Why Great Marketers Must Be Great Skeptics

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Rand Fishkin's presentation on the application of skepticism in web marketing.

Text of Why Great Marketers Must Be Great Skeptics

  • Rand Fishkin, Wizard of Moz | @randfish | rand@moz.com Why Great Marketers Must Be Great Skeptics
  • This Presentation Is Online Here: bit.ly/mozskeptics
  • Great Skepticism Defining
  • I have some depressing news
  • Does anyone in this room believe that the Earth doesnt revolve around the Sun?
  • The Earth (and everything in the solar system, including the Sun) revolves around our systems gravitational Barycenter, which is only sometimes near the center of the Sun.
  • Lets try a more marketing-centric example...
  • In 2009, Conversion Rate Experts built us a new landing page, and increased our subscribers by nearly 25%. What did they do? Via CREs Case Study
  • One of the most commonly cited facts about CREs work is the long landing page.
  • The Crap Skeptic The Good Skeptic The Great Skeptic Lets change our landing page to be a long one right now! We should A/B test a long landing page in our conversion funnel. How do we know page length was responsible? What else changed?
  • The Crap Skeptic The Good Skeptic The Great Skeptic I do believe sadly its going to take some diseases coming back to realize that we need to change and develop vaccines that are safe. Listen, all magic is scientific principals presented like "mystical hoodoo" which is fun, but it's sort of irresponsible. "The good thing about science is that it's true whether or not you believe in it."
  • In fact, weve changed our landing pages numerous times to shorter versions and seen equal success. Length, it would seem, was not the primary factor in this pages success.
  • What separates the crap, good, & great?
  • Assumes one belief-reinforcing data point is evidence enough Doesnt question whats truly causal vs. merely correlated Doesnt seek to validate
  • Doesnt make assumptions about why a result occurred Knows that correlation isnt necessarily causal Validates assumptions w/ data
  • Seeks to discover the reasons underlying the results Knows that correlation doesnt imply causality Thoroughly validates, but doesnt let imperfect knowledge stop progress
  • Will more conversion tests lead to better results? Testing
  • Obviously the more tests we run, the better we can optimize our pages. We need to build a culture of testing around here.
  • Via Wordstreams What is a Good Conversion Rate?
  • Via Wordstreams What is a Good Conversion Rate? Do Those Who Test More Really Perform Better?
  • Hmm Theres no correlation between those who run more tests across more pages and those who have higher conversion rates. Maybe the number of tests isnt the right goal.
  • Via Factors That Drive How Quickly You Can Run New Online Tests
  • Trust Word of Mouth Likability Design Associations Word of Mouth Amount of Pain CTAs UX Effort Required Process Historical Experiences Social Proof Copywriting CONVERSION DECISION Timing Discovery Path Branding Price (its a complex process)
  • How do we know where our conversion problems lie?
  • Ask Smart Questions to the Right People Potential Customers Who Didnt Buy Those Who Tried/Bought But Didnt Love It Customers Who Bought & Loved It Professional, demographic, & psychographic characteristics Professional, demographic, & psychographic characteristics Professional, demographic, & psychographic characteristics What objections did you have to buying? What objections did you have; how did you overcome them? What objections did you overcome; how?What would have made you stay/love the product? What would have made you overcome them? What do you love most? Can we share?
  • We can start by targeting the right kinds of customers. Trying to please everyone is a recipe for disaster.
  • Our tests should be focused around overcoming the objections of the people who best match our customer profiles
  • Testing button colors
  • Testing headlines, copy, visuals, & form fields
  • Designing for how customers think about their problems & your solution
  • THIS!
  • Does telling users we encrypt data scare them? Security
  • Via Visual Website Optimizer Could this actually HURT conversion?
  • Via Visual Website Optimizer
  • Via Visual Website Optimizer A/B Test Results They found that without the secure icon had over 400% improvement on conversions as compared to having the image. [Note: results ARE statistically significant]
  • We need to remove the security messages on our site ASAP!
  • We should test this.
  • Is this the most meaningful test we can perform right now? (Im not saying it isnt, just that we should prioritize intelligently)
  • Via Kayaks Most Interesting A/B Test vs.
  • Via Kayaks Most Interesting A/B Test A/B Test Results So we decided to do our own experiment about this and we actually found the opposite that when we removed the messaging, people tended to book less. - Vinayak Ranade, Director of Engineering for Mobile, KAYAK
  • Good thing we tested! Good thing we tested! Your evidence is no match for my ignorance!
  • What should we expect from sharing our content on social media? Social CTR
  • Just find the average social CTRs and then try to match them or do better. No brainer.
  • Via Signup.tos Analysis of CTR on Twitter
  • Via Signup.tos Analysis of CTR on Twitter
  • 306/701 = 43.6%... WTF??
  • Phew! Were not alone. Via Chartbeat
  • Assuming social metrics and engagement correlate was a flawed assumption. We need to find a better way to measure and improve social sharing.
  • OK. We can create some benchmarks based on these numbers and their averages, then work to improve them over time.
  • That is an insane amount of variability!
  • There are other factors at work here. We need to understand them before we can create smart metrics or useful expectations
  • Timing Source Audience Affinity Formatting Network-Created Limitations to Visibility Brand Reach Traffic Engagement
  • Lets start by examining the data and impacts of timing.
  • Via Facebook Insights
  • Via Followerwonk
  • Via Google Analytics
  • Theres a lot of nuance, but we can certainly see how messages sent at certain times reach different sizes and populations of our audience.
  • Comparing a tweet or share sent at 9am Pacific against tweets and shares sent at 11pm Pacific will give us misleading data.