RIMS RESTAURANT INDUSTRY SURVEY

History Started by Jim Sybert in late 80’s Retired in 2010 SIGMA started helping in 2008 # of Participants has varied between 15 and 38 Typically work with risk managers/analysts Some brokers submit data

Timeline Send out emails in December/January Request a 1/31 evaluation Deadline is early March Deadline is always extended We start calling people in first week of March Reminder e-mails are sent around every two weeks

Target Market RIMS members – risk managers RIMS members – brokers National Restaurant Association has shown little interest State restaurant associations California can be very vocal end up not sending us data

Data Request Completed in Excel Most are e-mailed back Cells are highlighted where user is to enter information

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Sample Data Request

Coverages Workers compensation General liability Property Product liability (never enough data)

Exposures Payroll Revenue Number of Restaurants Labor Hours Total Insured Value Guest Count and Transactions are requested but never enough data

Categories Quick Service Full Service Casual Dining Cafeteria/Buffet Some restaurants struggle with placing themselves in Full Service or Casual Dining.

Adjustment of Data Ask for ultimate losses we sometimes have to develop to ultimate Trend ultimate losses for pure loss rates We check for credibility of exposures and losses and will make adjustments if necessary

Stats We Calculate Severities Pure Loss Rates for each exposure Medical only and indemnity severities are calculated along with total overall severity in WC Pure Loss Rates for each exposure Indemnity Percentages Number of Claims per Exposure (Frequency)

Stats We Calculate Loss Development Percentages of Types of Claims Incurred Cost Percentages of Claims Each of the above percentages are then broken out further by category

Stats We Calculate All of the previous stats are calculated for both WC and GL Only a Pure Loss Rate (per Total Insured Value) and Severity is calculated for Property

WC Average Pure Loss Rates

WC Average Pure Loss Rates

WC Average Pure Loss Rates

WC Average Pure Loss Rates

WC Pure Loss Rates Conclusion Since there are different trends between the different exposures, the need to calculate each pure loss rate is necessary Payroll pure loss rates can show the affect of inflation, while revenue pure loss rates can show the affect of a slow economy

WC Average Development

GL Average Pure Loss Rates

GL Average Pure Loss Rates

GL Average Pure Loss Rates

GL Average Pure Loss Rate

GL Pure Loss Rates Conclusion For GL, the trending is more consistent between exposures than WC It may make sense to only analyze one exposure However, it may vary between participants which exposure they value over another

GL Average Development

Customized Individual Results Every statistic that is calculated in the previous slides is calculated for an individual restaurant that participates We then provide a side-by-side comparison of the individual restaurant, the rest of the industry and the specific category they belong Some restaurants leave blanks in the survey. If this occurs then that statistic is not calculated

Customized Individual Results This report, along with the presentation, is e-mailed the Friday before the conference These results prove to be a crucial part of analyzing their loss control programs, risk management programs etc. These results are also extremely valuable to each of the restaurants that participate to gauge themselves against their peers

Categories of Claims WC categories have remained consistent (Slips and Falls, Lifting, Burns etc.) GL categories needed to be narrowed The percentages of “Slips and Falls” and “All Other” were the majority of the claims The following categories were added to depict a better picture of the claims that were reported and paid: Alleged Food Poisoning Struck By/Against Foreign Object in Food

Categories of Claims The addition of “Alleged Food Poisoning” was not entirely welcomed at first Participants saw, however, that while there may have been many instances of food poisoning reported only very few proved to be substantial Those instances of actual food poisoning, however, often have a high severity and really affect their numbers It proves to be a very important statistic to analyze

Percentages of Types of WC Claims

Incurred Cost Percentages of WC Claims

Percentages of Types of GL Claims

Incurred Cost Percentages of GL Claims

Why Is It A Struggle To Obtain Data? Some restaurants never find the time to gather the data Sometimes there is a new person in that role Some data systems are “clunky” and prove to be difficult to pull certain data Money? Some restaurants rely on their broker to gather the data We have experienced instances where the restaurant really wanted to participate, but their broker “dropped the ball” and failed to gather the data

Questions/Comments We Have Received Can analysis be broken out by state – specifically CA? Some question the quality of other participant’s data Specifically when their restaurant has worse results compared to their peers in their category

Questions/Comments We Have Received Do participants include EPL and Liquor Liability in their GL numbers? Is there a guide we can provide that helps interpret the graphs? For example, there has been a participant that did not understand what a pure loss rate and severity were These definitions are explained at the presentation, but if a participant does not attend then the definitions can be difficult for them

Questions/Comments We Have Received Brokers have been more willing to submit their client’s data now that SIGMA performs the analysis instead of a competing broker Hesitation to provide data if their retention is a great deal higher than other participants Perhaps consider capping losses or layering the data received

Miscellaneous This group as a whole is very resistant to any change Few participants ask questions, but the ones that do are heavily involved Starting to attract non-traditional participants (ex. Quick-Marts) This past year, the majority of risk managers were younger They were very interested in the numbers and the value they provide Extremely interested in the analysis as a way to leave their mark and prove their value

Conclusion Brokers are now interested in other industry benchmarking analyses for their own clients or prospects The benchmarking analysis would be even more credible if the same participants would participate each year along with increasing the number of participants