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Circulation dashboards   UT San Diego
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Circulation dashboards UT San Diego

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Circulation dashboards   UT San Diego Circulation dashboards UT San Diego Presentation Transcript

  • Circulation Dashboards Easy to build Quick to refresh Multipurpose Time saving Useful
  • Drives DecisionsEnables You to Monitor  Fast Refresh 5 1  About 2 – 5 minutes to produce the report weekly Five Minute 4 Refresh 2  Stopped weekly DTI reporting  Saved many IT hours per week 3  Eliminated the majority of ad-hoc requests  Saved many research hours per week  Constancy  Same dashboard every week from one source  Multipurpose  Top level dashboard for executives  Pivot tables for advanced users  Raw tables for dataholics
  • Smart Reporter AutomatedWeekly Dashboard  Reporting Elements  Status  Frequency  Tenure  Start Sources  Stop Reasons  Schedule changes  Complaints  Plan Performance  Average Rates  Product Totals  Daily Deal Overlap  Email population  Mather Performance  Geographic performance  Demo/PRIZM Performance
  • The StepsCreation is Easy  Query Page  2-Year Base (2011-2012)  Current Fiscal Week !Avoid Macros! If you built properly  Have it auto-populate the current week you will never  Active Sub need them  Samples YES | NO (The confuse others)  Remove System Stops  Create a TAB for each Smart Report  Unless they are just a simple query count  It helps you stay clean and organized  NAME THEM WELL Query Report Dashboard Page Tabs
  • Smart Reporter AutomatedThe Query Page  Consolidate all queries onto one page.  They will never be shifted from changing data and you will always know where the reference is.  It makes things a lot easier
  • Make Your ReportsMulti-purpose  Make master reports to avoid ad-hoc reporting  All the granularity is already there  For Stops you could make many reports  By Stop Reason | By Week (Trending) | By Year (Trending) | By Frequency
  • Avoid The Mess of Data……..  Your smart report data could look like this………………….  Stop | Reason | Frequency | Week | Year
  • Same Amount of DataBut Simplified……  Compose your reports so you can take advantage of Pivot Tables and conditional queries  Keep the majority of your data in the Row Dimension.  Save the Column Dimension for years
  • Conditional QueryLeverage Excels Strengths  Conditional Query Example  Sumifs(Year=2012, Week = 9, StopReason = NonPaidStart, Frequency = DS) = 60 Raw Data Reported
  • Pivot TablesLeverage Excels Strengths  Pivot Table Example  I include a pivot table with all data I send out.  When people ask for more detail… they already have it.
  • Basic Data to Pull  Temporary Stop  ROWS: Restart/Will-call | Frequency | Week  COLUMN: Years  Permanent Stop  ROWS: Restart | Frequency | Week  COLUMN: Years  Service Change  ROWS: Upgrade/Downgrade | Frequency | Week  COLUMN: Years  Restart  ROWS: Start Source | Frequency | Week  COLUMN: Years  New Start  ROWS: Start Source/Channel | Frequency | Week  COLUMN: Years
  • Basic Data to Pull  Rate for Start/Stop  ROWS: Rate Code | Week  COLUMN: Transaction (Start or Perm Stop)  Rate for Active  ROWS: Rate Code  Branded Pub Transactions  ROWS: Publication | Week | Transaction  COLUMN: Year  Branded Pub Active  ROWS: Publication  Mather  Active by Group (A,B,C,D)  Transaction by Group (A,B,C,D)  Regional Zones and ZIP Codes  Active by Group (Geo)  Transaction by Group (Geo)
  • Basic Data to Pull  Subscriber Tenure  Complaints  Active  ROWS: Complaint | Week  Stop  COLUMN: Year  Basic Demos / PRIZM  Active  Start / Stop  Daily Deal Overlap  Active  Start / Stop  Repeat transactions  Multi-Start  Multi-Stop  Email population  Actives
  • And now for something completely different Web Server Logs?!?!?
  • Sure Why Not With lots of properties going to site registration and paywalls We have the ability to catch Subscriber ID | UserID | email from the server logs Then Match it to your subscriber records Authenticate Website/iPad DTI Login e-edition Summarize Server Log MaaX (Analytics & Reporting) (By UserID)
  • Raw Log Components LOTS of data!!!! In the raw log  Summarized from 32MM to 3MM  Per Month…..
  • Standard Web Measures Online usage  Devices  Page views  iPhone  Visits  iPad  Unique Visitors  Android  Pages per Visit  Frequency of visit  Referrals  Time on site  Direct  Social (Facebook/Google+) Online Interests  Google  Top headlines  Other site  Top Sections
  • Enhanced Intelligence  Web habits of subscribers  Which sections are most popular What is the frequency of visit  How does that fit within our segmentation  Paywall  What percentage of subscribers are hitting the wall  Advertising  Demographics by site section  Retention  Better customer service intelligence (Top Level Web Usership and frequency)  Would the pay wall effect this subscriber if they canceled?  Do they use mobile or the iPad app?  Acquisition  By Online habits and section preferences  Partner with the best referring sites  Better cross marketing  Email | Web | App | Print
  • Thank YouJoseph Gordon | Director of Research O: 619-293-2520 joseph.gordon@utsandiego.com UTSanDiego.com