 companies have seen their gross revenue numbers be a bit lower than we’d like as an industry.  This is because they are the equivalent of (very technically complex) plumbing for the industry.  There are plenty of places where they invest in intelligence, but the vast majority of their costs and their value deal with massive scale that they can handle, while being open to all the players in the ecosystem to plug in and add value.

Being a Wave 4 company implicitly means that you are able to leverage the existing sunk cost of these companies’ investment.  Thomas Friedman talks about this in “The World is Flat” – one of his core concepts is that every time an industry has seen (what he called) over-investment in enabling infrastructure, a massive economic benefit followed that had broad repercussions.  He cites the example of railroad investment that enabled cheap travel and shipping that led to a massive explosion of growth in the United States.  He cites the investment in data infrastructure globally that led to outsourcing of services to India and other third world countries on a massive scale.  And frequently those leveraging the sunk cost of these infrastructure plays make much more money from their own investments than those who created the opportunity.

So what should investors be watching for as we enter this fourth wave of ad tech innovation?

  1. Companies that are built on light cloud-based architectures that can easily and quickly plug into many other systems, and that don’t need to invest in large infrastructure to grow
  2. Companies that take advantage of the significant investments in big data, but in ways that synthesize or add value to the big data analysis with their own algorithms and optimizations
  3. Companies that can focus the majority of technical resources on innovative and disruptive new technologies – especially those that either synthesize data, optimize the actions of other systems, or fundamentally change the way that money is made in the advertising ecosystem
  4. Companies that are able to achieve scale quickly because they can leverage the existing large open architectures of other systems from Wave 3, but that are fundamentally doing something different than the Wave 3 companies
  5. Companies that take advantage of multiple ecosystems or marketplaces (effectively) are both risky but will have extremely high rewards when they take off

This is an exciting time to be in this space – and I predict that we’ll see significant growth in revenue and capabilities as Wave 4 gets off the ground that vastly eclipse what we’ve seen in any of the other waves.

Aug 14 2012
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Ad Ecosystem, Ad Technology, Media Agencies, Publishers, Startups

How real-time bidding works

By Eric Picard (First published on July 19, 2012 on iMediaConnection.com)

The real-time bidding ecosystem is still fairly new, and for many in our industry, there are a lot of misconceptions about how all the different parts of the ecosystem fit together. I’ve had a lot of requests from folks in the industry to explain how RTB works, and how the different players in our space fit together.

The biggest concept to get your head around with real-time bidding is the concept of programmatic buying and selling. The idea here is to streamline the buying and selling process by removing humans from the transaction. Now this is a very important thing to understand: By “the transaction,” I don’t mean that buyers and sellers no longer interact, or that there’s no role for sales in the equation. I simply mean that the act of booking the buy — let’s call it “order taking” — is completely automated. Ultimately this is a good thing for sales teams, as it lets them focus on building the relationship and selling the buyer on the value of their publisher brands. It lets the seller step away from the order-taking process.

Programmatic buying and selling is absolutely the future of this industry; it’s just a question of how long that transition will take. The lower cost of sales for publishers and more efficient buying for media agencies absolutely will make up for any hit to average CPM. And many (myself included) believe that we’ll actually see higher CPMs as a result of all this streamlining. Today most of the inventory that’s available is remnant, and it’s not the high-quality premium inventory currently handled by sales teams. Ultimately, all inventory will transact programmatically. But, like I said, sales will still play a very important role.

At the center of the RTB ecosystem are the ad exchanges. These platforms allow all the various players in the ecosystem to share supply and demand and create liquidity in the market. Examples of ad exchanges include Right Media, the DoubleClick exchange, AppNexus, and many others. On top of these pure-play ad exchanges are many ad networks and supply-side platforms that have essentially built ad exchanges on top of their existing products. The lines get very blurry between the “pure play” ad networks and the other aggregators of inventory that make that inventory available programmatically.

Similar to how stock or commodities exchanges allow inventory to be transacted upon at high volumes with maximum liquidity, the advertising exchanges play that central role. But it’s very important to understand that, just like in the financial services world, the big revenue opportunity is not with the exchange; it’s with brokers representing buyers or sellers. It’s these brokerages that represent the bulk of the value and that pull away the highest percentage of the transaction costs.

The equivalents of brokers in the RTB space are the ad networks, the supply-side platforms (SSPs), and the demand-side platforms (DSPs.) All of these ecosystem players have important roles and provide value. However, it should be noted that the lines are beginning to blur throughout the ecosystem. I predict that in the next few years, many DSPs will roll out SSP services, and many SSPs will become full-fledged ad exchanges. (But more on this in another article.)

So let’s follow the ecosystem participants from start to finish:

The impression starts with the consumer and runs through a web browser. (I didn’t put device in here, but note that even on mobile and tablets, there’s a browser involved.) The impression moves over to the publisher, through some SSP (or ad network) to the ad exchange, and then through to the DSP that is managed by an agency trading desk team on behalf of an advertiser.

There’s nothing very sophisticated about what I’ve drawn here — but note that this is the simplest way I could draw the RTB ecosystem. Here’s another view:

Note that even this is a simplified view, and that many of the various partners can service numerous blocks in the ecosystem. At the end of the day, the RTB ecosystem is made up of dozens of players (possibly hundreds), and they’re all scrambling to figure out their business models. This new ecosystem is definitely the future, but how all the pieces will ultimately fit together is still being determined.

The important thing to note in the RTB space is that from the moment consumers visit a web page, the entire transaction of selling and delivering the advertisements to them takes only a few hundred milliseconds. And this is where the revolution plays out; the competition over those impressions plays out in real time. The best ad for monetizing that user is theoretically shown, and the highest yield for the publisher is achieved. Thus, it should make the ad ecosystem function much better.

But there are many changes that have to take place, and I believe we’ll see it happen. First is that publishers need to push more and more of their premium inventory into the RTB environments. Publishers can make use of almost any ad exchange or SSP to create a private exchange where they can define advertiser-specific or agency-specific terms that are negotiated in advance, and the transaction simply makes use of the RTB infrastructure. Terms with specific advertisers can be reached in pre-decided negotiations, and the transaction takes place through the RTB infrastructure.

In a nuts-and-bolts summary article like this, I’ve glossed over a lot of the nuance and details, and I’m sure we’ll hear from a few parties about what I’ve missed or how I’ve not quite explained this correctly. But I welcome the dialogue. In the RTB space, I think there’s a lot of focus on the details, and not a lot of high-level framing going on — which alienates some of the industry folks who are looking to participate but haven’t dived in yet.

Mar 03 2012
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Ad Ecosystem, Ad Technology, Economics, Media Agencies, Publishers, Startups

3 ways that display advertising must change — or else

(Originally published in iMediaConnection, October 2011) by Eric Picard

Despite all the excitement in our industry about programmatic buying and selling of inventory (via ad exchanges, DSPs, SSPs, and a variety of direct-to-publisher vehicles like private exchanges and private marketplaces), the vast majority of dollars today are still spent the “old fashioned” way.

Since display ads began being sold in the mid-1990s, very little has changed in the way that the vast majority of ad dollars are spent. Most ad dollars are spent via a guaranteed media buy — either a sponsorship (the brand is placed on a specific location for all impressions served to it) or a volume guarantee (ad space of a specific volume is reserved against either a specific location on a page, or a specific group of pages, but will rotate out dynamically on a per-page view).

Sponsorships are great for buyers and sellers because they’re easy to manage. The buyer gets a fixed location, takes over every impression delivered to that ad location, and the seller doesn’t need to worry much about over- or under-delivery. (Sometimes they will sign up for a volume guarantee here, but many times they don’t.) And generally while sponsorships tend to yield low CPMs for the publisher, the ad buys are frequently for solid brands and the size of a sponsorship tends to be large on a dollar figure, if not large on CPM basis (e.g., it may be a multi-million dollar buy, but the CPM is probably low).

The oft-misunderstood publisher benefit of sponsorships, despite the low CPM, is that the cost of sales tends to be much lower. A sponsorship buy can be executed quickly and doesn’t require a lot of labor after the fact. I’ll discuss more about the issue of cost of sales when I touch on efficiency. But don’t underestimate the importance here.

Guaranteed volume-based buys are in many ways the cause of vast problems in our industry, despite being generally more lucrative and higher yielding on a CPM basis than sponsorships. First, they tend to be very sales and operations intensive, which means the cost of sales is often extremely high (frequently above 30-40 percent, and sometimes significantly higher for some of the most complex campaigns). There are several reasons why guaranteed volume-based buys are complex and costly.

First is that when inventory is sold in advance, there is some degree of prediction involved to determine how much inventory of any specific type or location will exist in the future. This inventory prediction problem is still one of the biggest issues we face as an industry. The ability to predict how many users will visit a specific section or page of a site is quite difficult on its own. Given the guaranteed nature of these buys, the prediction methods need to be extremely accurate, and getting accurate predictions is hard, even just based on seasonality and one or two locations. Once additional parameters, like various types of targeting, frequency capping, and various competitive exclusions are applied, the calculations are near impossible to calculate accurately.

This difficulty with predicting specific inventory in advance is the root of the second problem — optimizing buys on the publisher side during the life of the campaign. This rears its head in general, but much more so when the buy is targeted. Most buyers have no idea of the complexity of delivering these buys and how much work happens behind the scenes at most publishers to pull it off. Frequently there are daily (sometimes multiple daily) optimizations done behind the scenes to make sure a targeted campaign delivers against its goals. This can involve making changes to prioritization in the ad delivery systems, spreading the buy to larger pools of inventory, and bumping lower-paying campaigns out of the same inventory pool (at least temporarily) in order to ensure delivery.

Most publishers are not aware of the vast amount of labor done by ad agencies on their buys across publishers in order to ensure that advertiser goals are met. This can range from just ensuring that volumes that were agreed to are met, to ensuring that click or conversion rates driven by the buy are meeting a performance goal (for the direct-response advertisers). In either case, the amount of work done by agencies to optimize these buys, frequently across dozens of publishers, is huge.

Buying and selling inventory must get more efficient
This brings us to our first big problem that must be solved. Media buying and selling needs to get more efficient. If you compare efficiency (i.e., costs) of buying and selling traditional media versus online media, there’s a very clear difference. I’ve been told by numerous sources that the efficiency is between 10-15 times less efficient for big spenders for buying online versus offline media. And certainly there is a similar lack of efficiency for selling of online media.

One way that both buying and selling can become more efficient is through basic automation. Much of the back and forth of a media buy between buyer and seller is manual. There are not simple standard efficient means of automating the media buying process. There are numerous tools on the market that try to do this in the guaranteed space, but adoption has remained small so far. Between TRAFFIQ (full disclosure: I run product and engineering at TRAFFIQ), Centro, FatTail, isocket, Donovan Data Systems, DoubleClick, and others, there is plenty of choice to automate buying and selling of guaranteed between systems focused on the buy or the sell side of the problem.

And despite the promise of programmatic buying and selling removing much of the inefficiency from the space, most publishers are so worried about putting premium inventory into exchanges that we are still relegating exchanges to massive repositories of remnant inventory. Publishers must start using the private exchange and marketplace functionality that’s available to represent premium inventory.

This doesn’t mean that salespeople go away, and it doesn’t mean that publishers lose control of their inventory. It just means that much of the inefficient order-taking and campaign optimization that is done on both sides of the media buy can be removed from the system and automated. Sales become a more evangelical process, less work goes on behind the scenes, and salespeople stop spending so much time “order-taking.” Today publishers can set dynamic floor prices against exchange cleared inventory, buyers can automate their bids, and at the end of the day, the whole marketplace can get more efficient.

Publishers often say they don’t want this to happen because they fear a drop in the CPM of their guaranteed buys. The reality is that the cost of sales is so extreme on guaranteed media buys — especially targeted or frequency-capped ones — that publishers could easily skim 20-30 percent off their floor price if the cost of sales was significantly reduced.

One major reason that we’re having such trouble in the display industry is the predominance of performance or DR spend in our space. This overemphasis on DR for display has huge consequences to our space — from depressed CPMs to a focus on metrics and methodologies that require a lot of work. This leads us to our second major change that must take place.

Online display must become a brand friendly medium
Let’s face it. As a brand advertiser, you’re much better off putting your message on television or in magazines than on almost any digital vehicle. Our ads are too small to give the brand a proper emotionally reactive vehicle to reach audiences. Even the “brand friendly” 300×250 ad unit is tiny on today’s modern high-resolution screens. Luckily the IAB is responding to this problem with action, and there are many new larger standard ad sizes being promoted across the industry. But publishers have got to adopt them, and buyers have got to demand them as part of their RFPs. We should be moving much faster here — especially when you consider how many new tablet form-factor devices are moving into the hands of consumers.

But beyond the simple size of the ad, the design of most web pages leaves a lot to be desired from the perspective of a brand advertiser. There are too many ad units, not enough “white space,” too much noise on the page, and not enough back-and-forth value to the site’s own visitors or to the brands from the “advertising experience,” meaning the way ads are integrated with content. In a perfect world, the audience and the brand should be at the very least “neutral” in tension, and ideally the ads should be adding value to the viewing experience.

But there hasn’t been a huge outcry from the brands to fix this because they don’t see online as a medium that caters to them or is brand friendly. The flat CPM pricing is fine, but the lack of available GRP or TRP measurement in order to provide some cross-media evaluative metrics is a major roadblock.

Another reason that the biggest brands haven’t come online, beyond both the efficiency and brand friendliness issues, is that the ad units are shared with numerous less brand-centric advertisers, many of which run creatives that no brand advertiser would ever want running alongside their own creatives. This massive over focus we have on direct response or performance advertisers has somewhat tainted online display, and the willingness of publishers to liquidate every single available impression at fire-sale prices has led to overall much lower CPMs than media that have focused on brands as their primary customers. This issue leads to our third and final major change that must happen in online display.

Online display must increase overall CPMs of inventory
If we can transform display into a high-quality space for brand advertising, we should be able to demand higher CPMs. This sounds nice and wonderful to most publishers, but many of the people reading this article will somewhat cynically push back at this point and talk about the “reality” we face in online display today.

So let me dispel a few myths by explaining the economics of our space in terms many of you have probably never heard.

Every emerging media that I have researched or lived through has focused initially on DR advertisers as their primary target in the very beginning. There is an economic theory that drives this: budget elasticity. The idea is that a DR advertiser is theoretically managing spend based on pure ROI. That is, they only buy ads that drive profitable sales of product or services (i.e., the budget is “elastic”). This, in theory, means they will spend as much as they can as long as the media buy creates more revenue than ad spend. And because the media experience is new in an emerging media, and the advertising is novel, response rates to those new ads in new media types tend to start out much higher, and then they will eventually plateau.

The problem with this theory is that it only works out well for publishers catering to DR buyers when the conversion rate on their inventory is high enough to drive high CPMs. The type of inventory that drives high conversion rates is typically extremely well-targeted inventory, typified in our space by paid search advertising, where the users tend to be searching for the very thing that the advertiser is selling. There are some forms of display advertising that also drive high conversion rates. They are frequently driven by retargeting of search queries, very lucrative behavioral segments that show a user’s propensity to buy is higher than average, or similar principles.

Like all other emerging media, when display advertising first started out, the focus was on getting DR advertisers in the door. And like all other emerging media, the response rates on ads were relatively high in the early days. But unlike all emerging media before online display, we wrote software that managed media buys online right at the beginning of this industry. And all of the DR “knobs and dials” were locked down in code, which made it much harder to evolve out of DR into brand advertising. If response rates had grown or remained high, this wouldn’t have mattered. But like most “top of purchase funnel” ad experiences, the response rates are too low to justify high CPMs by the DR advertisers.

When a media type does not drive a very high conversion rate, DR advertisers are only willing to spend a very low CPM. There’s a magic point at which the price of the inventory is low enough that the DR formula for positive ROI starts to make sense even for low performing inventory. This inventory is generally cheaper than 50 cents and frequently cheaper than 5 cents. And there’s a ton of it available in our space. This overemphasis on DR has numerous unintended or unrealized consequences.

Many large publishers sell their guaranteed inventory at well above $3 on average, and many publishers average between $5 and $9 for what is sold by hand. But this typically represents well under half of their inventory, and for many publishers it’s more like 30-40 percent of their total inventory. Once you dip below the conversion threshold of a DR buyer on most ad inventory, you’re driving very hard toward the basement on your prices. And if more than half of your inventory is sold off for less than 20 percent of your total revenue, then something is very wrong with the way we’re managing our space.

Publishers would be much better off stripping half the ads off of their site, redesigning the site to accommodate larger brand-friendly ad units, selling a lot more sponsorships with their human sales force, and selling the remainder of those ads mostly through a very automated sales channel, such as a private exchange, or at the very least automating their sales with one of the available tools.

Even selling10-20 percent more ad inventory through premium channels would significantly increase yield for most publishers than all of the remnant sales that take place today. Simply repurposing the sales and operations teams away from the remnant inventory problem and focusing them on selling premium could solve this.

To conclude, if we can make buying and selling inventory across the online display space more efficient, more brand friendly, and significantly increase our CPMs, then we’re going to have a rapidly growing and expanding space — one that would rival venerable offline media like print and television in size and scale. And that would become the perfect vehicle for those media to travel through as they become “tablet-ized” and “streamed.” But with such a huge overemphasis on DR, massive inefficiencies in buying, and low CPMs, we have a ways to go.

Mar 03 2012
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Startups

3 ways to ensure your company is amazing

(Originally published in iMediaConnection, March 2011) by Eric Picard

Recently I was part of a fascinating discussion among some luminaries in the online advertising and media space. The conversation was kicked off when someone mentioned that a friend was starting an internet company. This led to a rollicking debate about the role of technologists in a startup.

This conversation was interesting because it’s an issue at the heart of my personal passion. I believe that technology has the power to transform business and to transform industries. I suppose that belief is founded upon a lot of evidence. But I found the discussion fascinating because people who wouldn’t argue with the idea that technology has the power to transform industries and businesses often don’t take the next logical leap in their thinking.

Powerful technology — the kind that can transform the world — only comes from the minds of amazing technologists. People who are rare and valuable and extremely creative.

I’ve been fortunate to work with many amazing technologists in my career. The kind of people who create new products and businesses and services — who invent as they breathe. I’ve viewed their willingness to work with me, to aim their big brains at ideas that I brought to the table, as a fantastic gift. And I am incredibly grateful for their collaboration and their partnership. (Thanks John, Phani, Brian, Tarek, Mike, Alex, Wayne, and oh so many others!)

Too often when a business is started by business people who bring technology people in behind them, or as an afterthought, a huge opportunity is wasted. There’s some kind of bad meme at work. There’s a common misconception about how best to build a technology startup. Many entrepreneurs believe that the right way to utilize the engineering resources of their team is to simply dictate to the team what they should build. This bad meme works itself out as something like this: Business team says why the product will be built and what it will be (the market requirements and product requirements), and the engineers just figure out how to build it and when it can get released.

In thousands of companies around the world, this is the path that is followed every day — the path of engineering as a solution provider. It is possible to build products this way; it’s been happening for a long time. But rarely does something world-changing, industry-changing, or even company-changing come out of this kind of process. Don’t use process as a fence. Don’t use process as a way to control your engineering team, or you’ll get crappy products without any spark of inventiveness. Give your amazing technologist room to breathe and experiment and invent.

There is a reason that technology companies tend to trade at much higher multiples than service companies. Technology has the ability to act as an incredible multiplier. It can supercharge a business. It’s the difference between recreating a process that existed in paper on a computer screen, and inventing a new recommendation engine that helps you find flavors of ice cream you never realized you might like. Don’t fall into the trap of thinking that because you’re a smart business person that you can remove all the thinking from the development process.

So, what direction can I give you to ensure that your business is not creating business solutions rather than game-changing technology?

1. Don’t hire a programmer to work for you; partner with an amazing, creative, and capable technologist.

I wasn’t kidding: I mean a partner, not an employee. If you can’t invent the future without a technologist, don’t be stingy — find the best you can, and invent the future together. In my experience, amazing technologists tend to be amazing at a lot of things. You might find that they were a race car driver, or a musician, or a professional cyclist. Or maybe they just write amazing code.

2. Only hire A+ people.

Once brought on board, your amazing technologist (unless he or she is just inexperienced and not quite as amazing as you thought) will not hire anyone who is not an amazing developer. So why are you going to hire a B- marketing person and a C+ sales person? A+ people will simply not work with people who aren’t also A+ players. Will not.

When you hire the niece of your VC’s sister to be a marketing assistant, you’re quietly killing your company. I’ve seen a lot of situations where amazing technologists simply refuse to listen to business teams because the business team is made up of idiots. Hiring anyone on any of your teams who is below the quality bar you’ve set for the engineering team will alienate the rest of the team. It will drive them nuts. Only hire people who are amazing, creative, argumentative, and who seek the truth. And I promise you harmony and success.

3. Don’t be stupid and set up the corporate structure with the product people reporting into marketing or sales.

If your company is making breakfast cereal or toasters, then maybe it makes sense to put product management under a marketing leader. And if you’re a media company, maybe it makes sense to put product management under the VP of sales (I don’t think so — but I won’t argue the point).

If you’re building a technology company, product management probably isn’t even necessary. But since nobody is going to listen to me about that, do the next best thing. Put product management under the right other person. If you listened to me with No. 1, then your amazing technologist is probably good enough to own product management as well as the developers and test team. But if for whatever reason that doesn’t work out, then you should keep product management reporting into the CEO or the COO.

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