Mobtown Labs

How Important is Giving Credit

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’.

Increasing the Performance of your Digital Media Spend with Cross-Channel Conversion Attribution (firstrate.co.nz)
Optimise paths to conversion, not channels (econsultancy.com)
From Clicks To Calls (searchengineland.com)

Tags: Advertising Cost per action Advertising network Conversion rate Affiliate network Google Pay per click

Attribution’s effect on The Ad Market - Demand Side

Image via Wikipedia

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!