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Impact of web latency on conversion rates
This document discusses how the speed of a website impacts key performance indicators (KPIs) for online businesses. It provides examples of companies that improved website speed and saw increases in important metrics like conversion rates and revenue. The document advocates for justifying investments in performance based on expected returns calculated from increased conversions and order values rather than just page load times. It calls for more experiments to better understand the relationship between website speed and business outcomes for different types of sites and user experiences.
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Impact of web latency on conversion rates
- 1. Performance ImpactHow Webspeed affects online business KPIs
- 2. Today’s HostsHooman Beheshti,VP Product, StrangeloopAlistair Croll,Analyst, BitcurrentAuthor of O’Reilly’s Complete Web Monitoring
- 6. 10 s1 s100ms10 msZzz!
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- 13. Impact of pageload time on average daily searches per user
- 15. Impact of additionaldelay on business metrics
- 16. Shopzilla had anotherangleBig, high-traffic site
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- 18. 8,000 searches asecond
- 19. 20-29M unique visitorsa month
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- 22. Page load from6 seconds to 1.2
- 23. Uptime from 99.65%to 99.97%
- 24. 10% of previoushardware needshttp://en.oreilly.com/velocity2009/public/schedule/detail/7709
- 25. 5-12% increase inrevenue
- 28. TransactionalSaaSBuy something(Amazon)Use anapp(Salesforce)MediaCollaborativeClick an ad(Google News)Create content(Wikipedia)
- 29. Tying web latencyto business outcomes
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- 35. It’s time foran experiment
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- 41. Average time onsite
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- 43. Conversion rate &order value
- 44. Justifying an investmentin performance()Currentdaily ordersIncreasedconversionsIncreasedorder value*+ROI(days)=Cost of performanceenhancement
- 45. Justifying an investmentin performance()*+0.16070.0551$10,000$2,15823.17days==$50,000$50,000Caveats
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- 47. This is justhow to think about itConclusionsLinks between performance and business KPIs are undeniable
- 48. By talking thesame language, IT and marketing can finally agree on what to do about it
- 49. Changing from “Xtimes faster” to “$Y more money” makes the business care
- 50. More research isneededWhat we need next# ofvisitsOptimized010,000VisitorlatencyDifferent visitors experienced different performance levels.
- 51. What we neednext# ofvisits21.58%better010,000VisitorlatencyRight now we have a single experiment, and a single resulting business impact.
- 52. What we neednextBest 5%Worst 5%# ofvisitsOptimized010,000VisitorlatencyVisitors who were optimized fall into a range – the 5th to 95th percentile
- 53. What we neednext24%18%14%Gajillions12%9.5%$ perday0010,000VisitorlatencyIf we have several experiments, we can understand the relationship better.
- 54. What we neednextGajillions$ perday0010,000VisitorlatencyEvery web business has a curve like this hidden inside it.
- 55. Questions? (Submit yourquestions using the GoToWebinar question tool)
- 56. More informationVisit: www.watchingwebsites.comVisit:www.bitcurrent.comTwitter: @acroll
Editor's Notes
- #4 Once upon a time, performance was a dark art. We struggled to deliver “good enough” without really knowing why.
- #5 We managed by anecdote. We were sure faster was better, but we couldn’t tie it to specific business outcomes.
- #6 The notion that speed is good for users isn’t new. The concept of “Flow” – a state of heightened engagement that we experience when we’re truly focused on something – was first proposed by mihalycsikszentmihalyi
- #7 It turns out that attention and engagement drop off predictably. At ten milliseconds, we actually believe something is physically accessible – think clicking a button and seeing it change color. At 100 milliseconds, we can have a conversation with someone without noticing the delay (remember old transatlantic calls?) At a second, we’re still engaged, but aware of the delay. At ten seconds, we get bored and tune out, because other things come into our minds.
- #8 How much was fast enough? It was anybody’s guess.
- #9 And guess they did.This is Zona’s formula for patience, the basis for the “eight second rule.” Unfortunately, things like tenacity, importance, and natural patience aren’t concrete enough for the no-nonsense folks that run web applications.
- #10 IT operators and marketers are completely different people. What convinces an IT person to fix performance doesn’t convince a marketer. They want to know how it will impact the business fundamentals.
- #11 By now, we know that everything matters. Usability, page latency, visitor mindset, and even sentiment on social media platforms all contribute to the business results you get from a site.
- #12 Fortunately, we’re getting better at linking performance to business outcomes.
- #13 One example of this is performance experimentation that Google’s done. Google’s a perfect lab. Not only do they have a lot of traffic, they also have computing resources to do back-end analysis of large data sets. Plus, they’re not afraid of experimentation – in fact, they insist on it. So they tried different levels of performance and watched what happened to visitors.
- #14 The results, which they presented at Velocity in May, were fascinating. There was a direct impact between delay and the number of searches a user did each day – and to make matters worse, the numbers often didn’t improve even when the delay was removed. You may think 0.7% drop isn’t significant, but for Google this represents a tremendous amount of revenue.
- #15 Microsoft’s Bing site is a good lab, too. They looked at key metrics, or KPIs, of their search site.
- #16 They showed that as performance got worse, all key metrics did, too. Not just the number of searches, but also the revenue (earned when someone clicks) and refinement of searches.
- #17 Shopzilla overhauled their entire site, dramatically reducing page load time, hardware requirements, and downtime.
- #18 They saw a significant increase in revenues
- #19 The site improvement increased the number of Google clicks that turned into actual visits
- #20 It also affected search engine scores. By improving load time, search engines (in this case Google UK) “learned” that this was a good destination. That’s right – Google actually penalizes sites that are slow by giving them a lower page ranking.
- #21 While this shows us metrics for large sites focused on sales and ad clicks, it doesn’t tell us about fundamentals.There are four fundamental site models, each of which has different business goals. An e-commerce site focused on transactions wants to convert visitors to buyers. A SaaS site wants to make subscribers renew. A media site wants to serve relevant ads and maximize searches or views. And so on.
- #22 If we want to convince marketing, we need to measure business metrics.
- #23 By tying performance and availability to Key Performance Indicators – KPIs – business and operations can finally have a conversation.
- #24 Whether those KPIs are shopping cart abandonment
- #25 Or visitor “bounce rate” (the number of visitors that leave immediately)
- #26 Or just traffic.
- #27 So what KPIs would we like to learn about? This is what web analytics folks work by, whether they’re running a media site, a SaaS platform, a transactional application, or a collaborative social network. It’s what the business cares about.
- #28 Strangeloop agreed to set up an experiment using their technology which would help measure this.
- #31 First, traffic. Despite splitting visitors to be optimized and unoptimized evenly, we had many more optimized sessions captured by the analytics. This may be a result of slower-loading pages failing to execute the analytics script, or abandoning the visit before the page had time to load.
- #32 Unoptimized visitors are roughly 1% more likely to leave the site immediately, without proceeding to other pages.
- #33 Strangely, the unoptimized visitors consisted of more new visitors than the optimized ones did. This seems counter-intuitive and warrants further study.
- #34 Optimized visitors spent more time on the site
- #35 And looked at more pages during their visit – if you’re a media property, this means more impressions for your advertisers.
- #36 On a second e-commerce site running roughly the same experiment, conversions were 16 percent higher and orders were 5.5% higher.