Attribution Reporting – Beyond last touch point

Accurate attribution has become increasingly important aspect in digital advertising due to the fact that users are being reached through multiple channels and touchpoints. And to determine the ROI from a particular channel or touch point, it is extremely important that returns are rightly attributed. This not only helps in better understanding of the results of the campaign already executed but it will also provide a great insight and direction on what should be the media planning for future campaigns.

The concept of purchase funnel which starts with creating awareness then generating interest and later invoking desire which finally results in an action, has not been built into digital advertising systems (which mostly depend on last click model). As these systems are maturing, attribution reporting and modeling is being added which will help in accurate ROI calculation.

Attribution reporting is basically retroactive reporting which helps to compare contribution of each type of touch point and ad events while attribution modeling is more like a proactive “what if” analysis which helps in optimizing the ways ad events occur based on attribution model. For example, a campaign which is creating awareness for a newly launched product will give most importance to last impression and reducing it with further with old impressions (decay). When a conversion happens, the attribution will be given to all the touch points which happened to be in the path to conversion based on the attribution model designed.

Attribution modeling can also help in better bidding with RTB systems as the bid will be determined using attribution model. It can also provide optimization opportunities based on consumer responses. But due to the impact it has, the modeling has to be done with caution. With Adatrix, we are building attribution reporting and providing ability to specify simple attribution models. In future, we will bring attribution modeling in bidding process and trend based optimization.

Brand Protection in Online Advertising

 

This presentation is part of research on several topics which was done to improve and enhance the features of our ad platform Adatrix™

This article discusses the topic of Brand Protection in Online Advertising. Companies invest a significant amount of time and money in creating brand awareness and establishing their brand. So when it comes to online advertising then want to make sure their brand is presented in the best light possible.

The guidelines of Brand Protection vary for Advertiser and Publisher. While the advertiser wants his ads to be kept away from inappropriate content, Publisher seeks malware protection and is concerned about the quality of ads being served to them.

Brand Protection for Advertisers

From the advertiser perspective, the most common requirements are listed as follows:

  • Avoid showing ads on offensive and illegal sites.
  • Avoid showing ads on bad-fit contents (e.g. showing ad of a travel site on article about accident).
  • Identify if ads are being displayed in desired locations (Geographic targeting)
  • Custom setting of tolerance levels (e.g. showing my ad on page containing alcohol/tobacco related content is OK but on page containing illegal content is NOT OK).

In order to meet those requirements, we should be able to show page-level content ratings of pages in our inventory. Also there should be a provision to maintain advertiser-specific blacklists and whitelists. At present, the feature of targeting/filtering based on geography,demography,category etc. is already provided.

Brand Protection for Publishers

From the publisher perspective, the most common requirements are listed as follows:

  • Ensure unwanted ads (e.g. poor quality ads) are not served which may effect user experience.
  • Protection from Malware

In order to meet those requirements, we are currently integrating a malware scanner which checks the media files before they are uploaded into our system (Adatrix). Also, publishers will be provided with dashboards where they can inspect the media/ad code being shown on their page at any particular time. Publishers should also be given an option to block unwanted ads through dashboard.

 

How DSPs, Exchanges and SSPs have evolved?

Last week during discussions on integrations with exchanges, DSPs etc. we were curious to know about how these have evolved and what is their uniqueness in digital advertising landscape. Briefly putting below my understanding on various jargons which are being used in online advertising today.

Considering the general workflow, advertisers hire agencies (for expertise) to spend their money, these agencies have buying desks which has relationships/partnerships with different entities on the supply side. Now two things have evolved during last 3 years mostly surrounding real time bidding.

Buying Desks along with the traditional buying relationships and partnerships now have something called Automated Trading Desks which are similar to DSP but are fully owned by agencies. Most of automated trading desks are running on technology either licensed from another company or acquisition (http://www.ustream.tv/recorded/16090215). The video highlights the point of conflict when any technology company works as an agency or vice versa. But this conflict is not occurring till now with Google’s display network which is pretty strange.

Ad networks or publisher networks started out with simple model of combining publisher sites into verticals, but due to abundance of networks and lack of differentiation, these networks have re-branded themselves as DSP and SSP. Even now many of them don’t have technology platform but provide a combination of licensed technology with inventory they had earlier. Many of these will now evolve to private exchange (providing the benefits of real time bidding along with the inventory they bring) which seems to be the next logical step. Private exchange concept is picking up to increase the spending of direct buy through real time bidding model. Exchange was mostly used for remnant stuff not just in terms of inventory that is left out but also in terms of money that is left out after premium buy.

Some links which will bring clarity on these are provided. Most of the links are interesting to read giving slightly different perspective and argument.

Definitions
DSP – Demand side platform – providing integrations with exchanges and ad networks to buy with RTB Ex: AdChemy, X+1, Media Math, DataXu
Exchange – RTB – Real Time Bidding – providing auction capability across different kinds of systems (DSP, SSP, Networks) Ex: DoubleClick, Right Media
SSP – Supply side platform – providing integrations with exchanges and ad networks to sell with RTB Ex: Rubicon, AdMeld, Pubmatic
Networks – Used to refer to ad networks which are basically publisher networks – Thousands of networks are there

Landing page optimization

­­­­­Landing page optimization (LPO) is a part of conversion rate optimization (CRO).  CRO aims at improving the percentage of visitors, from landing page (lead-form or a website) to become sales leads and customers. This is attained by making the landing page more appealing, relevant and helpful for the visitor.

Landing page is the webpage displayed to the visitor when then click on an advertisement or search engine result link. Landing page follows up on the advertisement by provides relevant content.

LPO aims to make landing page more apt in content and appearance that would make it more appealing to target audience.

Landing Page Optimization types can be classified based on targeting or experimentation.

LPO based on Targeting:

  1. Associative content targeting: Also knows as rule-base optimization and passive targeting, as in this case the page content is modified based on general information available about the user like visitor’s search criteria, geographic information, etc. This method can be used for generic parameters, for non-research-based consumer segmentation.
  2. Predictive content targeting: Also known as active targeting. In this case the page is modified using the information already available about the visitor, like prior purchasing behavior, demographic information, etc. Unlike in the passive targeting (associative content targeting), the page can be modified to anticipate (desired) future actions based on predictive analytics.
  3. Consumer directed targeting: Also known as social targeting. Here the page is modified based on the publicly available information, like reviews, rating, tagging, etc.
  4. Closed-ended experimentation: As the name suggests in this consumers are exposed to different variations of landing pages. Trend and user interaction with different pages is observed and at the conclusion of the experiment the final landing page is decided on the outcome.
  5. Open-ended experimentation: Unlike in the previous case, where we have a final landing page at the end of the experiment, this is an ongoing process and the page is continuously modified based on the available results. Hence the landing page generated is dynamic in nature.

LPO based on experimentation:

  1. Closed-ended experimentation: As the name suggests in this consumers are exposed to different variations of landing pages. Trend and user interaction with different pages is observed and at the conclusion of the experiment the final landing page is decided on the outcome.
  2. Open-ended experimentation: Unlike in the previous case, where we have a final landing page at the end of the experiment, this is an ongoing process and the page is continuously modified based on the available results. Hence the landing page generated is dynamic in nature.

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