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How to use Google Analytics for data driven design

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This presentation is an introduction into data driven design. It provides you with some basic knowledge about Google Analytics. And shows how to use Google Analytics to evaluate your implemented designs.

Read the full article on: http://www.sodastudio.nl/kennis-ideeen/google-analytics-data-driven-design-presentatie

Published in: Design

How to use Google Analytics for data driven design

  1. 1. HowtouseGoogle Analyticsfordata drivendesign an introduction into Google Analytics
  2. 2. Inthispresentation This presentation is an introduction into data driven design. It provides you with some basic knowledge about Google Analytics. And shows how to use Google Analytics to evaluate your implemented designs. After this presentation you are familiar with the glossary of (Google) Analytics. You will be able to gain relevant data from Google Analytics. And we provide you with some basic takeaways to implement data driven design in your projects.
  3. 3. Tableofcontent • Part 1: Data driven design? A short introduction. • Part 2: Google Analytics: The who, what & where. • Part 3: How to use in a project: setting up your data for succes. • Part 4: Analytics myths: busting some misconceptions.
  4. 4. Part1 Datadrivendesign? A short introduction
  5. 5. understand the data and applying it for the betterment of product and consumer understanding. We, as webdesigners, have a desire to move forward in the right way. To make sure that the right decision is made we try to
  6. 6. Whatisdatadrivendesign? • Quantitative vs Qualitative data • Track, track and track some more. • Numbers leave patterns, patterns tell stories • Understanding opportunities vs issues
  7. 7. 1. Quantitative data shows the who, what, when and where. Qualitative is non- numerical data that demonstrates the why or how. Quantitative vs Qualitative
  8. 8. 2. When it comes to data-driven design, the more data you have at your disposal the better; but only when that data is organised and manageable. Make sure you’re tracking your most important webpage elements (e.g. buy now button) first. Tracking data
  9. 9. 3. Start by looking at the data you have. Whether this is web page visits, goal completions or customer feedback. Look for common occurrences and try to correlate these numbers (e.g. a high exit rate in a sales funnel and a lot of users that enter the information page may indicate an issue). Numbers leave patterns, patterns tell stories
  10. 10. 4. By assessing and ranking issues and opportunities together, you can begin to identify where to add more research effort and which to work on first. Opportunities vs issues
  11. 11. The who, what and where Part2 Googleanalytics
  12. 12. Due to the vast amount of features it has become the most used Why Google Analytics? It’s a free and powerful tool that provides a lot of quantitative data. web analytics tool.
  13. 13. Glossary
  14. 14. The user’s activity on your site (including just the loading of a single page). Bounce The number of times a visitor enters a domain on a page but leaves before viewing any other page in the domain, divided by the total number of views of that page. Also generally expressed as a percentage. Bounce rate An activity carried out by the user which fulfils the intended web page purpose (product purchase, download, newsletter subscription etc.) Conversion Google Analytics uses a lot of terms that need some explanation to fully understand what the data is telling you. So here is a short but comprehensive glossary of the metric terms used in Google Analytics Glossary
  15. 15. The total number of pages viewed. Repeated views of a single page are counted. However, for an accurate number, it’s important to look at unique pageviews because a single visitor can trigger multiple pageviews in one session. Page views The average number of pages viewed during a visit to your site. Pages/Session A page is loaded or reloaded by a user. Page impression A group of interactions a single user has within a given time frame on your website. Sessions The number of times a visitor leaves your domain from a page, divided by that page’s total views. Generally expressed as a percentage. Exit rate The number of times visitors entered your site through a specified page or set of pages. Entrances
  16. 16. The total number of distinct devices that have accessed your site. Users The percentage of visits that were first time visits (from people who had never visited your site before). New sessions The total number of visits to your site, from unique or repeat visitors. Visits The number of unduplicated visitors to your website over the course of a specified time period. Unique visitors
  17. 17. Navigation
  18. 18. The Google analytics interface can be quite a daunting thing to understand. In the following slides we will explain where what kind of information can be found. Navigation
  19. 19. Dashboard Create your own custom dashboard with widgets.
  20. 20. Intelligence Events Custom alerts for specific events (change in number of sessions, conversions, etc..).
  21. 21. Real-Time Pretty self-explanatory…
  22. 22. Audience Who (demographics, location, devices, etc..) has viewed your website.
  23. 23. Acquisition How did the visitors navigate to your website? Which channels did they use?
  24. 24. Behavior What did the visitors do on your website? E.g. navigation flows and search queries.
  25. 25. Conversions Did they convert? Which route did they take and how much time did they spend on reaching their goal?
  26. 26. Crunchingnumbers
  27. 27. It’s possible to add another layer of information to most of the graphs & tables by adding a segment. This will make it possible to compare multiple types of information. E.g, in the following slides we show how to add a segment to compare all sessions (all traffic) with the mobile- and tablet traffic. Adding segments
  28. 28. Step 1 Audience (You can add segments to each of the information categories).
  29. 29. Step 2 Select: ‘Add Segment’
  30. 30. Step 3 Select the type(s) of information you wish to view in the graph / table.
  31. 31. Step 4 Analyse the data.
  32. 32. Importsegments
  33. 33. Lots of custom made segments are available from the Google Analytics community. These segments can be imported and can give new insights in your data, such as additional info about social media traffic or the level of engagement. Import segments
  34. 34. Step 1 Select: ‘Add Segment’
  35. 35. Step 2 Select ‘import from gallery’
  36. 36. Step 3 Select ‘import’ for the desired plugin
  37. 37. Filtermetrics
  38. 38. Filters allow you to isolate certain results in the data charts. E.g. these filters can be added to find pages with a high bounce rate, low exit rate or a high page value. In the following slides we show how to add a filter to find all pages with a bounce rate less than 60%. Filter metrics
  39. 39. Step 1 Select: ‘Advanced’
  40. 40. Step 2 Select include or exclude and the value type (e.g. exit rate, bounce rate, unique pageviews).
  41. 41. Step 3 Select: ‘Advanced’
  42. 42. Step 4 Select value and apply
  43. 43. Step 5 (optional) Add a segment to further focus your data.
  44. 44. Goals
  45. 45. Goals measure how well the site or app fulfils a target objective (convert). Google Analytics defines micro goals (e.g. step 1 in a sales funnel) and end goals (e.g. checking out). Goals
  46. 46. With a destination goal (e.g. buying a product) you can specify the path you expect the traffic to take. You can view information about this funnel in the Goal Flow and Funnel reports. In the following slides we will show you a couple of ways to view the goal data in Google Analytics. Destination goals
  47. 47. Conversions page This example shows all the completion data in one graph and one table. A bit messy in my opinion. Luckily there is another way to view this data..
  48. 48. Funnel visualisation Shows all the stats about the sales funnel in one easy to understand page!
  49. 49. Micro goal #1 Amount of completions of the first micro goal (e.g. first step of the funnel)..
  50. 50. Micro goal Open the dropdown to view another micro goal timeline.
  51. 51. Funnel visualisation This shows the conversion rate and the amount of people that enter and leave each step of the sales funnel.
  52. 52. Setting up your data for succes Part3 Howtouseinaproject
  53. 53. Thingtotakeinconsideration • Key to succes: be specific • Focus your efforts • Develop a common language • Quantitative & qualitative • The definition of succes is not always the same • Keep your data clean
  54. 54. The best kind of data is the kind that answers a specific question that can lead directly to a change in design. E.g. questions such as “how is the website performing?” won’t help you to improve your designs. Questions such as “in which step of the sales funnel do mobile visitors drop out?” are much more specific. Key to succes: be specific
  55. 55. You can’t isolate variables when looking across big aggregated metrics (e.g. overall page views or downloads). This makes it difficult to draw conclusions from your data. Key to succes: be specific
  56. 56. “All data in aggregate is crap” – Avinash Kaushik
  57. 57. Analysing data can be resource-intensive. Don’t just track pages and elements. Always use specific, empirical data — don’t offer “high-level” metrics. Find data points that answer specific design questions and, thus, illustrate whether design or content changes worked. Focus your efforts
  58. 58. Develop a common language with the analytics tool and your project team. Educate your team so that they understand the importance of metrics. Develop a common language
  59. 59. Use quantitative and qualitative data together while redesigning a page. Qualitative data will help you to answer the ‘why’ of the ‘what’ (quantitative data). Quantitative & Qualitative
  60. 60. Take the goals of individual pages and different users into consideration. A returning visitor might have different needs from a new visitor, a redirected visitor might have different needs from a visitor from an organic search. The definition of succes is not always the same
  61. 61. Polluted data will affect your design choices. Exclude your own traffic from reports and install spam blocking plugins to prevent (SEO) spam traffic filling up your data reports. Keep your data clean
  62. 62. Busting some misconceptions Part4 Analyticsmyths
  63. 63. Often people assume the bounce rate represents people who land on your site and leave straightaway. However, the number indicates the percentage of visitors that leave your website without visiting another page. So for single page websites a high bounce rate is inevitable. A high bounce rate is an awful thing
  64. 64. The unique visitors count the number of cookies dropped in a browser. Often people assume this shows the amount of people (and therefore size of audience) that are engaging on their website however this metric cannot indicate unique browser or even computers. Unique visitors are people
  65. 65. The average visit duration report underestimates the actual time sped on a site. The calculation used does not account for the time sped on the last page they view before leaving the site. So this report shows the average time sped navigating the site. Average Visit Duration reports shows how long people spend on a site.
  66. 66. Direct traffic shows the traffic from sources that are not indexed by search engines like: emails, instant message services, links in offline documents, redirect pages, bookmarks or a javascript link. Direct traffic comes from typing the address into an address bar
  67. 67. Inconclusion
  68. 68. Conclusion • Analytics are a great way to get information about the performance of a web service. • Be specific, gather your data empirically. • Always use the quantitative data in combination with the qualitative data for the best results.
  69. 69. Statisticsmaybedull, butithasitsmoments. Questions? E-mail me at mark@sodastudio.nl Good luck!

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