Media math july 11th classical wa sponder & matthews - part 1 deck


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This is a presentation given on July 11th at MediaMath on "Classical Web Analytics"

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Media math july 11th classical wa sponder & matthews - part 1 deck

  1. 1. What is “classical” Web Analytics and why to we need it?
  2. 2. What the activity of Web Analytics typically comprised of Image Source:
  3. 3. The Data Collection Process
  4. 4. Need to track events on you website using Analytics such as
  5. 5. Your stakeholders are drowning in data, but need help understanding it – that’s now your job.
  6. 6. Start with the Virtuous Cycle of Web Optimization
  7. 7. Evaluating Analytics implementations requires a clear set of questions
  8. 8. Tracking across Devices is challenging – often more heavily used at certain times of the day. Source: comScore Device Essentials, Monday 21st January 2013
  9. 9. The customer of today is complicated and distracted, can be hard to engage
  10. 10. Attribution Modeling – where is credit applied?
  11. 11. Attribution Models in Web Analytics
  12. 12. Getting “accurate” attribution models is challenging http://www.therain ia_Attribution.html
  13. 13. Pathing
  14. 14. Fallout
  15. 15. Troubleshooting Customers were expecting be reassured about site security and didn’t see this, so they mostly did not buy and left the site
  16. 16. Working with Stakeholders and Web Analytics Data – ..driving better results.
  17. 17. The ability to have Good Communication is Key to the successful deployment of Web Analytics in an enterprise
  18. 18. Ideal Analyst Scenario •Analyst: “We can increase PageViews on our website by 5% by sending out a weekly newsletter just before content updates which, by my estimates, would increase our advertising revenues by about $200,000 per week.” •Manager: “That’s sounds great! When can we implement your idea?”
  19. 19. Web Analytics works well mainly if/when your business is willing to make frequent changes to the website based on reports from the Analytics. If not, Website Analytics (Web Analytics) is almost useless. • The entire point of collected data with Web Analytics is to optimize. • Are you making recommendations on how to improve your Web site or simply just creating and sending reports to stakeholders with none of your own insight? Make sure that you do the former.
  20. 20. My own KPI definition: What Your Business (or You) Actually Care About
  21. 21. KPIs are not always a conversion rate. In the table, to the right, the sum of all Purchases and the sum of all Cart Additions can also be KPIs.
  22. 22. Sample: Success Events by Industry • Retail: ProductView, Checkout, Purchase • Media: Subscription, Contest Sign-up, PageView,VideoView • Finance: Application Submission, Login, Self-ServiceTools Usage • Travel: Booking (purchase), Internal Campaign (click-through), Search (pricing itinerary) • Telecommunications: Purchase, Leads, Self-ServiceTools Usage • HighTech: Whitepaper download, RFP, Form Completion, Support Requests • Automotive: Lead Submission, Request a Quote, Brochure Download
  23. 23. Metrics Convergence
  24. 24. Metrics, Metrics and more Metrics
  25. 25. Two types of Metrics (these are classified differently depending on the platform)
  26. 26. Example: Which Pages are Working? Not? Depending on how you want to define it (lets take “Bounce Rate” as a metric to follow”, Search Results seem to “bounce” less where as theWoman’s Merchandizing section of the site bounces the most.
  27. 27. Example: Which Pages get no traffic? Fashion detail pages got very little traffic, but that may be due to the very specific styles offered on each page.
  28. 28. Example: Which areas in your intended paths are weak and see a lot of dropout? We may need to do more to make our equipment page/s stand out.
  29. 29. Example: Which pages have high exits? We may need to do more to make our equipment page/s stand out.
  30. 30. Example: Which products are selling and which are not? This Adobe Site Catalyst instance does not have products defined.
  31. 31. We can segment Web Data to make it more actionable
  32. 32. Acting on Data once it’s been defined
  33. 33. Everyone needs a Plan – what's yours?
  34. 34. Goal(s): Audience: Location: Timing : Vehicle (how your going to do it): Venues (where your going to do it): Message (Call(S) to Action): Product / Service / Program Metrics/KPI’s among through/ with ask fans and customers to Regarding our Where Success will be judged by Here’s a template to help visualize “Your Plan”
  35. 35. Begin the Analytics Planning by defining your Business Goals so they are clear
  36. 36. Then match those “business goals” to goals your organization can accomplish online
  37. 37. Finally, match Digital Initiatives to KPIs that are tied to specific reporting from your Analytics
  38. 38. The end product is a Digital Strategy and Action Plan to help your organization realize its goals.
  39. 39. Dashboards and why we need them (or not)
  40. 40. Dashboards help if they are set up properly
  41. 41. Reports have several options and often populate dashboards
  42. 42. Summary We covered whatWebAnalytics is, what kind of questions it can answer and how to use define your key performance indicators in order to get answers from theWeb Analytics system.
  43. 43. Additional Sources of Information • Google Analytics Academy • AdobeTraining Services • ComScoreAcademy • Digital Analytics Association • Direct Marketing Association