Reading Kaushik (Part 6): Competitive Analysis

Competitive Intelligence Analysis: Metrics, Tips & Best Practices

What not to do?

Comparing conversion rates is hard: different business strategies
Pages / Content viewed is too individual and doesn’t really matter

What to do?

Share of Visits by your industry
Compare “up and downstream” against competition
Share of Search traffic
Share of brand and category key phrases
Discover new search key phrases
Traffic by media mix
Psychographic analysis

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

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

Reading Kaushik (Part 4): Tactical Analysis

Kick Butt With Internal Site Search Analytics

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

PPC / SEM Analytics: 5 Actionable Tips To Improve ROI

Compare keyword performance for different search engines / PPC sites
Focus on what’s changed, otherwise there’s just too much data
Look at your impression share
How is your ROI distributed – exceeds, meets or underwhelms expectations?
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

Non-Flirts, Potential Lovers: Page Depth bigger than 3 (depending on the distribution)
Social Media, Baby: Tag your links and track by referrer
Search Queries With Multiple Keywords [3, 4, 5, 10, 20]: Match keyword ->
```
^\s*[^\s]+(\s+[^\s]+){2}\s*$
```

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

Conclusion

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!

Conclusion

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.