1、THE STATE OF ONLINE RETAIL PERFORMANCE SPRING 2017 The State of Online Retail Performance2 While almost half of all consumers browse via their phones, only 1 in 5 complete transactions on mobile (page 5). Optimal load times for peak conversions ranged from 1.8 to 2.7 seconds across device types (pag

2、e 6). Just a 100-millisecond delay in load time hurt conversion rates by up to 7% (page 7). Bounce rates were highest among mobile shoppers and lowest among those using tablets (page 9). Optimal load times for lowest bounce rates ranged from 700ms to 1.2s across all device types (page 10). A two-sec

3、ond delay in load time hurt bounce rates by up to 103% (page 10). Pages with the lowest bounce rates had start render times ranging from 0.9 to 1.5 seconds (page 12). A two-second delay correlated with up to a 51% decrease in session length (page 14). KEY INSIGHTS The State of Online Retail Performa

4、nce3 Whats the cost of a few seconds in the digital marketplace? Retail businesses today face enormous challenges in delivering the fast, reliable, 24x7 omnichannel experience that users demand. And those users wont wait around for you to get it right: 53% of mobile site visitors will leave a page t

5、hat takes longer than three seconds to load.1 In recent years, many leading retailers have discovered that the page load times of their websites and apps have a significant and measurable impact on metrics like conversions and engagement: Walmart saw up to a 2% increase in conversions for every seco

6、nd of improvement in load time. Every 100ms improvement resulted in up to a 1% increase in revenue. Fanatics shaved two seconds off its median page load time and almost doubled mobile conversions. Staples improved page load times by 1-6 seconds and improved conversions by 10%. You may already be fam

7、iliar with the idea that page load times have a negative impact on user engagement and business metrics, but you may be surprised to learn that these impacts are being felt much earlier than have been reported in the past. This could be due to the fact that there have been no previous studies that a

8、nalyzed such a large dataset using such a nuanced set of correlated metrics. Or it could be due to accelerating changes in user expectations. Its too early to say. This research project marks the inauguration of a long-term benchmark study that seeks to answer these questions. 2.4 PAGE 5 In 2015, 57

9、% of Black Friday traffic came from mobile. 28% of customers wont return to a slow site The “sweet spot“ for peak conversions is 2.4 secs Just a 1.8 second slowdown resulted in an 8% increase in bounce rate Amazon accounted for more than a third of 2015 online holiday spending The State of Online Re

10、tail Performance4 1. Background and approach What this report is: A semi-annual (spring and fall) study of aggregated performance metrics for leading retail sites. We share performance metrics from three different perspectives IT, business, and user experience and explain how they intersect. How we

11、did it: We gathered one months worth of beacon2 data from leading retail sites, comprised of Akamai customers who have given permission for their data to be anonymized, aggregated and used in this type of research. Our data science team then used our industry-leading analytics engine to analyze the

12、data. Why this study is meaningful: Its the only study of its kind. No other web performance technology provider has the data gathering and analysis tools and the customer list to conduct a study like this. This study represents an unprecedented amount of user data. This study represents 27.7 billio

13、n beacons worth of data which equates to roughly 10 billion user visits. We have the only analytics engine that can correlate all this data to business-critical user experience metrics and KPIs such as bounce rate, conversion rate, and session length. This gives us unprecedented insight into how per

14、formance affects users and ultimately businesses. What we hoped to learn when we started this project: What is the “magic number” for page load time that yields the highest conversion rate? What is the impact of one second of performance improvement (or slowdown) on conversion rate/bounce rate/sessi

15、on length? How are high-performance pages different in terms of size, complexity and resources from pages that perform poorly? 2. Online retail in the age of Amazon How Amazon became the gold standard for user experience In 2016, e-commerce sales in the US grew 15.6%, representing 11.7% of total sal

16、es but many of those gains were swallowed up by Amazon.3 Amazon comprised 65.9% of the $53.1 billion growth in U.S. online retail last year, and 27.4% of the $127.6 billion increase in the total retail market. In fact, Amazons market cap is now greater than the combined total of the largest eight tr

17、aditional retailers in the US.4 For more than two decades, Amazon has excelled at innovating its digital business and motivating the rest of the industry to either adapt or lose market share. While the rest of the digital marketplace was simply driving visitors to websites, Amazon built an online bu

18、siness based on understanding those visitors through data science and performance analytics. Amazon was an extremely early adopter in the study of how seconds and sometimes even milliseconds of latency can affect bounce rates, conversion rates, sales and revenue. The company uses this knowledge to d

19、rive the evolution of better and better customer experiences. Today, Amazons ability to offer a superior buying experience has driven other businesses to extinction across multiple categories. 350 300 250 200 150 100 50 0 10 9 7 6 4 3 1 0 U.S. E-Commerce Sales however, these visitors are also much m

20、ore likely to bounce. While slightly more than half (50.9%) of bounced sessions happened on mobile devices, only 28.6% of non-bounced sessions took place on mobile. Bounce rates were highest among mobile shoppers and lowest among those using tablets. The median bounce rate for shoppers using mobile

21、devices was almost 58%, while tablet shoppers had a median bounce rate of almost 45%. 43% 51.8% 40.6% 48.1% 57.9% 44.5% 14.1% 14.2% 13.1% DesktopMobileTablet Average bounce rate Median bounce rate Lowest bounce rate Bounce Rates (by device type) Desktop 40.60% Mobile 50.94% Traffi c breakdown for al

22、l bounced sessions Traffi c breakdown for all non-bounced sessions Tablet 8.47% Desktop 61.44% Mobile 28.60% Tablet 9.97% The State of Online Retail Performance10 Optimal load times for lowest bounce rates ranged from 700ms to 1.2s across all device types. The closeness of this range suggests that u

23、ser expectations are similar despite the type of device they are using. And those user expectations are extremely high. On desktop, pages that loaded in 1 second experienced the lowest bounce rate (13.1%). On mobile, pages that loaded in 700 milliseconds experienced the lowest bounce rate (14.1%). O

24、n tablets, pages that loaded in 1.2 seconds experienced the lowest bounce rate (14.2%). A two-second delay in load time hurt bounce rates by up to 103%. Unlike the conversion rate findings, a 100-millisecond delay was not significantly impactful. However, as load times slowed down further, delays of

25、 one and two seconds had a powerful negative effect on bounce rates. These impacts were felt even on tablets and mobile, with mobile being the most affected by delays. For mobile users, the optimal load time which correlated to a 14.1% bounce rate was 700 milliseconds. Pages that loaded in 1.7 secon

26、ds experienced a median bounce rate of 21% representing an increase of almost 50%. At 2.7 seconds, the bounce rate was almost 29% representing a 103% increase. As with the conversion graphs in section 3, you may wonder: Why dont the fastest pages correlate with lower bounce rates? This is because th

27、e faster end of the bell curve is primarily comprised of 404 pages and other pages that, while fast, do not fall on the conversion path. Desktop Mobile Tablet Bounce Rate (%) 13.1% conversion rate 14.1% conversion rate 14.2% conversion rate 1 seconds 700 milliseconds 1.2 seconds 0.4% 0.2% 0.2% 18.4%

28、 49.8% 32.3% 102.9% 67.6% 62.1% DesktopMobileTablet Impact of 100ms slowdown on bounce rate Impact of 1s slowdown on bounce rate Impact of 2s slowdown on bounce rate Impact of page Slowdowns on bounce rates (by device type) The State of Online Retail Performance11 How does load time correlate to bou

29、nce rate? (desktop) How does load time correlate to bounce rate? (mobile) How does load time correlate to bounce rate? (tablet) The State of Online Retail Performance12 5. How does start render time correlate to bounce rate? Pages with the lowest bounce rates had start render times ranging from 0.9

30、to 1.5 seconds. Start render time the moment when content begins to render in the browser is a strong metric for measuring the user- perceived performance of a page. This metric is a solid measure of how long visitors are willing to look at a blank screen before they begin to leave a site. Not surpr

31、isingly, this user patience threshold is low. The optimal start render time for desktop users was under one second. Mobile and tablet user expectations were not far behind. On desktop, pages with a start render time of 900 milliseconds experienced the lowest bounce rate (18.1%). On mobile, pages wit

32、h a start render time of 1.3 seconds experienced the lowest bounce rate (23.1%). On tablets, pages with a start render time of 1.5 seconds experienced the lowest bounce rate (18.5%). Desktop Mobile Tablet Start render time (seconds) 0.9s 1.3s 1.5s 18.1% 23.1% 18.5% Correlation of start render times

33、with lowest bounce rate The State of Online Retail Performance13 How does start render time correlate to bounce rate? (desktop) How does start render time correlate to bounce rate? (mobile) How does start render time correlate to bounce rate? (tablet) The State of Online Retail Performance14 6. How

34、does load time correlate to session length? A two-second delay correlated with up to a 51% decrease in session length. There is an existing body of research that indicates faster pages compel shoppers to spend more time on retail sites, visit more pages and add more items to their carts. When examin

35、ed alongside metrics like bounce rate and conversions, session length (defined as the number of pages visited in a single visit) is a strong indicator of user engagement and satisfaction. Similar to the effects of slowdowns on bounce rate, the 100-millisecond increment had little to no impact on ses

36、sion length. At the one- and two-second points, however, the effects were significant. Sessions with median page loads that were one second slower than optimal speeds were also up to 25% shorter. A two-second delay correlated with a 51% decrease in session length for mobile users, a 47% decrease for

37、 desktop users and an almost 38% decrease for tablet users. Impact of Page Slowdowns on session length (by device type) -0.7% 0% -0.5% -24.9% -24.8% -15.3% -51% -46.9% -37.7% Impact of 100ms slowdown on session length Impact of 1s slowdown on session length Impact of 2s slowdown on session length De

38、sktopMobileTablet DesktopMobileTablet Session length (pages viewed) How does load time correlate to session length? 19 18 16 15 13 11 10 8 6 5 3 2 0 24 22 20 18 16 14 12 10 8 6 4 2 0 100 600 1100 1600 2100 2600 3100 3600 4100 4600 5100 5600 6100 6600 7100 7600 8100 8600 9100 9600 10100 10600 11100 1

39、1600 12100 12600 13100 13600 14100 14600 15100 15600 16100 16600 17100 17600 18100 18600 19100 19600 The State of Online Retail Performance15 7. Third-party scripts How does the number of third-party scripts correlate to conversion rate? The average page served to desktop and tablet contained 21.9 t

40、hird-party scripts. Not surprisingly, the average page served to mobile contained slightly fewer (18.7), no doubt partly due to the fact that some retailers serve leaner mobile-optimized pages. However, fewer third parties doesnt correlate to higher conversion rates. As the graphs on the following p

41、age show, the highest-converting pages served to mobile users tended to contain between 15-20 third-party scripts, and the highest- converting pages served to desktop and tablet users contained 20-25 scripts. This finding is consistent with a joint machine learning project conducted by Akamai and Go

42、ogle, which found that user sessions that converted contained 48% more scripts than sessions that did not convert.6 This does not mean that site owners should arbitrarily insert more third-party tags on their pages (as these tags could have a negative impact on page performance), but rather should b

43、e taken to mean that, used judiciously and optimized well, third parties have their place in a conversion optimization strategy. Desktop Mobile Tablet 21.9 scripts 18.7 scripts 21.9 scripts How third-party scripts correlate to conversion rates The State of Online Retail Performance16 5.5 5.0 4.5 4.0

44、 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0 converted sessions non-converted sessions conversion rate How does number of third-party scripts correlate to conversion rate? (Tablet) 0-5 5-10 10-15 15-20 20-25 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 85-90 155-160 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0% conve

45、rted sessions non-converted sessions conversion rate How does number of third-party scripts correlate to conversion rate? (Mobile) 0-5 5-10 10-15 15-20 25-30 30-35 35-40 40-45 45-50 50-55 55-60 60-65 65-70 70-75 75-80 80-85 85-90 90-95 105-110 6.0% 5.0% 4.0% 3.0% 2.0% 1.0% 0% converted sessions non-

46、converted sessions conversion rate How does number of third-party scripts correlate to conversion rate? (Desktop) 0-5 10-15 20-25 30-35 40-45 50-55 60-65 70-75 80-85 90-95 100-105 110-115 120-125 130-135 140-145 150-155 160-165 170-175 180-185 190-195 200-205 210-215 220-225 230-235 240-245 255-260

47、265-270 295-300 310-315 430-435 575-580 The State of Online Retail Performance17 Takeaway You can compete like Amazon. When it comes to the performance of your retail properties, even milliseconds can matter. How can you create and monitor the user experience your customers demand? The complexities

48、of online business and the sophistication of digital marketing are evolving rapidly. Understanding and managing digital performance has never mattered more than it does now. Only those retail brands that adapt their current approaches can survive. To compete with Amazon, you need to understand how r

49、eal users are experiencing your site, and how even small or intermittent slowdowns could be hurting your business. The challenge for online businesses today is to adopt Amazon-like culture, practices and tools to enable end-to-end digital performance management continuously and in real time. In order to compete like Amazon, you need: A unified view and control of customer experience, business, and IT performance Real-time visibility into your marketing campaigns