1. RIMS RESTAURANT INDUSTRY SURVEY

2. 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

3. 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 s are sent around every two weeks

4. 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

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

6. Sample Data Request

7. Sample Data Request

8. Sample Data Request

9. Sample Data Request

10. Sample Data Request

11. Sample Data Request

12. Sample Data Request

13. Sample Data Request

14. Sample Data Request

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

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

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

18. 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

19. 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)

20. 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

21. 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

22. WC Average Pure Loss Rates

23. WC Average Pure Loss Rates

24. WC Average Pure Loss Rates

25. WC Average Pure Loss Rates

26. 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

27. WC Average Development

28. GL Average Pure Loss Rates

29. GL Average Pure Loss Rates

30. GL Average Pure Loss Rates

31. GL Average Pure Loss Rate

32. 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

33. GL Average Development

34. 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

35. Customized Individual Results
This report, along with the presentation, is ed 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

36. 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

37. 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

38. Percentages of Types of WC Claims

39. Incurred Cost Percentages of WC Claims

40. Percentages of Types of GL Claims

41. Incurred Cost Percentages of GL Claims

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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