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eCommerce performance, what is it costing you and what can you do about it?

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A presentation I gave at Internet World 2013 in London
http://www.internetworld.co.uk/page.cfm/Action=Visitor/VisitorID=4356

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eCommerce performance, what is it costing you and what can you do about it?

  1. 1. eCommerce Performance what is it costing you, and what can you do about it? Peter Holditch Technologist pholditch@appdynamics.com
  2. 2. The Business Impact of One Second “One second increase in Amazon‟s page load would annually cost $1.6 billion in sales” Borland Research - March 2013
  3. 3. Because a 1 second delay equates to… 3 11% fewer page views A 16% decrease in customer satisfaction A 7% loss in conversions
  4. 4. Google and Microsoft research • Experiments to introduce delay into web searches to measure the impact 4 http://velocityconf.com/velocity2009/public/schedule/detail/8523 http://vimeo.com/5310021
  5. 5. Server Delays Experiment: Results • Strong negative impacts • Roughly linear changes with increasing delay • Time to Click changed by roughly double the delay DistinctQueries/UserQuery RefinementRevenue/User AnyClicks Satisfaction TimetoClick (increaseinms) 50ms - - - - - - 200ms - - - -0.3% -0.4% 500 500ms - -0.6% -1.2% -1.0% -0.9% 1200 1000ms -0.7% -0.9% -2.8% -1.9% -1.6% 1900 2000ms -1.8% -2.1% -4.3% -4.4% -3.8% 3100 - Means no statistically significant change
  6. 6. Impact measured by • Slower performance  abandoned searches • More active users more sensitive to this • Effect got worse over time, and persisted once performance was restored 6 dailysearchesperuserrelativetocontrol wk1 wk2 wk3 wk4 wk5 wk6 -1%-0.8%-0.6%-0.4%-0.2%0%0.2% 200 ms delay 400 ms delay actual trend Impact of Post-header Delays Over Time dailysearchesperuserrelativetocontrol wk3 wk4 wk5 wk6 wk7 wk8 wk9 wk10 wk11 -1%-0.8%-0.6%-0.4%-0.2%0%0.2% delay removed Persistent Impact of Post-header Delay 200 ms delay 400 ms delay actual trend
  7. 7. Conclusion • Revenue is a function of user behaviour • User behaviour is quite sensitive to performance • Effects of poor performance outlast the problems • It is necessary to have a constant watch on performance of critical transactions, fix problems quickly and continuously improve over time 7
  8. 8. BIG DATA Hadoop Cassandra MongoDB Coherence Memcached CLOUD Amazon EC2 Windows Azure VMWare This is made very hard by the modern technology landscape DistributedMonolithic Login Search Flight View Flight Status Make Reservation Weblogic Oracle .NET MQ ATG, Vignette, Sharepoint SQL Server JBoss Tomcat Tomcat Mule, Tibco, AG ESB .NET Tomcat SOA WEB 2.0 Browser Logic AJAX Web Frameworks Release 3.4 Release 3.5 Release 3.6 Release 4.0 AGILE Release 1.1 Release 1.2 Release 1.23 Release 1.5 Release 4.4 Release 4.5 Release 4.6 Release 5.0 Release 2.4 Release 2.5 Release 2.6 Release 3.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 8
  9. 9. BIG DATA Hadoop Cassandra MongoDB Coherence Memcached CLOUD Amazon EC2 Windows Azure VMWare Where and what is the problem? Weblogic Oracle .NET MQ ATG, Vignette, Sharepoint SQL Server JBoss Tomcat Tomcat Mule, Tibco, AG ESB .NET Tomcat SOA WEB 2.0 Browser Logic AJAX Web Frameworks Release 3.4 Release 3.5 Release 3.6 Release 4.0 AGILE Release 1.1 Release 1.2 Release 1.23 Release 1.5 Release 4.4 Release 4.5 Release 4.6 Release 5.0 Release 2.4 Release 2.5 Release 2.6 Release 3.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 9 Login Search Flight View Flight Status Make Reservation
  10. 10. BIG DATA Hadoop Cassandra MongoDB Coherence Memcached CLOUD Amazon EC2 Windows Azure VMWare Where and what is the problem? Weblogic Oracle .NET MQ ATG, Vignette, Sharepoint SQL Server JBoss Tomcat Tomcat Mule, Tibco, AG ESB .NET Tomcat SOA WEB 2.0 Browser Logic AJAX Web Frameworks Release 3.4 Release 3.5 Release 3.6 Release 4.0 AGILE Release 1.1 Release 1.2 Release 1.23 Release 1.5 Release 4.4 Release 4.5 Release 4.6 Release 5.0 Release 2.4 Release 2.5 Release 2.6 Release 3.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 10 Login Search Flight View Flight Status Make Reservation
  11. 11. BIG DATA Hadoop Cassandra MongoDB Coherence Memcached CLOUD Amazon EC2 Windows Azure VMWare What if the problem is outside the application? Weblogic Oracle .NET MQ ATG, Vignette, Sharepoint SQL Server JBoss Tomcat Tomcat Mule, Tibco, AG ESB .NET Tomcat SOA 11 Login Search Flight View Flight Status Make Reservation WEB 2.0 Browser Logic AJAX Web Frameworks Release 3.4 Release 3.5 Release 3.6 Release 4.0 AGILE Release 1.1 Release 1.2 Release 1.23 Release 1.5 Release 4.4 Release 4.5 Release 4.6 Release 5.0 Release 2.4 Release 2.5 Release 2.6 Release 3.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0
  12. 12. Real-User Monitoring gets Real Results* 12 >10% decrease in end-user complaints >30% increase in App Availability >91% transaction completion End-users „completely satisfied‟ BusinessesdoingRealUser Monitoring BusinessesNOTdoingRealUser Monitoring *Source:AberdeenGroup,July2012
  13. 13. BIG DATA Hadoop Cassandra MongoDB Coherence Memcached CLOUD Amazon EC2 Windows Azure VMWare And beyond performance monitoring… Weblogic Oracle .NET MQ ATG, Vignette, Sharepoint SQL Server JBoss Tomcat Tomcat Mule, Tibco, AG ESB .NET Tomcat SOA WEB 2.0 Browser Logic AJAX Web Frameworks Release 3.4 Release 3.5 Release 3.6 Release 4.0 AGILE Release 1.1 Release 1.2 Release 1.23 Release 1.5 Release 4.4 Release 4.5 Release 4.6 Release 5.0 Release 2.4 Release 2.5 Release 2.6 Release 3.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 Release 1.4 Release 1.5 Release 1.6 Release 2.0 13 Login Search Flight View Flight Status Make Reservation
  14. 14. Case Study – One Year Dev QA Ops Business ProductionPre-Production • Agile Releases 12 > 18 • Spent 3,060 hours less firefighting • Delivered More Innovation • Identify & Fix Defect 20 hours > 13 hours • Spent 4,024 hours less testing • Faster Time to Market • Availability 99.91% > 99.95% • MTTR 40 hours > 22 hours • 1,528 hours less troubleshooting • End User Experience 500ms > 150ms • $167,475 lost revenue savings • $627,691 productivity savings • $795,166 Total savings 14
  15. 15. Thank You! Peter Holditch Technologist pholditch@appdynamics.com

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