Iterative Testing Methods

✨ What does iterative multivariate testing look like? Take a look at the chart below. A couple of years ago we helped a client make a big decision: should they enter a new market? Serious investment would be required, and the company’s board wanted evidence that the company could generate demand in a market with a lot of established competitors. The company had ZERO knowledge of the new market (and the market had no knowledge of the company). Together, we developed hypotheses about what might work to position the company for success. I want to note the plural in that last sentence: HYPOTHESES. That’s how multivariate testing works. You test MULTIPLE hypothetical strategies at once with MULTIPLE audiences. It’s very different from how most people approach strategy, which is to test (if they test at all) that one perfect strategy. Multivariate testing of strategy is incredibly powerful. In the chart below, you can see the results of the first set of tests—those first lumps of traffic and revenue on the left. Clearly there’s something there, but nobody’s killing it, right? Wrong. Averages are deceiving. In the second wave of testing, we dropped the losing strategies and audiences and focused on the winners. Things started to pick up. By the third wave of testing (which was really a series of mini-waves), we weren’t just finding what worked—we were optimizing it. We call this sort of testing HEAT-TESTING, because it finds the ‘hot spots’ between strategy and audience. What does heat-testing tell you? Which audiences are most receptive How large those early audiences are How to position your product Which user flows are most productive in generating interest or revenue The cost to acquire a customer Whether you should move to the next step of a big investment I’ve been a strategist my entire career and here’s what I know: no amount of competitive analysis, focus groups, and surveys will deliver the one perfect strategy. Testing multiple strategies, ideally in an environment that gives you real-life, behavioral feedback, gives you raw material to iterate your way to a validated strategy. Always be testing.

Rapid testing is your secret weapon for making data-driven decisions fast. Unlike A/B testing, which can take weeks, rapid tests can deliver actionable insights in hours. This lean approach helps teams validate ideas, designs, and features quickly and iteratively. It's not about replacing A/B testing. It's about understanding if you're moving in the right direction before committing resources. Rapid testing speeds up results, limits politics in decision-making, and helps narrow down ideas efficiently. It's also budget-friendly and great for identifying potential issues early. But how do you choose the right rapid testing method? Task completion analysis measures success rates and time-on-task for specific user actions. First-click tests evaluate the intuitiveness of primary actions or information on a page. Tree testing focuses on how well users can navigate your site's structure. Sentiment analysis gauges user emotions and opinions about a product or experience. 5-second tests assess immediate impressions of designs or messages. Design surveys collect qualitative feedback on wireframes or mockups. The key is selecting the method that best aligns with your specific goals and questions. By leveraging rapid testing, you can de-risk decisions and innovate faster. It's not about replacing thorough research. It's about getting quick, directional data to inform your next steps. So before you invest heavily in that new feature or redesign, consider running a rapid test. It might just save you from a costly misstep and point you towards a more successful solution.

𝗔𝗜 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀: 𝗧𝗵𝗲 𝗕𝗿𝗶𝗱𝗴𝗲 𝗳𝗿𝗼𝗺 𝗣𝗼𝗖 → 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗖𝘀 𝘄𝗼𝘄. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗽𝗮𝘆𝘀. The gap from PoC → production is real. Your demo dazzles in a controlled pre-production setup, but by day two in the real world, cracks appear. The root cause? Many builds skip a continuous, iterative evaluation framework anchored to rigorous acceptance criteria. Acceptance criteria differ from success criteria—and grasping this is crucial for reliable scaling. 𝗤𝘂𝗶𝗰𝗸 𝗲𝘅𝗮𝗺𝗽𝗹𝗲: Almond grading with 25 defect classes. We spent ~6 months building a golden set (~2,000 images per class) and only green‑lit when two bars were hit: 90% F1 on a blind holdout (success) and the production line met the business bar — low false rejects (≤2%), line‑rate throughput, and a unit‑cost ceiling (acceptance). 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 𝘃𝘀. 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 (𝗻𝗼𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲) • 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 (𝗯𝘂𝗶𝗹𝗱‑𝘁𝗶𝗺𝗲): fast signals for iteration—task win‑rate, RAG groundedness, tool‑call accuracy, unit tests. • 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 (𝗴𝗼‑𝗹𝗶𝘃𝗲 & 𝘀𝗰𝗮𝗹𝗲): the business bar—success rate, time‑to‑task, cost per task, risk/safety, and operability (observability, canary, rollback). If this isn’t met offline, don’t ship. If it slips in production, auto‑rollback. 𝗔𝗰𝗰𝗲𝗽𝘁𝗮𝗻𝗰𝗲 𝗳𝗼𝗿𝗺𝘂𝗹𝗮 (𝗲𝘅𝗮𝗺𝗽𝗹𝗲) Ship only if Success rate ≥ X%, Time‑to‑task ≤ Y minutes, Cost per task ≤ $Z. 𝗧𝗵𝗿𝗲𝗲 𝗺𝗼𝘃𝗲𝘀 𝘁𝗵𝗮𝘁 𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗶𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻𝘀:  𝗚𝗼𝗹𝗱𝗲𝗻 𝘀𝗲𝘁 + 𝘀𝗰𝗼𝗿𝗶𝗻𝗴 𝗴𝘂𝗶𝗱𝗲. Curate 50–100 real tasks. Define a simple scoring guide (what “good” looks like), align reviewers, and version the dataset and the guide. Keep a blind holdout and track how often reviewers agree. Gate with thresholds (e.g., ≥80% first‑pass resolution in <2 minutes, ≤2% escalations). 𝗟𝗟𝗠‑𝗮𝘀‑𝗷𝘂𝗱𝗴𝗲—𝘄𝗶𝘁𝗵 𝘀𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀. Use pairwise comparisons, 2+ judge models, and human spot‑checks. Monitor judge disagreement/drift in CI and block merges on preference win‑rate drops. Log evaluation cost and latency so tests don’t balloon spend. 𝗦𝗰𝗼𝗿𝗲 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗲𝘅𝘁. • Agents: goal completion, steps‑to‑success, tool‑call success and preconditions, safe‑action %, rollback/undo rate. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗔 𝘀𝘁𝗲𝗽 𝗺𝗼𝘀𝘁𝗹𝘆 𝗺𝗶𝘀𝘀𝗲𝗱 Name an Evaluation Owner with approve authority. Run weekly evaluations and publish the scoreboard. Tie every score to success rate, time‑to‑task, and cost per task. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 • Make evaluations your ship/no‑ship gate tied to KPIs. • Start with a 100‑task golden set and a guardrailed LLM‑judge in CI. • Always score execution and keep a visible, weekly scoreboard. Treat evaluation like a product—owned, versioned, and tied to outcomes—and you’ll ship AI that sticks and scales. #AgenticAI, #AIEvaluation, #EnterpriseAI, #RAG, #MLOps

The key to effective usability testing? Approaching it with a Human-Obsessed mindset. This is crucial. It determines whether your improvements are based on assumptions or real user insights. It guides how you engage with: → User needs → Common tasks → Pain points → and Preferences throughout their journey on your site. Usability testing isn’t straightforward. It requires a deep understanding of user behavior and continuous refinement. How do you start a Human-Obsessed usability testing approach? Follow these steps: 1. Set Specific Goals — Focus on areas like navigation and checkout.  — Know what you aim to improve. 2. Match Test Participants to Users — Ensure your participants reflect your actual user base.  — Diverse feedback is key. 3. Design Realistic Tasks — Reflect common user goals like finding a product or making a purchase.  — Keep it real. 4. Choose the Right Method — Decide between moderated (in-depth) and unmoderated (scalable) tests.  — Pick what suits your needs. 5. Use Effective Tools — Leverage tools like UserTesting or Lookback.  — Integrate analytics for comprehensive insights. 6. Create a True Test Environment — Mirror your live site.  — Ensure participants are focused and undistracted. 7. Pilot Testing — Run a pilot test to refine your setup and tasks.  — Adjust before full deployment. 8. Collect Qualitative and Quantitative Data — Gather user comments and behaviors.  — Measure task completion and errors. 9. Report Clearly and Take Action — Use visuals like heatmaps to present findings.  — Prioritize issues and recommend improvements. 10. Keep Testing Iteratively — Usability testing should be ongoing.  — Regularly test changes to continuously improve. Human-Obsessed usability testing is powerful. It’s how Enavi ensures exceptional user experiences. Always. Use it well. Thank us later.

I made a common mistake early on in my work with ligand conformational strain: assuming a single conformational search iteration would find the true global minimum conformation. The result? ❌ Strain energy estimates were higher than they should have been. ❌ Conclusions and guidance were made based on incomplete data. What I’ve learned is this: The energy landscape of a ligand is often incredibly complex, especially for macrocyclic peptides or flexible small molecules. A single search can miss lower-energy states hidden in this landscape, leading to overestimated strain and less-than-optimal outcomes. What changed? By adopting iterative conformational searches and ensemble modeling, I’ve been able to uncover more accurate global minima. This approach not only refined strain estimates but also improved binding predictions and stability, leading to better drug candidates. ‼️Takeaway: Don’t settle for a single snapshot. Iterative refinement gives you the full picture and sets the stage for success. 💬 Have you faced similar challenges when modeling conformations? How did you overcome them? Share your thoughts in the comments! #DrugDesign #MedicinalChemistry #ConformationalStrain #DrugDevelopment #LessonsLearned

PDCA (Plan-Do-Check-Act) is a continuous improvement cycle used in Lean, quality management, and operational excellence systems. It provides a structured, iterative approach for problem-solving and process enhancement. The Four Phases of PDCA 1. PLAN This phase focuses on identifying a goal or problem and creating a plan for addressing it. Steps: Define the problem clearly. Understand the current state (using tools like process mapping, root cause analysis, etc.). Set measurable objectives. Develop hypotheses for solutions or improvements. Plan the implementation (who, what, when, how). Example: If customer orders are often late, analyze the current order-to-delivery process and plan to streamline approval steps. 2. DO In this step, implement the plan on a small scale or in a pilot/test environment. Steps: Execute the plan with selected team members or within a pilot area. Train staff as needed. Collect data on the process and outcomes during implementation. Example: Pilot the revised order process for one region or product category to see if cycle times improve. 3. CHECK Assess and analyze the results of your test implementation. Steps: Compare actual results with the expected outcomes. Use metrics and KPIs (Key Performance Indicators). Identify what worked, what didn’t, and why. Document findings. Example: Determine whether the new process reduced order time, improved customer satisfaction, or revealed new issues. 4. ACT (or ADJUST) Based on what you learned, take action: If successful: Standardize the solution and roll it out more broadly. If not: Refine the plan and go through the cycle again (iterative learning). Steps: Apply improvements organization-wide. Update procedures, documentation, and training materials. Start a new PDCA cycle if problems remain or new ones emerge. Example: If the pilot succeeded, train other departments and implement the process company-wide. Why PDCA Works Iterative: You continuously learn and improve. Data-driven: Based on measurable outcomes. Scalable: Works for small tasks or full organizational change. Risk-minimizing: Tests ideas before wide deployment. Typical Uses of PDCA Quality improvement initiatives Operational process redesign Reducing waste in Lean systems Strategic deployment Safety and compliance efforts

📣 Our paper is out pulling together 12 A/B tests showing *big* efficiency gains with ongoing rigorous, rapid, and regular testing in education. So excited about this paper and broader approach. We optimize tutoring programs, which are famously effective but hard to scale. Highlights below 👇 ➡️ Learning is iterative and cumulative. We show how randomized trials can be at once rigorous, while also more rapid and regular. We put together over 12 A/B tests which we ran starting in 2020, once every school term, in Botswana. ➡️ We include two types of tests: (1) Effectiveness-enhancing. Add to see if the program is more effective. (2) Cost-reducing. Reduce to see if the program = as effective at lower cost. We often test the former, but rarely the latter. We find cost-reducing tests yield substantial and consistent efficiency gains ➡️ Some of the effectiveness-enhancing tests generate large gains in learning at very minimal cost, such as engaging caregivers more, ranking in the upper percentile of the cost-effectiveness literature. ➡️ We measure practitioner teacher beliefs and show they update accurately towards identifying "what works," revealing the power of embedded learning to drive change on the front lines Huge shout out to our team of implementers, donors, researchers, and thought partners who have helped us work on this set of 12 tests: Youth Impact, What Works Hub for Global Education, Blavatnik School of Government, University of Oxford, Mulago, The Agency Fund, Jacobs Foundation, Foreign, Commonwealth and Development Office - Research, Science and Technology, UBS Optimus Foundation, Centre for the Study of African Economies, University of Oxford, Marshall, and many more who are supporting this type of iterative learning across a broad array of programs & sectors. https://lnkd.in/edNZQ_U3 cc Claire Cullen Janica Magat