Industry Analysis Techniques

Data is only powerful if people understand and act on it That’s why just pulling numbers isn’t enough. A good report tells a story, answers key business questions, and helps decision-makers take action. To ensure your analysis actually gets used: ✅ Start with the right question – If you don’t understand what stakeholders really need, you’ll spend hours on the wrong metrics. It’s okay to ask clarifying questions. ✅ Make it simple, not just accurate – Clean tables, clear charts, and insights that anyone (not just data people) can understand. ✅ Provide context, not just numbers – A 20% drop in sales is scary… unless you also show seasonality trends and explain why it’s normal. ✅ Anticipate follow-up questions – The best reports answer the next question before it's asked. ✅ Know your audience – A C-suite executive and a product manager don’t need the same level of detail. Tailor accordingly. Your work should make decision-making easier. If stakeholders are confused, they won’t use your report No matter how technically correct it is. The best data professionals don’t just crunch numbers. They translate data into impact. Have you ever spent hours on an analysis only for no one to use it?

Communicating complex data insights to stakeholders who may not have a technical background is crucial for the success of any data science project. Here are some personal tips that I've learned over the years while working in consulting: 1. Know Your Audience: Understand who your audience is and what they care about. Tailor your presentation to address their specific concerns and interests. Use language and examples that are relevant and easily understandable to them. 2. Simplify the Message: Distill your findings into clear, concise messages. Avoid jargon and technical terms that may confuse your audience. Focus on the key insights and their implications rather than the intricate details of your analysis. 3. Use Visuals Wisely: Leverage charts, graphs, and infographics to convey your data visually. Visuals can help illustrate trends and patterns more effectively than numbers alone. Ensure your visuals are simple, clean, and directly support your key points. 4. Tell a Story: Frame your data within a narrative that guides your audience through the insights. Start with the problem, present your analysis, and conclude with actionable recommendations. Storytelling helps make the data more relatable and memorable. 5. Highlight the Impact: Explain the real-world impact of your findings. How do they affect the business or the problem at hand? Stakeholders are more likely to engage with your presentation if they understand the tangible benefits of your insights. 6. Practice Active Listening: Encourage questions and feedback from your audience. Listen actively and be prepared to explain or reframe your points as needed. This shows respect for their perspective and helps ensure they fully grasp your message. Share your tips or experiences in presenting data science projects in the comments below! Let’s learn from each other. 🌟 #DataScience #PresentationSkills #EffectiveCommunication #TechToNonTech #StakeholderEngagement #DataVisualization

Clear communication of research findings is one of the most overlooked skills in UX and human factors work. It’s one thing to run a solid study or analyze meaningful data. It’s another to present that information in a way that your audience actually understands - and cares about. The truth is, most charts fall short. They either say too much, trying to squeeze in every detail, or they say too little and leave people wondering what they’re supposed to take away. In both cases, the message gets lost. And when you're working with stakeholders, product teams, or executives, that disconnect can mean missed opportunities or poor decisions. Drawing from some of the key ideas in Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic, I’ve been focusing more on what it takes to make a chart actually work. It starts with thinking less like an analyst and more like a communicator. One small but powerful shift is in how we title our visuals. A label like “Sales by Month” doesn’t help much. But a title like “Sales Dropped Sharply After Q2 Campaign” points people directly to the story. That’s the difference between describing data and communicating an insight. Another important piece is designing visuals that prioritize clarity. Not every chart needs five colors or a complex legend. In fact, color works best when it’s used sparingly, to highlight what matters. Likewise, charts packed with gridlines, borders, and extra labels often feel more technical than informative. Simplifying them not only improves readability - it also sharpens the message. It also helps to think ahead to the question your visual is answering. Is it showing change? Comparison? A trend? Knowing that upfront lets you choose the right format, the right focus, and the right amount of detail. In the examples I’ve shared here, you’ll see some common before-and-after chart revisions that demonstrate these ideas in action. They’re simple changes, but they make a real difference. These techniques apply across many research workflows - from usability tests and survey reports to concept feedback and final presentations. If your chart needs a walkthrough to make sense, it’s probably not working as well as it could. These small adjustments are about helping people see what’s important and understand what it means - without needing a data dictionary or a deep dive.

“If you’re not thinking segments, you’re not thinking.” - Theodore Levitt Here’s a brief history of market segmentation: 1950s: Segmentation started with basic demographics—age, location, gender—because that was the easiest data to collect and analyze. 1960s: Marketers began adding psychographics, gathering insights into customer attitudes and traits to create more specific profiles. 1970s: The rise of large transaction databases enabled real-time point-of-purchase data collection, leading to segments based on purchase behavior. 1980s: Needs-based segmentation emerged, driven by powerful computers and advanced clustering techniques. This allowed researchers to group customers based on desired product features and benefits. While needs-based segmentation was a step forward, it often missed the mark because customers aren’t product engineers. They struggle to articulate what specific products or features they need. But here’s the thing: Customers excel at describing the outcomes they want to achieve when using a product to get a "job" done. When discussing their desired outcomes, they can identify 100 to 150 different metrics to describe success at a granular level. Today's most effective market segmentation? It focuses on understanding how customers rate the importance and satisfaction of each outcome. This insight allows marketers to craft targeted messages and develop products that resonate deeply with each segment. Here’s 3 examples of Outcome-Based Segmentation in action: 1. J.R. Simplot Company identified a segment of restauranteurs who needed a French fry that stays appealing longer in holding, leading to a tailored product solution. 2. Dentsply found a segment of dentists who believed that the quality of a tooth restoration depended on consistently achieving solid bonds, allowing them to tailor their products to this need. 3. Bosch discovered a segment of drill–driver users who primarily wanted a tool optimized for driving, rarely using it as a drill. This insight helped Bosch create targeted and effective marketing strategies. Outcome-based segmentation represents a significant leap forward. It focuses on real opportunities... ...and measurable activities that are underserved by the competition. Outcome-based segments provide a clear path to innovation and market success.

Imagine you've performed an in-depth analysis and uncovered an incredible insight. You’re now excited to share your findings with an influential group of stakeholders. You’ve been meticulous, eliminating biases, double-checking your logic, and ensuring your conclusions are sound. But even with all this diligence, there’s one common pitfall that could diminish the impact of your insights: information overload. In our excitement, we sometimes flood stakeholders with excessive details, dense reports, cluttered dashboards, and long presentations filled with too much information. The result is confusion, disengagement, and inaction. Insights are not our children, we don’t have to love them equally. To truly drive action, we must isolate and emphasize the insights that matter most—those that directly address the problem statement and have the highest impact. Here’s how to present insights effectively to ensure clarity, engagement, and action: ✅ Start with the Problem – Frame your insights around the problem statement. If stakeholders don’t see the relevance, they won’t care about the data. ✅ Prioritize Key Insights – Not all insights are created equal. Share only the most impactful findings that directly influence decision-making. ✅ Tell a Story, Not Just Show Data– Structure your presentation as a narrative: What was the challenge? What did the data reveal? What should be done next? A well-crafted story is more memorable than a raw data dump. ✅ Use Clean, Intuitive Visuals – Data-heavy slides and cluttered dashboards overwhelm stakeholders. Use simple, insightful charts that highlight key takeaways at a glance. ✅ Make Your Recommendations Clear– Insights without action are meaningless. End with specific, actionable recommendations to guide decision-making. ✅ Encourage Dialogue, Not Just Presentation – Effective communication is a two-way street. Invite questions and discussions to ensure buy-in from stakeholders. ✅ Less is More– Sometimes, one well-presented insight can be more powerful than ten slides of analysis. Keep it concise, impactful, and decision-focused. Before presenting, ask yourself: Am I providing clarity or creating confusion? The best insights don’t just inform—they inspire action. What strategies do you use to make your insights more actionable? Let’s discuss! P.S: I've shared a dashboard I reviewed recently, and thought it was overloaded and not actionably created

Here's my cheat sheet for a first-pass quantitative risk assessment. Use this as your “day-one” playbook when leadership says: “Just give us a first pass. How bad could this get?” 1. Frame the business decision - Write one sentence that links the decision to money or mission. Example: “Should we spend $X to prevent a ransomware-driven hospital shutdown?” 2. Break the decision into a risk statement - Identify the chain: Threat → Asset → Effect → Consequence. Capture each link in a short phrase. Example: “Cyber criminal group → business email → data locked → widespread outage” 3. Harvest outside evidence for frequency and magnitude - Where has this, or something close, already happened? Examples: Industry base rates, previous incidents and near misses from your incident response team, analogous incidents in other sectors 4. Fill the gaps with calibrated experts - Run a quick elicitation for frequency and magnitude (5th, 50th, and 95th percentiles). - Weight experts by calibration scores if you have them; use a simple average if you don’t. 5. Assemble priors and simulate - Feed frequencies and losses into a Monte Carlo simulation. Use Excel, Python, R, whatever’s handy. 6. Stress-test the story - Host a 30-minute premortem: “It’s a year from now. The worst happened. What did we miss?” - Adjust inputs or add/modify scenarios, then re-run the analysis. 7. Deliver the first-cut answer - Provide leadership with executive-ready extracts. Examples: Range: “10% chance annual losses exceed $50M.” Sensitivity drivers: Highlight the inputs that most affect tail loss Value of information: Which dataset would shrink uncertainty fastest. Done. You now have a defensible, numbers-based initial assessment. Good enough for a go/no-go decision and a clear roadmap for deeper analysis. This fits on a sticky note. #riskassessment #RiskManagement #cyberrisk

A case study in overcoming the challenges to implement Theory of Constraints Holistically. ITT Night Vision, under the leadership of Neil Gallagher achieved remarkable success using the 5 Focusing Steps of Theory of Constraints (TOC) at a very strategic level. Their success story is a powerful example of how the deliberate management of the system constraint can lead to exceptional results. Rather than identifying the constraint they decided where they wanted the constraint to be - in production - and made sure they had enough customers to sell all their capacity at highest gross margin/constraint time. It enabled them to double sales in just 3 years and dominate their industry for decades. In this keynote, Dr. Eli Goldratt interviewed Neil to find out what rules they had to change to get these results, which rules got the most resistance and how they got buy-in for these. In summary, the key rules they had to change that faced most resistance included: Product Pricing Rule: The CEO of ITT was one of the pioneers in using Cost Accounting. It enabled ITT to win government orders and make 100’s of acquisitions. But when they decided that the constraint should be Production, they needed a Flexible Pricing Rule to evaluate existing and new business using Throughput Accounting (not Cost Accounting) to ensure they could sell all their capacity at highest Gross Margin/Constraint resource.    Production Scheduling Rule: ITT has a very complex manufacturing process. Both MRP and Just-in-time failed to improve their operational performance. With TOC they realized that by scheduling only the Capacity Constrained Resources (CCRs) they could make more reliable commitments while maximizing throughput and lowering total cost. Continuous Improvement Rule: And they used Buffer management analysis to identify what is causing most of the Throughput losses and Lead Time Delays to determine what resources to improve (to better exploit) or to invest in (to elevate) to keep growing Throughput. How did they get buy? They developed a Business Analysis system using Throughput Accounting principles to overcome the resistance in moving to flexible pricing. They used the Goldratt Production Simulator game in training to get buy-in for the TOC rules in production scheduling, prioritization and continuous improvement Like many fast-growing companies, the biggest challenge ITT faced was when key people with deep TOC or System Thinking expertise left. It left major voids that ultimately harmed operational and financial performance Key Take Away Using TOC at a strategic level means you must be proactive, not only in deciding where you want the constraint to be and to change rules in conflict with that decision. It also means being proactive to identify these key roles in your organization, and creating (through education), or hiring, enough of them so they do not become the system constraint Follow Dr Alan Barnard for daily insights on make better faster decisions

I wish I had learned this framework earlier in my career, when I was a Staff Accountant. At the time, I was booking journal entries and putting reconciliation schedules together from one month-end to the next. I remember finding things I thought management should be worried about but nobody seemed to listen when I would bring them up. Well now, I know that if I was applying this buy-in framework, things would have been much different. So if you want to be the go-to person for strategic recommendations in your organization and help others do the same, do these 4 things consistenly. 𝟏 - 𝐆𝐞𝐭 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤𝐢𝐧𝐠 Get in the habit of reading other companies’ financial statements and audit reports, especially if they are within your industry. [ Hint: Public companies and large not-for-profits usually have their financial statements available online. ] Start by downloading these documents and diving into the details. Comparing different companies’ financials will give you a broader industry perspective. 𝟐 - 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐞 𝐊𝐞𝐲 𝐑𝐚𝐭𝐢𝐨𝐬 Use the financial data to calculate essential ratios like current ratio, debt-to-equity ratio, and return on equity. These metrics are critical for benchmarking against industry standards and understanding where your company stands relative to others. How do you know that your current profit margin makes sense if you don't know the bigger picture? 𝟑 - 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 𝐊𝐏𝐈𝐬 Identify and track key performance indicators (KPIs) such as revenue growth and operating cash flow. Compare these metrics with those of other companies in the industry to gain insights and identify best practices. 𝟒 - 𝐂𝐨𝐧𝐯𝐞𝐫𝐭 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 Use the following framework to turn your analysis into actionable insights and get buy-in on your recommendations: > Observation: What does the data show? (i.e., "Revenue growth has slowed over the last two quarters.") > Analysis: Why is this happening? (i.e., "This could be due to increased competition and higher production costs.") > Implication: What does this mean for the business? (i.e., "If the trend continues, it could impact our profitability and market share.") > Recommendation: What should be done next? (i.e., "We should explore cost-cutting measures and evaluate new market opportunities to boost revenue.") By following this framework, you not only leverage your company’s data but also incorporate industry benchmarks to provide context. This helps stakeholders understand the broader landscape, see the implications clearly, and align with your recommendations, especially if you use an easy-to-understand format. What do you think?

Are traditional competitive sets of your hotel obsolete? An interesting question raised by my industry friend Fabian Bartnick! Some in the industry believe that the era of the traditional comp sets are no longer relevant because of the rising hyper competition. Here is my take. I believe monitoring your traditional competitive set (comp set) still provides value, but you should broaden your horizon and acknowledge that there other forces at play here. Today, every hotel in the world has not one, but THREE categories of direct competitors that should be taken into account in your revenue management practices: * Official Comp Set: This is your traditional comp set, included in the property's dSTAR Report by STR or rate shopping report by Fornova. These reports provide real value by benchmarking how well your property is performing against the “official” competition. * Digital Comp Set: These are properties that dominate the search engine results pages (SERPs) on the search engines for keyword terms that are very relevant to your property's product. Your digital competitors are all the properties with comparable to your property core services and amenities that rank on top or above your property in the search engines. Most likely these properties are not part of your “official” comp set. Ex. Google: which are the hotels listed on page one of Google’s SERPs (Search Engine Results Pages) when you search for your hotel category in your destination Ex. “Boutique hotels in downtown Houston” or “4-star hotels near Times Square Manhattan”. The hotels listed there are your true “digital comp set.” Ultimately, travel consumers are the ones who decide who are your property’s true competitors; your comp set should not be decided arbitrarily by the owner or the GM. Since Google “owns” the Dreaming and Planning Phases of the Digital Customer Journey, and more recently is increasing very aggressively its presence in the Booking Phase, you are losing more potential guests to your digital competitors than to your dSTAR comp set. * Vacation Rental Competitors: Today short-term rentals satisfy 1/4 of accommodation demand. In addition to monitoring and benchmarking your “classic” and digital competitors, you should monitor closely Airbnb's, Vrbo's and other vacation rental properties in your market. Research and identify the rental properties in your neighborhood, and what are their typical amenities and features. Introduce weekly and monthly rates for both rooms and suites. A weekly rate is NOT a daily rate multiplied by seven. A monthly rate is NOT a nightly rate multiplied by 30! Make sure that your CRS, WBE (Website Booking Engine) and Channel Manager can support weekly and monthly rates. Create an “Airbnb product” emphasizing the value and additional complimentary services guests will experience by booking the hotel at a comparable price, including free breakfast, connected rooms, parking, swimming pool, WiFi, luggage storage, etc.

𝗕𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 & 𝗗𝗮𝘁𝗮: 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘁𝗼 𝗡𝗼𝗻-𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 Data analysts often face a big challenge not just analyzing data, but explaining it in a way that makes sense to business team. A great analysis is useless if decision-makers don’t understand it! Here are some ways analysts can communicate better with non-technical stakeholders: ↳ 𝗧𝗲𝗹𝗹 𝗮 𝗦𝘁𝗼𝗿𝘆, 𝗡𝗼𝘁 𝗝𝘂𝘀𝘁 𝗡𝘂𝗺𝗯𝗲𝗿𝘀:– Instead of sharing raw data, focus on the key takeaway. What does the data mean for the business? ↳ 𝗔𝘃𝗼𝗶𝗱 𝗝𝗮𝗿𝗴𝗼𝗻:– Terms like "p-value," "ETL," or "normalization" might not be familiar to everyone. Use simple language that connects with your audience. ↳ 𝗨𝘀𝗲 𝗖𝗹𝗲𝗮𝗿 𝗩𝗶𝘀𝘂𝗮𝗹𝘀:– A well-designed chart is more powerful than a table full of numbers. Choose the right visual to highlight the key insight. ↳ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗧𝗵𝗲𝗶𝗿 𝗡𝗲𝗲𝗱𝘀:– Before presenting data, ask stakeholders what decisions they need to make. This helps you focus on relevant insights. ↳ 𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀:– A two-way conversation ensures stakeholders fully understand the data and feel confident using it. Great analysts don’t just crunch numbers, they bridge the gap between data and decision-making. What strategies have helped you communicate better with non-technical teams? #dataanalytics