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MeasureWorks - Tying web performance to analytics
This document discusses the importance of customer experience and web performance monitoring from a complete perspective. It notes that web analytics can provide "hard data" on what users do on a site, usability testing can show how users interact with a site, and performance monitoring can indicate if users can complete desired tasks. A holistic view is needed to understand user behavior, motivations, community sentiment, and competitors. The document argues that optimizing website performance through techniques like acceleration can significantly improve key metrics like conversion rates, time on site, bounce rates, and revenue. It notes performance impacts will vary across users and more data is needed to map the relationship between latency and business outcomes for a given site.
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MeasureWorks - Tying web performance to analytics
- 1. Who cares aboutcustomer experience? A Complete Web Monitoring perspective Web Analytics Conference 2010 in collaboration with MeasureWorks
- 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. What did theydo? Web analytics
- 4.
- 5. Accounting Optimization http://www.flickr.com/photos/sanchom/2963072255/ http://www.flickr.com/photos/thomasclaveirole/538819881/
- 6. How did theydo it? Web Interaction Analytics
- 7.
- 8. Why did theydo it? Voice of the Customer
- 9.
- 10. Could they doit? Performance & availability
- 12. What were theysaying? Community monitoring
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- 14. What were theyup to? Competitive analysis
- 15.
- 16. Online marketers wantto maximize their revenues.
- 17.
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- 24.
- 32. Websites have adirty little secret http://todaystatus.files.wordpress.com/2009/04/ww11-secret.jpg
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- 41. 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
- 42. 10 s 1s 100ms 10 ms ! Zzz
- 45. http://www.flickr.com/photos/spunter/393793587 http://www.flickr.com/photos/laurenclose/2217307446
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- 48. Shopzilla had anotherangle • 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
- 49. 5-12% increase in revenue.
- 52. Tying web latencyto business outcomes.
- 53. http://www.flickr.com/photos/spunter/393793587 http://www.flickr.com/photos/laurenclose/2217307446 KPIs
- 55. http://www.flickr.com/photos/ mrmoorey/160654236
- 57. 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
- 59. 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
- 60.
- 61. Traffic levels 9.000 Total number of visits 6.750 4.500 8.505 2.250 4.740 0 Optimized Unoptimized Visitor experience
- 62. Bounce rate 20 Visits that bounced 15 10 13,38% 14,35% 5 0 Optimized Unoptimized Visitor experience
- 63. % visits marked“new” % of visits that had no returning cookie 14 11 7 13,61% 10,85% 4 0 Optimized Unoptimized Visitor experience
- 64. That means... 9000 Value Number of visits 6750 4500 7.582 4.095 2250 923 645 0 Optimized Unoptimized
- 65. Average time onsite 31 Time on site (minutes) 23 16 30,17 23,83 8 0 Optimized Unoptimized Visitor experience
- 66. Pages per visit 16 Average pages seen 12 8 15,64 11,04 4 0 Optimized Unoptimized Visitor experience
- 67. Conversion rate and order value 20 Difference due to optimization 15 10 16,07 5 5,51 0 Conversion rate Order value
- 68. This is justone case LOTS # OF VISITS OPTIMIZED 0 0 VISITOR LATENCY 10,000 Different visitors experienced different
- 69. 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
- 70. 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
- 71. Lots of differentresults LOTS 24% 18% $ PER 14% 12% DAY 9.5% 0 0 VISITOR LATENCY 10,000 If we have several experiments, we can
- 72. You have yourown curve LOTS $ PER DAY 0 0 VISITOR LATENCY 10,000 Every web business has a curve like this hidden inside