Comments on: Measuring Desirability http://blog.jimnovo.com/2008/04/26/measuring-desirability/ Moving from a Low Accountability to a High Accountability Business Model Sun, 20 Jul 2008 19:13:25 +0000 http://wordpress.org/?v=2.5.1 By: Alan Osetek http://blog.jimnovo.com/2008/04/26/measuring-desirability/#comment-24085 Alan Osetek Tue, 13 May 2008 15:26:07 +0000 http://blog.jimnovo.com/?p=200#comment-24085 Enjoyed reading this blog post and wanted to comment on one other way of testing mis-allocated budgets related to your comment about marketers ‘over-spending’ issues. This is related to consumers that would have come back to an advertisers brand vs. under spent budgets on getting consumers to come back who are less likely or unlikely to come back. In addition to using an email vendor to accomplish a test, you can accomplish the same test with your display media budgets and paid search budgets. We have a few clients that have accomplished this via ‘attribution analysis’ or display cookie based analysis. These companies looked at multiple marketing touchpoints across media channels to give them a more accurate understanding of how various multi-channel activities would ultimately affect the final click (or conversion). By analyzing marketing campaign data (including cookie-level raw data) across all websites and other media channels, building a unified consumer data warehouse, and applying specific business-driven algorithms, they learned the following insights: 1. Organizations must consider multiple channels. CPA (and sometimes CTR) doesn't provide accurate data for optimizing campaign spends. Given that CPA is calculated based on the last impression, our clients were not giving appropriate credit to other channels and the influence these channels have on consumer behavior. Such an adjustment in attribution and media optimization based on the new data led to an increase of 12.5 percent ROI on media spend with one of our clients. 2. Assigning higher budgets for certain publisher websites, based on historically low CPAs, without understanding the bandwidth of these sites can lead to wasted money and missed opportunities gained by spreading the spend to niche online publisher sites. 3. Cookie data analysis is a much more effective to way to set frequency caps on media placements. Most advertisers set frequency caps on media placements based on the initial planning data they receive from third-party sources such as comScore or A.C. Nielsen. For our clients, they found cookie data analysis changed the optimal frequency for each campaign, site, placement and creative. Impressions were being wasted, and in some cases a few more impressions would have generated a substantially larger number of new conversions. By placing the correct frequency caps, they gained efficiencies of about 6-9 percent of media spend in the first few months. In summary, via these tests, our clients learned that they achieved significant marketing campaign conversion improvement when taking into consideration the "sphere of influence" of multiple consumer touchpoints. Focusing on and measuring the final conversion click is simply not enough. Alan Osetek Visual IQ Enjoyed reading this blog post and wanted to comment on one other way of testing mis-allocated budgets related to your comment about marketers ‘over-spending’ issues. This is related to consumers that would have come back to an advertisers brand vs. under spent budgets on getting consumers to come back who are less likely or unlikely to come back. In addition to using an email vendor to accomplish a test, you can accomplish the same test with your display media budgets and paid search budgets.

We have a few clients that have accomplished this via ‘attribution analysis’ or display cookie based analysis. These companies looked at multiple marketing touchpoints across media channels to give them a more accurate understanding of how various multi-channel activities would ultimately affect the final click (or conversion). By analyzing marketing campaign data (including cookie-level raw data) across all websites and other media channels, building a unified consumer data warehouse, and applying specific business-driven algorithms, they learned the following insights:

1. Organizations must consider multiple channels. CPA (and sometimes CTR) doesn’t provide accurate data for optimizing campaign spends. Given that CPA is calculated based on the last impression, our clients were not giving appropriate credit to other channels and the influence these channels have on consumer behavior. Such an adjustment in attribution and media optimization based on the new data led to an increase of 12.5 percent ROI on media spend with one of our clients.

2. Assigning higher budgets for certain publisher websites, based on historically low CPAs, without understanding the bandwidth of these sites can lead to wasted money and missed opportunities gained by spreading the spend to niche online publisher sites.

3. Cookie data analysis is a much more effective to way to set frequency caps on media placements. Most advertisers set frequency caps on media placements based on the initial planning data they receive from third-party sources such as comScore or A.C. Nielsen. For our clients, they found cookie data analysis changed the optimal frequency for each campaign, site, placement and creative. Impressions were being wasted, and in some cases a few more impressions would have generated a substantially larger number of new conversions. By placing the correct frequency caps, they gained efficiencies of about 6-9 percent of media spend in the first few months.

In summary, via these tests, our clients learned that they achieved significant marketing campaign conversion improvement when taking into consideration the “sphere of influence” of multiple consumer touchpoints. Focusing on and measuring the final conversion click is simply not enough.

Alan Osetek
Visual IQ

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