ricklentz/moving_around_the_no_collecting_issue.txt

## moving_around_the_no_collecting_issue.txt
Darren,
  I like your Excel tool and found the side-by-side visual helpful in understanding the changes in power metric conveyed by the author.
  I was curious this past weekend and set out to replicate data gathering practice outlined in the Zhang paper.  I found that even as a registered Facebook developer, it is intentionally hard to pull public user data using the graph API within the current Terms of Use (even for just brand pages).
  I am struggling to connect the dots on how the paper's benchmarks integrate with a real company's marketing strategy metrics.  I see that there is value in the structure of a social graph and methods to measure a node's relative power.  But I feel that capturing and proving value directly from these metrics in a restricted data collection ecosystem is no longer feasible.
   SAP, Oracle, IBM, and Acxiom acquired data aggregators of historical, individual-level transaction data to help brands efficiently run marketing campaigns.  Then as social network adoption grew, behavioral analysis startups leveraged detailed individual-level social data to build better customer segment and behavior models.  Datalogix, BlueKai, Silverpop, LiveRamp, RelateIQ, Cognea, Cloudant, Aspera, TOA Technologies, SeeWhy, Fieldglass, Concur... have been gobbled up and are used to augment the big player's service offerings.
 Maybe part of my dot-connecting discomfort is that the Zang paper's premise is based on prior work that shows high engagement leads to increased purchases and revenue. Zang states,  "...we examine the effectiveness of our strategy in identifying target users who will display high user engagement with the focal brands."  But to me, I would like to see what improvement these metrics offer the big four, specifically regarding the incremental increase to the performance of current state of the art offerings.
  As a step forward, it would be nice to envision how one could help flush out these metrics in the field for a real company.  I'd guess you would want to approach a brand that likely has clean datasets with individual-level purchase history.  Then maybe use a tool like Watson Analytics [1] to build out a benchmark campaign on the firm's data as well as an augmented campaign that narrowed targeting.  Both campaigns could run and use traditionally collected individual-level feedback measures.  Proving the performance improvement at a few different firms may make your consultancy an attractive acquisition target. Thoughts? ;-)


[1] https://www.ibm.com/blogs/business-analytics/how-to-analyze-purchasing-habits-to-reach-the-right-customers-with-watson-analytics/
	Darren,
	I like your Excel tool and found the side-by-side visual helpful in understanding the changes in power metric conveyed by the author.
	I was curious this past weekend and set out to replicate data gathering practice outlined in the Zhang paper. I found that even as a registered Facebook developer, it is intentionally hard to pull public user data using the graph API within the current Terms of Use (even for just brand pages).
	I am struggling to connect the dots on how the paper's benchmarks integrate with a real company's marketing strategy metrics. I see that there is value in the structure of a social graph and methods to measure a node's relative power. But I feel that capturing and proving value directly from these metrics in a restricted data collection ecosystem is no longer feasible.
	SAP, Oracle, IBM, and Acxiom acquired data aggregators of historical, individual-level transaction data to help brands efficiently run marketing campaigns. Then as social network adoption grew, behavioral analysis startups leveraged detailed individual-level social data to build better customer segment and behavior models. Datalogix, BlueKai, Silverpop, LiveRamp, RelateIQ, Cognea, Cloudant, Aspera, TOA Technologies, SeeWhy, Fieldglass, Concur... have been gobbled up and are used to augment the big player's service offerings.
	Maybe part of my dot-connecting discomfort is that the Zang paper's premise is based on prior work that shows high engagement leads to increased purchases and revenue. Zang states, "...we examine the effectiveness of our strategy in identifying target users who will display high user engagement with the focal brands." But to me, I would like to see what improvement these metrics offer the big four, specifically regarding the incremental increase to the performance of current state of the art offerings.
	As a step forward, it would be nice to envision how one could help flush out these metrics in the field for a real company. I'd guess you would want to approach a brand that likely has clean datasets with individual-level purchase history. Then maybe use a tool like Watson Analytics [1] to build out a benchmark campaign on the firm's data as well as an augmented campaign that narrowed targeting. Both campaigns could run and use traditionally collected individual-level feedback measures. Proving the performance improvement at a few different firms may make your consultancy an attractive acquisition target. Thoughts? ;-)


	[1] https://www.ibm.com/blogs/business-analytics/how-to-analyze-purchasing-habits-to-reach-the-right-customers-with-watson-analytics/