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Liberty Hill - using web metrics

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Using web metrics to improve your site

Using web metrics to improve your site
A presentation for Liberty Hill Foundation

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    Liberty Hill - using web metrics Liberty Hill - using web metrics Presentation Transcript

    • Using web metrics to improve your site A presentation for Liberty Hill Foundation Dana Chinn April 2010
    • • What’s noise vs. signals • Questions to ask of your data What data will give you answers • Understanding web metrics • Deciding which metrics to use, what your site needs to have www.slideshare.net/danachinn @danachinn 2
    • “After the disaster in Haiti, [our site] hit 168.6 million pageviews in the month of January. A new record.” --The ”famous metrics” term comes from web analytics guru Avinash Kaushik 3
    • 4
    • web analysis usually starts here... ...and ends here 5
    • Internal vs. external metrics Decision-making Advertising, marketing • Census data • Panel data 100% of all visitors, visits, page Activity from a sample of self- views for all sections selected people. Only total site data for a limited number of sites. • Analysis, decisions, • Marketing, trending, actions, evaluation external comparisons • Omniture • comScore Google Analytics Nielsen WebTrends Compete etc. etc. • Web Analytics • Interactive Association Advertising Bureau 6
    • Recognizing the signals amid the noise Noise: Everything, every one, every second of every day Signals: Metrics you select -- Mark Smiciklas, IntersectionConsulting.com for decision-making 7
    • Only look at the signals you need 1 You need to know • How your site traffic changes due to external 2 events so you can determine whether your actions made a difference (or not) 3 • Whether your actions led to the results that you anticipated 8
    • Two types of web metrics What people do (behavioral) Who they are, what they think (attitudinal) 9
    • Unique visitors visit websites, generate page views. 10
    • A “unique visitor” is actually a “unique computer” 11
    • Unique visitors may be over- or undercounted Work =33 unique visitors = unique visitors Hotel Home = 1 unique visitor Work 12
    • Questions to ask your data You Your Data Did people visit our site more than once last week? Was this more or less than previously? If more, was it due to an outside event, OR was it something we did? Was the increase as much as expected? If less, was there a holiday, OR did we not do something? Was the drop as much as anticipated? 13
    • For every question... Did people visit our site more than once last week? ...there’s an answer hiding in Google Analytics... visits per unique visitor 14
    • ...but you have to go past the Dashboard... visits per unique visitor 15
    • ...change the time period, and customize the reports for your decisions default report shows a 5-week period Use weekly metrics, full- week time periods so you can identify unusual movement quickly 16
    • On average, last week, how many stories did people see with each visit? Did most visits come from returning visitors OR new ones? Are we hooking in new visitors? 17
    • On average, last week, how many stories did people see with each visit? Are we hooking in new visitors? page views per visit percent of visits from new visitors page views per new-visitor visit percent of visits from returning visitors page views per returning-visitor visit 18
    • When someone came to our site, what was the first page they saw? Did they leave immediately when they saw it? Did new visitors leave more than returning ones? 19
    • When someone came to our site, what was the first page they saw? Did they leave immediately? bounce rate of the page where people enter your site most often 1. Overall 2. Visits from new unique visitors 3. Visits from returning unique visitors 20
    • Example Home page bounce rate = over 50% Over half of the visits to the CNN.com home page left CNN.com without clicking into any other pages Best (?) cases: Came only to get the headlines Home page has dynamic content not captured with page views Worst cases: Couldn’t find what they wanted Didn’t like what they saw Source: “Can CNN, the Go-To Site, Get You to Stay?” by Brian Stetler, New York Times, Jan. 17, 2009 21
    • Measure engagement... No. of podcasts -- put on iPod -- played -- listened to the end ...and construct your site to maximize success Put “please donate by going to ourspecialcampaignURL.com” at the beginning 22
    • The perfect (measurable) Tweet • A call to action to participate, engage with you Look at this. Go here. What do you think? • A link To get news, information Tweets are now a primary news source, the new home page To respond to the call to action • A #hashtag and/or keywords • Handle specific to person/topic • A comment 23
    • “Perfect” tweets are less than 120 characters RT/via @handle + call to action/comment + link + #hashtag 100 characters 111 characters Watch handle, hashtag sizes Lost the link 24
    • Two types of web analytics metrics What people do (behavioral) Who they are, what they think (attitudinal) 25
    • Are you reaching the audiences you meant to reach and in the numbers you want? Is it your content? Is it your design? Is it you? 26
    • Some questions that can’t be answered from web traffic data 1. What was the purpose of your visit today? 2. Were you able to complete your task today? 3. If not, why not? 4. If you did complete your task, what did you enjoy most about our site? 27
    • Evaluation assignment What are the indicators that your web site is - or isn’t - contributing to your project? 28
    • How do we get more [type of people] to register? 29
    • Is our site selling the number of Upton Sinclair Dinner tickets, sponsorships and ads that we want? 30
    • How do we get more [type of people] to register? Look at trends after flyer is mailed to each audience type -- visits per unique visitor by week -- home page bounce rate -- pop-up bounce rate 31
    • How do we get more [type of people] to register? Does our reg form capture the info we need? What’s our goal? -- No. on print newsletter list [by zip code, business, name] -- New vs. returning - “...register...even if you’ve been a supporter for decades” --Percent of registered people [by type] who buy Upton Sinclair dinner tickets 32
    • Is our site selling the number of Upton Sinclair Dinner tickets, sponsorships, ads, eBay items that we want? -- Total sold last year from all sources, by time period -- Tickets (premier, standard); sponsorships (by type); ads (by type) --Percent of registered people [by type] who buy tickets, sponsorships, ads 33
    • Where are we losing people? -- Percent, no. of visits that started with dinner overview page, ended with completed payment (ad option is below this screen fold) 34
    • Web analytics is not easy... • Have clearly defined, accountable goals, objectives on which everyone agrees each of which is someone’s direct responsibility • Know the limitations of your data, metrics Guess rather than rely on bad data, metrics • Dedicate people, processes - budget - to analytics Technology, software are just tools • Let the hippos decide whether metrics will really be used for decisions Use only what you need HIghest Paid Person’s Opinion 35
    • Dana Chinn Blog http://www.newsnumbers.com Lecturer chinn@usc.edu 213-821-6259 Analytics for news orgs bookmarks http://www.delicious.com/ danachinn Presentations http://www.slideshare.net/ danachinn Twitter: DanaChinn 36