AI Agents for Customer Segmentation

Based on our market survey knowledge regarding autonomous vehicles, compare their sentiment towards autonomous mobility. Compare two cohorts (18–34-year-olds and 45–64-year-olds) who live in North America, who have annual household incomes of more than $50K and who currently own a vehicle. State which of the two cohorts has the most positive overall sentiment and state their weighted averages. Weight the survey results as follows: - Very Negative: 1 - Negative: 2 - Neutral: 3 - Positive: 4 - Very Positive: 5 Round all calculations to the first decimal place. Print your findings as a reply to me here.

What will the year 1 revenue across EM1, EM2 and EM3 be if we launch a solar system for households at $6,000 price point? Assume the company will be able to acquire 7.5% of users who are planning to install the solar system soon and have their maximum willingness to spend more than the price of solar system. The number of households across markets is as follows: EM1 - 200,000 EM2 - 400,000 EM3 - 700,000 Use the survey data for these markets for estimation. Report your responses here, and round revenue numbers to nearest integer.

We have conducted surveys across 3 emerging markets - EM 1, EM 2, and EM 3 to establish market potential for solar systems. 1) Based on the survey-related documents for EM1, report the percentage of small businesses that don't use solar systems in EM1 2) There were errors in the responses we received for EM1. The agency has shared an updated responses sheet that captures the correct responses for users where there was an error. Keep the original information in case the cell is blank. Also, there might be new users in this sheet. Add those users to the original file. What percentage of small businesses don't use solar systems in EM1 after accounting for this new information? Return your answers as a message here. Round all percentage values to two decimal places.

The client wants to do market sizing for an autonomous ride-hailing service in Europe. Estimate the market size (in $ Mn) based on the lifetime value (LTV) and adoption rate (%) of users. - Use information available in the user survey, as well as additional information received from the agency. - Assume the total population of Europe is 700 million and the survey is a representative sample set of the entire population. - Take full LTV for market size estimation. Assume adoption % is only a function of the user's likelihood of using an autonomous ride-hail service. Present your findings to me as a message. Round the final numbers to the nearest integer.

Let's do an analysis and find out what capacity of solar systems will be required in the Emerging Market 1 (EM 1) in the next year (in kW). We should also find out what the split of this demand will be between Urban, Semi Urban, and Rural regions in percentage terms. For the analysis, please use the Solar EM1 user survey and responses. Let's assume all the users who don't have a solar system installed currently but are very likely to install one in the next 12 months will end up purchasing a solar system, and all other users will not purchase in the next 12-month window. We should also assume that the required capacity will be equal to the average of the capacity range they intend to install, and a capacity of 0kW for users who have mentioned <1KW or 'not sure'. For users who mentioned >10kW, let's take the average capacity of 15kW. Total population in terms of user type and location is captured in the demographics file. Round capacity to the nearest integer (kW) and percentages to two decimal places. Share what you find as a message to me here.

I need to compare our results from the autonomous vehicle survey and the survey questionnaire against the attached 2025 launch targets. For these three metrics, calculate the percentage points gap between what we measured and our target: 1) What percentage of European respondents expect AVs to go mainstream within the next 5 years (inclusive of 5 years), compared to our Europe target? 2) What share of total global respondents would pay over $100 per month for AV subscriptions versus our $100+ tier target? 3) What is our high-trust percentage (scores of 4-5) compared to the consumer trust target? Please provide each answer as a reply to me in here, rounded to whole numbers.

Evaluate the maximum total potential liability for Star Tankers International Ltd. compared to Cooper/Jeffries Energy Corporation under the Oil Pollution Act for the incident with the M/V Red Room. Draft a message to me here, stating which entity has a greater liability if found to be the sole responsible party for the incident with the M/V Red Room. Calculate and include the potential maximum liability for each party. Give the values in 100s. Use the BLPL Claim Summary, the relevant legal authority, and the Hull and Machinery Survey for your analysis.

I want to forecast total annual revenue for ServiceNow based on the competition's price benchmarking information available to us. For this analysis, calculate revenue for each of their tier/plan (like ITIL User (Base), ITOM Module etc.) based on the attached information. Optimal price multiplier refers to the percentage points above the minimum price point where the tier/plan will be priced. Report total forecast revenue in $m, rounded to one decimal place. Print your response here.

Can you use the files on Deal Win Rate Target and the transaction history in Brightpath Deal Transactions to determine the Implied Deal Volume required to reach the Expansion target share of revenue for the 'Upper Mid-Market' segment? Apply the target expansion share from the Target Revenue Mix Strategy to the segment's total realized revenue in the deal transactions dataset (from "Won" deals only), to get the specific number of deals the sales team must close, based on the actual average size of an Upper Mid-Market Won expansion deal. Also, calculate the Required Pipeline Capacity for the 'Europe - Enterprise' sector, assuming the target ARR is equal to the sum of all "Won" deals in the Enterprise segment in Europe in the dataset, and assuming the sales team achieves the Target Win Rate for Europe Enterprise deals. Round numeric outputs to the nearest whole number and round currency outputs to the nearest dollar. Return your findings as a message to me here.

Using Brightpath's Discount Approval Logs, review each approver’s total score and rank. Reply to me with a short message here, outlining your findings. Scores are determined using four criteria: 1. Violated Policy Threshold: Score 1 goes to the approver with the most deals exceeding the policy threshold; score 4 goes to the fewest. Scores 2–3 follow their ranking. 2. Negotiation-Based Discounts: Score 1 for approving the most negotiation-driven deals exceeding the threshold; score 4 for the fewest. Scores 2–3 follow. 3. Pilot-Program Discounts: Same logic as in #1, scoring based on deals exceeding the threshold due to pilot-program discounts. 4. Level of Approval: Score 1 for approving the fewest CFO-level deals within policy; score 4 for the most. Scores 2–3 follow. Notes: - Round all scores to the nearest whole number. - Ties receive the same score (e.g., both highest = 1, both fewest = 4, middle = 2). - For ties in total score, use Director-level approval counts from criterion (4) as the tiebreaker.

Can you use the feature usage file and identify the average adoption rate percentage, average monthly usage, and average retention impact score for each tier except Team? Print the output for me here.

We need to redo the analysis of the new survey response dataset. Can you re-calculate the standard deviation for Brightpath Software's efficiency dataset? Using both the recalculated average efficiency score for Brightpath Software (by all Brightpath users) and the average efficiency score for Brightpath Software by only Brightpath users who ranked AI capabilities as top priority, please also calculate the fraction of a standard deviation that the two scores differ by. Round all final answers to four decimal places. State the output directly to me here as a reply.

Refer to the latest (v1.0) customer segmentation data and the final pricing model. Calculate the % change of Customer Segmentation expected revenue (use target customers and average ARR for the pricing) relative to Configuration A expected revenue (use target customers and effective pricing) for each segment. Account for churn in both scenarios based on each scenario's respective data source. Reply with your results here, with final numbers rounded to the nearest 0.01%.

Using the latest pricing version, the revenue data by segment, and the discount approval logs, determine the average discount percentage for each of the Business and Growth tiers separately (use the midpoint of the Company Size range as the user count). Then, calculate the %variance of each tier's discount relative to its average policy threshold. Provide the discount for each tier (rounded to the nearest 0.01%) as well as the variance from policy threshold (rounded to the nearest 0.01%) directly here as a reply.

For 2024 Won/Upsold deals with NCV ≥ 50k, determine the policy-friction risk per deal as NCV × Discount × tier multiplier × tier PFI, where tier PFI is the benchmark mix-weighted sum of Software Customer User Satisfaction Survey Results. After you rank the regions by the total policy-friction risk, please give me the top 3 regions and their respective total policy friction risk (in $M, rounded to three decimal places) in any order. Refer to the following three files: 1) Deal Transactions sheet, 2) the Customer User Satisfaction Survey Results chart in the software pricing trends doc, and 3) the attached policy mix and multiplier charts. Give me your answers as a reply right here.

Use the churn and WinLoss data, and assume the following: - Competitor Loss Ratio = (Total Contract Value of Lost deals/Total Contract Value of all deals) - If competitor-lost deal value exceeds the retained renewal ARR for that tier: Increase the churned ARR for that tier by 15% - If retained renewal ARR exceeds competitor-lost deal value: Reduce the competitor-lost contract value by 50% - Severity Score = Adjusted Competitor Pressure + (Adjusted Churn Rate x (Adjusted Competitor Lost Value/ Original ARR)) - Competitive Exposure Multiplier = Highest Severity Score ÷ Adjusted Competitor Pressure of that tier - Scenario Sensitivity Factor = Highest Severity Score * (Adjusted Competitor Pressure + Adjusted Churn Rate) Answer the following questions: 1. Which pricing tier has the highest severity score? 2. What is the highest severity score? 3. For the tier identified with the highest severity score, what is the single most frequent competitor appearing in lost deals? (If multiple competitors, return the alphabetically first) 4. For the tier identified with the highest severity score, calculate the Severity Score to Adjusted Churn Rate ratio 5. For the tier identified with the highest severity score, calculate the Competitive Exposure Multiplier? 6. For the tier identified with the highest severity score, calculate the Scenario Sensitivity Factor? Return the responses to the questions right here as a message. Round all final outputs to 2 decimal places.

Using the estimated market share chart and Brightpath customer segmentation, please calculate the potential revenue for the SMB Accounting segment if it achieved the target share. Include an analysis stating the percentage point difference (rounded down) between Target and Actual Enterprise share for Consulting Firms and the revenue gap (to the nearest dollar) for Mid-Market IT Services. Return your findings in a short message here

Using the discount approval logs and the KPI chart, I'd like to get one number that tells me how risky our discounting behavior is right now. Looking at deals where the final approved discount exceeded policy, classify the severity using the chart, apply the risk sensitivity, and calculate the revenue exposure. Assume Policy Breach % is the difference between final approved discount and the policy threshold. Return to me a message with the Policy Breach Stress Index (rounded to 2 decimal places), which is the average revenue at risk per policy-breaching deal.

I would like to analyze the current proportion of Brightpath Churn and Annual Recurring Revenue. 1. Based on the ARR from the discount approval file and the Churned ARR from customer segmentation file, calculate the required reduction in $ in Churned ARR for every Pricing Tier whose current Churned ARR proportion exceeds 0.5% of its Overall ARR, so that the proportion for that tier is reduced to exactly 0.5%. 2. Calculate the number of additional deals (each valued at the average ARR per deal from discount approval report) required to meet a target of 0.5% Churned ARR as a percentage of total ARR. Print your response here. Round final dollar amounts to the nearest whole dollar. Round the number of deals up to the nearest whole number.

Using the latest renewal / churn data (v1.0) and pricing log, identify the average seat changes (increase or decrease) by Brightpath customers from every renewal with seat changes. When doing the calculation, change v4.2 pricing effective start date year to 2023 and make use of monthly price per user to calculate the seat changes. Present your result, printing it right in here to the nearest 0.01.

Use the baseline seat utilization data against the attached 2025 strategic targets for the following two metrics. 1) What is the Seat Purchased Surplus (Actual Seats minus Target Seats) for the Medium utilization band in the Enterprise tier? 2) What is the difference in percentage points between the Target High (>80%) share for the Business tier and the Actual share? Please provide both answers as a reply here, rounded to the nearest whole number.

Using the state of fashion beauty 2025 report: 1. Identify the percentage of global beauty sales in 2024 occurring through the combined channel of grocery/big box, drugstores/pharmacies and department stores, rounded to a whole number. 2. Calculate the expected relative percentage change in that combined channel's share of global beauty sales in 2030 compared to 2024, rounded to one decimal point. 3. Assuming total global beauty sales grow at 5% CAGR from 2024 to 2030 and total sales in 2024 are $593B, calculate the net dollar change in sales for this combined channel by 2030, rounded to the nearest billion. Please return your answer straight here.

Using the State of Fashion Beauty report for 2024 sales and sub-category shares, alongside the attached data for Mass Market vs. Premium share by sub-category, calculate the difference (in $M) between the North American Mass Market and Premium segments for Skincare and Fragrance. Assume North American breakdowns by sub-category and segment match those of the global benchmarks. Report values as positive if Mass Market is larger and negative if Premium is larger. Using the data from the internal financials spreadsheet, determine Lumea's North American market share of the Premium segment of each sub-category (Skincare and Fragrance). Assume North American revenue percentages are uniform across all segments, all Lumea sales are Premium, and Lumea's Skin and Body units fall under Skincare. Round currency outputs to the nearest $0.1M and round percentage outputs to the nearest 0.1%. Provide your findings to me here in a reply.

Using the State of Fashion Beauty report for June 2025: 1. Identify whether consumers are more likely to save or splurge on facial serum and brow product. 2. State the net intent percentages of consumers who are likely to save or splurge for each of those two products, rounded to the nearest full percentage. 3. Calculate the average net consumer intent percentage for each of the three main categories, including only products with a positive net consumer intent percentage. Round to one decimal point. Report which category customers are least likely to splurge on. Just write your response straight here for me.

Which product has the highest DTC conversion rate, defined as units sold divided by views on website across all quarters? What was its conversion rate, rounded to two decimal places? Assuming no change in website views, how many units of this product would Lumea need to sell in order to reach an average conversion rate across all products of 8.5%? Use the Q1-Q4 DTC performance files for the analysis. Return your final outputs as a message right in here.

Using Lumea's product portfolio (Q4) file, can you calculate the weighted average Q4 gross margin (%) per product category? Percentages and percentage points should be rounded to one decimal place and $ values should be rounded to the nearest dollar. 1. Compare each percentage to the midpoint of the industry 2024 gross margin range by category (from Lumea's beauty market analysis). Report the difference in percentage points. 2. For any category where Lumea is below the industry midpoint, assume Lumea closes half of the gap to the industry midpoint and revenue stays constant. Estimate the incremental Q4 gross profit opportunity for each underperforming category. Reply back to me with your findings here.

Using the June State of Fashion report, identify the top benefit consumers look for when shopping for beauty in Japan and South Korea. Print out the percentage of customers from each of Japan and South Korea who rated this their top benefit, rounded to the nearest percentile. State the brand value that had the highest percentage of "Very Important" responses from the survey, and determine whether this brand value is the same as, or different from, the top benefit sought after in Japan and South Korea identified above. Treat variations in terminology as the same factor if they describe the same functional benefit or product standard. Respond with a message for me in here.

Please use the State of Fashion Report to identify the top three regions (by global beauty retail sales market size) in 2024. Also, please provide 2019 and 2030 market size for these regions, using the base case assumptions in the report. Report the top regions by 2024 rank. Report final numerical answers in $Billions for 2019 and 2030, adhering to one decimal place. Respond to me with your findings here.

Prepare a memo which identifies the delta (in basis points) between McKinsey (June State of Fashion report) and Lumea's (final market sizing model) 2030 projections for Global beauty sales by channel %. Match channels between Lumea and McKinsey's projections as follows: - Online_marketplace = Ecommerce - Mass_retail = Grocery / Big Box - Specialty_retail = Specialty / Mono Brands - Pharmacy = Drugstores / Pharmacy - Department_store = Department Stores Respond the information in a new Document docx. Give final answers rounded to the nearest whole number.

Calculate the projected Operating Profit contribution ($ Millions) from the Europe Growth segment from updated growth bridge charts, assuming this new revenue achieves the 2030 Operating Margin target. Also, calculate the Total Incremental Gross Profit ($ Millions) generated by the combined International Expansion, assuming these new markets achieve the 2030 Gross Margin target. We need to use the results from the Lumea Financials Report and the updated Strategic Growth Bridge (attached) from the client. Output all financial values rounded to whole numbers (no decimals). Print your answers right in here.

Can you please go through the state of fashion beauty report to calculate the difference in average spend for US consumers for 2025 vs. 2023 for each of the beauty sub-categories. Do weighted average by considering mid-points of spend ranges and taking the same spend number for the highest spend bucket. Report the final spend differences in $ to one decimal place. Print your response straight here to me.

Using Lumea's 2025 financial report and the industry operating margin benchmarks in Lumea's beauty market analysis: 1. Identify the 75th percentile operating margin for DTC beauty brands (assuming the benchmark range represents a uniform distribution). 2. Calculate the percentage point difference between Lumea's 2030 operating margin target and the 75th percentile DTC benchmark 3. If Lumea were to operate at the 75th percentile DTC operating margin in 2030, calculate the implied operating profit and how that compares with Lumea's current 2030 target. Return all results to me as a reply right here. Express all $ values in millions to one decimal place, and round % to one decimal place.

Using the State of Fashion report and the customer survey, for the Drugstores/pharmacies, E-commerce, and Department Stores channels, return the percentage point difference between the percentage of survey respondents who ranked each channel in their top 2 channels for purchase and the 2024 global market share. When calculating the survey percentages, exclude the 18-24 age group and any non-responses. Report a positive value if the survey share value is higher than the market share value, and a negative value if the survey share value is lower than the market share value. Write back to me with what I want. Round final numbers to one decimal place.

Use the State of Fashion Beauty report for June 2025 to identify which 3 countries have the most customer engagement in live social media shopping events, measured by the percentage of customers who have ever attended a beauty live shopping show. Calculate the percentage of customers who have ever engaged in live shopping shows in these 3 countries, and then state the percentage of customers who have ever engaged in live shopping shows in the US. Round answers to the nearest whole percent and do not round any intermediate calculations Respond with your findings in a new document that you create.

I need additional information for North America's sales for 2024, both online and In-store. Values are based on the Nielsen report and State of fashion beauty report. Assume that the US numbers split in the report on stores are same as for North America for this analysis and take base case growth rates. Use state of fashion report for the 2024 numbers. Also, I need you to forecast these numbers to 2030 based on average share split of these two channels for the last 4 years (including 2024) as a proxy. Note that the total numbers in Nielsen's report has a discrepancy and we want to use only the numbers for split on the page with 6 year historical numbers. Reply to me here with your values, rounded to the nearest million USD.

Can you take a look at Lumea's 2025 financial report and the global beauty market analysis and help me double check their margin target? Specifically: a) If Lumea were to reach the midpoint of the premium-player operating margin range (instead of their 2030 target), what would their operating profit be in 2030? b) What's the delta with Lumea's 2030 operating profit target? c) How big is that delta as a % of Lumea's 2030 revenue target? Provide all your answers directly to me here as a message. Round the dollars to the nearest million and % to one decimal.

Using Lumea's market sizing model, calculate the 2024 percentage share for Department Stores and DTC Sites in North America and only give me the following answers: 1. Identify the benchmark share for those 2 channels shown in Lumea's beauty market analysis. 2. Calculate the percentage-point difference for these 2 channels between the model-implied share and the benchmark share shown in Lumea's beauty market analysis. 3. Calculate the dollar delta (in $B) for Department Stores and DTC Sites between Lumea's market sizing model and the benchmark. Return all this information to me in here, with the final numeric values rounded to one decimal place.

Using Lumea's product portfolio datasets (Q1 through Q4), calculate the weighted yearly profit margin (%) for the Body Care category and return only the following answers: 1. The difference, in percentage points, between Lumea's weighted annual profit margin and the midpoint of the industry 2024 profit margin range for the Body Care category, shown in Lumea's market beauty analysis. 2. The absolute dollar value that this gap represents, based on annual revenue. 3. The annual profit opportunity if Lumea were to close 30% of this margin gap. Print back the answers I requested. % and % points should be rounded to one decimal place, and $ to the nearest dollar.

I'm trying to get a sense of which HarFeast employees are most ready for the digital training rollout. Can you pull the workforce survey data and identify all employees who are above their role type's median readiness score, willing to pilot new tools, willing to spend >2 days in training with dedicated training time, and above the overall median digital comfort score? Once you've identified that "high-priority" group, can you tell me: 1. How many employees qualify and what percentage of the total workforce that represents 2. How many hours these employees spend on manual entry / searching / fixing errors, and what percentage of total manual entry / searching / error fixing hours that represents 3. How many "high-priority" employees there are for each role type Just give me the final answers as a reply in this box, rounded to one decimal place.

I want to quantify the average annual productivity loss at a cost level for each employee in each primary role based on the sum of average hours spent doing manual entry, searching data, and fixing errors. Then, I want to calculate the total productivity loss cost HarFeast faces every year, company-wide. Note that the survey responses represent one week of work. Report your final answer as a message here, rounded to the nearest dollar.

The client sent us employee wage data (attached), so we need to update our assumptions in the financial analysis section of the survey analysis report to display the updated annual productivity loss figures (in 000s). Find the average hourly salary of employee roles and use that to update the annual productivity loss estimate (rounded to 1 decimal place). Assume average hourly wages in the data are fully-loaded costs. Assume the following activities are non-productive: manual data entry, searching for data, and fixing errors. Report final answers here, written out in a short message.

Use the v1 version of the survey responses to identify the number of respondents who received any kind of training on digital tools. Of those respondents, return the percentage of respondents for each training quality rating. Reply back here to me.

Would you please review the 50-State Survey of Telemarketing Laws document and identify whether or not there are any states missing from the list in the document? Then, send me the list the of missing states that have enhanced senior protections. Write out your reply in here.

I want to inform client discussions around what action to take regarding sourcing of materials and the speculated tariffs. Can you summarize averages of the following 3 data points pertaining to tariffs from the latest group of supply chain expert witness interviews: 1. % of competitors that are shifting sourcing to domestic US material suppliers. 2. % of competitors that are exploring international diversification. 3. Average % increase in material costs for companies that moved sourcing exclusively to the USA. Take an average of the values identified for each data point, and adjust for outliers by removing any values that are more than 1.5 population standards deviation from the mean. Also only consider data from expert witnesses with a seniority level of Director, VP, or Chief in their job titles. Write your reply straight here. Your final outputs should be rounded to the nearest .01%.

Calculate and report the following metrics: 1. The Overall Vitamins Purchase Intent Score for each buyer segment. 2. The buyer segment with the highest average purchase intent score for Vitamins 3. The Vitamin Purchase Intent Index 4. Whether the Vitamin Purchase Intent Index Rating is desirable or undesirable Vitamin Purchase Intent Index is a composite metric calculated as: Average of Vitamins Purchase Intent Score across all segments × Segment Weight (i.e., Average of Population Size of buyer segment with highest purchase intent score for Vitamins × Average of Loyalty Score of buyer segment with highest purchase intent score for Vitamins) x PureLife Awareness Factor (i.e., Survey Respondents who said ‘Definitely yes’ or ‘Probably yes’ to trying PureLife divided by total Survey Respondents who have heard about PureLife). Vitamin Purchase Intent Index Rating is determined as: Vitamin Purchase Intent Index of >=8 is considered 'Desirable' other wise is considered 'Undesirable'. Base your analysis off the survey data and buyer behaviour data. Percentage values must be formatted to the nearest 0.01%. Dollar values must be formatted as US$ with 2 decimal places All other number calculations should be formatted to two decimals. Post your answer straight here.

Using the consumer perception data and pricing details from the buyer profile, determine how many survey respondents in each market have never heard of PureLife and are willing to pay more than a 30% premium above average price for trusted brands. Give it as both a count and as a percentage by market. In which of the three surveyed markets would these customers be willing to pay more than $20 for a trusted brand? State both the market(s) and the price(s). All intermediate and final calculations should be rounded to three decimal places. Return all of the outputs to me as a message in here.

We need additional analysis for 2025 cost per activated member (CPAM) under 3 scenarios. I want you to edit the PnL workstream draft deck with this new information you'll calculate--> the 2025 CPAM ($ per activated member) for As reported, Profitable acquisition, and Industry normalized. Use the loyalty marketing memo for total 2025 CPAM and loyalty + reactivation spend, the PnL master profitability spreadsheets file for active and segment member volumes and segment , and the segment profitability assumptions spreadsheets file for segment level economics. Use marketing_benchmark.pdf for industry activation benchmarks. First, report the "as-reported CPAM" exactly as documented in the materials, corresponding to the 2025 full year projected CPAM. Second, calculate the "profitable acquisition CPAM" by focusing only on traveler segments that generate positive EBITDA per member. Allocate Summit's total 2025 loyalty and reactivation spend across segments based on each segment's share of total customer acquisition cost as calculated using the segment level numbers. Determine each segment's activated members using an assumed activation rate of 53%. Use loyalty and reactivation cost and active members to compute the the total cost per activated member across only profitable segments. Third, for the industry-normalized case, use the profitable segment scenario analysis and replace Summit's actual activation rate with the benchmark activation average from the industry data in marketing_benchmark.pdf. The benchmark activation average should include all competitors in the file and leverage midpoints for ranged values. Round all CPAM values to two decimal places for the final deck.

Using only 2024 values from IHG's annual report, tell me: 1. Calculate the room-weighted average occupancy rate for IHG across all of its hotel brands, globally. 2. Using regional occupancy rates and ADRs by brand, calculate IHG's global room-weighted average RevPAR. 3. Calculate IHG's room-weighted average RevPAR as a % of the average RevPAR for the industry, globally. ## Notes 1. Use Fee Business data where possible. 2. ADR refers to the 'Average Daily Rate'. 3. Use the definitions of RevPAR and ADR as stated in the annual report. 4. Where ADR or occupancy data for a brand is not available in a given region, assume the value for that brand is equal to the room-weighted average across all brands with available data within the relevant region. 5. Derive global RevPAR using occupancy rates and ADRs by brand, rather than the report’s stated RevPAR values by brand. Report occupancy rates as a % and RevPAR as a $ value. Report all numerical values to 1 decimal place. Do not round calculation steps. Print out the values to me in here.

What are the projected 2026 aggregate free night bookings for Marriott, Hilton, and Hyatt in New York, London, and Tokyo combined? Assume all of Summit's 2026P free night certificate redemptions from the PNL (in thousands) are projected to be exclusively in either New York, London, or Tokyo. What would Summit's expected free night relative market share be in each market? Provide values to one decimal place. Express RMS as a percentage. Print your reply here.

Give me the scores for Summit’s segments, using the Summit-Specific Survey. Your scoring system must follow these rules: - For the average satisfaction with status benefits from the file “14. Summit_Tier_Status_Experience_Raw_vtest.xlsx”, grant 1 point if it is greater than 2, grant 1 point if it has had no status loss in the past 2 years. Also grant 2 points if more than 23% of respondents in the segment have a current tier equal to "none". - Deduct 1 point if the loyalty score from the file “9. Summit_Loyalty_Outcome_Summary_Raw_vtest.xlsx” is below 2 but add 2.5 points if it's above 2. Present the results in a new one-slide deck. Give all answers rounded to one decimal point.

Determine which years have the highest and lowest average stay lengths for Bronze Tier members. For each of those years, report the average stay length for Bronze Tier members who stayed at a Summit Express property, segmented by whether they used a co-branded credit card or not. Use the latest merged member profile data spreadsheets file and the stay & booking data spreadsheets files for the analysis. Use check-in date to determine the date of stay. Return your answers to me here, with numerical values rounded to the nearest 0.1.

Can you use the Summit's business case and provide the revised sum of net cash flow ($m) for scenarios 1, 2, and 3? Apply the scenario-specific benefits multipliers provided in the attached file to adjust the annual benefits relative to the base case, while keeping all costs unchanged. Can you make sure to round your final answers to two decimal places? Provide your answer straight here as a message.

Using 'Accor Presentation.pdf', 'Hilton Annual Report.pdf', 'Hyatt 10K.pdf', 'IHG Investor Presentation.pdf', and 'Marriott-2024-Annual-Report.pdf': 1. Calculate, for each of Summit's key competitors, the total number of available room nights in 2024. 2. Calculate the standard deviation of available room nights for Summit's key competitors. 3. Determine whether or not Summit's available room nights is at least one standard deviation above the mean of its key competitors'. Notes and Assumptions 1. Summit's key competitors are Accor, Hilton, Hyatt, IHG, and Marriott. 2. Assume Summit had 741,237 rooms as of 31 December 2024. 3. Use each competitor's latest reported room count in your analysis. 4. Assume all rooms were available every day of the year. 5. Include all owned, leased, managed, and franchised rooms as well as rooms specifically called out as "unbranded" or "strategic partner"/"exclusive partner" in the room count. For those not explicitly labeled in this fashion, use stated room totals. Output your response directly to me here, reporting the available room nights and the standard deviation of available room nights in millions. Report all numerical values to 1 decimal place.

Determine CAGR for total points sold over the entire covered time period from the Profit and Loss Master Profitability Workbook. Apply that growth rate to average points per member from the Member Points Summary (Balance & Expiry) Sheet to project points per member (PPM) per year, 2 years out. Assume the average in this data set is the year end 2025 average. Return the state Average PPM and CAGR of points sold for the base year and each of the two years out in a New spreadsheet. Round to two decimals.

Was the data Ecan provided during the meeting (noted in the team sync-up from 11/21/25) on the number of members that have stayed during Q3 2025 correct? Use our data from the 11/17 stay and booking data to assess it. Report here if Ecan provided the correct data or not. If not, give the correct value.

Estimate the contribution (in $M) that Summit's loyalty program members will make to its 2025E Program EBITDA in $ millions. Assume that Summit's loyalty program members will contribute, as a % of 2025E Program EBITDA, an amount equal to the member-weighted average % of stays by members in the most recent financial year across Hilton, Hyatt, and Marriott. Use our profitability workbook, the 10Ks for Hilton and Hyatt, and Marriot's annual report for 2024. - If data is unavailable for any of the three competitors listed, discard that competitor in your member-weighted average % of stays by members calculation. - If a range is stated instead of an exact numerical member count (e.g. we have 'over 10 million members') use the headline value ('10 million'). Write your answers out here. Report all numerical values to 1 decimal place.

Prepare a new memo, and put it a new document file you make. I will be sending it to James Brown, CEO of Summit, on behalf of The Strategy Team. It should outline the total cost of labor for each phase of Summit’s turnaround effort based on the operational gantt RACI. Effort is calculated using 20 workdays per month and 8 hours per workday. The duration of the task “Property-level benefit alignment” must be adjusted so that its total duration equals the combined durations of all tasks beginning in Month 1. Using the same data, identify which team contributes the greatest total effort, assuming that teams tagged “R” in the RACI table generate 70% of the total effort required for each task, teams tagged "A" generate 20%, and the remainder is split evenly between remaining tag categories. Include the name of the team and the value of their total effort in the memo. Then, assuming all employees working on the turnaround effort are impacted by the launch staff training program (as outlined in the loyalty turnaround strategy) and that training only applies to these employees, calculate and state the average amount of time each employee from the most contributing team will need to dedicate to efforts where they are tagged as "R", rounded to three decimals. Assume that 50% of employees impacted by the launch of the training program belong to the most contributing team. Present values as integers unless I told you otherwise.

You are analyzing the results from the customer survey. The survey asked what Helios' top 3 capabilities are. The initial results came back incomplete, and there are now additional responses available to analyze (attached). Your goal is to calculate what percentage of all total responses each capability received. Only calculate these values for respondents who responded "Slightly Important" or "Not Important" for question 2. You may also utilize the survey questions file for reference. Round final answers to two decimal places please. Send your reply here.

We want to understand the most important investment areas for pureplay EV respondents. The previous file contained incomplete information, so please use the newly attached updated file. Identify the #1 and #2 most important investment areas for pureplay EV respondents. Using only respondents who selected those two areas as their #1 and #2 priorities, calculate the average score for: (1) Relevance of legacy suppliers and (2) Level of redesign required. Then compare these average scores to the averages calculated using only Auto Parts respondents. Round all final scores to two decimal places and output the results to me here as a short message.

Use the Helios customer survey to calculate average NPS scores for Pureplay EVs and EV/ICE. - If the average is 2 or below, they are promoters. They are passives if they are between 2 and 3, and detractors are above 3. - NPS is defined as: (% Promoters - % Detractors) x 100. Based on these values: If the overall NPS score is above 20, state that Legacy manufacturers have a competitive advantage. If it is below 20, state that Legacy manufacturers do not have a competitive advantage. Make sure to note the count of promoters, detractors, and passives. Print your answers here.

The LATAM market customer count is expected to continue growing at its 2024-2025B CAGR. Complisure could capture a quarter to half of the two largest LatAm players’ latest share of customers if they were to expand into that the region. I am defining the largest LatAm players by their number of LatAm customers. What would you forecast CompliSure’s 2030 revenue range, given this upside? Remember: - Refer to the five-year forecast file for the original 2030 revenue estimate. - Assume each competitor's contract size is the same for all of their customers based on 2025B figures and does not change over time. - Round the answer to the nearest thousand. Reply to me with your answer back in here.

For each of the four competitor firms use use our financial dataset for 2016 - 2025 to calculate the average customer acquisition cost (US$) for 2016-24 and identify the competitor with the highest churn rate (%) in 2024 and calculate the percentage difference relative to their average between that competitor with the highest churn rate (%) and each of the remaining competitors. Round all percent final answers to the nearest 0.01%. Round $ amounts to the nearest whole $. Print your answer to me here as a message.

Calculate overall customer sentiment score using the customer surveys. For each section, compute the section sentiment score as the simple average of the available question-level scores within that section (omit any question with missing scores). Then, calculate the customer sentiment score as the weighted average of all section sentiment scores, using the weights specified in the chart from the attached score guide. For the NPS score, adjust for the scale difference by using 50% of the average NPS value before including it in the weighted aggregation. Round your final answer to 4 decimal places, and reply back to me with it here.

Based on the attached table, with the asking price for each company, which competitors would have a higher 2024 ARR multiple than CompliSure even if their SMB customer count declined by 75%? State the updated 2024 revenue ARR multiple for each of those companies. Use the KPI dashboard, expanded financial dataset, and competitors client share files along with the attached files for this analysis. You can write your response back to me in here.

Please check how KO and MDLZ differ in expected upside once we apply the ESG and GLP-1 filters and account for each investor’s maximum allowable ESG risk level. Use the survey data and the ESG risk thresholds to determine which respondents are eligible to hold each company. Then calculate the confidence weighted average and standard deviation of expected annual return for KO and MDLZ. Show each company’s weighted average and standard deviation of expected annual returns. Round only the final results, going to two decimal places.