Has an Executive Sponsor Got Your Back?
- Without executive sponsorship it’s hard to overcome organizational inertia
- Senior executive should within a key stakeholder group for WA (e.g. ecommerce, marketing, etc)
- They should have enough power and influence
- Depending on the WA maturity the sponsor executive may vary (tactical to strategic)
- Executive sponsor responsibilities
- Align WA program with corporate strategy
- Protecting the WA team from other initiatives/corporate politics
- Solving problems like budget constraints
- Promoting the success of the WA program
- Effective executive sponsor should be committed and involved
Online Accountability: Are You Data-Driven or Merely Data-Informed?
- Without accountability your organization is just data-informed
- Establish clear goals
- Regularly talk about the performances
- Give feedback and maybe rewards – can be adversarial
- Accounting should start at the top. Lead by example
- Expand beyond web KPIs
Soft vs Hard Bounces: A Closer Look at Bounce Rate
- Hard bounce rate = Bounce Rate on new visitors
- Soft bounce rate = Bounce Rate on returning visitors
Switching to a Data-Driven Culture
- You have appeal to the rational and emotional sides
- Sometimes resistance is lack of clarity
- Laziness can be exhaustion
- Look at what works well instead of was is not working well – easier to promote
- Provide actions that have to be taken to change
- What does this mean for the near future?
- Surprise people – testing stuff is a great tool
- Try to achieve lots of small goals instead of one big one
- See failures in execution as learning not as failing
- Provide a data-driven environment
- Build habits – repeat, repeat, repeat
- Provide workshops for homogeneous groups
Five Times to Test: 4 — When you spot an opportunity in your analytics
- Often hypotheses drive testing
- But you can generate hypotheses with analytics
- They took the ~50 top-selling products and plotter conversion rate and avg. selling price
- Look for outliers
- Positive outliers: try to promote them more prominently
- Negative outliers: Check at least the page – is it broken? No content?
Is Your Data-Driven Organization Heading into a Lake?
- Data should inform and shape not dictate or control
- It’s like science: intuition helps to understand and inspire, data helps to check and reject
- It helps you question your assumptions
- Data-informed: nice to know this information
- Data-driven: acting on the information
Are You Using Web Analytics To Power & Improve Your Testing?
- Don’t test randomly, test with hypotheses
- Benefits of WA:
- Helps you to understand your testing efforts in context
- Helps you to prioritize testing areas
- Helps you to improve your decision maing
- Provides insights that help you to make even better tests
- Things you should do:
- Analyze your conversion funnels
- Start higher up the funnel (note: in contrast to previous article)
- Check your top landing pages that have high bounce rates
- Check heat maps for your testing pages – what is the customer intent?
- Set alerts for new highly visited pages
- Improve your test plans with analytics insights
- WAs and testing people should work together
Never a Failed Test
- Testing is a long-term strategy
- Does every test answer a clear business question?
- Do you know before the test what you do, depending on the results, afterwards?
- Negative lift is even good lift – you learned something!
- Do you think testing is valuable or risky?
- Do you have to hit the big wins? – This can interfere with your learning ambitions
Why we do what we do: Garbage in and Garbage Out — Congruence Bias
- “the tendency to test hypotheses exclusively through direct testing, in contrast to tests of possible alternative hypotheses”
- Example: Hypotheses: Button 1 opens the door, not Button 2; Test: Just press Button 1 and check if it opens the door
- In web testing: Test picture against no picture, or CTA against no CTA
- It’s easy to get big results but not great answers
Great summary/overview: Digital Governance: Best Practices from the Trenches
Tip #3: Turbocharge Your SEM/PPC Analysis
- Measure your Bounce Rate
- Understand how vendors work
- Measure cannibalization rate vs. organic
- Experiment and Test
- Understand the multi goal of your site
- Measure the value of long tail keywords
Tip #4: Make Your Analysis/Reports “Connectable”
- Make dry stuff more approachable
- e.g. Flirters = Visitors with three pages or less
- You can always include the definition if people are interested in it
- You can link it up in a persona way
Tip #7: The Adorable Site Abandonment Rate Metric
- Site Abandonment Rate = [1 – (total orders placed on the website) / (Total add to cart clicks)]
- Checkout Abandonment Rate = [1 – (total number of people who complete checkout) / (total number of people who start checkout)]
- Now you can segment, test and improve your rates
Tip #13: Measure Macro AND Micro Conversions
- Macro Conversions: Buy something on your site
- Micro Conversions: Write a review, sign up the email newsletter, etc.
- Not all visitors want to buy something, therefore measuring micro conversions reveals more truth
Tip #15: Measure Latent Conversions & Visitor Behavior
- Don’t just focus on immediate results — look a month later on the behavior of the acquired visitors
- Especially for community-based websites (social networks, boards, etc.) later behavior is more important than just the sign up
- Measure Loyality, Requency, Frequency
Tip #16: Brand Evangelists Index
- Survey: Not at all satisfied, not satisfied, satisfied, very satisfied, extremely satisfied
- Problem: Satisfaction rate is not very informative
- Aim for delight
- Penalize for negative rating
- Index the results for communication
- => Brand Evangelist Index (BEI): [[(Very Sat + Ext. Sat.) – (Not Sat. + Not At All Sat.)] / #Responses ] * 100
Tip #18: Make Love To Your Direct Traffic
- Direct traffic is traffic that is driven by people who seek you actively out
- Problem: Direct traffic can result from improperly tagged links / urls
- Missing WA tags on landing pages
- Untagged campaigns
- Improperly tagged campaigns
- Improperly coded redirects / vanity urls
- Non Async tag
- Links encoded in JS can be problematic
- https to http and vice versa don’t send referrers
- Multi and sub domains problems
Tip #19: Identify Website Goal [Economic] Values
- What is the economic value of micro conversions?
- Assign campaign codes & track offsite converting goals
- Track online micro-conversions in offline systems
- Get the current “faith based” number from Finance
- Estimate relative goal values
- If everything else fails, just use $1
I read Occam’s Razor for quite a while now and I really like Avinash’s style and insights. I thought it would be nice to reread most of his stuff and as a nice extra, I will post my notes on here.
I oriented each section by the section defined in his overview of all articles.
The 10 / 90 Rule for Magnificent Web Analytics Success
- There is lots of data but no insights
- Rule: 10% in tools and 90% people/analysts
- may seem over the top but
- med-large websites are complex
- reports aren’t meaningful by default
- tools have to be understood
- there is more than clickstream to analytics
- If you don’t follow the 10 / 90 Rule
- Get GA account
- Track parallel to expensive solution
- Find a metrics multiplier, so you can compare GA to old data
- Cancel your contract and hire an smart analyst which will probably deliver more insights for less money
Trinity: A Mindset & Strategic Approach
- The goal is to generate actionable insights
- Behavior analysis: clickstream data analysis
- Outcomes analysis: Revenue, conversion rates, Why does your website exist?
- Experience: Customer satisfaction, testing, usability, voice of customer
- Helps you understand what customer experience on your site, so that you can help influence their behavior
The Promise & Challenge of Behavior Targeting (& Two Prerequisites)
- We have so much behavior data but you get the same content regardless whether you are here to buy or get support
- There are BT systems but you have still think about the input
- You have to first understand your customers good enough to create suitable content
- Test content ideas first to learn what works and as evidence for HiPPOs
Six Rules For Creating A Data Driven Boss!
- Paradox: The bigger the organization the less likely it is data driven in spite of spending lots of money on tools
- It is possible to achieve this but you have to actually want to do and fight for it
- 1. Get over yourself: Learn how to communicate with your boss and try to solve his problems
- 2. Embrace incompleteness: Data is messy, web data is really messy but still better than completely faith based initiatives.
- 3. Give 10% extra: Don’t just report data, look at it. Give him insights he didn’t asked for. Make recommendations and explain what’s broken.
- 4. Become a marketer: Great analysts are customer people. Marketer as internal customer (like account plannner)
- 5. Don’t business in the service of data: Data should provide insights not just more data. Ask: how many decision have been made based on data that have added value to the revenue?
- 6. Adapt a Web Analytics 2.0 mindset:
Lack Management Support or Buy-in? Embarrass Them!
- HiPPOs may be don’t listen to you but they better listen to customers & competitors
- 1. Start testing
- 2. Capture Voice of Customer: Surveys, Usability tests, etc.: Let the customer do the talk
- 3. Benchmark against the competition, e.g. use Fireflick
- 4. Use Competitive Intelligence
- 5. Start with a small website
- 6. Ask outsiders for help
How To Excite People About Web Analytics: Five Tips.
- 1. Give them answers
- 2. Talk in outcomes / measure impact
- 3. Find people with low hanging fruit and make them a hero
- 4. Use customers & competitors
- 5. Make Web Analytics fun: Hold contests, hold internal conferences, hold office hours
Redefining Innovation: Incremental, w/ Side Effects & Transformational
- 1. Incremental innovation, e.g. Kaizen
- 2. Incremental innovation with side effect, e.g. iPod or Adsense
- 3. Transformational innovation, e.g. invention of the wheel
- Web analytics can’t probably create 3
- Clickstream alone is also not enough for 1.
- generally the more the better (Web analytics 2.0)
Six Tips For Improving High Bounce Rate / Low Conversion Web Pages
- Purpose gap between customer intent and page
- 1. Learn about traffic sources / keywords(!)
- 2. Do you push your customers against their intent? Identify jobs of each page and focus on your call to actions.
- 3. Ask your customer what they are looking for
- 4. Get insights from site overlays
- 5. Testing!
- 6. Get first impressions from people, e.g. fivesecondtest
Online Marketing Still A Faith Based Initiative. Why? What’s The Fix?
- Faith based initiatives like TV, magazines, etc.
- Online marketing gives us useable data
- and allows us to test easily
- The web is quite old yet it is not in the blood of executives
- Old mental: shout marketing, instead of new inbound marketing
- Lousy standards for accountability
- Let the customers speak
- Benchmark against competition
Win With Web Metrics: Ensure A Clear Line Of Sight To Net Income!
- Focus on the bottom line, i.e. profits
- 1. Identify your Macro Conversion
- 2. Report revenue
- 3. Identify your Micro Conversions
- 4. Compute the economic value
- Net income = Unit Margins * Unit Volumes
- Unit Margins = Price – Cost
- Unit Volumes = Market Share * Market Size
- Because Net Income is the goal, you have to measure Price, Cost, Market Share or Market Size
- Which metrics help doing that? And if not, why do you track/report this metric?
- They also depend on the strategies or more general goals of the organization
- — let your “boss” decide what matters most to him/organization
- identify clear metrics / KPIs for each used strategy
- use the web analytics measurement framework as a reporting foundation (more to this later)
- find actionable insights with segmented analysis
Digital Marketing and Measurement Model
- Marketing with measuring helps you to identify success and failure
- Digital Marketing & Measurment Model
- Set business objectives (should be DUMB)
Identify goals for each objective
Get KPIs for each goal
Set targets for each KPI
Identify segments of people / outcomes / behavior to understand why things succeeded or failed
What scope has the model to cover?
- Acquisition: How do people come on your site? Why? How should it be?
- Behavior: What should people do on your site? What are the actions they should take? How do you influence their behavior?
- Outcomes: What are the goals? (see previous summary)
11 Digital Marketing “Crimes Against Humanity”
- Not spending 15% of your marketing budget on new stuff
- Not having a fast, functional, mobile-friendly website
- Use of Flash
- Campaigns that lead to nowhere
- Not having a vibrant, engaging blog
- “Shouting” on Twitter / Facebook
- Buying links is your SEO strategy
- Not following the 10/90 rule
- Not using the Web Analytics Measurement Model (previous summary)
- Using lame metrics: Impressions, Page Views, etc.
- Not centering your digital existence on Economic Value
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.