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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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Today I read an article on SAI about how Online video that was re-posted from Dave Rayburn’s Streamingmedia.com.  The article inferred that online video will not replace TV in the near future because Akamai finally released some numbers on the actual number of live video streams during the World Cup and the numbers were underwhelming.

My thoughts are that there are some systematic problems with online video but this should not be confused with consumer demand for it.

Online video is pre-mature because of a couple things.  First of all you can’t get a decent stream.  Now this could be the CDN’s fault, the ISP’s fault for throttling bandwidth, or the content provider’s fault for purposefully not investing in decent quality video through the internet channels.  But the truth is it goes through the same line so it’s more of a business decision rather than a technical limitation.

The counter argument of, well people prefer to watch things on TV….. I’m not buying it.  Cable TV’s menu user experience is terrible.  Boxee, Apple TV, Hulu, Netflix, every one of them has a better UI, more selection and better customer experiences.  Also, now any computer can be plugged into a TV with an HDMI cable and give excellent picture and an iPhone or Droid can be used as the remote, so saying people prefer TV is just wrong.

Just like everything else in media it’s tied to advertising and the attribution model.  TV has a great racket going on for commercial spots.  The fact that you buy on a spot basis or TRP or GRP measurement basis is just silly.  The technology is there to buy and track it better, content providers, ISP’s and CDN’s are just choosing not to do it.  Right now they have created a similar setup to homepages online.  

The truth is Cable TV could be bought like exchange display advertising and the diversity and segmentation that’s going on in display will bring in a flood of new advertisers and faster moving transactions and I also believe competition will be driven up for the premium spot buys for national premier series like American Idol spots much like Yahoo’s homepage in Q4.

Right now companies like TidalTV and Simulmedia are working on solutions for this but don’t yet have access to do the transactions and sales (to my knowledge).  I look forward to this opening up, but I’m not waiting for them.

Attribution can be done on Cable TV right now and we will begin modeling it out in coming months and I look forward to integrating this attribution model with online.

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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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Working at a big company you get very used to how things are done.  Data has a certain availability and you need to work around this and play within the confines you are given as any major infrastructure changes could take months or years and there is always the fear of killing the golden goose and switching costs from going to from one system to another.  Basic rule of thumb is use what is currently working and innovate within the confines of it unless there is a major loss or need to change things.

I’m specifically talking about adserving technology, the databases that store information that is gleaned out of adserving and then the structure of this data to query and report on.

I got in a discussion with the CEO of a emerging data company who I have a ton of respect for recently whom also has an extensive technology background.  Our discussion was around real-time data reporting and the feasibility thereof.  Typically most adservers dump logs into massive long-term storage databases using either hadoop, neteeza, or even oracle to store this.  There is definitely a maximum number of records you can insert per second, limitations on the structure of this data which could complicate how you pull it out later for reporting, and furthermore the more data you store in one place, the harder it is to pull out a tiny piece of it on a quick recall.

When talking about advertising, for an individual advertiser or marketer, you are talking about between 10 and 100 million display ad impressions per day along with tens or hundreds of thousands of clicks and tens or hundreds of thousands of page views on a daily basis so to break that down lets say you need to store:

100 mm impressions

300k clicks

20 k page view events

Per day totaling 100,316,000 event records

so that’s 4,179,833 events per hour

or 69,663 per minute

or  1,161 per second

Then you have to think about how that number will spike during certain hours of the day and then lets say you definitely want to design the system to handle a lot of advertisers so let’s say 1,000 advertisers….you are talking about being able to handle between 1 MM and 20 MM events inserted into the database per second.

So how do you do this while managing costs? And is it even doable?

One thing is for sure that you need to own the data source so importing data from third party adservers or publishers is off the table because the server to server transfer alone will add valuable seconds on to your process and if you ever plan to do calculations on that data like conversion rates, match processes, click thru rates, you are adding time on to process that, and we haven’t even gotten to querying that data out of the database yet…..we’re just inserting it.  That said querying it out if structured properly will be a lot quicker and easier because you just have one or two users querying at a time per advertiser’s data set.

Anyway…..just thinking out loud about the problem at hand and the gap between real-time bidding and actually pulling the data based on real-time bidding into a reporting interface so a human being can actually look at a marketing problem and address it or examine it.

As a business side person myself, would love to see anyone else’s commentary on what database structures they have used and the feasibility of a project like this.

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So I’ve been trying to keep myself contained waiting but it has finally happened….

Someone has officially linked the offline product sales tracking world to the online advertising world.

Yes, that’s right, I know you’ve all been waiting for it too!

Stickybits has opened up their api to the public http://code.google.com/p/stickybits/

Mark my words, this point in time is the opening of the proverbial floodgates of online to offline sale tracking.  Now any old schmuck who can get their hands on a python developer can put Ticketmaster, OpenTable, Live Nation, and any major retailer out of business.  I mean seriously….these guys ought to just file for bankruptcy now…cut your losses!!!

Why is this, might you ask?

Well for those who don’t know Stickybits is a company that issues bar codes and connects them to a url.  This url can theoretically be anything.  It can call an image, it can call a website, and it can also trigger a purchase or redemption through a URI call.

I will be curious to see what restrictions and what type of load Stickybits handles.  Also there’s the fact that taking a picture of a barcode on a smart phone is kind of unreliable and a clunky user experience, but all of that will work it’s way out the base framework for the business model is there.

I look forward to seeing the innovation that comes out of this.

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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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