Decision-Making Process Optimization

The biggest mistake in decision-making has nothing to do with the solution. It’s focusing on the answer before you've understood the real question. This creates confusion, wastes resources, and burns out your team. The fastest way to a great decision isn't speed, it's clarity. 6 steps to make better decisions every time: 1️⃣ Define the actual problem. ↳ Don't just treat the symptom. Ask "Why?" five times to find the root cause. A solution to the wrong problem is worthless. 2️⃣ Involve the right people. ↳ Get input from those who will do the work. But keep the decision-making circle small. More voices don't mean a better choice, they just mean more noise. 3️⃣ List your constraints. ↳ What are the absolute limits on time, budget, and resources? Being honest about your boundaries forces creative and realistic solutions. 4️⃣ Generate multiple options. ↳ Never fall in love with your first idea. Force yourself to come up with at least three viable paths. This simple step prevents confirmation bias. 5️⃣ Stress-test your top choice. ↳ Before you commit, ask the most important question: "If this fails, why did it fail?" Identify the weaknesses in your plan before the world does it for you. 6️⃣ Decide, commit, and communicate. ↳ A good plan executed now is better than a perfect plan next month. Make the call, empower your team to act, and clearly explain the "why" behind your decision. Stop looking for the right answer. Start by finding the right question. What's one rule you follow for making better, faster decisions?

Before you make a decision, define what you know! In every real-world decision problem, people rush to design a policy. But what they skip is foundational: 🔍 What do you know right now? 🔁 How does the world change when you act? These two questions define your state variable and state transition function. And if you skip them, your policy won’t stand on solid ground. In the frameworks I used in industry, every decision model I create starts with: 1️⃣ State variable: What information do you have right now that’s relevant to the decision? • Inventory levels • Open orders • Current locations • Weather forecast • System status 2️⃣ Transition function: How does the state evolve based on: • Your decision • Random events • Time This has nothing to do with your policy. It’s just the physics and flow of your system. But here’s where I have seen most people go wrong: They mix the two. They write code that implicitly assumes the policy while updating the state. So when you want to try a new decision rule, you realize… the whole model has to change. ⚠️ That’s a design failure. Instead: • Let the state transition function handle how the world updates. • Let the policy decide what to do in the current state. That’s it. This separation gives you flexibility, testability, and long-term maintainability. It’s how you move from one-off scripts to decision systems. Want to optimize? Forecast? Simulate? Learn? You can’t do any of that until you’ve framed the problem!

Nearly 60% of CEOs evaluate their strategic decision capability based on outcomes rather than the quality of their decision-making process (PwC). It’s easy to see why. Outcomes are tangible, measurable, and at the end of the day, they’re the bottom line. Yet, decades of research show that using smart decision processes thoroughly beats congratulating yourself on outcomes. This is because outcomes are influenced by factors outside your decision scope—like market shifts, new regulations, or good old-fashioned luck. You could have a positive result because the market suddenly changed in your favor, or because a competitor stumbled. Or, a great decision could lead to an unfavorable outcome simply because of unexpected variables—like an economic downturn or an unforeseen risk. By the way, some of the most brilliant, value-creating moves I’ve seen came after a bad misstep or unexpected event prompted exec teams with stellar decision practices to re-evaluate and take advantage of the new conditions. (Insert your favorite example from early COVID here!)  When you evaluate your strategic decisions through the lens of the quality of your decision-making process it can reveal key insights: ✨ Clarity of information: Did you gather the right data? Were there gaps in your information? ✨ Diverse perspectives: Did you get a variety of viewpoints? Did you challenge assumptions? ✨ Navigating uncertainty: What risks were identified? Did you fully explore what you were unclear about? ✨ Alignment with values and mission: Did your decisions consistently reinforce the org’s larger vision? Were the decisions aligned with your org’s core values? ✨ Flexibility and agility: Did you stay flexible to new information or changing circumstances? ✨ Room for improvement: What worked well? What changes might be made next time? Focusing on the quality of your decision-making process reveals whether your decisions are based on thorough analysis, aligned with your strategic goals, and designed to be repeatable for long-term success. What could change for your team if you started measuring success by increasing the quality of your decisions instead of waiting for the results?

Consensus feels safe. It is also slow. Your job is not to keep everyone happy. Your job is to make the next right decision, own the risk, and move. Consensus tries to average preferences. Operators create direction. The difference is costly: consensus optimizes for feelings, direction optimizes for outcomes. Here is a simple operating view of decision-making that scales from a 3-person team to a 300-person org: 1️⃣ Define the decision and the owner ↳ One DRI. One clock. One sentence problem statement. ↳ Timebox debate. “We decide by Tuesday 3:00 PM.” 2️⃣ Separate door types ↳ Reversible (two-way): bias to speed and small tests. ↳ Irreversible (one-way): slow down just enough to protect downside. 3️⃣ Gather signal, not noise ↳ Ask for the strongest counterargument and the cheapest test, not opinions. ↳ Pull data that shrinks uncertainty, not decks that grow it. 4️⃣ Force alternatives ↳ At least two viable options with trade-offs stated plainly. ↳ Include a “do nothing” case to anchor costs. 5️⃣ Decide in writing ↳ One page, max: • Decision: X • Why now: drivers, constraints • Options considered: A/B (+ trade-offs) • Risks & mitigations: top 3 • Success metric & review date 6️⃣ Communicate for alignment (not agreement) ↳ “We chose X because Y. We will measure Z. We will recheck on [date].” ↳ Invite dissent before the call, commitment after it. 7️⃣ Close the loop ↳ Log the decision. Set the review. If wrong, fix fast, do not assign blame. Learning speed beats perfect aim. Decision hygiene beats decision theater. You do not need more meetings. You need clearer ownership, tighter clocks, and smaller experiments. When should you slow down? ↳ One-way door with existential risk. ↳ High cost of reversal, long tail liability, or brand trust at stake. ↳ When the cheap test is still expensive. Otherwise, ship the test. Leader’s checklist for “hard and clear”: ↳ Name the owner and the deadline out loud. ↳ Refuse vague language: “maybe,” “kinda,” “circle back.” ↳ Tie every decision to one measurable and one de-risking action. Use this micro-template in Slack/Email: ↳ Decision: Launch pricing test at $X for Segment Y ↳ Why now: Competitor moved, CAC rising ↳ Options: A/B/C (trade-offs noted) ↳ Risks: Churn ↑, margin ↓, confusion → Mitigations: FAQ, support script ↳ Metric: Net revenue per signup ↳ Review: 14 days, DRI: Pat Three moves you can make today: ✅ Pick one stalled decision and set a 24-hour clock. ✅ Write a one-page decision note and share it for alignment. ✅ Assign a DRI to every open decision and schedule the review. Hope this helped! How could it be improved? 👇 ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

How we choose policies for sequential decision problems   There is a long tradition in deterministic optimization to design algorithms to find the optimal “x”.   When we make decisions over time, the situation is more complicated. In rare cases, we can find optimal policies, but as a rule optimal policies are out of reach.   There are a variety of methods for making decisions – each of these fall somewhere in the four classes of policies. But how do we evaluate policies? It turns out, it is more complicated than simply finding the one that works the best.   In practice, we evaluate policies based on the following criteria:   o Average performance – Minimize cost, maximize profit, other metrics. o Worst-case performance – How poorly does it sometimes work? o Transparency – Can we explain the decision? o Computational complexity - How long does it take to compute? o Reliability – Does it always work? o Methodological complexity – How hard is it to create and implement? o Data requirements – Do we have the data needed?   These are not issues that arise in deterministic optimization. It is an important dimension of sequential decision problems, largely overlooked in the literature, but absolutely recognized by everyone in practice.

Decision-making is a necessity in almost every aspect of daily life. However, making sound decisions becomes particularly challenging when the stakes are high and numerous complex factors need to be considered. In this blog post, written by The New York Times (NYT) team, they share insights on leveraging the Analytic Hierarchy Process (AHP) to enhance decision-making. At its core, AHP is a decision-making tool that simplifies complex problems by breaking them down into smaller, more manageable components. For instance, the team faced the task of selecting a privacy-friendly canonical ID to represent users. Let's delve into how AHP was applied in this scenario: -- The initial step involves decomposing the decision problem into a hierarchy of more easily comprehensible sub-problems, each of which can be independently analyzed. The team identified criteria impacting the choice of the canonical ID, such as Database Support and Developer User Experience. Each alternative canonical ID choice was assessed based on its performance against these criteria. -- Once the hierarchy is established, decision-makers evaluate its various elements by comparing them pairwise. For instance, the team found a consensus that "Developer UX is moderately more important than database support." AHP translates these evaluations into numerical values, enabling comprehensive processing and comparison across the entire problem domain. -- In the final phase, numerical priorities are computed for each decision alternative, representing their relative ability to achieve the decision goal. This allows for a straightforward assessment of the available courses of action. The team found leveraging AHP proved to be highly successful: the process provided an opportunity to meticulously examine criteria and options, and gain deeper insights into the features and trade-offs of each option. This framework can serve as a valuable toolkit for those facing similar decision-making challenges. #analytics #datascience #algorithm #insight #decisionmaking #ahp – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Spotify: https://lnkd.in/gKgaMvbh https://lnkd.in/gzaZjYi7

Decision-making can be daunting. Strategy fundamentally involves making choices—determining where to focus, what trade-offs to accept, and how to allocate resources to achieve desired outcomes. The brutal fact: We don’t always have all the answers, but decisions can’t wait until we fully understand every detail. One of the decision-making frameworks I keep in my pocket - 𝐂𝐲𝐧𝐞𝐟𝐢𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 It is based on the nature of the situation (complexity) and the level of predictability. 𝟏. 𝐊𝐧𝐨𝐰𝐧 𝐊𝐧𝐨𝐰𝐧𝐬: 𝐓𝐡𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐚𝐛𝐥𝐞 ➡️ What it means: You know what's happening and understand it fully. ➡️ Example: A customer support team spends hours every day categorizing and assigning incoming tickets manually, following a consistent set of rules. ➡️ How to decide: 1. Sense: Understand the facts of the situation. 2. Categorize: Match it to a known framework or pattern. 3. Respond: Apply a straightforward solution, as the answer is often obvious. 🔔 Key Tip: Stick to tried-and-true methods for efficiency and consistency. 𝟐. 𝐊𝐧𝐨𝐰𝐧 𝐔𝐧𝐤𝐧𝐨𝐰𝐧𝐬: 𝐓𝐡𝐞 𝐁𝐥𝐢𝐧𝐝 𝐒𝐩𝐨𝐭 ➡️ What it means: You know there’s a problem but don’t fully understand it yet. ➡️ Example: User churn rates are high, but the reasons behind it are unclear. Analytics show patterns, but they don’t provide definitive insights into why users are leaving. ➡️ How to decide: 1. Sense: Gather all relevant data and inputs. 2. Analyze: Use expert opinions, tools, or detailed studies. 3. Respond: Choose the best course of action from multiple viable options. 🔔 Key Tip: Don’t rush. Use analysis and expertise to guide decisions. 𝟑. 𝐔𝐧𝐤𝐧𝐨𝐰𝐧 𝐊𝐧𝐨𝐰𝐧𝐬: 𝐓𝐡𝐞 𝐔𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 ➡️ What it means: There are things you don't realize but could understand if you investigated. Patterns exist but are not obvious upfront. ➡️ Example: Discovering unconscious biases affecting hiring dynamics. ➡️ How to decide: 1. Probe: Conduct safe-to-fail experiments to uncover hidden factors. 2. Sense: Observe the results to identify emerging patterns. 3. Respond: Adapt based on the insights gained. 🔔 Key Tip: Be open to exploration and learning; flexibility is crucial. 𝟒. 𝐔𝐧𝐤𝐧𝐨𝐰𝐧 𝐔𝐧𝐤𝐧𝐨𝐰𝐧𝐬: 𝐓𝐡𝐞 𝐔𝐧𝐞𝐱𝐩𝐞𝐜𝐭𝐞𝐝 𝐂𝐡𝐚𝐨𝐬 ➡️ What it means: You’re blindsided by events you couldn’t predict or prepare for. ➡️ Example: A sudden industry-disrupting technology or a global crisis like COVID-19. ➡️ How to decide: 1. Act: Take decisive steps to establish stability like emergency measures. 2. Sense: Identify areas of order or stability amid the chaos. 3. Respond: Gradually transition to a more manageable situation by creating structure. 🔔 Key Tip: Speed is critical; act first, then refine your approach. And when faced with 𝐜𝐨𝐧𝐟𝐮𝐬𝐢𝐨𝐧 (𝐝𝐢𝐬𝐨𝐫𝐝𝐞𝐫)—a completely unclear state— break it down into smaller parts and assign each to its appropriate category for clarity. Have you used this framework? #productmanagement #strategy

How to master quick decision-making in business (A guide): 1. Perfectionism is the enemy of progress. Don’t delay decisions waiting for the perfect solution - an imperfect decision is better than none. You can always adjust later. 2. Speed up your experiments. The faster you can learn from a decision, the quicker you can iterate and improve. 3. Consult trusted voices and reflect quietly. Your intuition, honed by experience, is a critical tool for making swift decisions. Trust it. 4. Difficult decisions require open dialogue. Whether it’s a spontaneous chat or a scheduled check-in, make sure your team feels safe to discuss challenges. 5. Recurring meetings provide a safety net. Even if not always used, they ensure decisions are made promptly rather than dragged out over weeks. 6. After gathering input, take time to reflect and journal. This process sharpens your thinking and helps refine your decisions. 7. Regular check-ins with coaches or peers help you continuously optimize strategies and keep your decision-making sharp. 8. Once you’ve made a decision, act on it immediately. Clarity without execution is wasted potential. 9. Encourage a culture where difficult issues are openly discussed. This not only strengthens decision-making but also builds trust and resilience within the team. 10. Your gut feeling, informed by experience and reflection, is often your best guide in complex situations. Hone it like a skill. 11. Keep decision-making straightforward. Complex processes slow you down - simplicity is your ally in speed. 12. Regular updates and feedback from trusted advisors help refine your approach and ensure you’re making the best decisions possible. Make the last 4 months of 2024 yours. Don’t wait - start making faster, more informed decisions today.

𝗨𝗻𝗹𝗼𝗰𝗸𝗶𝗻𝗴 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲: 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗚𝗿𝗼𝘄𝘁𝗵 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 1PD + 3PD → Customer Data + AI = Decision Intelligence → Top Growth Blueprint Simple concept, yet many falter at the foundational level: Customer Data. Here's your roadmap to optimize data initiatives and unlock decision intelligence: 𝟭. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗬𝗼𝘂𝗿 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗮𝗯𝗹𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 • Know your audience: Outline target verticals and expected customer profiles • List information needed for a 360° view • Tailor content to your customer profile across channels 𝟮. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲 𝗙𝗶𝗿𝘀𝘁-𝗣𝗮𝗿𝘁𝘆 𝗗𝗮𝘁𝗮 (𝟭𝗣𝗗) • 1PD is your foundation • Capture intent signals, behavioral data, demographics, transactions, and engagement • Maintain data quality: CRM data can degrade by 34% annually without intervention 3. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐆𝐚𝐩𝐬 𝐢𝐧 𝐘𝐨𝐮𝐫 𝟏𝐏𝐃 • Map your customer journey to spot missing data points • Consult sales, marketing, and customer service teams for insights 𝟰. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗵𝗶𝗿𝗱-𝗣𝗮𝗿𝘁𝘆 𝗗𝗮𝘁𝗮 (𝟯𝗣𝗗) • Find accurate sources to fill your data gaps via a single source if possible (iCustomer) • Seek vendors with expertise in Identity level depth • Consider non-traditional options (e.g., waterfall enrichment services) 𝟱. 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲 𝟭𝗣𝗗 𝘄𝗶𝘁𝗵 𝟯𝗣𝗗 • Layer 3PD onto your 1PD foundation • Use matching, mapping, and survivorship techniques • Prevent duplication, inaccuracies, and redundancy 𝟲. 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 • Derive actionable insights from unified customer data • Identify funnel friction and optimize customer journeys • Enrich data with relevant labels to reveal hidden segments and dark funnel intent • Automate data quality processes 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁: ✅ Quick, frictionless decision-making ✅ Contextualized outreach ✅ Top-line growth 🚀 Stuck at any point? DM me—I'd love to help you navigate this journey! #DecisionIntelligence #DataStrategy #AI #GrowthBlueprint

Companies are often described as decision factories. Based on the academic literature (see comments section) and extensive interviews, I created this decision-making framework. ❶ DECISION ENABLERS Decisions are not made in a vacuum. There are five enablers that dictate the quality of decisions: • The quality of supporting materials is a key enabler of good decision making. Our interviews show that decision makers almost uniformly would like to see an improvement of the underlying materials.   • Scenarios should be possible to run in real time. Most executives find this lacking. Instead, a new scenario make take up to 2–3 days to run for the business analysts. • Decisions should be timely. Often, decision making takes too long. A typical strategic planning process takes 3–5 months. Some issues seen at the outset may not be material by the end, and new issues may have emerged. • The right people have to be involved. T-shaped expertise is required: some should be generalists with broad experience, while some should be specialists with deep knowledge. • A collaborative environment is an important enabler. Executives are more committed when heard. ❷ DECISION CONTEXT The enablers lead to to a decision context: • The most important pain point we heard in our interviews was that the issues to be decided on were not framed correctly, or not at all. Instead the decision making process (e.g., for the strategic plan) became a process of checking the numbers. • The decision processes are usually viewed as inefficient, especially in higher level processes like strategic planning, even though outcomes may be good. ❸ DECISION MAKING The actual decision making flows from the enablers and the context. There are two ways of making business decisions: rationale and intuitive. • Rationale decision making has gradually become more important over the last century. This is because executives are now better educated and much more information is available in digestible form. • But, intuitive decision making is important and will continue to be so. When rational decision making becomes more efficient, there is more space to have higher quality in intuition. ❹ CORPORATE PERFORMANCE Improving high level decision making like strategic planning is often the highest ROI effort available to a company. First, more effective processes will save money. There are 0.7M corporate planning analysts in the United States, and 2.5M worldwide, who support decisions. Second, resource allocation is improved with better decisions. This is especially true of the decisions coming out of strategic planning cycles. Third, capturing opportunities becomes more precise. This leads to top line growth which over time adds up to a major benefit for the company. —  —  — [2023-09-09] #strategy #management #ceo