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Project Justification

Maximise RevPAR through in-depth analysis of Rooms Segments and Business Mix EAME Project #84848 Project Sponsor – Sue Finlay, DoSM Black Belt – Peter Cullen Master Black Belt – Paul James. Project Justification.

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Project Justification

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  1. Maximise RevPAR through in-depth analysis of Rooms Segments and Business MixEAME Project #84848Project Sponsor – Sue Finlay, DoSMBlack Belt – Peter CullenMaster Black Belt – Paul James

  2. Project Justification Ahead of setting our Rooms Revenue budgets for 2005, we feel there is a key opportunity to fully analyse the impact and interaction of our different market segments. This exercise is to ensure that the budget we submit is both accurate, realistic and challenging and that our proactive Sales activity is focused on the highest yielding areas of our business. Our aim is to hypothetically model a 'perfect' business mix to maximise RevPAR and profitability and identify how this mix changes through the year and then to come up tactical plans to achieve this ‘perfect’ mix

  3. Project Justification 2005 budget RevPAR = £87.59 Jan 02 Jun 04 Jun 08 If RevPAR continues to grow at the same rate, we will have to wait until June 2008 to ‘naturally’ reach the budgeted RevPAR target for 2005. Therefore, we need to do something different

  4. Sigma Level of Daily RevPAR results The Year End budgeted RevPAR for 2005 is £87.59. If we continue to generate similar RevPAR figures as we do today (and take every day that we do not achieve this threshold as being a ‘defect’), the resulting sigma level is likely to be 1.05 (671,740 DPMO) Cpk of less than 0 shows that our current RevPAR performance is not capable of hitting our year end goal for 2005

  5. Comparative Analysis Running the daily RevPAR data through an X-Bar control chart quickly showed the data points that were particularly high and those that were particularly low. MINITAB automatically gives us a list of the data points that failed SPC test 1 (more than 3s away from the mean) These ‘failure points’ were then loaded into Excel and Lookup functions were used to cross-reference the business mix that led to each of these results X-bar Chart: Revpar TEST 1. One point more than 3.00 sigmas from center line. Test Failed at points: 2 4 5 6 82 142 176 177 178 179 199 200 201 212 227 228 229 230 232 235 266 267 268 269 273 304 356 357 370 371 376 377 433 476 477 503 528 529 586 587 588 589 590 591 592 593 598 599 600 625 626 627 638 675 705 720 721 733 761 803 809

  6. These ‘failure points’ were then loaded into Excel and Lookup functions were used to bring-forward the rooms mix data corresponding to those dates. This enabled us to cross-reference the business mix that led to each of these particularly high or low results Pivot Tables were then used to format the data into graphical summaries

  7. Comparative Analysis The rooms business mix from the dates that ‘failed’ the control chart, were flagged as either particularly ‘HIGH’ or particularly ‘LOW’. This flag was then used to stratify the business mix percentages as seen below. Initially, we worked at a high level looking only at the general mix between Transients & Groups It can clearly be seen from this bar chart that days where there is no Group business lead to particularly low RevPAR results whereas a 55% : 45% split between Group & Transient rooms is the mix that has historically led to particularly high RevPAR results. This suggests that a stronger presence of Group Rooms will lead to an increased RevPAR result. However, we needed more detail

  8. Comparison of business mixes that resulted in particularly high and particularly low RevPAR The same method was used again but stratified to the next level of segment detail to see, again, what business mix characterised High and Low RevPAR days Dates that had a particularly high RevPAR were characterised by a fairly even mix of business across all the different segments. By contrast, days with a particularly low RevPAR are characterised by very high concentrations of rooms in the Transient Leisure segments and by almost-complete absence of any Retail and Group rooms.

  9. Optimum mix to maximise RevPAR based on historical data

  10. Comparison of business mixes that resulted in particularly high and particularly low RevPAR – split by Weekdays and Weekends A similar pattern is seen when split between Weekdays and Weekends. The days with particularly low RevPAR results are characterised by high concentrations of Transient Leisure rooms and an absence of premium rate Retail rooms and Groups. By contrast, the High RevPAR days show a more even spread of business across all major segments

  11. Comparison of business mixes that resulted in particularly high RevPAR – split by Weekdays and Weekends Summary – Highest midweek RevPAR comes from roughly a 40:60 split between Transients & Groups Highest weekend RevPAR comes from the opposite, a 60:40 split between Transients & Groups Our analysis shows that these are the business mixes that have historically resulted in the highest RevPAR results. If we can now replicate this business mix more often then, logically, we should enjoy an improved overall RevPAR performance

  12. Comparison of business mixes that resulted in particularly high – Seasonality Furthermore, our Sales & Marketing Dept wanted to know whether this mix changed with seasonality. Run Charts of RevPAR over time clearly show a seasonal cycle of low and high periods…but did the business mix change to reflect this at different times of year? Additionally, our Revenue Management software system (TLPE) operates with ‘seasonal’ parameters. These same parameters were used during this analysis Summary – (figures below are approximated) Low Season = 55 : 45 split between Transients and Groups Shoulder Season = 55 : 45 split between Transients and Groups Peak Season = 40 : 60 split between Transients and Groups August = 55 : 45 split between Transients and Groups

  13. Verification of Business Mix Given the results seen so far, the Sales & Marketing dept thought that the influence of Standard Life may have been effecting the figures. Standard Life was our biggest volume corporate account up until 2002 when we made a strategic decision to terminate the contract on the basis that the rate was too low and it was diluting our ADR The Standard Life contract was terminated in April ’03 so we decided to run the same analysis only from May ’03 onwards to see if there was any marked difference The results stayed exactly the same. Minitab Boxplots were another way of displaying the same data. These two boxplots clearly show that the High RevPAR days (green) occur when there is roughly 40% Transient rooms and 60% Groups

  14. Verification of Business Mix Further verification was done using the monthly figures from the hotel’s P&L rather than the daily figures from the Revenue Plan. This meant that we had much fewer individual data points but there were some interesting patterns within this data that had not shown up in the daily figures. For instance, the Group Leisure segment showed a strong correlation (p=0.001 and r = 0.68). Analysis of this segment shows a strong curvilinear relationship with the highest R-sq value achieved using a cubic regression model. This model shows that RevPAR increases along with Group Leisure rooms up to a threshold of roughly 700 rooms a month. Beyond this, the lower rates in this segment begins to dilute the overall RevPAR and pull it back down Average number of occupied rms per month over the same data collection period was 5859. So, an optimum of 700 Group Leisure rooms out of 5859 in total = 12% (which verifies exactly the % mix for this segment from the comparative analysis – see slide 16) Up to a threshold of 700 rooms per month (12% of total mix) the Group Leisure segment contributes positively to overall RevPAR. Beyond this threshold, an increasing level of transient leisure rooms begins to dilute the overall yield

  15. Further Verification of Business Mix The analysis so far had been based on percentages of occupied rooms and did not take into account variation in the actual number of occupied rooms. To filter out these effects, the data was filtered to use only dates that had exactly the same number of occupied rooms. The highest frequency was found with 254 occupied rooms, for which we had 21 separate dates. These 21 data points, each with 254 occupied rooms were then subjected to the same analysis as before Owing to the much smaller sample size, there were actually no ‘Low’ days within this dataset (254 rooms = 97% occupancy so this is not surprising). Again, the main difference that characterises the ‘High’ days is the higher percentage of Group rooms (esp Grp Assoc.) and reduced volumes of Transient (esp Trans Corp Contracted)

  16. Effect of Business Mix on Rooms Profitability One key concern was whether all this work on increasing RevPAR would ultimately make it’s way to the bottom line. In order to test this, we ran regression plots of RevPAR against Rooms Division profit figures from the hotel’s P&L Correlation between RevPAR and Rooms Division Profit shows an extremely strong relationship with 98.4% of the variation in profit being explained by the changes in RevPAR. The relationship is a positive linear one showing that the greater RevPAR which can be achieved, the higher the profit. This shows clearly that our efforts in increasing RevPAR will translate into increased profits Interestingly, using a cubic regression model, the relationship shows a slight upward curve as the RevPAR increases. This suggests that as RevPAR increases beyond about £90, the profit increases more-than-proportionately. Given the relatively fixed cost-base of occupying a room, this makes perfect sense! As, once those fixed costs are covered, any additional RevPAR will flowthrough to the profit line at a higher and higher percentage

  17. Effect of Increased Levels of Group Business on Revenues Putting a financial value to this project centred around to main areas. 1) The improved ADR that we get from Group segments will displace and outweigh any negative impact on transient business 2) As Group Rooms usually bring other revenue generating elements with them (eg banqueting, room hire etc), there proved to be a significant differential between incremental spend of Transient and Group rooms Rolled-up Monthly ADR figures for Groups & Transients show that Groups (£113) command a significantly higher ADR than Transients (£104). Increasing our proportion of Group Rooms should improve our overall ADR and RevPAR 1 4 0 1 3 0 Group Incremental Rev per Room Night = £115.78 Transient Incr Revenue per Room Night = £42.27 1 2 0 1 1 3 . 0 0 3 R 1 1 0 D 1 0 4 . 1 8 5 A 1 0 0 9 0 8 0 7 0 g r o u p t r a n s s e g m e n t

  18. Process Improvements • How do we sell more Group Rooms? • Sales Team Strategy • Property Sales Team move to spend 70% of their sales time concentrating on accounts that will generate Group Rooms rather than the current situation where they spend 70% account managing transient corporate clients • Sales Team to take enquiries through to contracting stage themselves to improve conversion. Currently the sales team hand-off all enquiries to Events as soon as an enquiry ariives • Sales Team to have regular reviews with local DMC’s, Tourist Board, Edinburgh Convention Bureau and Edinburgh International Conference Centre to ensure that we are aware of future city-wide events and forecasted residential demand • Events Team Strategy • Re-structure of Events team from multi-skilled all-rounders into specialist ‘sellers’ and ‘planners’ to, again, improve conversion. ‘Sellers’ report to Events Manager with dotted line reporting to Sales Manager. ‘Planners’ report only to Events Manager • Weekly meeting between Sales & Events ‘Sellers’ to review business and focus on converting existing tentative groups • Implementation of an MS Access Group & Event Enquiry Log to track the source, size and type of bookings that are coming in. Data from this tool is then used to actively call back turned-down business, track rate sensitivity, geographical source (which is then linked into planning of sales trips etc). While Opera S&C system can capture this data, slow speed issues meant that operators were not recording this data rather than wait for the system to catch-up

  19. Process Improvements • Pricing • Increase commissions payable to booking agents from an uncompetitive 8% to a local industry standard 10% • Use TLGO system to develop an at-a-glance price banding tool in order to offer a Best Available Group Rate on any given day. Price banding is based on existing business and forecasted demand • Development of a Group forecasting and ‘Pace’ report • Linked F&B Strategy • Develop a ‘social calendar’ of local events that are currently being held at competitors that we can actively target and ‘steal’

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