Brian Tomasette talking about advertising, music, technology and startups. Scroll down a bit to start reading, or a bit more to read more about me or subscribe to the feed.
DOM - Living in America
Yesssssssssssss…..heard this on the radio driving in to work, didn’t know who it was and finally found it by typing in random lyrics to google.
Thank you, google for search, the internet for inventing itself and making this possible, python for text scan search, whoever invented the mp3 and everyone else who worked to make this moment possible.
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So I have been hard at work at my new job here at Double Positive and we should be publicly launching a new product in the next month or two. This post is not about taking credit for that product or giving others credit for building it (that said they do deserve it, as I work with some incredible people here and they are building some amazing things). It’s about tracking online advertising and the obsession of getting credit for conversions and giving credit for conversions.
Some quick definitions:
Last Click - The ad that is the last to be clicked on before a user converts within a certain window of time (typically 30 days) is the one who gets credit for driving that conversion.
Last View - The ad that is the last to be viewed before a user converts within a certain window of time (default in most adservers is 14 days but people adjust this one a lot more) is the one who gets credit for driving that conversion.
Last Click Trumps Last View - In this model if there are no clicks prior to a conversion the last ad viewed wins the conversion but if there are any clicks prior to the conversion the views are disregarded and the last click wins the conversion.
These are the most classic conversion models. Last view seemed to be sweeping the marketplace a couple years back but I see a lot of big brands going back to the tried and true last click model.
The truth is that everyone knows that it’s a combination of ads that typically drive a consumer to convert but the problem is most adservers these days can’t handle a multiple ad attribution model. The other thing that gets in the way is that a lot of affiliates and ad networks want to offer a CPA model and make some margin for their hard work (which is totally deserved) and the Advertiser and the Affiliate/Network need to have a rock solid audit-able method of tracking this so as to not over-pay for sales.
Given some of the research we have been doing over the past couple months we have found that there might be an opportunity to have your cake and eat it too. That said with all reward comes risk with it. We’ve been messing around with different methods of statistical significance using Tagman and some home-grown tools and of course a lot of excel sheets to determine if a model can be built that will evolve with the campaign.
This is predicated on a couple things:
1. You can track all conversions that are going through an advertiser’s site. [mandatory]
2. You can track all clicks and natural traffic that are going to the advertiser’s site. [mandatory]
3. You can track all ads purchased to drive users to an advertiser’s site. [optional]
4. You can throttle ads up and down by channel. [optional but possibly mandatory]
Number 1 is mandatory because you need one (and only one) count of all the sales coming in to the site and a user id that you can match upstream to the clicks.
Number 2 is mandatory because you need to know how and when users arrive on your site. The how is not as important as the when. The idea here is if a user sees a display ad and then types in the advertisers url, you can link those three events if they are in a chronological time series and then by the user id from the adserver.
Number 3 is optional because first off, there’s no way Google will let you get a cookie dropped on search ad views and what you are really looking for is statistical significance in a chronological time series of advertising, click/type-in to site, and then conversion so tracking all ad views isn’t critical but tracking as much as you can without breaking the bank in adserving fees definitely helps.
Number 4 is optional/mandatory because in a perfect world all ads are RTB enabled and you can write a computer program to do a time series of cascading ad bursts and then track statistical significance of when a particular ad channel is increased and decreased and watch conversions spike and trough and then you can build your model out as to which ones actually drive sales. The fact is that the world isn’t perfect so some ads need to be bought in bulk (homepages), others need to be reserved, and others need to be bidded up and down and hope that the volume of ad impressions follow. So just like #3, do your best but don’t worry about being too much of a perfectionist.
Now that you have all of that set up, the goal will be to make changes over specific periods of time with each channel of your advertising and identify key pathways and then assign a coefficient of statistical significance to the incremental conversion boost you get (or don’t get) and adjust your budget and price (or bid) you are willing to pay for that channel of ads moving forward.
This should help you develop a model for buying advertising based more on throttling price and budget based on what is driving incrementality rather than obsessing about ‘giving credit’.
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This is a continuation of my post on Friday discussing how the Supply side was affected by attribution modeling and, though I’m confident it will be a good thing for the ad market because I think online ads sell for too cheap these days, I can see how publishers are worried and advertisers could be too opportunistic. That said let’s examine the Demand Side.
Supply = Publishers (Friday’s Post)
Demand = Advertisers (This Post)
Demand
Attribution modeling is truly an Advertisers dream. Many publishers attempt to offer it like Doubleclick and Atlas’ Engagement mapping or the Advertising.com custom data mining offerings but there are inherent flaws because data is limited, the whole puzzle isn’t visible to the publisher, or advertisers are just skeptical that there could be a conflict of interest in that they are just trying to sell you more advertising.
Every marketer’s job is to buy advertising and make creative and then align the cost of doing so in an attempt to make it drastically cheaper to acquire new customers than what you are charging the customer. The problem with this is that without a solid attribution model, you never truly know what piece of media and creative is actually driving that customer. Furthermore it rarely is just one ad impression so the question becomes what combination of media over what time period (and what time interval) is the most cost efficient.
Couple that with the fact that you either need to pay for each individual ad (like a homepage) or you pay for a result and you have no control over or have no way to track how and when the ads were shown (like a pay per click search text link) and you have quite a messy scenario.
So in the grand scheme of things there is only upside for the advertiser with attribution modeling but there is a major philosophical flaw. Most marketers if they look at their marketing plan have customers that they think are for free. Natural search traffic, organic traffic to your site, customers walking in or calling in and just buying a product.
So I will be bold enough to lay down the Cardinal Rules of Attribution Modeling:
1. There is no such thing as free traffic
2. There is no such thing as a free customer
3. If you have free traffic and free customers, your attribution model is BROKEN.
I will end with a ridiculous scenario and prediction. I personally think that the demand side of attribution modeling could be the fix to ad market growth and economic growth. The reality is that we are doing less work and incurring less costs to acquire new customers, so therefore companies like Amazon, Google, and Yahoo can deliver sales to customers at a much lower overhead than newspaper, TV, and radio have done in the past. This concentrates dollars in marketplace pricing scenarios and those dollars are then compressed because these businesses use their low TAC to offer low ad prices.
Based on economic principles alone, if every advertiser knew what ads truly drove their sales right now, they would double down their ad dollars. That would double the ad market and pump twice as much money back into our economy and all problems would be solved. THE END!
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So I recently engaged in a debate about how solving the attribution modeling problem could effect the ad market. The conversation was broken into supply and demand where:
Supply = Publishers (this post)
Demand = Advertisers (the next post)
Supply
On the supply side solving the attribution modeling issue could affect publishers in a drastic way. Since ad inventory is becoming so fluid as a result of companies creating marketplace environments out of their ad inventory, the value that an advertiser sees in an ad, could really affect a publisher’s business.
We already know that marketplace buying drastically compresses ad dollars. We see that with advertisers who have pulled money out of newspapers and TV and put it into google. Google takes these dollars and since they can deliver the eyeballs and customers at an incredibly inexpensive overhead cost, they allow advertisers to essentially set the price of each keyword ad. Since there are so many keywords and relatively few bidders on each keyword and such low cost to deliver each incremental customer, this works extremely well so there is plenty of diversity for google to rely on their prices to continually rise as competition increases.
For display advertising it’s not the same. A homepage 300x250 ad typically only has one advertiser per day and with 365 days in a year and between $50k - $2mm per day (depending on the site) that’s a lot of eggs in one basket for a publisher.
So they are highly incented to keep advertisers from knowing the real value of their advertising and they can create scarcity in a high competition placement to drive up price.
The threat here is that if the small group of advertisers (it can’t be more than 365) all know exactly how much this placement is worth to them, they can hold the publisher hostage on price.
I think that gives an idea of two polar opposite sides of the spectrum for the supply side. One area with a lot of diversity and little competition where having true attribution and REAL pricing will not threaten a publisher’s business model and another where little diversity and high competition if attribution is introduced could curb that high competition and cause problems for a publisher.
There is also the fact that REAL pricing and a sound attribution model could make advertisers identify what really drives incremental sales in which case all publishers win!
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I came across a post by Brad Terrell talking about the Appnexus Innovation summit and he highlights the topic of targeting sites vs. targeting audiences.
He goes on to talk about two different ad networks, their differences and gives emerical evidence of his thoughts:
Adam also illustrated the value creation potential of the audience-driven approach to targeting by pointing out how the enterprise value of a representative “old school” ad network, Burst Media, paled in comparison to the much higher enterprise value of a newer “audience-driven” ad network, interCLICK
- Please read the whole post as this could be considered out of context and its worth reading the whole post http://loca.ly/couk6j
So I think about this from a entrepreneur’s perspective and I completely agree. Advertisers and Marketers cling to what is new and there is continual pressure to do a better job and take advantage of the newest technologies and methods possible.
That said, DON’T OVERLOOK SITE TARGETING. Audience targeting if done properly with re-targeting or search keyword re-targeting such as with Magnetic.is can be very powerful and literally spin gold when it comes to bottom of the funnel and making sure you get in-market sales.
Beyond this however lies the ocean of ad impressions that are still being boiled by the likes of the big ad networks and exchanges. I recently had a conversation with the VP of Operations at one of the larges online ad agencies pitching my business and knowing that I used to sell to him I asked, well what do you think I should be buying. He responded, that when you are doing direct response advertising where you need to hit a specific ROI to continue to invest, what you do is buy all of the ad networks and optimize the best you can. He said DSP’s are great for getting that re-targeting audience and a couple other segments but largely a lot of the audiences do not work and there are so many of them that even if there is an optimal audience, you will waste a lot of money trying to find it.
From my experience on the sales side on any given campaign there were 2-3 audiences that really worked on any given campaign (one being re-targeting) and the rest of the heavy lifting was done by site optimization and frequency within site optimization. In fact the most important driver is something that I rarely hear people selling on or creating business models on, which is site frequency and the fact that the first few impressions (1-3) are in fact the most valuable by a long shot. Most publishers know this and technology lends to selling this easily but for some reason it is not sold out there on the open market.
Furthermore there is also a lot of evidence that brand name new sites and mail sites such as LA Times, NY Times, Yahoo Mail, AOL Mail and others similar create the most ROI impact if bought at the right frequency and attributed properly…..which leads to attribution modeling which is a whole other post which I probably need to write more on. That said, if bought through an ad network or exchange often you get the higher site-frequency impressions and these sites don’t appear to work as well. If bought directly, you have the burden of paperwork and exorbitantly high prices to get access to the right impressions of this media.
So because they have such a broad view of their buys I think a great new frontier for ad networks SSP’s, and DSP’s is to sell based on site-frequency. They have this data and often know where they are in the publisher daisy chain and can aggregate supply across many sites and can efficiently convert this into a fluid buy.
And, I would buy it…..
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So Invite Media was bought by Google today. To me that means Realtime Bidding is officially ON. We all knew it’s valuable and the mechanism to do it has been validated.
But that still leaves wide open, what to do with it now that you have the mechanism to do it. What is the use of real-time bidding if you can’t have real-time reporting and you can’t have a real-time dashboard to examine, analyze and control that.
There is a critical problem there. Decisioning on the price in real-time if you already know who a user is has been made readily available with DSP’s like Invite Media, however, what about real-time estimation on that impression and what about crafting your algorithm to learn and adjust things like frequency, ad sequence, and offer based on that real-time bid. I’m hoping google will quickly string together Teracent and Invite to be able to do this. Only time will tell.
But on the Media Buyer or Agency side where I now sit, other things come to mind. Like if this data is flowing in and I can bid in real-time then I should probably get access to map this data to my marketing plan and report on this based on my marketing plan so that my marketing plan becomes a living breathing thing.
To do this you need real-time Reporting to match your real-time bidding!
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The more I think about attribution the more it leads down the road of connecting the ads with the true direct response vehicle. I’m not particularly talking about direct response advertising as it has evolved to today as that is really just optimization of the bottom of the funnel.
And contrarily brand marketing has still done a good job of keeping itself disconnected from direct response. So what would happen if you started cutting that branding budget and relying on just direct response advertising?
Or what if you cut the direct response dollars and just tracked all the brand impressions back to the direct response mechanisms i.e. call centers, sales people, and online commerce carts. We can do this. So why don’t we?
Well I have to give google credit. They passed up the brand dollars. In their earlier days they offered cpm advertising at the top of keywords. They later cut this out and moved over to an all cpc model.
Now we are seeing facebook doing this as well.
So these are the two biggest marketing vehicles of our recent history and they are passing up the branding dollars which could have a major backlash on our industry.
We already saw the compression of ad dollars when google swooped in and captured massive ad budgets. Well arguably they are getting the same results as when people would spend 10X what they spend on google on TV, Newspaper, and Magazines. Now Facebook is coming in and offering the best targeting our industry has ever seen and is passing up the brand dollars.
I think we will see another wave of ad dollar compression industry wide from the facebook price change backlash.
Also, I think it’s time for disruption at the big agencies and starting to use some of the tools that we have with ad servers, scoring, data warehouses, and call tracking to connect the brand dollars with the call centers, sales channels, and ecommerce carts.
Maybe a new smarter view thru metric will be the catalyst for this…..
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A year or two ago you started to see a lot of buzz in the marketplace about attribution modeling and analytics. Atlas and Doubleclick started coming out with products they called “Engagement Mapping” and agencies and marketers dug in! Finally someone would try to crack the nut of the age old question by John Wannamker:
“I know half the money I spend on advertising is wasted, but I can never find out which half. ”
So what ever happened with this?
You really don’t hear too much talk about Engagement Mapping anymore. Everything is more about “Audiences on Demand” or “Reatlime Bidding” or owning your own data warehouse and retargeting audiences.
Is this because people did the Engagement Mapping and figured out their attribution model and found the ‘half’ that is not a waste and is trying to buy that at the cheapest rate possible?
I don’t think so. What I think happened is that we started to dig into the Engagement Mapping and Attribution model tools and first of all realized warehousing all of that data and keeping it and analyzing it over the long term proved to be incredibly expensive, incredibly cumbersome to compute and quickly access and the Engagement Mapping product could not be easily productized and made into a nimble nicely packaged tool where marketers and agencies could quickly make changes and show results, they bagged it and went for the next best thing.
Essentially they took the high road on Attribution Modeling. Realtime Bidding and targeting audiences essentially productized and made Demand Side Platforms able to quickly and nimbly scale the ‘stuff that works’.
To better explain lets go back to John Wannamaker. He said that he knows half of his ad spend doesn’t work. Well we have come a long way and with technology now we are down to knowing that half of our ad spend doesn’t work, about 5-10% of our ad spend definitely does work and we can really do a lot of work and gain a lot of efficiencies in that 5-10% and I think that’s what DSP’s and a lot of the data targeting is doing.
Retargeting we know is a gold mine and for large advertisers, it’s reasonably scaleable. We also know that search data from companies like Magnetic.is is really valuable but we’re still finding out how to scale that and which keywords and from what sources will that work from.
These are great findings and our industry and marketers are much better served by knowing that these audiences are some new more robust pillars to stand on.
That said there is a whole lot more and I think we quit too early and there is a lot more innovation to do on Attribution Modeling. The data is there, the capabilities are there, it just needs to be figured out and the technology needs to be optimized because right now even with hadoop, cassandra, cascading, and some of the methods the guys at Flightcaster are using, data is still not normalized enough and inexpensively scalable enough. Also, the big dollars are being spent on the media company side (i.e. google, yahoo, AOL) and not on the advertiser or agency side because those dollars don’t exist.
I think as agencies expand their margins and as the DSP and Data space starts to cool and consolidate the next frontier will be Demand Side Attribution Model Software or DSAS or DeSAMoS or DemSAMoSo or maybe someone else can work on a cool acronym while I work on the product…….
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I recently read an article in the Economist and then a few other blog posts on an emerging concept of offering cloud computing processing and content delivery on a bidded pricing scheme. The basic idea is what is being called a “follow the moon” strategy. This is based on the fact that in geographic areas where it is night time or a cooler environment, computers and servers are either sitting idle or need less energy to cool. You may have heard that Google is setting up data centers on mountains or in the arctic so that they don’t have to pay for air conditioners to cool down their servers and can do more data processing and storage for cheaper.
Well with the dawn of virtual machines and a software layer that splits the application and processing algorithms from the actual hardware that does the storage and processing it is now possible to float your application and load balancing from cloud to cloud to get the best price.
Amazon is already offering a service they call “Spot Instances” this reminds me a lot of RTB (Realtime Bidding) in the ad world. People are starting to choose to run a server instance and have more content delivery power based on the price to do so. For someone that is streaming a ton of video and costs could expand rapidly can technically build this into their revenue model and essentially choose to serve customers based on the current going rate to deliver that content or compute that request.
Amazon offers bidding through an API and with their DevPay option I think it would be interesting to offer a service layer to optimize pricing over time and estimate yield management from an ad supported site. For example, lets say you use Rubicon Project for your SSP to control yield management on your site and maybe you’re promoting your site out there with ads on a DSP and you’re using an SEM to bid into search. It would be interesting to build a piece of middle-ware between these to estimate yield management on what you think you are going to make on a server call and process based on how much you can make from your SSP and the n+1 of acquiring an additional user via your DSP and SEM and essentially decision on that in realtime…..or near realtime….or even hourly for that matter.
Currently AppNexus is offering a seemingly open source stack to do this but adding in the yield management on the computing costs could essentially put you in the driver’s seat of controlling your cost model, revenue model, and better estimate your growth plans. Basically you’re just left with the Marketing Idea, and the Business Model itself!
Here’s an interesting site that tracks the price of Spot Instances on Amazon.
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I kind of hope Twitter has a cool and useful revenue model.
I was thinking about this today after talking to a friend of mine at the wharton school about the life sciences startup world vs the tech startup world and how a lot of the life science startups take a lot more money and a lot more time to produce an exit or value whereas you could have a web app startup company 3-5X over in the time it takes to evolve a drug or procedure or piece of technology in the life sciences field.
This lead me to think about Myspace and Facebook. Myspace, first to get huge and basically just plugged in ad networks and then sold brand advertising and then tried to cobble together targeting methods. Facebook, now with an ad platform with pretty solid targeting and a lot more scalable for small to medium advertisers.
But are these really useful and interesting. I mean Myspace has a massive database of bands and comedians and entertainers that use their site as their homepage and their portfolio. Why haven’t they evolved enterprise tools for these users that are more useful. Facebook with its apps and api and facebook connect, why have they stalled on peer to peer payments and are going toward the ad model.
Is the ad model the easy way out? Are ads and ad networks a conduit to not having to innovate? Is facebook and myspace short selling themselves when it comes to being useful to the world and it’s users? Are they stopping short of disrupting companies like Ticketmaster, Paypal, Bank of America? Could Facebook go head to head with Bank of America?
Now what for Twitter?
From ReadWriteWeb quoting Peter Kafka:
- Ads will be tied to Twitter searches, in the same way that Google’s (GOOG) original ads did. So a search for, say, “laptop”, may generate an ad for Dell. The ads will only show up in search results, which means users who don’t search for something won’t see them in their regular Twitterstream.
- The ads will use the Twitter format — 140 characters or less — and will be distributed via the third party software and services that use Twitter’s API. The services will have the option to display the ads, and Twitter will share revenue with those that do.
- Twitter will work with ad agencies and buyers to seed the program, but plans on moving to a self-serve model, like Google offers, down the road.
Read full ReadWriteWeb Article http://loca.ly/ade2jo
I mean surely they will make money from an ad platform but really…..is this the best that they can do? Isn’t there an opportunity for them to put Reuters and Associated press out of business and revolutionize real-time news and real-time headlines? C’mon now, lets be useful!
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