Using Web Analytics


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Overview of web analytics for MKT 556/Internet

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Using Web Analytics

  1. 1. Using Web AnalyticsMKT 556/Internet Marketing 556/I t t M k ti October 2011 Dana Chinn Twitter: @danachinn
  2. 2. Aren’t allbusinessesdata driven? 2
  3. 3. From Analysis Ninja... …to HIghest Paid Person’s Opinion -- Avinash Kaushik, Google “Streaming and DVD by mail are becoming two quite different businesses…different businesses different benefits that need to be marketed differently…. “Another advantage of separate websites is simplicity for our members… p y “….if you need to change your credit card or email address, you would need to do it in two places if you rate or review places….if a movie on Qwikster it doesn’t show up on Netflix, and vice versa.” Netflix blog, Sept. 18, 2011
  4. 4. “Analytics will be the backbone of our multi-faceted web design, email, content, content video and advertising efforts.” efforts 4
  5. 5. Get an A in web analytics class answer #1: So S what. 5
  6. 6. The work of a spreadsheet monkey Our site has 5,000 monthly unique visitors. Last Tuesday that story got 20,000 page views. The average time spent on our site last week was 24 minutes. minutes Our iPhone app was downloaded 10,000 times.We have 2,000 fans on our Facebook page. We have 5,000 Twitter followers. “If you can’t take action, some action, (any action!), based on your analysis, why are you reporting data? analysis data?” --Avinash Kaushik 6
  7. 7. Web analytics is the analysis of data “to drive a continual improvement of the online experience… which translates into your desired outcomes.” y 7from Web Analytics 2.0 by Avinash Kaushik
  8. 8. A desired outcome is whatever you say it is……but you need to define the starting point but and the goals with the right metrics 8
  9. 9. Internal metrics External metrics for for Strategic Planning Marketing, Advertising• Census data • Panel data 100% of all visitors, visits, page Activity from a sample of self- views in a site selected people. Only total site data for a limited number of sites.• Analysis, decisions, • Marketing, trending, actions, evaluation competitive analysis• Omniture • comScore Google Analytics Nielsen WebTrends Compete etc. etc etc.• Web Analytics • Interactive Advertising Association Bureau 9
  10. 10. Is this site a success? Our site has 5,000 monthly unique visitors. Last Tuesday that story got 20,000 page views. The average time spent on our site last week was 24 minutes. Our iPhone app was downloaded 10,000 times.We have 2,000 fans on our Facebook page. We have 5,000 Twitter followers. 10
  11. 11. Get an A in web analytics class answer #2: It depends. Not all traffic is equal 11
  12. 12. Old …to… Eyeballs… …advertisers Advertisers have to pay for Ad ti h t f access to all of them
  13. 13. New Only some eyeballs… …to… …advertisers Advertisers want to pay for only those eyeballs they think are current or potential custo e s customers based o how e gaged they are on o engaged t ey a e with selected content… “The more insight a publisher has into its audience, audience the more it can charge advertisers.” Alan …because they now have Pearlstein, Cross-Pixel Media, many ways – including y y g Ad Age, 8/8/11 Age directly – they can reach and interact with (almost) exactly who they want
  14. 14. Word of mouth: Probably hasn’t changed since the beginning of time and probably never willused to be advertising? 14
  15. 15. Audiences, actions, metrics differ by channel SITES SOCIAL MEDIA * Totals1. Who? How many? In target audience? ? ? ? ? ? ? ?2. No. f i i ?2 N of visits? How often? ? ? ? ? ? ? ?3. What did they see? ? ? ? ? ? ? ? Did they get want they wanted?4. Did they interact? y ? ? ? ? ? ? ? What did they do? How much? 15 * Different metrics, methodologies for each channel!
  16. 16. Two types of web analytics dataBehavioral research What people did when they came to your site, as captured by an action taken on a keyboard or mouseAttitudinal research What people say they did what they think and why as captured by surveys, focus groups, social media, usability studies 16
  17. 17. Unique visitors visit sites and generate page views 17
  18. 18. Key Performance Indicator: Visits A visit is counted every time someone comes to a site t it Visits: the strongest metric available An increase in visits? Always good. A decrease in visits? Always bad bad. 18
  19. 19. Strong vs. weak metrics Strong metrics are useful tools that give clear indications of what’s successful or notc. Kyle Taylor Weak metrics… -- are conceptually flawed “so what?” counts of things so what? -- are technically flawed metrics calculated by c. Kyle Taylor web analytics systems b l ti t in ways that give unclear indications …could be so misleadingg they could lead to bad decisions 19
  20. 20. Really weak metric #1: Unique visitors A unique visitor is really a unique computer. Unique visitors are either over-counted… 20
  21. 21. …or under-counted. You don’t know when or by how much.* y library, school, ? Internet cafe* It doesn’t matter anyway….better to measure outcomes (didpeople do what you wanted?) than the number of people who came toyour site. 21
  22. 22. Really weak metric #2: Page views An increase in page views can be good - or bad.* Bad design, navigation, site architecture? design navigation Lots of page views, annoyed users ? A redesign improved usability? Fewer page views, happier users Content that should be there but isn’t? Lots of page views, annoyed users Dynamic content? Fewer page views, happier users (probably)* It doesn’t matter anyway….better to measure outcomes (didpeople do what you wanted?) than the number of pages people wentto when they came to your site. 22
  23. 23. Really weak metric #3: Time spent on site An increase in average time spent on g p site can be good - or bad.* Bad design, navigation, site architecture? ? Lots of time spent, annoyed users A redesign improved usability? Less time spent, happier users spent* It doesn’t matter anyway….better to measure outcomes (didpeople do what you wanted?) than how much time people spent onyour site. 23
  24. 24. Systems only measure the time spent in b t i between pages on a site, so… it? The time spent of a user who g p goes only to y one page is NOT included in the time spent calculation. 1 minute The time spent on the last page of a site isn’t counted at all. 10 minutes Time spent = 1 minute Site X 24
  25. 25. When people came to your site, did they stay? Key Performance Indicator: Bounce rate percent of the landing page where most visits start “I came. I saw. I puked.” -- Avinash Kaushik on bounce rateA bounce: a visit with only one page view 25
  26. 26. A large number of visits that start with the home pagebounce, or leave the site without going to another page 100% 51% 8,331 Home page bounce rate: 43% 16,304 visits visits started on content pages 49% 7,973 57% 43% left the site 4,547 3,426 without going visits went to started at least to another on the one page other home page page Week of Sept. 11, 2011 26
  27. 27. How did people get to your site?Key Performance Indicator: Visits by traffic source 27
  28. 28. How “loyal” are people who come to you s te your site? Key Performance Indicator: Visit frequency q y Visit daily or 19% more frequently 12,410 visits , New visitors 101-201+ times 41% 27,087 visits from new visitors 13% 8, 95 s ts 8,495 visits 26-100 times 9% 5,846 visits 18% 9-25 times 12,126 i it 12 126 visits 2-8 times Occasional visitors i itTotal visits Sept. 11-Oct. 8, 2011: 65,964 28
  29. 29. When was the last time someone came to your site? you s te Key Performance Indicator: Previous visit recency y Visitors who came back after a long absence 3% 393 visits from people whose most recent 6% visit was 31-120 days 899 visits from people whose most recent visit was 15-60 days 1% 23 visits from people whose most recent visit was 121-365+ days New visitors 16% 2,490 visits from people 41% whose most recent visit was 6,333 visits 1-7 1 7 days before from new visitors Recent visitors 33% 5,026 visits from people whose most recent visit was earlier that dayTotal visits Oct 2-8, 2011: 15,267 29
  30. 30. Let’s cut to the chase! Key Performance Indicator: Sales funnel completion rate p A lot in in… …not as much t h outFunnel example by Josh Podell, USC MBA class of 2011
  31. 31. Home Page – Main Statistics Numbers are examples only. 640 Enter the Home Page 64 (10%) of those Visitors Click on the Donate Button 576 Leave the Home Page to Go Somewhere ElseFunnel example by Josh Podell, USC MBA class of 2011
  32. 32. Community Coalitions Funnel Numbers are examples only. People Entering Each Step People Exiting Each Step 1. 1 Search 514 Total in = 514 + 72 +59 = 640 1. 1 Exit 230 2. Direct Traffic 72 2. About US 112 3. Referring Sites 54 Home Page 3. Campaigns 74 640 Conversion Rate = 10% 4. Events 122 64 Continue…640 – 64 = 5. Action Center 38 576 576 1. About Us 10 Total in = 64 + 46 = 110 1. Exit 4 2. Campaigns 6 2. Home 2 3. 3 Action Center 1 Donation Page – 3. 3 About US 1 Payment Info 4. Events 11 4. Events 2 CR = 90 % 5. Gala Dinner 18 99 Continue 5. Gala Dinner 2 46 11 Exit 11 Placed Donations Confirmation Page 99 15.47% Funnel Conversion RateFunnel diagram by Josh Podell, USC MBA class of 2011
  33. 33. Questions for a e-commerce company Who came to our site? e.g., previous vs. new; high vs. low potential How did they get here? What did they look at? Were they successful in getting what they wanted? A simple e-commerce data story “Current and potential customers who typed in “t-shirts” in Google arrived on our t-shirts landing page. 1.5% of them made a purchase.” 33-- Corey Koberg, web analytics consultant
  34. 34. “You need to know the cost to yourbusiness when you don t learn from don’tyour customers, as well as dialoguewith them ” them. -- Nilofer Merchant, strategist and author of “The New How” 34
  35. 35. Metrics that indicate interactivity are essential Facebook Insights – daily stats* Key Performance Indicators: No. of active users No. of likes No. No of comments 35* Enter daily numbers in a spreadsheet for trending, rolling up into weekly/monthly totals
  36. 36. Start with smart campaign design “Connect with us to find valuable wellness tips” tips 36
  37. 37. Does this page answertheth call to action, reinforce brand? ll t ti i f b d? Wasn t Wasn’t this an Alta Dena site? What’s Mayfield Dairy Farms? PET Dairy? Where are the wellness tips? 37
  38. 38. Be honest with the metricsDo 538 peopleREALLY “Like”this? Or do h O d they jjust want another sweepstakes entry? 38
  39. 39. Assess context, sentiment together with comment counts t th ith t tOnly 2 comments comments…… and from peoplesaying they can’t y g yenter thesweepstakes or getthe additional codeDoes theperson/people fromthe milk companyhave a name? “Coupon Fairies” but no coupon 39
  40. 40. 40
  41. 41. Measurable tweets have have…1. A call to action Go here…look…tell me 2. li k that 2 A link th t you track with link t k ith li k and site metric tools 3. #Hashtags and/or keywords 4. Topic or person-specific handles …120 or fewer characters, not 140! 41
  42. 42. Mapping metrics to business goalsBusiness goal/objective: Site/social media metrics:No. of Korean BBQtacos sold… …to people who saw the truck location on Twitter and went there i d h “Where else should we send our trucks?” Where people have p p asked, via Twitter 42
  43. 43. Business goal/objective: Site/social media metrics:No. of cars & truckssold… …to people who became a member of the GM community… …after voting for the 1969 Pontiac when we asked them …after going to our site from Twitter to find fi d out about GM b hybrid powertrain systemBusiness goals are achieved with more than just social media,site 43
  44. 44. Two types of decision-making HIghest Paid Person’s Opinion Person s -- Avinash Kaushik, Google Decision-making with data • S t specific, quantifiable site goals Set ifi tifi bl it l • Use meaningful metrics; monitor weekly; y; distinguish between traffic from external events vs. internal actions • Analyze traffic by audience segment • Understand site goals and traffic before tackling attitudinal survey research, social media metrics, mobile metrics 44
  45. 45. “A good analyst has the capacity to analyze data and y generate insight.” Data dexterity with basic overall site metrics and web y analytics tools Pattern recognition of the trends most important to the business Attention to detail, and an understanding of the importance of data integrity p g y Commercial awareness, or knowing how the data should be interpreted given the decisions that need to be made Positive presence and the ability to communicate what the organization needs to know 45from “5 Things to Look for in an Analyst,” by Neil Mason, ClickZ, 8/2/11