Who cares about customer
experience?
A Complete
Web Monitoring
perspective


    Web Analytics Conference 2010
  in collab...
“Hard” data

  Analytics         Usability     Performability
(what did they   (how did they    (could they do
  do on the...
What did they do?
Web analytics
http://www.flickr.com/photos/diegocupolo/3511117614/
Accounting                                       Optimization


                                                          ...
How did they do it?
Web Interaction Analytics
http://www.flickr.com/photos/trekkyandy/189717616/
Why did they do it?
Voice of the Customer
http://www.flickr.com/photos/karola/3623768629/
Could they do it?
Performance & availability
What were they saying?
Community monitoring
!
What were they up to?
Competitive analysis
http://www.flickr.com/photos/31690139@N02/2965956581/
Online marketers want to
maximize their revenues.
http://www.davidross.com.mx/admin/wideimagerepository/sergioZymanAmp.jpg
http://www.goal-posts.net/media/fbl-181_cat_07_160_x_160.jpg
http://www.johnsoncontrols.com/publish/etc/medialib/jci/ps/Media_Kit.Par.25973.File.dat/complex_machinery_HIRES.JPG
http://www.infovisual.info/02/img_en/078%20Different%20land%20animals%201.jpg
http://upload.wikimedia.org/wikipedia/commons/0/0e/Soccer_Youth_Goal_Keeper.jpg
Websites
 have a dirty
 little secret


http://todaystatus.files.wordpress.com/2009/04/ww11-secret.jpg
http://www.inquisitr.com/2097/site-meter-causing-internet-explorer-failure/
http://www.flickr.com/photos/aleermakers/3455786409/
http://www.octulipfestival.com/images/Picture%20144.jpg
Figure 3          Interactive user productivity versus computer response time for human-intensive
                        ...
10 s
 1s

100 ms
10 ms
        !   Zzz
http://www.flickr.com/photos/spunter/393793587   http://www.flickr.com/photos/laurenclose/2217307446
Everything	
  is	
  interwoven.
Shopzilla had another angle

•   Big, high-traffic site       •   16 month re-engineering
•   100M impressions a day      •...
5-12% increase in
    revenue.
Tying web latency to
business outcomes.
http://www.flickr.com/photos/spunter/393793587   http://www.flickr.com/photos/laurenclose/2217307446




     KPIs
http://www.flickr.com/photos/
    mrmoorey/160654236
ATTENTION            ENGAGEMENT CONVERSION
             NEW
 SEARCH     VISITO
   ES         RS
             GROWT        ...
VISITOR        STRANGELOOP          WEB
                ACCELERATOR         SERVER
                   Decide
             ...
What we learned:
Traffic levels
                         9.000
Total number of visits




                         6.750



                ...
Bounce rate
                      20
Visits that bounced




                      15



                      10

       ...
% visits marked “new”
% of visits that had no returning cookie


                                           14



        ...
That means...
                         9000
Value Number of visits




                         6750



                  ...
Average time on site
                         31
Time on site (minutes)




                         23



               ...
Pages per visit
                     16
Average pages seen




                     12



                     8     15,64...
Conversion rate
                                      and order value
                                 20
Difference due t...
This is just one case
 LOTS


 # OF
VISITS

             OPTIMIZED
    0
         0     VISITOR LATENCY   10,000

Differen...
With one outcome
 LOTS


 # OF
VISITS
             21.58%
             BETTER
    0
         0    VISITOR LATENCY   10,000...
With one outcome
 LOTS
         Best 5%            Worst 5%
 # OF
VISITS
                   21.58%
                   BETT...
Lots of different results
 LOTS       24%

                  18%
$ PER                   14%
                             ...
You have your own curve
 LOTS


$ PER
 DAY


   0
        0    VISITOR LATENCY    10,000

Every web business has a
curve l...
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
MeasureWorks - Tying web performance to analytics
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MeasureWorks - Tying web performance to analytics

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Performance Matters! Learn how web performance ties directly to conversion and the bottom line....

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MeasureWorks - Tying web performance to analytics

  1. 1. Who cares about customer experience? A Complete Web Monitoring perspective Web Analytics Conference 2010 in collaboration with MeasureWorks
  2. 2. “Hard” data Analytics Usability Performability (what did they (how did they (could they do do on the interact with what they site?) it?) wanted to?) Complete Web Monitoring Community VoC Competition (what were (what were (what are they they saying?) their up to?) motivations?) “Soft” data
  3. 3. What did they do? Web analytics
  4. 4. http://www.flickr.com/photos/diegocupolo/3511117614/
  5. 5. Accounting Optimization http://www.flickr.com/photos/sanchom/2963072255/ http://www.flickr.com/photos/thomasclaveirole/538819881/
  6. 6. How did they do it? Web Interaction Analytics
  7. 7. http://www.flickr.com/photos/trekkyandy/189717616/
  8. 8. Why did they do it? Voice of the Customer
  9. 9. http://www.flickr.com/photos/karola/3623768629/
  10. 10. Could they do it? Performance & availability
  11. 11. What were they saying? Community monitoring
  12. 12. !
  13. 13. What were they up to? Competitive analysis
  14. 14. http://www.flickr.com/photos/31690139@N02/2965956581/
  15. 15. Online marketers want to maximize their revenues.
  16. 16. http://www.davidross.com.mx/admin/wideimagerepository/sergioZymanAmp.jpg
  17. 17. http://www.goal-posts.net/media/fbl-181_cat_07_160_x_160.jpg
  18. 18. http://www.johnsoncontrols.com/publish/etc/medialib/jci/ps/Media_Kit.Par.25973.File.dat/complex_machinery_HIRES.JPG
  19. 19. http://www.infovisual.info/02/img_en/078%20Different%20land%20animals%201.jpg
  20. 20. http://upload.wikimedia.org/wikipedia/commons/0/0e/Soccer_Youth_Goal_Keeper.jpg
  21. 21. Websites have a dirty little secret http://todaystatus.files.wordpress.com/2009/04/ww11-secret.jpg
  22. 22. http://www.inquisitr.com/2097/site-meter-causing-internet-explorer-failure/
  23. 23. http://www.flickr.com/photos/aleermakers/3455786409/
  24. 24. http://www.octulipfestival.com/images/Picture%20144.jpg
  25. 25. Figure 3 Interactive user productivity versus computer response time for human-intensive interactions for system A E 600 - 3 T -" INTERACTIVE USER PRODUCTIVITY (IUP) w -HUMAN-INTENSIVE COMPONENT OF IUP 7 MEASURED DATA (HUMAN-INTENSIVE E 500 - A z " COMPONENT) U E - w E 0 > - > - - 400 3 n F 2 0 0 300 - 200 - 100 - 0 0- I 1 I I I 0 1 2 3 4 5 COMPUTER RESPONSE TIME (SI (1981) A. J. Thadhani, IBM Systems Journal, Volume 20, number 4
  26. 26. 10 s 1s 100 ms 10 ms ! Zzz
  27. 27. http://www.flickr.com/photos/spunter/393793587 http://www.flickr.com/photos/laurenclose/2217307446
  28. 28. Everything  is  interwoven.
  29. 29. Shopzilla had another angle • Big, high-traffic site • 16 month re-engineering • 100M impressions a day • Page load from 6 seconds to 1.2 • 8,000 searches a second • 20-29M unique visitors a • Uptime from 99.65% to month 99.97% • 100M products • 10% of previous hardware needs http://en.oreilly.com/velocity2009/public/schedule/detail/7709
  30. 30. 5-12% increase in revenue.
  31. 31. Tying web latency to business outcomes.
  32. 32. http://www.flickr.com/photos/spunter/393793587 http://www.flickr.com/photos/laurenclose/2217307446 KPIs
  33. 33. http://www.flickr.com/photos/ mrmoorey/160654236
  34. 34. ATTENTION ENGAGEMENT CONVERSION NEW SEARCH VISITO ES RS GROWT CONVERSI TWEETS NUMB H PAGE ON ER TIME RATE S MENTI PER ON x OF SITE ONS VISIT ORDER VISITS LOSS VALUE ADS BOUN CE SEEN RATE
  35. 35. VISITOR STRANGELOOP WEB ACCELERATOR SERVER Decide whether to optimize Normal Accelerat Receive content page Insert Process scripts Optimize? segment marker Send analytic Unacceler GOOGLE ANALYTICS
  36. 36. What we learned:
  37. 37. Traffic levels 9.000 Total number of visits 6.750 4.500 8.505 2.250 4.740 0 Optimized Unoptimized Visitor experience
  38. 38. Bounce rate 20 Visits that bounced 15 10 13,38% 14,35% 5 0 Optimized Unoptimized Visitor experience
  39. 39. % visits marked “new” % of visits that had no returning cookie 14 11 7 13,61% 10,85% 4 0 Optimized Unoptimized Visitor experience
  40. 40. That means... 9000 Value Number of visits 6750 4500 7.582 4.095 2250 923 645 0 Optimized Unoptimized
  41. 41. Average time on site 31 Time on site (minutes) 23 16 30,17 23,83 8 0 Optimized Unoptimized Visitor experience
  42. 42. Pages per visit 16 Average pages seen 12 8 15,64 11,04 4 0 Optimized Unoptimized Visitor experience
  43. 43. Conversion rate and order value 20 Difference due to optimization 15 10 16,07 5 5,51 0 Conversion rate Order value
  44. 44. This is just one case LOTS # OF VISITS OPTIMIZED 0 0 VISITOR LATENCY 10,000 Different visitors experienced different
  45. 45. With one outcome LOTS # OF VISITS 21.58% BETTER 0 0 VISITOR LATENCY 10,000 Right now we have a single experiment, and a single
  46. 46. With one outcome LOTS Best 5% Worst 5% # OF VISITS 21.58% BETTER 0 0 VISITOR LATENCY 10,000 Visitors who were optimized fall into a range – the 5th to
  47. 47. Lots of different results LOTS 24% 18% $ PER 14% 12% DAY 9.5% 0 0 VISITOR LATENCY 10,000 If we have several experiments, we can
  48. 48. You have your own curve LOTS $ PER DAY 0 0 VISITOR LATENCY 10,000 Every web business has a curve like this hidden inside

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