The Secrets of WFMJonty Pearce

How are Calls Distributed?

Average Handling Time of 5 minutes

How are Calls Distributed?

Average Handling Time of 5 minutes

Poisson Distribution

How Calls are Actually Distributed

The Erlang DistributionNot many calls of “average” length - Many short calls, some very long callsThis is one of the reasons why Erlang Calculators appear to overstaff at low levels

Average Handling Time of 5 minutes

https://www.callcentrehelper.com/how-is-average-handling-time-distributed-it-is-not-how-you-think-97688.htm

The Hourly Rule of Thumb

• A lesser-known “rule of thumb” says that a call centre could expect to get at least 40 per cent of its hourly calls within the first 15 minutes of the hour, then around 30 per cent after the first half hour, and the final 30 per cent in the final quarter of the hour.

• “It’s human nature to look at the clock and ring in after the clock strikes the hour,” says Dave Appleby.

• Have a look at this and you will see it in your numbers.

https://www.callcentrehelper.com/7-ways-to-improve-the-accuracy-of-your-contact-centre-forecasts-29032.htm

15 Minute Forecast Intervals Usually Better

• Watch for overhang – Make sure AHT is less than 50% of reporting periodThese calls overhang to the next interval

https://www.callcentrehelper.com/multi-channel-contact-centre-calculator-96321.htm

Maximum Occupancy

• Occupancy is the percentage time that a contact centre advisor spends handling customer contacts

• Agents can not work constantly at very high occupancy levels

• Above Maximum Occupancy, AHT and Attrition will increase

• Maximum Occupancy ranges

• from 85% (in-house)

• to 90% (more performance driven)

85%

https://www.callcentrehelper.com/how-to-work-out-how-many-staff-you-need-in-a-contact-centre-96591.htm

Applying Shrinkage

Shrinkage is a measurement of anything that takes an agent away from their

ability to take customer contacts.

What is an average shrinkage figure for the industry?

Most contact centre professionals seem to agree that shrinkage seems normally

to come out between 30 and 35%.

Dimension Data in their Global Benchmarking Report give an average shrinkage

figure of 35%.

Shrinkage

External Shrinkage (out-of-centre shrinkage)• Sickness (either run rate or desired level)

• Holiday

• Public holidays

• Paid breaks (if applicable)

• Absenteeism

• Lateness (or tardiness)

Internal Shrinkage (in-centre shrinkage)• Team meetings

• Coaching

• Training

• One to ones

• System downtime

• Unplanned facility breaks

• Time spent helping other departments

• Special projects (e.g. time spent fixing broken processes)

https://www.callcentrehelper.com/how-to-calculate-contact-centre-shrinkage-90353.htm

Errors in Erlang Calculators

• Many people use free Erlang Calculators downloaded from the Internet. But many of these contain errors.

• Please check your Erlang Calculator to ensure that it produces the right results

• All the calculators use Average Handling Time of 5 minutes 300 seconds, 80% of calls answered in 20 seconds and 0 Shrinkage

Calls per 30 minutes

Traffic Intensity Erlangs

Agents(Right Answer)

AgentsWrong Answer)

100 16.7 21

1000 166.7 176 167,168

2000 333.3 345 333,334

3000 500 513 501

6000 1000 1015 347, 1001

Errors in Erlang Calculators

The correct answer is 1015 Agents6000 Calls per Half Hour, AHT 5 minutes 300 seconds, 80% of calls answered in 20 seconds and 0% ShrinkageThe Traffic Intensity is 1000, so the number of agents cannot be less than 1000

https://www.callcentrehelper.com/online-call-centre-staffing-calculator-77780.htm

Errors in Commonly used Erlang Calculators

The correct answer is 1015 Agents6000 Calls per Half Hour, AHT 5 minutes 300 seconds, 80% of calls answered in 20 seconds and 0% ShrinkageThe Traffic Intensity is 1000, so the number of agents cannot be less than 1000

Tests carried out 23 March 2017

Why the Errors in Erlang Calculators?

• The Erlang Formula uses big Powers and Factorials

• Most floating point systems have a limit on size of numbers –for example in Excel the limit is 1x 10307

• Above 142 agents many Erlang calculators can be unpredictable, often not throwing an error but returning wrong results

=POWER(A,N) =FACT(N)

A N A Power of N N Factorial

1 2 1 2

2 3 8 6

3 4 81 24

4 5 1024 120

5 6 15625 720

10 11 1E+11 39916800

15 16 6.56841E+18 2.09228E+13

20 21 2.09715E+27 5.10909E+19

50 51 4.44089E+86 1.55112E+66

100 101 1E+202 9.4259E+159

120 121 3.8101E+251 8.0943E+200

140 141 4.0184E+302 1.8981E+243

141 142 1.5457E+305 2.6954E+245

142 143 5.9873E+307 3.8544E+247

143 144 #NUM! 5.5503E+249

144 145 #NUM! 8.0479E+251

145 146 #NUM! 1.175E+254

150 151 #NUM! 8.6272E+264

160 161 #NUM! 7.5907E+286

169 170 #NUM! 7.2574E+306

170 171 #NUM! #NUM!

171 172 #NUM! #NUM!

172 173 #NUM! #NUM!

173 174 #NUM! #NUM!

Try this yourself in Excel

Forecast Accuracy

• How not to do it

Actual Forecast Difference Forecast Accuracy %

Monday 1200 1000

Tuesday 1500 1000

Wednesday 900 1000

Thursday 800 1000

Friday 600 1000

Total 5000 5000 0 0%

Forecast Accuracy

• How to do it – MAPE (Mean Absolute Percentage Error)

Actual Forecast AbsoluteError

MAPE %

Monday 1200 1000 200 16.7%

Tuesday 1500 1000 500 33.3%

Wednesday 900 1000 100 11.1%

Thursday 800 1000 200 25%

Friday 600 1000 400 66.7%

Total 5000 5000 1400 28.0%

Forecast Accuracy

• But you really should do it on an interval basis

Actual Forecast AbsoluteError

MAPE %

08:00 16 20 4 25.0%

08:15 33 25 8 24.2%

08:30 47 40 7 14.9%

… … … … …

17.15 17 20 3 17.6%

Total 1200 1100 270 22.5%

In this exampleA daily total gives 100 / 1200 = 8.3% Forecast AccuracyAn interval total gives 270 / 1200 = 22.5% Forecast Accuracy

Forecast Methods

• Manual / Graph

• Spreadsheet / Graph

• Trend Plotting

• Holt Winters

• ARIMA

Holt Winters Forecasting Method

• Also known as Triple Exponential Smoothing

• Level (Last Forecast) , Trend, Seasonality

• Forecast for next period = (Last Forecast + Trend) * Seasonality

• Built in to most WFM platforms

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Seasonality

Month Seasonality

Jan 95%

Feb 98%

Mar 107%

Apr 99%

May 113%

Jun 105%

Jul 82%

Aug 84%

Sep 111%

Oct 123%

Nov 109%

Dec 74%

ARIMA

• Auto-Regressive Integrated Moving Average

• Latest forecasting “kid on the block”

• Now used by the Met Office

• Correlates latest data against past patterns

• Maths is very complex

• Generally beyond spreadsheets

Spreadsheets vs WFM

• Spreadsheets OK up to 35 agents

• Above this use WFM Software

• WFM + Spreadsheets for Forecasting

• WFM for Scheduling

The Secrets of WFMJonty Pearce