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A b-testing-101

  1. 1. A/B Testing 101 The Basics & Best Practices
  2. 2. A/B Testing: Why? USER EMPATHY: COLLECT, ANALYZE & SOLVE USER PAIN POINTS INCREASED ROI REDUCED BOUNCE RATE TAKE INTELLIGENT RISKS DATA DRIVEN DECISION MAKING INTRODUCE & SCALE CHANGE INTELLIGENTLY Problem statements: • Increased unqualified leads? Lower engagement? Higher bounce rate? Answer: A/B Testing
  3. 3. A/B Testing: Scope Web pages (Headlines, images, Call to action texts/buttons, mentions, badges etc) API end points: Platform as a service API or Data as a service API or Software as a service API (the developer network uses APIs which APIs has higher rate of conversions)
  4. 4. A/B Testing: Definition A/B Testing = showing 2 variants to different target personas (at same time) + comparing the results to determine which variant has higher ROI Example: Landing page changes to determine which change results in higher metrics of conversion. Examples of metrics of conversion: • # of products purchased • # of leads generated
  5. 5. A/B Testing: How it works? Research Collect data on usage by visitors=>track metrics=>Identify problems=>Root-cause- analysis of the problem Hypothesis Build a hypothesis based on the findings from research. Alternatives Build alternatives to existing solutions to validate the hypothesis. Validate Validate by testing alternatives in parallel for a defined duration. The test duration can be based on: •existing state vs future state •# of alternatives •total # of personas/users •% of personas/users to be tested. Implement Analyze the test results from the validation Measure, Iterate & Improve alternatives until the best outcome is achieved Implement the winning alternatives
  6. 6. A/B Testing: Best practices • Plan for optimization: Measure, iterate, improve & scale intelligently • Prioritize & focus while testing alternatives (Quantity vs Quality) • Use “Smart statistics” (Data science) to eliminate bias in driving outcomes from a test (Probability, Duration & Comparison of Improvements) • Control vs challenger (test & compare with vs without hypothesis) • Build test samples intelligently • Set a goal for your test • Look for best tool selection • Collect feedback from users during the test • Consider external factors
  7. 7. Examples of A/B testing by domains A/B Testing for media: Engagement & Growth for online users A/B Testing for B2B: # of qualified leads, # of trials, # of conversions A/B Testing for search: search modals, search relevance, search results A/B Testing for eCommerce: # of orders (Successful vs Cancelled) A/B Testing for Platform as service or Data as Service or Software as service: # of successful API calls, engagement & growth
  8. 8. A/B Testing Tools Convert Experiences Google Analytics Instapage Pricing Paid: Higher price (trial) Freemium Paid: Lower price (trial) Product direction Strong Available Strong Support High quality support Low Meets expectation Ease of use Can be better Meets expectation Great Variation Testing Strong Meets expectation Meets expectation Reporting Strong Meets expectation Meets expectation Personalization Strong Meets expectation Meets expectation Industry SMB, Mid-market SMB, Mid-market, Enterprise SMB, Mid-market ** Disclaimer: Based on my experience of using these tools & information available on internet.
  9. 9. References • https://www.g2crowd.com/categories/a-b-testing • https://vwo.com/ab-testing • https://offers.hubspot.com/an-introduction-to-ab-testing

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