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Reading Kaushik (Part 3): Strategic Analysis

Path Analysis: A Good Use of Time?

  • Customers like the path the want to, not the one you force
  • The tool don’t show which page in the path was most influential
  • Most tools don’t track the path correctly
  • Exception: Landing Page experience
  • Possible solution: Group relevant pages
  • Show most influential pages/li>
  • Make segmentation easier

Stop Obsessing About Conversion Rate

  • Overall conversion rate doesn’t allow for actionable insights
    • It only covers a small minority of all visitors
    • People are going to research on your site
    • People who want to learn about your company
    • People who need help with a product of yours
    • People that do something completely different
  • That is, you neglect a big part of your visitors
  • You’ll focus on short term gains
  • Better metric: Task Completion Rate
    • Helps you to cover all customers
    • Successes outside from conversions
    • Ultimately understand your customers better

Getting Started With Web Analytics: Step One – Glean Macro Insights.

  • Understand the macro level first
  • 1. How many visitors are coming to your site?
  • 2. Where re they coming from?
  • 3. What is the purpose of your website? What are your top three web strategies you currently working on?
  • 4. What are you visitors actually doing?

Consultants, Analysts: Present Impactful Analysis, Insightful Reports

  1. No data overload: Give value instead of data, provide recommendations
  2. Tie your data to business outcomes
  3. Use other data than just Clickstream
  4. Don’t make it boring
  5. Connect insights with actual data
  6. Meet the “exceptions of scale”: If you are a big agency or written a book on WA, then people expect more from you
  7. Do something unique

Paid Search Analytics: Measuring Value of “Upper Funnel” Keywords

  • Upper Funnel / Longtail Keywords can neglected because of the single session mindset
    1. Understand each stage of the customer purchase life cycle
    2. Map your keywords to each of those cycles
    3. Measure success for each cycle differently
    • Category Keyword: Bounce Rate
    • Category / Brand: Time on Site
    • Brand: Visitor Loyalty
    • Conversion/Product: Conversion Rate / Leads

Aggregation of Marginal Gains: Recession Busting Analytics!

  • Often web analysts don’t focus on the immediately achievable improvements
  • Simple things:
    1. Figure out where you are making money
    2. Check errors in your email campaigns
    3. Use funnels
    4. Fix your top landing pages
    5. Compare organic and paid keywords: Where are gaps between these two and why?
    6. Ask your customer
    7. Fix dumb stuff: e.g. check 25-point Website Usability Checklist

Analyze This: 5 Rules For Awesome Impromptu Web Analysis

  • Question: What would you change on this website?
  • Useful things to remember:
    1. Don’t start with your opinion: you are a proxy for customers / visitors => Better: State hypothesis
    2. Always offer alternatives
    3. Offer data, even when you don’t have access to the site’s data.
    4. State your assumptions about the site’s objectives
    5. Focus on obvious and non-obvious things: micro and macro conversions, competitor’s site, customer satisfaction, ideas for testing, demographic trends, etc.

Web Analytics Segmentation: Do Or Die, There Is No Try!

  • Pick at least some segments in: Acquisition, Behavior and Outcomes
  • #1: Acquisition: Where does the company spent its most money on?
  • How to segment:
    • Context: How many visits?
    • (Optional): How many were new?
    • Bounce Rate
    • What was the cost of acquisition?
    • What value could we extract at a per visit level?
    • How many were able to accomplish their goal?
    • Was what the total value added to our organization?
  • #2: Behavior
    • What are the visitors doing?
    • What do people want to do on your site?
    • How deep are they browsing on your site?
    • How long did they take time till conversion?
    • How often did they visited your site?
    • => Traffic source, conversion, average order value, actions, etc?
  • #3: Outcomes
    • Loot at macro and micro conversions!
    • i.e. video clicks, adding products to wish list, applying for a trial, downloading white papers, etc. etc. etc.

5 + 4 Actionable Tips To Kick Web Data Analysis Up A Notch, Or Two

  1. Go deeper — Don’t stop at the obvious border: compare off and online data, create CLV for ecommerce,
  2. Join the People against lonely metrics club
  3. Measure the complete site success
  4. Don’t just report one month: at least three months, understand your business’s cycles, create annotations
  5. Make insights in your data obvious: Better visualizations
  6. Segment your data (previous summaries)
  7. Don’t just look at the top 10 rows
  8. Step away from one-session thinking (later summary)
  9. Achieve multichannel analysis (previous summary)

Win Big With Web Analytics: Eliminate Data & Eschew Fake Proxies

  • Most reports are overloaded with data
  • Though, the readers want context and insights
    1. Eliminate all useless metrics and data in your reports
    2. Understand the desired outcomes

Rebel! Refuse Report Requests. Only Answer Business Questions, FTW.

  • Context is important – no business is the same
  • Answer business questions – what’s the driving request for the data?
  • Attributes of a business question:
    • They are usually open-ended and on a higher level
    • They need likely more information than just visits, bounce rate, etc.
    • They are seldom answered with tables

The Difference Between Web Reporting And Web Analysis

  • Data puking isn’t web analysis
  • Signs that you are doing web analysis:
    1. Actions instead of data
    2. Work with the business, measure economic value
    3. Use the web analytics measurement model (previous summary)
    4. You are doing a bit advanced statistics
    5. If you work with targets
    6. You provide context
    7. You segment your data effectively
    8. If you can provide an impact for a recommendation
    9. If you use less than four metrics in a table
    10. If you use multiple data sources

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