Reading Kaushik (Part 6): Competitive Analysis

Competitive Intelligence Analysis: Metrics, Tips & Best Practices

  • What not to do?
    1. Comparing conversion rates is hard: different business strategies
    2. Pages / Content viewed is too individual and doesn’t really matter
  • What to do?
    1. Share of Visits by your industry
    2. Compare “up and downstream” against competition
    3. Share of Search traffic
    4. Share of brand and category key phrases
    5. Discover new search key phrases
    6. Traffic by media mix
    7. Psychographic analysis

The Definitive Guide To (8) Competitive Intelligence Data Sources!

  1. Toolbar data: e.g. Alexa
  2. Panel data: comScore, Nielsen
  3. ISP (Network) data: Hitwise, Compete
  4. Search Engine data: Google AdWords, Keyword Tool, Search-based Keyword Tool, Insights for Search, Microsoft adCenter Labs
  5. Benchmarks from WA vendors: Fireclick, Coremetrics and GA
  6. Self-reported data: Quantcast, Google AdPlanner
  7. Hybrid Data: Google Trends, Compete, DoubleClick AdPlanner
  8. External VOC data: iPerceptions, ACSI

Reading Kaushik (Part 4): Tactical Analysis

Kick Butt With Internal Site Search Analytics

  1. Understand site search usage: What are they looking for?
  2. On which site do people search?
  3. How good are the search results?
  4. What do people search for after they search for one term?
  5. Measure outcomes

PPC / SEM Analytics: 5 Actionable Tips To Improve ROI

  1. Compare keyword performance for different search engines / PPC sites
  2. Focus on what’s changed, otherwise there’s just too much data
  3. Look at your impression share
  4. How is your ROI distributed – exceeds, meets or underwhelms expectations?
  5. How are the keywords matched?

Analysis Ninjas: Leverage Custom Reports For Better Insights!

  • Start with goals: Where is the company spending money? How is the bonus of your boss calculated? What is the worst thing on your company’s website?
  • Include outcomes
  • Reduce the number of reports
  • Match metrics up to reader – Personalize
  • Talk to people and understand what motives them

3 Advanced Web Analytics Visitor Segments: Non-Flirts, Social, Long Tail

  1. Non-Flirts, Potential Lovers: Page Depth bigger than 3 (depending on the distribution)
  2. Social Media, Baby: Tag your links and track by referrer
  3. Search Queries With Multiple Keywords [3, 4, 5, 10, 20]: Match keyword ->

Three Amazing Web Data Analyses Techniques For Analysis Ninjas

  • Calculate costs / profits for micro-conversions
  • Bring ratios / quotas into context: 0.01% conversion = 100 of 1,000,000 visitors convert
  • Not every visitor is convertible: only return and of return only valuable traffic (e.g. people visiting the pricing info)

Video: Successful Web Analytics Approaches by Avinash Kaushik

Again a great video, this time about Web Analytics by Avinash Kaushik. I just love his no-BS style.

  • Ask the metric: So what? Three times, if it don’t give an action it’s useless
  • Data should drive action
  • Give people the information they need – don’t send them everything => no death by data
  • Home pages of websites, are no longer the home page you want
    • Where do people come from?
    • What are they looking for?
  • Context matters: previous months, years, etc.
  • Relative numbers more important than absolute numbers
  • Compare different metrics, e.g. conversion rate and page views
  • non e-commerce sites:
    • averages hide truth effectively
    • How often do they visit?
    • How recent did they visit?
    • Depths of visit
    • => Understand the value: Loyalty
  • Segment people
  • Survey people: What do they think about the content?
  • Bounce rate: Came and left
    • Segment by source, entry-page, landing pages, etc.

Rules for Revolutionaries

  • 10/90 Rule: $10 Tools, $90 People: Understand Data & Business, Able to analyze => to extract value
  • Reporting is not analysis: Reporting -> provide data; Analysis -> prove insights
  • Data Quality can be low, but is still better than other data
  • Faith-based initiative: e.g. magazine ad without tracking
  • Make decisions, don’t argue about the quality of the data
  • Over time understand why quality is different -> confidence will get better


  • Decision making is a journey, not a destination
  • => Put some level of process in place, mostly for tasks, e.g. what happens to implement a test, etc.
  • if HiPPo (highest paid person’s opinion) makes the most decisions
  • => make experiments
  • Learn to be wrong, quickly
  • => You probably don’t know what your customers want
  • => Experimentation

#81/111: Web Analytics 2.0

What is it about?

Visitors, Page views, Time on site. Everything familiar? Time to move to the next level. Avinash Kaushik talks about using Web Analytics in decision processes. He tries to reduce the information to help you create KPIs for better management decisions.

What can I learn?

Critical Few: You should try to reduce the data into insights. Create meaningful KPIs (Key Performance Indicators) and explain it to your employees/bosses. The future main goal will be to increase these critical few.

Bounce Rate is your friend: The bounce rate is a very insightful measurement (however not for blogs). It tells you how many percentage of your customers just visited one site and then left the page. This is an easy metric to minimize with a huge profit.

Segmentation works: A big advantage of online analytics is that you can segment customers/visitors more easily. Try to segment people by traffic source or geographical sources. This helps you to target your campaigns better. If you want to improve your advertising campaigns, you should look first if most people convert in the first two to fives visits. If you, it’s okay and you can directly look on the best conversing traffic source. However if most of the people buy at a later time it’s gets really fuzzy how to value each source. Caution!


Web Analytics 2.0 is huge. Avinash Kaushik talks about so much different topics from A/B testing to event tracking. This is sadly one of the criticizing points. Somehow it feels to shallow to be useful. However, it’s brutally hard to write about such an extensive topic like (web) analytics. Good book.