What is Call Center?“A place where calls are made and received.”

The objective of this workshop is to provide an introduction to call center operations and the role of WFM in running a typical call center

Type of Call Centers

1. Inbound – HDFC Bank Customer Service

2. Outbound – Car or Bike helpline Telesales

3. Blended – Incoming calls with Outbound calls or emails or Web chat

Geographic Scope1. Internal – serves only internal customers, internal Transport

helpdesk

2. Local- Serve customers in limited geographic for example local taxi or bus service

3. Regional-Services beyond local but limited by region for example Videocon/Aircel Mobile Companies

4. National-Take calls from all states for example Indian Railways

5. International- Take calls that extend beyond national boundaries

Financial Structure

1.Profit Center :- • Call center that generate revenue for example Indian

Railways Reservation center

2.Cost Center:-• Call center that generate no direct revenue

CALL LIFE CYCLE FROM CUSTOMER TO AGENT

Customer

1800-969-4545

• PSTN : public switched telephone network• PBX : Public Branch Exchange • CTI : Computer Telephony Integration• CMS : Call Management System • TTS : Text To Speech • IVR : Interactive voice Response

PSTN

KT

▷ Echo (Order System)

CTI CMS RecordingPBXAuto

ordering TTS

Local Network (LAN)1800-414-4545………

Order/SR

1800-266-4545

Call Master

Agent

Call Line Quan-tity

- 5 -

Work force planning (WFM)

“Right number of people, at the right time to take right number of volume”.

1. Forecasting2. Scheduling3. SD4. MIS

FORECASTING:

The process of predicting future values using the current or historical information on causes, customer behavior or performance

"It is tough to make predictions, especially about the future.“

FORECASTING-A BLEND OF ART & SCIENCE

Art– Predicting the future– The Human Factor

– Intuition– Instinct– Judgement

Science Uses tools Pencil, paper Calculator Statistical techniques Spreadsheet software Forecasting software

FORECASTING HORIZON

• Long-range (more than 12 months ahead):– Used to plan contact center capacity

• Intermediate-range (3 to 12 months ahead):– Used to plan workforce levels, allocate funds

among divisions, and develop training schedules• Short-range (1 to 12 weeks):

– Used to schedule workforce, intra-day forecasting and re-forecasting

FORECASTING METHODS• Judgmental methods

– Expert opinion– Market surveys/tests– Delphi

• Statistical methods– Point estimate– Simple average– Simple moving average– Weighted average– Weighted moving average– Time series models– Regression (or causal) models

• Combination method– Statistical forecasting with judgmental adjustments

Judgmental methods– Expert opinion– Market surveys/tests– Delphi

FORECASTING METHODS

Expert Opinion:

Gathers judgments and opinions of key personnel based on their experience and knowledge

– Workforce managers are a good source for the initial volume forecasts for new services and special events with no past history

Market Surveys/Testing:

Utilizes questionnaires, telephone contacts, field tests, or personal interviews

– Testing the new billing statement format with a select group of customers to determine its impact on call volumes and AHT

Delphi:

Experts are individually questioned about their opinion of future contact volumes and AHT.

• Their responses are summarized by an outside party and returned to the experts until a consensus is reached by the group.

FORECASTING METHODS

• Statistical methods– Point estimate– Simple average– Simple moving average– Weighted average– Weighted moving average– Time series models– Regression (or causal) models

POINT ESTIMATE

Takes data from a point in the past, assumes the next point will be the same. For example Weather Prediction of the high temperature for June 1st of coming year could be derived by simply using number from last June1st.

Shortcomings Doesn’t account for long-term change Cannot determine whether original data was typical

or unusual Doesn’t recognize recent trends

SIMPLE AVERAGE

Sum all data points and divide by the number of data points used

Shortcomings• Weighs all events equally• Every day likely to be like any other day• No underlying changes• Assumes data consistent across time

SIMPLE MOVING AVERAGEMoving average is one where old data is dropped out as new information is added. For example if June 1st Temperature is 95 degrees, then oldest piece of information(83 degrees in 1990)

Shortcomings• Uses only a fixed span of data• Significance of recent trends is over emphasized

WEIGHTED AVERAGE

Emphasizes the most recent events and recognizes something has fundamentally changed

• Assumes:– Today better predictor of tomorrow than past data– More weight should be given to recent data

• Best for call centers with unpredictable fluctuations• Accounts for fundamental shifts in underlying call

patterns

WEIGHTED MOVING AVERAGE

Combines the moving average and weighted average methods• New data is continually averaged in with old and

weighted more heavily• Older values are retained• Statistical value of sample improves over time• Used by most workforce management tools

TREND AND SEASONALITY

• Trend is the long-term change in call volume expressed as the average monthly increase or decrease.

• Seasonality is the month to month fluctuation in call volume that can be expected to recur each year.

PRINCIPLES OF TREND AND SEASONALITY

Call volume is influenced over timeTrend and seasonality are the influencesCalculating these influences is a 2-step process

Calculate trendDetrend--bring old data up to the current point

CALCULATING TREND

Preferably with 24 months of data, calculate annual % change in actual call volume for each month from year to year:((Year B - Year A) / Year A) * 100 = % of change

Calculate the simple average of each month’s percent of change

To convert to monthly trend rate, sum each month’s percent of change, divide by number of months of data used

Monthly Call Volume Forecast

Monthly Call Volume

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REGRESSION ANALYSIS

Where volume is dependent upon an event or variable. For example if HS18 advertise their product on a newspaper and has kept

records of catalogs mailed and corresponding number of incoming calls.

Month Call Volume Number of CatalogsSep 72000 123000Oct 85000 135000Nov 92000 140000Dec 101000 155000Jan 54000 86000Feb 55000 76000Mar 76000 94000Apr 102000 164000May 81000 128000Jun 86000 131000

According to the Table greater the number of catalogs shipped, the greater the calls received.

The data collection process Data Validation Dealing with Aberrations Impact of sample sizes Sources of call data Business drivers & factors Simple, Moving & weighted averages Time Series Analysis Calculating Trend Identifying seasonal Patterns Calculating day of week factors Identifying time of day patterns The impact of special events Shortcut Forecasting approaches Calculation & Formula Review

Combination method

DATA COLLECTION PROCESS

•The most reliable data source is Avaya or any Platform Call Center is working on.•The data has to be collected on split skill basis & not on any other parameter.

DATA VALIDATION-1

Validating of data is CRITICALMust use normalized data, adjusted for aberrationsAberrations

Some “normal” or “repeatable”

DATA VALIDATION-2How much data do I need to start?Suggested sample sizes:

To determine seasonal patterns and annual growth rate: 2 yearsTo determine day of week patterns: 4-6 weeksTo determine hour of day patterns: 4-6 weeks of hourly data

Brand New Call Center?Marketing StudiesBenchmarking StudiesClientLogic dataClient DataOthers are “special”,Store aberrant data as future predictorAlways note on reports whether electronic or paperLong distance carrier, Genesys,Computer Telephony IntegrationAgent hash marks, Marketing projectionsTools range from manual to automaticACD reports,Telephone company

DATA LIMITATIONS

Technology: (Variations among ACDs)Types of dataDifferent technologyDifferent features

Call routing influences reportingDefault Queues Transfer calls Overflow callsSkill-based routing

CONSIDER RANDOM VARIATIONS

Past events may be used to forecastIdentify predictable variations

Capture aberrationsIdentify Unpredictable Variations

One time eventsActs of nature, etcMay not know when it will happen again, but will know

what it will look like

CALCULATING DAY OF WEEK PATTERNS

• Daily Call Volume Estimate:• Choose several recent “clear” weeks• Calculate average daily total and weekly total• Calculate day of week %’s for each day of week• Expected percentage is derived from past and

applied to future• Account for specific call center fluctuations

based on the nature of the business

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INTRA DAY VOLUME PATTERN

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CHECKING THE STATIONARITY - REMOVE TREND

Differencing: Method of removing upward / downward trend in a data series. After the data has no trend, the data becomes STATIONARY.

Microsoft Excel Worksheet

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Adjusting special days

Take the weekly data for 6 weeks prior to the special week.Calculate the median of first 3 weeks and the last 3 weeks. Average the two medians. Calculate the growth rate of volume in special week over this

average. Average the Growth rates.If the average GR falls between +/- 10%, call it normal or

else special. Inflate / deflate the special days with these factors and get

the final daily numbers.

REMOVE SEASONALITY

Estimating the Seasonal Index / Seasonal Dummies:

Microsoft Excel Worksheet

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SEASONALITY

Seasonality is the normal fluctuations experienced in the same months from year to year. Different depending on your business.

Once trend is identified, it is removed as factor; what’s left is the effect of seasonality.

“Detrending” Is the process of removing trend from the data.

TIME SERIES MODEL

A time series is a set of observations measured at successive points in time

TIME SERIES MODELS

Time series models analyze historical data to determine patterns and dependencies for forecasting future values• Patterns

– Trend– Seasonal

• Correlation and dependencies between two or more time series

TIME SERIES - TREND

• Trend– A time series with trend gradually shifts to

relatively higher or lower values over a period of time

LINEAR OR NON-LINEAR REGRESSION

Trend may be– Increasing, decreasing or non-existent– Linear or non-linear

SEASONALITY

• A regular pattern of variability in time series data that repeats over a year or shorter periods

TIME SERIES - PATTERNSSeasonality in contact volumes and AHT. Intra-day. Daily. Monthly. Quarterly. Annual

CORRELATION COEFFICIENTCorrelation between two time series is a measure of dependence scaled to [-1, 1]

For two positively correlated time series, if the value of one is .high. in one period, the other is likely to be “high” also

For two negatively correlated time series, if the value of one is .high. in one period, the other is likely to be “low”

DEPENDENCIES BETWEEN VOLUMES, AHT AND INDICATOR VARIABLES

EXCEL CORREL FUNTION - CORRELATION COEFFICIENT FOR TWO TIME SERIES

Correlation between two time series is a relative measure of dependency. Values closer to 1 or -1 represent strong relationshipsCoefficient is also know as relative standard deviation (RSD)

SIMPLE DAILY MOVING AVERAGES

DAY OF WEEK AND MEAN DAY OF WEEK FACTORS

WEIGHTED MOVING AVERAGESWeighted (e.g. percentages) Moving AveragesAssigns higher weights to some actual values (usually the most recent ones) than the others

DAY OF WEEK FACTORS WITH THE WEIGHTED MOVING AVERAGES METHOD

INCLUDING WEEKLY GROWTH FACTORS WITH THE WEIGHTED MOVING AVERAGES METHOD

INTERVAL FORECASTS

Interval level forecasts are generated from daily forecasts using the interval factors (%) calculated by applying the same approach used to compute the day of week factors

TIME SERIES MODEL COMPONENTS

Special Events

. Known calendar events such as July 4th (US Independence day), 1st day of a month. Planned events such as campaigns, mailings. Unknown events such as computer viruses, storms

SPECIAL EVENTS

SPECIAL EVENT DAY FORECASTINGForecasting with special eventsStep 1: Identify special event days in the past and over theforecast horizon

Step 2: Regular day forecasting. Forecast contact volume and AHT for the past special event days. Replace the past special event day actuals with the forecasted values. Forecast future contact volumes and AHT values over the forecastinghorizon

Step 3: Special event day forecasting. Forecast future contact volumes and AHT for the special event daysover the horizon using past special event day actuals. Replace the base forecasts found in Step 2 with the special event dayforecasts from this step

TIME SERIES MODELS - OUTLIERS

An outlier is an actual that issubstantially higher or lower than theactuals observed in the similar periods

. Outliers are caused by one-time or recurringunpredictable events. Events causing an outlier may be known(e.g. web site problems increasing call volumes due to civil aggitation)or unknown

OUTLIERS

Outliers should be excludedin forecasting to improve forecast accuracyby replacing them with forecasted values

OUTLIERIf not detected and replaced, outlier andmissing values will affect your forecasts andaccuracy negatively

ConclusionCall centers with good forecasts do not necessarily have inherently stable environments. Rather, they have established good ties with other departments, pulled in the data required, and established a forecasting process they are continually working to improve.

They recognize that the responsibility for producing a good workload forecast cannot rest solely on the person or group who "does" the forecasting. Instead, they view forecasting as a call center-wide process and work on all contributing factors.

WFM-SCHEDULING

WHAT IS CALL CENTER SCHEDULING?

Scheduling right number of people at the right time, to take right number of calls, to maximized the sales and customer satisfaction.

SCOPE OF CALL CENTER SCHEDULINGScheduling scope includes creating/generate staffing schedules that optimize a wide range of factors :-

• Agent availability• Work rules• Skills• Holidays• Breaks• Service levels• Center budgets

How do you SCHEDULE• Hour of operations

• 24 hrs • 12 hrs• 8 hrs

• SLA Target ASA (Average Speed of Ans 30 Sec)• 80/20• 90/20• 95/30• 99/30

• Type of Scheduling• European (49% - 56% ASH)• North America (30% - 35% ASH)• Indian (20% to 28% ASH)

• Week Off/Day off preference• Create fine balance between volume forecast & Agent Week off preferance• Needs to maintain fairness tracker so that everyone get equal Week Off

preference• Agent’s Week Off preference request for Week• Scheduling Lunch and Break so that Ans capacity doesn’t impact overall SLA

How do you SCHEDULE• Call Volume Trending

• Day of the week factor, when volume is less give more week-off

• Staffing based on Skill priority and Que priority

SCHEDULE after-effectsOhh good !!!!

I got what I asked for

Why I am not getting week Off on Sunday

for last 4 weeks

Nooo.. Why my planned leaves are not reflecting

If you are not getting what you want it can be because of• Your Skill Priority• Your Que Priority• Call Volume for that week• Any special marketing campaign• Festival session and everyone is asking for leaves

How do you SCHEDULEScheduling is needed to answer below mentioned questions

• Achieving Performance Target/Quality of Service Target• ASA (Average Speed of Ans 30 Sec)

• Week Off/Day off• Create fine balance between volume forecast & Agent Week off

preferance• Needs to maintain fairness tracker so that everyone get equal Week

Off preferance• Agent’s Week Off preference request for Week• Scheduling Lunch and Break so that Ans capacity doesn’t impact

overall SLA

• Hours of operations• 24 hrs • 12 hrs• 8 hrs• Call Volume Trending

• Including agent availability• Work rules• Skills• Holidays• Breaks• Service levels• Center budgets

Thank you

WHAT IS THE IMPACT OF ONE AGENT?• 175 calls per half-hour, 5 minute AHT (280 talk/20 acw)

• (29.2 erlangs)

Number of Staff

Average Speed of Answer

Service Level (in

30 sec)30 298 sec 24%31 107 sec 46%32 54 sec 62%33 30 sec 74%34 18 sec 82%35 11 sec 88%

Staff Occupanc

y.97.94.91.88.86.83

6 am 10 pm

Peo

ple

Think of your shifts as LEGO building blocks. The more sizes and types of blocks, the better you can build your model.

SCHEDULE INFLEXIBILITY

WHAT IF CALLS TAKE 30 SECONDS LONGER TO HANDLE?

Forecast versus Actual

Time of Day

Forecast Calls

Forecast AHT

Forecast Staff

Actual Calls

Actual AHT

New Staff

Net Staff

6:00 280 320 56 280 350 61 - 5

6:30 310 320 62 310 350 68 - 6

7:00 350 320 69 350 350 76 - 7

7:30 380 320 75 380 350 82 - 7

8:00 420 320 82 420 350 90 - 8

8:30 450 320 88 450 350 96 - 8

WHAT IF VOLUME IS 10% BELOW FORECAST?

Forecast versus Actual

Time of Day

Forecast Calls

Forecast AHT

Forecast Staff

Actual Calls

Actual AHT

New Staff

Net Staff

6:00 280 320 56 252 320 51 + 5

6:30 310 320 62 279 320 56 + 6

7:00 350 320 69 315 320 63 + 6

7:30 380 320 75 342 320 68 + 7

8:00 420 320 82 378 320 75 + 7

8:30 450 320 88 405 320 80 + 8

6 am 10 pm

Peo

ple

The Result: A better fit with less understaffing and

overstaffing

OPTIMIZED SCHEDULE

REPORTING SERVICE LEVEL• Activity

• An executive calls and wants to know how service level looked yesterday.

• Your service level goal is 80% in 20 sec.

• Questions:Did you meet your goal?Why or why not?What are the possibilities for reporting the numbers?Which method do you think is best?

PAYBACK EXAMPLE Real-Time Adherence Monitoring

Number of Agents

@ $15/hour wage rate

@$20/hour wage rate

100 Rs 32,500 $ 43,333

250 $ 81,250 $108,062

500 $162,500 $ 216,125

1000 $325,000 $ 432,250

Assumption: 5 minutes savings per agent per day