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 …

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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  • A study by Borland identified an overwhelming correlation between sales-generated traffic rises and increases in website response times – a nightmare situation for any retailer hoping to capitalize on the seasonal online rush of bargain-hunting consumers. Research has shown that even minor delays to website response times can have a sizable impact on customer satisfaction, page views, conversion rates and site abandonment. A one second delay in website response time equals11% fewer page views,16% decrease in customer satisfaction and a 7% loss in conversions.The study thus concludes that a one second increase in Amazon’s page load would annually cost $1.6 billion in sales, and  38% of UK online shoppers abandon websites or apps that take more than 10 seconds to load.The average online shopper expects web pages to load in 2 seconds or less, after 3 seconds, up to 40% will abandon the site. Seventy four per cent of users will abandon a mobile site after waiting only five seconds for it to load.Once visitors leave, it’s very difficult to get them back.  88% of online consumers are less likely to return to a site after a bad experience.Play.com, the UK arm of the Rakuten Group, saw performance drop by 500% as its site slowed from a load time of 2 seconds to 12 when site traffic peaked on the 4th January. Other online retailers that also suffered significant increases in load times during the first few days of the January sales included John Lewis, Amazon.co.uk, Asos.com and Tesco.com. Increases ranged between 3 and 4.5 seconds for their landing page to load.“There is lots of data available showing that users are losing patience with poor performing websites,” said Archie Roboostoff, product director at Borland. “It looks like a number of the sites monitored over the seasonal period will have missed out on potential revenue as a result of their website’s inability to process high levels of traffic. The sites we monitored in the UK had normal load times averaging 2.9 seconds, but saw load times increase by an average of 4.5 seconds during peak traffic periods – a 55% deterioration.Developing a robust performance strategy takes time, and peak period preparation should begin early with testing starting about six months beforehand. Putting in this groundwork is crucial if retailers are to take full advantage of peak shopping times throughout the year.”http://www.retail-digital.com/retail_technology/one-second-delay-on-amazon-16-billion-loss-a-year[source data: http://www.aberdeen.com/aberdeen-library/5136/RA-performance-web-application.aspx]
  • http://velocityconf.com/velocity2009/public/schedule/detail/8523
  • The application landscape is complex, and so is the transaction landscapeSome transactions will be more important to track than others – with conventional monitoring it’s impossible to focus on the important things, and impossible to understand if monitoring anomalies have any business impactMoreover, it’s impossible to troubleshoot the important things – just
  • Find the point of a problem quicklyGather enough detail to troubleshoot it in situDo the same during development, to avoid issues getting to production
  • Find the point of a problem quicklyGather enough detail to troubleshoot it in situDo the same during development, to avoid issues getting to production
  • Find the point of a problem quicklyGather enough detail to troubleshoot it in situDo the same during development, to avoid issues getting to production
  • http://v1.aberdeen.com/launch/report/perspective/8371-AI-application-performance-management.asp?lan=US
  • Find the point of a problem quicklyGather enough detail to troubleshoot it in situDo the same during development, to avoid issues getting to production
  • Objective of SlideHighlight our value proposition across Development, QA, Operations and the business.ScriptFor example, here’s a customer case study from Edmunds.com which highlights the annual benefits of AppDynamics across their organization and lifecycle.Development was able to double their innovation as a result of spending less time firefighting, and implementing more business requirements.QA were able to detect performance defects twice as fast, therefore increasing testing productivity and accelerating time to market.Operations increased application availability by .04%, and cut MTTR in half which had a significant impact on the business.All these benefits translated an enhanced end user experience combined with significant lost revenue and productivity annual savings totaling almost $800,000.Bank of New Zealand, Expedia and Fox News also had similar savings to Edmunds.com.

Transcript

  • 1. eCommerce Performance what is it costing you, and what can you do about it? Peter Holditch Technologist pholditch@appdynamics.com
  • 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. Because a 1 second delay equates to… 3 11% fewer page views A 16% decrease in customer satisfaction A 7% loss in conversions
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Thank You! Peter Holditch Technologist pholditch@appdynamics.com