Cloud Connect 2013: Web acceleration and front-end optimization [SLIDES]

If you ever get a chance to hear Hooman Beheshti speak at a conference, drop everything else and go. Hooman has a way of talking about performance that’s incredibly accessible (read: he’s really good at not making you feel bad that he’s so much smarter than you), and every time he speaks at an event he gets rave reviews that I would be jealous about if we weren’t such good friends.

Last week, Hooman spoke at Cloud Connect in Santa Clara, and I asked him if I could post his slide deck here. It’s an excellent overview of the business value of performance, mobile performance issues, measurement and tools, front-end problems and front-end optimization (FEO), the role of CDNs in performance, and some great case studies and examples.

I also wanted to call out a couple of bits of eye candy, because I know there are always a few of you out there on the prowl for new biz value graphics to use in your own presentations. It’s also really interesting to see familiar data presented in a fresh way, which can trigger new insights.

First is the oft-quoted Google research about the impact of load time slowdowns on search. You may have heard these stats before, but this simple graph really drives those numbers home.

Cloud Connect 2013: Impact of page load time on average daily searches (Google)

A reduction of 0.6% may not sound like much, but when you consider that this is a result of a slowdown of less than half a second, and if you also consider the net impact across all of Google’s traffic, it’s pretty staggering.

I also really like this graphic interpretation of Bing’s well-known research, in which they segmented their traffic and served slower pages — in some cases up to 2 seconds slower — to different segments. It’s easy to see at a glance the impact on queries, clicks, revenue, and user satisfaction.

Cloud Connect 2013: Impact of additional delay on business metrics (Bing)Again, you can see how little it takes to hurt metrics. Revenue dropped by more than 1% with just a 500-millisecond delay, and by more than 4% with a 2-second delay. It’s also interesting to note that while the 200-millisecond delay may not have directly affected revenue, it did have a measurable impact on user satisfaction, which is a good reminder of just how sensitive users are to even the smallest changes in load time.

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