Competitor Benchmarking Methods

✈️ Airline Cost Structure: The Ultimate Professional Reference Every network decision is shaped by cost. This guide breaks it down clearly, precisely, and strategically. It’s your essential reference for evaluating route economics, understanding cost drivers, and aligning financial insight with network strategy. Keep this guide at hand for planning sessions, budget reviews, and strategic discussions. It’s your shortcut to clarity when analyzing route economics, cost allocation, and financial performance. The complete airline cost framework unfolds like this: 𝗗𝗶𝗿𝗲𝗰𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗖𝗼𝘀𝘁𝘀 (𝗗𝗢𝗖): Costs directly tied to flight operations, split into: • 𝗙𝗶𝘅𝗲𝗱 𝗗𝗢𝗖: Costs that remain constant regardless of flight activity, aircraft ownership (lease or depreciation), maintenance infrastructure, crew salaries, and insurance. • 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗗𝗢𝗖: Costs that scale with flight activity, driven by four key dimensions:   - 𝘾𝙮𝙘𝙡𝙚-𝙗𝙖𝙨𝙚𝙙: Costs per flight cycle, such as landing fees and outsourced handling charges   - 𝘿𝙞𝙨𝙩𝙖𝙣𝙘𝙚-𝙗𝙖𝙨𝙚𝙙: Costs that increase with flight length, such as fuel burn and en-route charges     - 𝙋𝙖𝙨𝙨𝙚𝙣𝙜𝙚𝙧-𝙗𝙖𝙨𝙚𝙙: Costs tied to passenger count, such as catering and GDS cost      - 𝙍𝙚𝙫𝙚𝙣𝙪𝙚-𝙗𝙖𝙨𝙚𝙙: Costs linked to ticket sales, such as credit card fees and commissions 𝗜𝗻𝗱𝗶𝗿𝗲𝗰𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗖𝗼𝘀𝘁𝘀 (𝗜𝗢𝗖): Support functions that enable airline operations, such as sales, marketing, administration, and overhead. These are essential for sustaining commercial performance and organizational alignment. Understanding the hierarchy and drivers of cost is key to forecasting, route evaluation, and strategic decision-making. This guide helps decode that structure with clarity. This quick reference guide below connects every critical cost component with precise definitions, airline-standard classifications, and strategic relevance for network planning and financial modeling. 𝗪𝗵𝗮𝘁'𝘀 𝗜𝗻𝘀𝗶𝗱𝗲: • Clear hierarchical structure of airline cost categories • Industry-standard definitions and classifications • Strategic insights for route evaluation and cost optimization 𝗟𝗶𝗸𝗲 𝘁𝗵𝗶𝘀 𝗽𝗼𝘀𝘁: 💾 Save this post for quick reference 🔄 Share with your network and spread the knowledge Which cost driver was the biggest surprise in your last network review?  Share your experience below 👇 and join the conversation with industry peers. #Air52Insights #Aviation #Airlines #AviationStrategy #NetworkPlanning

Search Query Performance is a Goldmine for Ranking Insights! If you’re not leveraging Search Query Performance (SQP) to its fullest, you’re missing out on some hidden nuggets that can supercharge your ranking strategy. By adding few extra columns to the report, you can uncover valuable trends: ■ CTR Metrics: • Market CTR • Brand/ASIN CTR • Delta CTR (%): The difference between your Brand CTR and Market CTR. A positive delta means you’re outperforming the market; a negative delta means you’re lagging behind. ■ CVR Metrics: • Market CVR • Brand/ASIN CVR • Delta CVR (%): Similar to CTR, this shows how your conversion rate compares to the market average. As we know CTR and CVR have a huge impact on ranking, and you can visualize this in your data. When you sort your data by Brand Click Share(highest to lowest) and look at the organic ranking. There is a clear relationship: ➤ Higher click share correlates with better organic rank (makes sense—you’re getting more clicks and likely ranking higher). ➤ Higher CTR and CVR Delta (in green) are often at the top of the list, showing you’re outperforming the market. ➤ Lower down the list, CTR and CVR Delta turn red, indicating underperformance compared to the market average. There are exceptions, of course, like competitor brand names. But the overall trend is clear: ✅ Higher organic rank --> Higher Delta CTR and CVR The Takeaway? If you’re investing in a ranking strategy, prioritize keywords with low click share (poor organic ranking) but high CTR and CVR Delta. These terms have the potential to drive better ranking gains with focused effort. Have you analyzed your SQP data like this? Does your data reveal the same trends as in the image below?

One of My Favorite Use Cases of Search Query Performance! The Impressions to Search Volume Ratio is one of the best ways to leverage Search Query Performance (SQP) data. If you want to quickly assess a product’s visibility and determine whether it appears for all relevant searches, SQP provides the key data points you need. By comparing the impressions your ASIN receives on a specific search term to the total search volume for that term, you can gauge its visibility: ✅ If your ASIN’s impressions are equal to or greater than the search volume, it means the product is highly visible. ✅ If impressions are significantly lower than the search volume, there’s still room for growth—your ASIN needs to rank better for that term. This simple yet powerful analysis helps you identify opportunities to improve ranking and increase visibility on Amazon.

𝗠𝗲𝗮𝘀𝘂𝗿𝗶𝗻𝗴 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 I've been asked this at least 3 times in the last two months. "How do I know that my leaders are improving?" This is where we distinguish knowing from application. 10% of capability comes from learning from formal sources. 20% comes from networks and interactions. 70% comes from application to portfolios and projects. One thing that sets this all apart are data points. Even if I apply skills to my projects, how do I know I did it well? Most large companies have a 360-degree or leadership assessment process in place. So, I'll share my thought process for this in case you are attempting to develop this for your own organization. Step 1: Determine organizational strategy and business outcomes. This is necessary to align expectations of desired behaviors. This is where a Balanced Scorecard can come in handy. Step 2: Assess expectations of leaders. You'll then assess them across leadership behaviors for new, mid and even senior managers. Granularity of differences supports focus and clarity. Often, a list of pre-existing behaviors/competencies are used to make the exercise easier. Validated psychometric tools such as the 16PF help to anchor it to scientific rigor. Organizational psychologists like me conduct surveys to gather insights. Then, focus groups are used to drill down to details information. After that, we'll create categories basedon the information and produce working behavior-based definitions. Step 3: Prioritize the list Now, the leadership team decides which behaviors are more important by way of ratings. Step 4: Build the 360 We then build a 360-degree feedback survey questions. These questions are reviewed for validity. Step 5: Allocate the survey A system specializing in the 360 (there are many) can be used. Feedback Recipient selects 6 to 12 people to rate them. In organizations, to avoid selection bias, leaders of the feedback recipient can review and veto the people doing the rating. Then, the participant does the survey too (self-rating) Step 6: Debrief of survey Usually, participants need guidance from a trained coach who understands feedback requirements. This is to provide grounding and objective input. Often, 360 surveys tend to be met with resistance unless the coach is skilled in facilitating the reflection conversation. Step 7: Action Planning The participant then produces a set of actions for improvement. This plan and the priority of focus should be made known to the feedback givers. Step 8: Pulse Surveys After a designated time (within 6 to 12 month period) a validated pulse survey is set up for the observers to rate improvement in specific behaviors. Step 9: Continued Leadership Coaching, Mentoring and Peer Support A combination of these can be used to enhance development. Step 10: Final Comparison Survey Toward the end of the year, a comparison survey is done to see how the key areas have improved or not. ---

Chemical engineers have spent 144 years mastering industrial scale-up, but climate change demands we accelerate this process from 35 years to a fraction of that time. The authors introduce L-RAMP, merging early-stage research with industrial risk assessment practices. While this approach shows promise, its real test will be whether it can significantly reduce the current 35-year average commercialization timeline. Key questions remain about how academic labs with limited resources can implement such comprehensive assessments. A particularly valuable contribution is their analysis of second law efficiencies across different process classes - from gas separations to electrochemical reactors. This provides researchers with concrete benchmarks to evaluate new technologies against thermodynamic limits. The paper's discussion of surrogate modeling techniques is timely, showing how modern computational tools can accelerate scale-up. It offers a compelling case study of carbon capture technology development at Lawrence Livermore National Laboratory, where this framework led to the rapid optimization of novel packing structures for CO2 absorption. Kudos to Thomas Moore, Drew Wong, Brian Giera, Diego Oyarzun Dinamarca, Aldair E. Gongora, Tiras Lin, Wenqin Li, Christopher Hahn, Sarah Baker, and many more authors from Lawrence Livermore National Laboratory.

Most Amazon agency owners ask the wrong questions when performance drops. They obsess over raw numbers: impressions, clicks, purchases. The problem? Without context, these numbers don’t mean much. Your CTR isn’t “bad” until you know how it compares to the rest of the market. Same with impression share. Same with purchase share. The fix: Search Query Performance Report (SQPR). It shows you your market share at every stage of the funnel: impressions → clicks → add-to-carts → purchases Here’s what that looks like in practice: 𝗦𝗶𝗴𝗻𝗮𝗹 𝟭: 𝗖𝗹𝗶𝗰𝗸 𝘀𝗵𝗮𝗿𝗲 > 𝗜𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘀𝗵𝗮𝗿𝗲 Listing is compelling (title/image/price) Move: Expand reach on the right queries 𝗦𝗶𝗴𝗻𝗮𝗹 𝟮: 𝗖𝗹𝗶𝗰𝗸 𝘀𝗵𝗮𝗿𝗲 < 𝗜𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝘀𝗵𝗮𝗿𝗲 You’re seen but ignored Move: Fix hero image/title/price or mismatched keywords 𝗦𝗶𝗴𝗻𝗮𝗹 𝟯: 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 𝘀𝗵𝗮𝗿𝗲 < 𝗖𝗹𝗶𝗰𝗸 𝘀𝗵𝗮𝗿𝗲 Traffic is there, doesn’t buy Move: Reviews, bullets, offers, PDP assets 𝗦𝗶𝗴𝗻𝗮𝗹 𝟰: 𝗣𝘂𝗿𝗰𝗵𝗮𝘀𝗲 𝘀𝗵𝗮𝗿𝗲 > 𝗖𝗹𝗶𝗰𝗸 𝘀𝗵𝗮𝗿𝗲 Strong when found, not found enough Move: Increase coverage/budget on winners Where SQP really matters, it's the trend reads: -Flat impressions & clicks, but purchase share falling = conversion dip -Impressions + share both down = losing visibility, fix rank + add support -Impressions down, but share flat = category demand decreasing, diversify queries What it won’t tell you: who’s dropping off. For that, use AMC → see who clicks vs who buys and retarget. SQP shows you the “why” behind the drop, not just the drop itself.

If you aren’t using Search Query Performance data in brand analytics to measure and drive incrementality on Amazon, you’re recycling the same bottom-of-funnel demand and cannibalizing organic sales Most brands use the Search Query Performance report to track keywords (if they use it at all) That is a tactical misuse of one of the most powerful strategic tools Amazon has ever released True growth isn't about winning the spend war on high-volume terms; it’s about identifying where your brand is under-indexing relative to the market and capturing the flywheel effect To drive incremental sales through SQP, we use a surgical three-step framework (the last is the most important 1. The Market Share Gap: Identify queries where your Add to Cart rate is high but your Purchase share is low. This indicates a conversion friction point, not a traffic problem. 2 The Creative Pivot: If your Impression share is dominant but your Click share is lagging, your creative is failing to create a pattern interrupt. 3. The Investment Shift: Reallocate ad spend from winner or branded keywords where you’ve already capped organic reach toward potential queries where a slight bump in rank will yield the highest marginal return. In fact you can test pulling back spend on branded keywords and winner keywords where your conversion share is high and see whether your conversion share decreases If your share doesn’t decrease, your spend wasn’t incremental Stop looking at SQP as a rearview mirror for past performance. Treat it as a roadmap for where your next dollar of incremental profit actually lives It’s the difference between using first-party data to find new customers and paying Amazon a tax to keep the ones you already have

Forecasting/Budgeting for Procurement: When forecasting and budgeting procurement costs - especially direct material costs - several factors need to be taken into account. The starting point is an understanding of the underlying cost structure. The first step is to identify the key cost drivers such as raw materials (commodities) and other blocks such as wage or process costs. The entire procurement portfolio should be segmented into categories based on comparable cost drivers. Only through this structuring is a targeted and reliable budget planning possible. The application of the identified cost drivers then forms the input for the procurement budget. To validate these approaches, it is advisable to analyze historical cost trends. The analytical forecast method is used to estimate the price development of central cost blocks. It starts at the macro level with economic and political framework conditions. These overarching assumptions must be agreed with management, as they serve as the basis for all further derivations. They are then refined in a multi-stage process - starting with global commodity markets and industry-specific developments through to product-specific factors such as specifications, batch sizes and delivery times. This results in a well-founded, comprehensible forecast that provides a solid basis for the procurement budget. Another key aspect is the choice of planning approach: top-down or bottom-up. In the top-down model, management defines financial targets that are cascaded downwards. With the bottom-up approach, planning takes place at operational level, based on specific requirements and detailed knowledge. In practice, a hybrid approach is often recommended in order to combine both strategic control and operational realism in the planning of direct material costs. Finally, basic principles for budget and forecast planning in procurement must be observed. These range from avoiding unrealistic expectations and focusing on relevant cost drivers to clearly assigning process responsibility. It is particularly important to emphasize that budget cuts should never be made purely top-down without defining responsibilities for individual material costs and savings projects. Only methodically sound and organizationally embedded planning can sustainably strengthen the role of procurement and lay the foundation for efficient decisions and strategic development. Dr. Mario Büsch, PURCHNET.de

Maintenance Cost Analysis Maintenance cost analysis is the detailed evaluation of all expenses related to managing, preventing, and resolving breakdowns in machinery or plant equipment. Costs can be divided into three main categories: 1. Direct costs: immediately linked to maintenance activities, such as labor, consumables, spare parts, and tools. 2. Indirect costs: include general management expenses, auxiliary equipment, IT systems for monitoring, and support staff. 3. Induced costs: refer to production losses due to equipment downtime, delivery delays, general inefficiencies, and, in some cases, collateral damage caused by failures. --- 🧭 How to Perform a Cost Analysis The first step is to map all assets (machines, systems, production lines) and define their useful life and criticality. Then, distinguish between ordinary maintenance (scheduled, recurring tasks) and extraordinary maintenance (unexpected or major repairs). Historical data should be collected on costs, intervention times, and failure frequency. Key performance indicators (KPIs) should be used, such as: MTBF (Mean Time Between Failures): average time between two failures. MTTR (Mean Time To Repair): average repair time. Availability: the percentage of time a machine is operational versus total time. --- 💸 Main Cost Categories Preventive maintenance includes recurring tasks such as lubrication, cleaning, inspections, tightening, and scheduled replacement of worn parts. These costs are generally lower but more frequent, helping to reduce unexpected breakdowns. Corrective (extraordinary) maintenance involves major repairs, replacement of critical components, and urgent interventions. These can be very expensive, especially when they cause long downtimes or affect valuable equipment. There are also downtime-related costs: when a system stops working, work hours are lost, delays pile up, and entire orders can be compromised. --- 📊 Preventive vs Predictive Maintenance Preventive maintenance is based on a planned schedule: actions are taken before failures occur, through regular inspections and replacements. This strategy can reduce emergency costs by up to 40% compared to reactive approaches. Predictive maintenance, on the other hand, uses sensors and artificial intelligence to monitor machine conditions in real-time. It can detect early signs of failure and intervene before problems arise. Companies using predictive maintenance often report a 25–30% overall cost reduction and up to 70% less downtime. --- ⚙️ Tools for Cost Control To manage costs efficiently, it's recommended to use a CMMS (Computerized Maintenance Management System). This type of software helps to: schedule interventions, track costs per asset, manage spare parts, generate reports and KPI analysis. Additionally, good cost accounting practices help assign expenses to the correct production units, highlighting areas in need of optimization.