Blog Posts
Reading “Straight Talk for Hard Times”
In his article published this Christmas Eve, "Straight Talk for Hard Times: Using Ethnographic Thinking in Interstitial Moments," Jay Hasbrouck explores the challenges of navigating a volatile job market, especially in the field of applied research. This insightful piece is a guide for professionals at any career stage, focusing on how to use ethnographic thinking to reevaluate career paths and make informed decisions during these uncertain times.
Key takeaways from the article include:
Understanding a Company’s Core Value: It's crucial to comprehend how a company makes money and what it fundamentally offers. This understanding shapes your ability to determine if a company's focus aligns with your passion and skills, which is vital for long-term job satisfaction.
Assessing Organizational Culture and Fit: Investigate whether a company's culture and values align with your own. This includes understanding the company's practices, reward systems, and how decisions are influenced. I think feedback from others about your work can be important in this assessment.
Recognizing and Navigating Power Dynamics: Understand the official and unofficial power structures within a company. This knowledge helps in identifying key stakeholders and decision-makers who may impact your role.
Leadership Analysis: Analyze the leadership style and effectiveness within the organization. Leadership gaps can be indicators of either challenges or opportunities for change and impact.
Approach to Innovation: Inquire about how a company approaches innovation. Whether it's an integral part of product development or a separate entity, it can significantly influence your role and the value of your contributions.
Emphasizing Humility: Assess whether the company culture values humility, learning from mistakes, and supporting each other. This can significantly impact your job satisfaction and growth.
Dealing with Job Responses: Hasbrouck advises on how to handle different types of job responses – 'no,' 'maybe,' and He s’ He suggests using each response as a learning opportunity to understand more about the company and your own candidacy.
Broadening Career Perspectives: Careers are often non-linear. Be open to opportunities that might not initially seem appealing, as they can offer significant growth.
Career Game Realities: Finally, Hasbrouck notes that while navigating one's career, it's essential to enjoy the journey and not be overly fixated on 'winning' the career game, as this rarely correlates with happiness or fulfillment.
Actionable Insights:
From reading the article, here are some actionable items researchers can try:
Conduct Thorough Research: Before applying or interviewing, research the company's business model, culture, and values to ensure alignment with your own.
Seek Diverse Feedback: Regularly seek feedback about your work from various sources to understand your strengths and areas for growth.
Engage in Strategic Networking: Understand the power dynamics within an organization and identify key influencers.
Evaluate Leadership: Pay attention to leadership styles and strategies, and think about how they align with your approach to work.
Consider Innovation’s Role: Reflect on how you fit into the company's innovation strategy, whether in a dedicated team or as part of product development.
Cultivate Humility and Learning: Look for environments where mistakes are viewed as learning opportunities and humility is valued.
Be Open to Various Opportunities: Expand your job search to include roles and industries that may not be your first choice but offer potential for growth and learning.
Enjoy the Career Journey: Focus on the experience and growth offered by each role rather than solely on career advancement.
Hasbrouck's article serves as a comprehensive guide for professionals navigating today's challenging job market, emphasizing the importance of aligning personal values and strengths with potential employers for a fulfilling career path.
Help plan Boston's World Information Architecture Day 2024
Julia DeBari, has stepped up to lead the next World IA Day Boston on March 2, 2024. This is exciting because it will be Boston’s first event since 2021!
Julia is looking for people to help make this happen. There are lots of ways to participate: event planning, helping to find sponsorship, or supporting marketing/communication/social media, graphics, A/V, and other day-of-event stuff.
Would you like to be involved, or do you know someone else who would?
If you would like to hear more, please reach out to Julia directly.
Wherever you are, don't forget to book March 2 on your calendar to attend or livestream a WIAD event.
Optmizely: Lessons learned from running 127,000 experiments.
The recently published "Final Benchmark Study" by Optimizely, based on the analysis of over 127,000 experiments, provides invaluable insights to inform your A/B testing and experimentation program.
These are some interesting insights from the study:
💡 88% of tests don't win (This is why it's SO important to test - Our intuitions about what will succeed are often not correct)
💡 Only a third of experiments test more than one variation, but experiments that have more variations are 3x as impactful (i.e., we should do more ABCD tests when possible)
💡 Tests that make significant changes to the user experience (pricing, discounts, checkout flow, data collection, etc.) are more likely to win and with higher uplifts.
💡 Experiments that include targeting are 16% more likely to win when compared to untargeted experiments.
💡 The median company runs 34 experiments per year. The top 3% of companies run over 500. To be in the top 10%, you need to be running 200 experiments annually.
Other key findings
Experimentation Win Rates and Company Practices
About 12% of experiments win on their primary metric, while 88% do not.
The median company runs 34 experiments per year, with the top 3% conducting over 500 annually.
Companies are increasing their experimentation velocity by 20% year over year.
Most experiment uplifts decrease to 80% of their initial value after a year, except for revenue-related uplifts, which retain 91%.
Experimentation Evolution and Strategies
Companies are transitioning from client-side testing to more mature experimentation frameworks, with feature experimentation growing to 36% of all tests since 2016.
Experiments involving more complex changes and multiple variations are more successful.
Advanced analytics and integrated Customer Data Platforms (CDPs) significantly enhance experimentation success.
Industry and Metric Variations
Win rates and experiment success vary across industries, influenced by experimentation maturity and metric selection.
The choice of primary metrics for experiments differs by industry, reflecting varying goals and priorities.
Team Performance and Experiment Design
Experimentation teams tend to maintain consistent performance over three years. Improvement requires altering research, creativity, and development processes.
High-impact experiments often involve substantial changes and multiple variations.
Greater complexity in experiments, such as multiple change types, leads to higher returns.
Micro-Conversion and Personalization
Focusing on micro-conversions (like search rate and add-to-cart rate) can lead to a higher experiment impact than solely targeting revenue.
Personalized experiments targeting specific user segments are 41% more impactful than general ones.
Resource Allocation and Traffic Models
Effective resource allocation, including developer time, is crucial. The most productive setup is running one experiment per developer per two-week sprint.
Machine-learning models like Stats Accelerator and Multi-Armed Bandit, which dynamically allocate traffic, significantly enhance experiment outcomes compared to standard A/B tests.
Successful experimentation in digital commerce hinges on advanced analytics, complex experiment designs, focus on micro-conversions, personalization, and efficient resource allocation. These insights can guide executives to foster a culture of innovation and optimize their digital strategies effectively.
Check out the report
Dive in to start reading The Evolution of Experimentation research from Optimizely.
