Data Quality Sucks, Let’s Just Get Over It

• Data quality in the web is not great
• Six step plan:
1. Don’t dive deep into the data to find discrepancy between data
2. Assume a level of comfort with the data
3. Start making decision that you are comfortable with
4. Over time start understanding small areas of data
5. Get more comfortable over time with your data
6. Absolute numbers rarely matter, segmented trends do

Tips for Web Analytics Success for Small Businesses

1. Get top key phrases from search
2. Get top referring URLs
3. Which content is popular on your site?
4. Percentage of Visitors on the homepage
5. Check segmented click densities
6. Learn about your site’s bounce rates

Measuring Success for a Support Website: A Point of View

• Moment of Truth: hold or lose customers — web: often support problems
1. Don’t measure unique or total visitors
2. Identify top methods to customers find information
3. Click Density Analysis for the Top FAQ pages
4. What percent of site visitors call the support phone number?
5. Measure: Problem resolution, timeliness, likelihood to recommend
6. Check if the top solutions are corresponding to the top real problems (call center, user forums, blogs, etc.)

Seven Steps to Creating a Data Driven Decision Making Culture

1. Go for the bottom-line (outcomes): What motives the people around you?
2. Reporting is not Analysis
3. Depersonalize decision making
4. Proactive insights rather than reactive: Deliver insights before someone asks
5. Empower your analysts: They are not reporting monkeys. Give them strategic objectives
6. Solve for the Trinity: What, Why, How much?
7. Create a understandable, repeatable framework for making decisions
8. Business/Strategy should own web analytics

Five “Ecosystem” Challenges for Web Analytics Practitioners

1. Lack of relevant talent / skills: No real format training; too much experience requested (5+ years) although the field is moving fast
2. Entrenched mindsets: Decision makers still thinking in the old way
3. The web is no longer a monologue
4. It’s not about you, it’s about your customers: Less clickstream, more experimentation, usability, integration of multiple sources
5. Web analytics is the first step

Web Analysis: In-house or Out-sourced or Something Else?

• In the long run: in-house team
• strategic implementation of WA can’t exist in a silo
• Qualitative analysis is also needed
• Tribal knowledge helps the decision making
• But, it depends on the stage:
• Stage 1 – No WA -> Implement and show promise from data
• Stage 2 – Too much data -> Hire WA, customize dashboards, tag everything
• Stage 3 – WA rocks; Asking the why -> Start testing, collect qualitative data, expand your team
• Stage 4 – Trinity implemented -> new data, more people, a self-sustaining process

Five Rules for High Impact Web Analytics Dashboards

1. Benchmark & Segment: Provide context
2. Isolate your critical few metrics: less than 10 metrics
3. Include insights
4. Don’t create more than one single page
5. Constantly adapt your dashboard to changes in the environment

I Got No Ecommerce. How Do I Measure Success?

• Visitor Loyalty: How often does one person visit my website?
• Recency: How long has it been since a visitor last visited your website?
• Lengths of Visit: How long does each session last?
• Depth of Visit: How many pages did they visit?

Convert Data Skeptics: Document, Educate & Pick Your Poison

• Understand how your tools actually measure
• Document your findings
• Present everybody touching the data your findings
• Report high level trends between the tools
• Pick the best tool for each metric

Is Conversion Rate Enough? It’s A Good Start, Now Do More!

• There is more stuff than just conversions
• Esp. for non-ecommerce businesses, ask:
• Have you found what you were looking for?
• Will you go to our store?
• Will your recommend our website?

History Is Overrated. (Atleast For Us, Atleast For Now.)

• Value of web analytics data decays in time
• Your visitors change too much: browsers, cookie deletion, etc.
• Your computations change too much: new computations, maybe other tagging, etc.
• Your systems change too much: Other hosts, new technology, etc.
• Your website changes too much
• Your people change too much
• => no real tie to legacy tools and data
• => keep some major benchmarks for comparison

“Engagement” Is Not A Metric, It’s An Excuse

• Engagement is unique – therefore say what you actually measure instead of saying that you measure engagement.
1. What’s the purpose of the website?
2. How do you measure success?
3. Define your metrics
4. Call them what they are
• Ideas:
• Question: Are you engaged with us?
• Question: Likelihood to recommend website
• Use primary market research
• Customer retention
• RF of customers

In Web Analytics Context Is King Baby! Go Get Your Own

• Never report data in aggregate, or by itself. Always always always test to see if you are including context!
• Compare trends over different time periods
• Compare key metrics and segments against site average
• Report multiple metrics
• Benchmark
• Tap into the tribal knowledge

The “Action Dashboard” (An Alternative To Crappy Dashboards)

• Why do dashboards suck?
• Don’t provide a interpretation
• Readers don’t trust the providers of dashboards
• Not enough company context in the interpretation
• Providers don’t have enough experience
• How to make a good one?
• Report only 3-5 most critical metrics
• Show a trend in a graphic for a metric (segmented)
• Give key trends and insights
• Recommend actions
• What are the impacts of the company

Multichannel Analytics: Tracking Offline Conversions. 7 Best Practices, Bonus Tips

• Track your online store locator, directions, etc.
• Use unique 800 numbers
• Use unique coupons / promotions
• Connect online and offline – e.g. club cards, delivery, etc.
• Ask your customers (survey)
• Conduct controlled experiments
• Do primary research

Multichannel Analytics – Tracking Online Impact Of Offline Campaigns

• Use vanity urls: Permanent redirects help you to differentiate between offline and online referals
• Use unique coupons / offer codes
• Survey, survey, survey
• Correlate traffic patterns with offline ad patterns
• Experiment

Slay The Analytics Data Quality Dragon & Win Your HiPPO’s Love!

1. Change your boss
2. Compare web data with their favorite source
3. Put the data quality problem aside and give them actionable insights
4. You get trends rather fast even without a complete WA implementation
5. Start with the outcomes
6. One WA is probably enough
7. Realize if the data quality is good enough
8. If you don’t have enough traffic, care about more traffic first
9. There are inaccurate benchmarks and illegal customer behavior – don’t care too much about it
10. Fail fast, i.e. test

Barriers To An Effective Web Measurement Strategy [+ Solutions!]

• Note: Tools aren’t the real problem, still lots of people talk about them

1. Lack of resources (45%): Start for free and ask your right for a budget
2. Lack of strategy (31%): Change your job. If your a VP maybe you can help create one
3. Siloed organization (29%): Start small and offer value
4. Lack of understanding (25%): Do and show
5. Too much data (18%): Limit yourself on the critical few metrics.
6. Lack of senior mgm buy-in (18%): see previous summaries
7. Difficulty reconciling data (17%): Whatever.
8. IT blockages (17%): Show lost revenue by delay
9. Lack of trust in analytics (16%): previous summaries
10. Finding staff (12%): Don’t be to narrow minded
11. Poor technology (9%): Concerns mostly only brand new technologies / platforms

Who Owns Web Analytics? A Framework For Critical Thinking

• The biggest problem is most often the organization structure.
• How long has the company been doing WA? -> New: find accepting division to embarrass the seniors – Older: see next point
• Analytical maturity? New: find accepting people – Older: identify power centers
• Who owns the power to make changes to the site?
• Which model: Centralized? Decentralized? Something else? Agile team with satellites
• Which division / department is best for WA? Marketing if they have the power -> Ideal situation: own department

10 Fundamental Web Analytics Truths: Embrace ‘Em & Win Big

1. If you have more than one clickstream tool, you are going to fail: Implementing, understanding and communicating one tool is hard enough
2. Expensive tools won’t give you insights: Real problems are lack of skills, terrible orga, no structure or no courage.
3. It is faster to fail and learn then wait for an “industry case study” or find relevancy in a “industry leader white paper”
4. You are never smart enough not to have a Practitioner Consultant on your side (constantly help you kick it up a notch)
5. Your job is to create happy customers and a healthier bottom-line
• Go to your own website
• Read your own email campaigns
• Buy something on your own website
• Return a product on your own website
• Do the same stuff on competitor’s websites
• Do usability studies
• Be a customer and ask yourself: What will create happier customers tomorrow?
6. If you don’t kill 25% of your metrics each year, you are doing something wrong
7. A majority of web analytics data warehousing efforts fail. Miserably: Too much irrelevant data, mostly anonymous, full of holes, BI are bad at answering WA questions and DWs are too slow
8. There is no magic bullet for multi-channel analytics: see previous summary
9. Experiment, or die.
10. The single most effective strategy to win over “stubborn single-minded” HiPPO’s is to embarrass them.

Measuring Incrementally: Controlled Experiments to the Rescue!

• Test everything
• You may need additional personnel for conducting and analyzing the experiment
• It gives you excellent insights in different methods
• Act on your results as fast as you can