Sales forecasting

SALES FORECASTING

PT. Sosro, January 2010

INTRODUCTION

Module 1

Introduction• Find your partner• Introduce your partners to the

class

1. What is your name?2. What is your place of

employment? How long have you been with your companies? What are the areas of your responsibility?

3. What is your expectations of the course? What is one question that you hope to get answered during the class?

4. Tell us one fun thing that you like to do on weekend?

Administrative Tasks

• Hours• Locations• Emergency Phone• Parking Lot• Smoking Policy• Attendance List• Name Tents• Training Manuals

Training Agenda – Day 109:00 – 10:30 Module 1: Introduction to sales forecasting

10:30 – 10:45 Break

10:45 – 12:00 Module 2: Indicators Affecting Sales Forecasting

12:00 – 1:15 Lunch

13:15 – 14:30 Module 3: Moving Average Forecasting Techniques

14:30 – 14:45 Break

14:45 – 15:30 Module 4: Linear Regression Forecasting Techniques

15:30 – 16:00 Day One Wrap Up and Preview Day Two

Training Agenda – Day 209:00 – 10:30 Module 5: Multiple Regression Forecasting

Techniques

10:30 – 10:45 Break

10:45 – 12:00 Module 6: One Way Anova Forecasting Techniques

12:00 – 13:15 Lunch

13:15 – 14:30 Module 6: Two Way Anova Forecasting Techniques

14:30 – 14:45 Break

14:45 – 15:30 Module 7: Forecasting as a Strategic Business Tools

15:30 – 16:00 Course Summary and Wrap Up

What is Forecasting?

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• Process of predicting a future event

• Underlying basis of all business decisions– Production– Inventory– Personnel– Facilities

Types of Forecasts by Time Horizon

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Short-term vs. Longer-term Forecasting

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Influence of Product Life Cycle

4-10

Strategy and Issues During a Product’s Life

Introduction Growth Maturity Decline

Standardization

Less rapid product changes - more minor changes

Optimum capacity

Increasing stability of process

Long production runs

Product improvement and cost cutting

Little product differentiation

Cost minimization

Over capacity in the industry

Prune line to eliminate items not returning good margin

Reduce capacity

Forecasting critical

Product and process reliability

Competitive product improvements and options

Increase capacity

Shift toward product focused

Enhance distribution

Product design and development critical

Frequent product and process design changes

Short production runs

High production costs

Limited models

Attention to quality

Best period to increase market share

R&D product engineering critical

Practical to change price or quality image

Strengthen niche

Cost control critical

Poor time to change image, price, or quality

Competitive costs become critical

Defend market position

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HDTV

CD-ROM

Color copiers

Drive-thru restaurants

Fax machines

Station wagons

Sales

3 1/2” Floppy disks

Internet

INDICATORS AFFECTING SALES FORECASTING

Module 2

Types of Forecasts

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Seven Steps in Forecasting

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Sales over 4 Years with Trend and Seasonality

Year1

Year2

Year3

Year4

Seasonal peaks Trend component

Actual demand line

Average demand over four years

Sale

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pro

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or

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Random variation

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Actual Demand, Moving Average, Weighted Moving Average

Actual sales

Moving average

Weighted moving average

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Realities of Forecasting

• Forecasts are seldom perfect• Most forecasting methods assume that

there is some underlying stability in the system

• Both product family and aggregated product forecasts are more accurate than individual product forecasts

Forecasting Approach

Overview of Qualitative Methods

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Jury of Executive Opinion

• Involves small group of high-level managers• Group estimates demand by working together

• Combines managerial experience with statistical models

• Relatively quick• ‘Group-think’

disadvantage

4-20© 1995 Corel Corp.

Sales Force Composite

• Each salesperson projects their sales• Combined at district & national levels• Sales rep’s know customers’ wants• Tends to be overly optimistic

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Delphi Method

• Iterative group process• 3 types of people

– Decision makers– Staff– Respondents

• Reduces ‘group-think’

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Consumer Market Survey

• Ask customers about purchasing plans• What consumers say, and what they actually

do are often different• Sometimes difficult to answer

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Overview of Quantitative Approaches

• Naïve approach• Moving averages• Exponential smoothing• Trend projection

• Linear regression

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Time-series Models

Associative models

4-25

What is a Time Series?

• Set of evenly spaced numerical data– Obtained by observing response variable at regular

time periods

• Forecast based only on past values– Assumes that factors influencing past and present will

continue influence in future

• ExampleYear: 2005 2006 2007 2008 2009Sales: 78.7 63.5 89.7 93.2 92.1

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TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Time Series Components

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• Persistent, overall upward or downward pattern

• Due to population, technology etc.• Several years duration

Trend Component

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• Regular pattern of up & down fluctuations• Due to weather, customs etc.• Occurs within 1 year

Seasonal Component

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• Repeating up & down movements• Due to interactions of factors influencing

economy• Usually 2-10 years duration

Cyclical Component

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• Erratic, unsystematic, ‘residual’ fluctuations• Due to random variation or unforeseen

events– Union strike– Tornado

• Short duration & nonrepeating

Random Component

MOVING AVERAGE FORECASTING TECHNIQUES

Module 3

4-32

• Any observed value in a time series is the product (or sum) of time series components

• Multiplicative model– Yi = Ti · Si · Ci · Ri (if quarterly or mo. data)

• Additive model– Yi = Ti + Si + Ci + Ri (if quarterly or mo. data)

General Time Series Models

Naive Approach

• Assumes demand in next period is the same as demand in most recent period• e.g., If May sales were 48, then June sales will be 48

• Sometimes cost effective & efficient

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Naïve Seasonal Model

Naïve Seasonal Model

• Formula (1) : Yt+1 = Y1

– Y’24+1=Y24 , Y25=650– E25=Y25 – Y’25=850-650=200

• Formula (2) : Y’t+1=Yt+(Yt-Yt-1)– Y24+1=Y24+(Y24-(Y24-1) =650+(650-

400) = 650+250 = 900– E25=Y25-Y’25= 850-900=-50

Year Quarter Sales Forecast2004 1 500

2 3503 250 2004 400 150

2005 5 450 5506 350 5007 200 2508 300 50

2006 9 350 40010 200 40011 150 5012 400 100

2007 13 550 65014 350 70015 250 15016 550 150

2008 17 550 85018 400 55019 350 25020 600 300

2009 21 750 85022 500 90023 400 25024 650 300

2010 25 850 90026 600 105027 450 35028 700 300

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• MA is a series of arithmetic means

• Used if little or no trend

• Used often for smoothing– Provides overall impression of data over time

• Equation

MAMAnn

nn Demand inDemand in PreviousPrevious PeriodsPeriods

Moving Average Method

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You’re manager that sells beverages. You want to forecast sales (000) for 2011 using a 3-period moving average.

2006 42007 62008 52009 32010 7

Moving Average Example

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Moving Average SolutionTime Response

Yi Moving Total (n=3)

Moving Average

(n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3 = 5 2010 7 2011 NA

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Moving Average

(n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3 = 5 2010 7 6+5+3=14 14/3=4 2/3 2011 NA

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Moving Average

(n=3) 2006 4 NA NA 2007 6 NA NA 2008 5 NA NA 2009 3 4+6+5=15 15/3=5.0 2010 7 6+5+3=14 14/3=4.7 2011 NA 5+3+7=15 15/3=5.0

95 96 97 98 99 00Year

Sales

2

4

6

8 Actual

Forecast

Moving Average Graph

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• Used when trend is present – Older data usually less important

• Weights based on intuition– Often lay between 0 & 1, & sum to 1.0

• Equation

WMA =WMA =ΣΣ(Weight for period (Weight for period nn) (Demand in period ) (Demand in period nn))

ΣΣWeightsWeights

Weighted Moving Average Method

Actual Demand, Moving Average, Weighted Moving Average

Actual sales

Moving average

Weighted moving average

Moving Average Technique

Moving Average TechniqueYear t Sales Y't et

2,004 1 275 2 291 3 307 4 281

2,005 5 295 6 268 290 (22) 7 252 288 (36) 8 279 281 (2)

2,006 9 264 275 (11) 10 288 272 16 11 302 270 32 12 287 277 10

2,007 13 290 284 6 14 311 286 25 15 277 296 (19) 16 245 293 (48)

2,008 17 282 282 - 18 277 281 (4) 19 298 278 20 20 303 276 27

2,009 21 310 281 29 22 299 294 5 23 285 297 (12) 24 250 299 (49)

2,010 25 260 289 (29) 26 245 281 (36) 27 271 268 3 28 282 262 20 29 302 262 40 30 285 272 13

Double Moving Average

Double Moving Average ForecastTime Weekly 3 weeks Double Column1 Column2 Forecast (Y16) Column3

sales MA MA Value Value Y15+p = a+bpt Yt Mt M"t a b (p=1) et1 6542 6583 665 6594 672 6655 673 670 665 675 56 671 672 669 675 3 681 -107 693 679 674 684 5 678 158 694 686 679 693 7 690 49 701 696 687 705 9 700 1

10 703 699 694 704 5 714 -1111 702 702 699 705 3 710 -812 710 705 702 708 3 708 213 712 708 705 711 3 711 114 711 711 708 714 3 714 -315 728 717 712 722 5 717 1116 727

Disadvantages of Moving Average Methods

• Increasing n makes forecast less sensitive to changes

• Do not forecast trend well• Require much historical data

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• Form of weighted moving average– Weights decline exponentially– Most recent data weighted most

• Requires smoothing constant ()– Ranges from 0 to 1– Subjectively chosen

• Involves little record keeping of past data

Exponential Smoothing Method

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• Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3

+ (1- )3At - 4 + ... + (1- )t-1·A0

– Ft = Forecast value

– At = Actual value

– = Smoothing constant

• Ft = Ft-1 + (At-1 - Ft-1)– Use for computing forecast

Exponential Smoothing Equations

Exponential Smoothing Example

• You’re organizing a meeting. You want to forecast attendance for 2011 using exponential smoothing ( = .10). The 2006 forecast was 175.

2006 1802007 1682008 1592009 1752010 190

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© 1995 Corel Corp.

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Ft = Ft-1 + (At-1 - Ft-1)

TimeTime ActualForecast, Ft

(= = .10.10))

20062006 180 175.00 (Given)

20072007 168168

20082008 159159

20092009 175175

20102010 190190

20112011 NANA

175.00 +175.00 +

Exponential Smoothing Solution

4-53

Ft = Ft-1 + (At-1 - Ft-1)

TimeTime ActualActualForecast, Forecast, FFtt

(( = = .10.10))

20062006 180180 175.00 (Given)175.00 (Given)

20072007 168168 175.00 +175.00 + .10.10(180 (180 - 175.00- 175.00)) = 175.50 = 175.50

20082008 159159

20092009 175175

20102010 190190

20112011 NANA


Ft = Ft-1 + (At-1 - Ft-1)

Time ActualForecast, Ft

(= .10)


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Year

Sales

140150160170180190

06 07 08 09 10 11

Actual

Forecast

Exponential Smoothing Graph

Exponential Smoothing Techniques

Exponential Smoothing

• Y’4=0.6*S3 + 0.4*Y’3

• Dumping factor (1 – α) = 0.4

• α = 0.6• Β = 0.4

Time Sales Forecast Error1 500 #N/A #N/A2 350 350 - 3 250 350 100 4 400 290 (110) 5 450 356 (94) 6 350 412 62 7 200 375 175 8 300 270 (30) 9 350 288 (62)

10 200 325 125 11 200 250 50 12 200 220 20 13 550 208 (342) 14 350 413 63 15 250 375 125 16 550 300 (250) 17 550 450 (100) 18 400 510 110 19 350 444 94 20 600 388 (212) 21 750 515 (235) 22 500 656 156 23 400 562 162

Multiplicative Seasonal Model• Find average historical demand for each “season” by

summing the demand for that season in each year, and dividing by the number of years for which you have data.

• Compute the average demand over all seasons by dividing the total average annual demand by the number of seasons.

• Compute a seasonal index by dividing that season’s historical demand (from step 1) by the average demand over all seasons.

• Estimate next year’s total demand• Divide this estimate of total demand by the number of

seasons, then multiply it by the seasonal index for that season. This provides the seasonal forecast.

SIMPLE LINEAR REGRESSION MODEL

Module 4

• Shows linear relationship between dependent & explanatory variables– Example: Sales & advertising (not time)

Y Xi i= +a b

Dependent (response) variable

Independent (explanatory) variable

SlopeY-intercept

^

Linear Regression Model

Linear Regression Techniques

Summary Output

SUMMARY OUTPUT

Regression StatisticsMultiple R 0.863488967R Square 0.745613197Adjusted R Square 0.713814846Standard Error 2.725453111Observations 10

ANOVAdf SS MS F Significance F

Regression 1 174.1752427 174.1752 23.44817 0.001284315Residual 8 59.42475728 7.428095Total 9 233.6

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 32.13592233 4.408587726 7.289392 8.48E-05 21.96970081 42.30214385 21.96970081 42.30214385Price (x) -14.53883495 3.002445334 -4.84233 0.001284 -21.4624863 -7.6151836 -21.4624863 -7.6151836

Yt = 32.13 – 14.53 (Xt)

Simple Linear Regression

Simple Linear Regression

Week Price (x) Sales (Y) % Status Forecasting %2 Status21 1.3 10 13 2 2 6 -40% 3 -77%3 1.7 5 -17% 7 142%4 1.5 12 140% 10 39%5 1.6 10 -17% 9 -14%6 1.2 15 50% 15 65%7 1.6 5 -67% 9 -40%8 1.4 12 140% 12 33%9 1 17 42% 18 49%10 1.1 20 18% 16 -8%

14.4 112 112

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• Slope (b)– Estimated Y changes by b for each 1 unit

increase in X• If b = 2, then sales (Y) is expected to increase by 2

for each 1 unit increase in advertising (X)

• Y-intercept (a)– Average value of Y when X = 0

• If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0

Interpretation of Coefficients

4-67

• Variation of actual Y from predicted Y• Measured by standard error of estimate

– Sample standard deviation of errors– Denoted SY,X

• Affects several factors– Parameter significance– Prediction accuracy

Random Error Variation

4-68

Least Squares Assumptions

• Relationship is assumed to be linear. Plot the data first - if curve appears to be present, use curvilinear analysis.

• Relationship is assumed to hold only within or slightly outside data range. Do not attempt to predict time periods far beyond the range of the data base.

• Deviations around least squares line are assumed to be random.

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Text uses symbol Yc

Standard Error of the Estimate

n

yxbyay

n

yyS

n

i

n

iiii

n

ii

n

iii

x,y

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• Answers: ‘how strong is the linear relationship between the variables?’

• Coefficient of correlation Sample correlation coefficient denoted r– Values range from -1 to +1– Measures degree of association

• Used mainly for understanding

Correlation

MULTIPLE REGRESSION MODELModule 5

Multiple Regression Analysis

• Y = Konstanta + B1X1 + B2X2+… BnXn

Case Study

• Sosro produce 3 products (Tea, Coffee and Milk). How can I forecast the sales base on historical unit price per product?

Historical DataMonth Sales Tea Coffe Milk

1 44439 515 541 9282 43936 929 692 7113 44464 800 710 8244 41533 979 675 7585 46343 1165 1147 6356 44922 651 939 9017 43203 847 755 5808 43000 942 908 5899 40967 630 738 682

10 48582 1113 1175 105011 45003 1086 1075 98412 44303 843 640 82813 42070 500 752 70814 44353 813 989 80415 45968 1190 823 90416 47781 1200 1108 112017 43202 731 590 106518 44074 1089 607 113219 44610 786 513 839

Data Analysis

Summary OutputSUMMARY OUTPUT



Regression 3 42856229.89 14285410 9.102365 0.001126532Residual 15 23541260.74 1569417Total 18 66397490.63

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 35102.90045 1837.226911 19.10646 6.11E-12 31186.944 39018.8569 31186.944 39018.8569Price Tea 2.065953296 1.664981779 1.240826 0.233727 -1.482871344 5.614777936 -1.482871344 5.614777936Price Coffee 4.176355531 1.681252566 2.484074 0.025288 0.592850531 7.759860531 0.592850531 7.759860531Price Milk 4.790641037 1.789316107 2.677359 0.017223 0.976804052 8.604478023 0.976804052 8.604478023

Sales Prediction = 35,102.90+2.06 (Price Tea) +4.17 (Price Coffee) + 4.79 (Price Milk)

Forecast Chart

Sales Forecast44439 42863.9943936 43307.0744464 43657.6641533 43564.3146343 45326.5444922 44674.4843203 42773.3743000 43650.1940967 42744.0448582 47324.0345003 46535.2744303 43473.542070 42659.1644353 44752.0745968 45315.4747781 47559.1643202 44169.5144074 45298.81

42879.18

Case Study

• How can I forecast the sales if qualitative factors (season, event, etc) involves?

Data ConversionYear Quarter Sales GDP Unemp Int

2000 1 2007 2431 5.9 9.42000 2 2562 2640 5.7 9.42000 3 2385 2595 5.9 9.72000 4 2520 2701 6 11.92001 1 2142 2785 6.2 13.42001 2 2130 2509 7.3 9.62001 3 2190 2570 7.7 9.222001 4 2370 2667 7.4 13.62002 1 2208 2878 7.4 14.42002 2 2196 2835 7.4 15.32002 3 1758 2897 7.4 15.12002 4 1944 2744 7.4 11.82003 1 2094 2582 8.3 12.82003 2 1911 2613 8.8 12.42003 3 2031 2529 9.4 9.32003 4 2046 2544 10 7.92004 1 2502 2633 10.7 7.82004 2 2238 2878 10.4 8.42004 3 2394 3051 9.4 9.12004 4 2586 3274 8.5 8.82005 1 2898 3594 7.9 9.22005 2 2448 3774 7.5 9.82005 3 2460 3861 7.5 10.32005 4 2646 3919 7.2 8.82006 1 2988 4040 7.4 8.22006 2 2967 4133 7.3 7.52006 3 2439 4303 7.1 7.12006 4 2598 4393 7 7.22007 1 3045 4560 7.1 8.92007 2 3213 3487 7.1 7.72007 3 2685 4716 6.9 7.42007 4 3213 4796 6.8 7.4

Q1 Q2 Q3 GDP Unemp Int1 0 0 2431 5.9 9.40 1 0 2640 5.7 9.40 0 1 2595 5.9 9.70 0 0 2701 6 11.91 0 0 2785 6.2 13.40 1 0 2509 7.3 9.60 0 1 2570 7.7 9.220 0 0 2667 7.4 13.61 0 0 2878 7.4 14.40 1 0 2835 7.4 15.30 0 1 2897 7.4 15.10 0 0 2744 7.4 11.81 0 0 2582 8.3 12.80 1 0 2613 8.8 12.40 0 1 2529 9.4 9.30 0 0 2544 10 7.91 0 0 2633 10.7 7.80 1 0 2878 10.4 8.40 0 1 3051 9.4 9.10 0 0 3274 8.5 8.81 0 0 3594 7.9 9.20 1 0 3774 7.5 9.80 0 1 3861 7.5 10.30 0 0 3919 7.2 8.81 0 0 4040 7.4 8.20 1 0 4133 7.3 7.50 0 1 4303 7.1 7.10 0 0 4393 7 7.21 0 0 4560 7.1 8.90 1 0 3487 7.1 7.70 0 1 4716 6.9 7.40 0 0 4796 6.8 7.4

Data Analysis

Summary OutputSUMMARY OUTPUT



Regression 6 3181157.823 530193 9.659845 1.56789E-05Residual 25 1372157.052 54886.28Total 31 4553314.875

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 2468.149196 565.3001581 4.366086 0.000193 1303.891736 3632.406655 1303.891736 3632.406655Q1 98.63689332 118.1751743 0.834667 0.411811 -144.7494321 342.0232188 -144.7494321 342.0232188Q2 68.23580594 118.146654 0.577552 0.568732 -175.0917808 311.5633927 -175.0917808 311.5633927Q3 -175.311164 117.2567064 -1.49511 0.147404 -416.8058691 66.18354178 -416.8058691 66.18354178GDP 0.274026559 0.069927467 3.918726 0.00061 0.130008246 0.418044872 0.130008246 0.418044872Unemp -47.2973846 36.21216334 -1.30612 0.203403 -121.8777304 27.2829613 -121.8777304 27.2829613Int -56.5799921 21.56606503 -2.62357 0.014617 -100.9961341 -12.16385012 -100.9961341 -12.16385012

Sales forecastSales Forecast

2007 2423.1142562 2459.422385 2177.22520 2252.5242142 2279.952130 2336.7062190 2112.512370 2081.0782208 2192.2922196 2099.261758 1884.0481944 2203.8762094 2159.1081911 2136.2022031 2016.5162046 2246.7062502 2342.3022238 2359.2922394 2170.8442586 2466.6762898 2658.6762448 2662.5762460 2414.6642646 2704.7662988 2860.982967 2900.3322439 2735.4522598 2934.4823045 2978.073213 2721.4682685 2841.1043213 3043.044

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Sample Coefficient of Correlation

n

i

n

iii

n

i

n

iii

n

i

n

i

n

iiiii

yynxxn

yxyxnr

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• You want to achieve:– No pattern or direction in forecast error

• Error = (Yi - Yi) = (Actual - Forecast)

• Seen in plots of errors over time

– Smallest forecast error• Mean square error (MSE)• Mean absolute deviation (MAD)

Guidelines for Selecting Forecasting Model

^

4-86

Time (Years)

ErrorError

00

Desired Pattern

Time (Years)

Error

0

Trend Not Fully Accounted for

Pattern of Forecast Error

Multi Regression Forecasting

ONE WAY ANOVAModule 2

One Way Anova

Summary Out

Anova: Single Factor

SUMMARYGroups Count Sum Average Variance

Front 5 45 9 2.5Back 4 56 14 3.333333Middle 3 33 11 1

ANOVASource of Variation SS df MS F P-value F crit

Between Groups 55.66667 2 27.83333 11.38636 0.003426 4.256495Within Groups 22 9 2.444444

Total 77.66667 11

2 Way Anova

Two Way Anova SummaryAnova: Two-Factor Without Replication 17.5

SUMMARY Count Sum Average VarianceDistrik 1 4 26 6.5 28.33333 -11Distrik 2 4 59 14.75 4.916667 -2.75Distrik 3 4 85 21.25 10.91667 3.75Distrik 4 4 70 17.5 31 0Distrik 5 4 110 27.5 69.66667 10

-17.5Rep 1 5 75 15 63.5 -2.5Rep 2 5 67 13.4 59.3 -4.1Rep 3 5 102 20.4 104.3 2.9Rep 4 5 106 21.2 67.7 3.7

ANOVASource of Variation SS df MS F P-value F crit

Rows 970.5 4 242.625 13.95065 0.000182 3.259166727Columns 225.8 3 75.26667 4.327743 0.027588 3.490294821Error 208.7 12 17.39167

14.44645 4.003547Total 1405 19

Distrik 2 Distrik 2Rep 4 forecast Rep 3Mean 18.45 17.65Lower 10.44291 9.642906Upper 26.45709 25.65709

JUDGEMENTAL FORECASTINGModule 4

Multiple Regression Analysis

Any Question?

• The unique challenges associated with providing effective customer service to phone callers.

• Identify the strengths and weaknesses of your telephone styles and techniques.

• Identify effective telephone skills

MANAGING THE FORECASTING PROCESS

Module 5

Learning Objectives

• Identify methods for diffusing customer anger or hostility

• Develop strategies for handling difficult customers

• Identify which verbal and non-verbal messages exacerbate a difficult situation and which diffuse a difficult situation

Strategies to Handle Difficult Customer Situation

1. Listen– Use active and reflective listening

skills

2. Empathize– Putting yourself in customer shoes– Connect with persons feeling

• making a statement that tells the person we understand the feeling

• paraphrasing his or her words to show the person we understand the issue

– Stick to what Company can and can’t do

Strategies to handle Difficult Customer Situation

3. Respond professionally– Use customer’s name– Maintain friendly manner– Use appropriate body

language

4. Recognize underlying factors– Customer act for a reason– Negative emotion

Strategies to Handle Difficult Customer Situation

5. Ask question– Be sure to listen to everything

6. Give feedback– Treat the public as customer

seeking service– Play tour tone of voice

7. Summarize– Communicate what you will

do and when you will do it– Remember to under promise

and over deliver

Limited English Speaking• Be patient and concentrate

– Remember, the customer is just as frustrated as you are

• Speak slowly and distinctly– Don’t speak so slowly that it appears to be an insult

• Be extra courteous– you really do care and want to help

• Avoid using slang or industry jargon– Use plain, simple English. Don’t use terms or phrases that will only add to

the confusion

• Speak in normal tone of voice– Don’t shout. Speaking loudly won’t help

• Don’t try to listen to every word– Listen carefully for key words and phrases

Limited English Speaking• Reiterate what has been said

– Once the customer has told you what the problem is, summarize

• Don’t ask “do you understand?”– The customer may feel you are insulting him or her.

• Avoid humor– Stick to the problem. Different cultures view humor in different ways.

• Write it down– Use simple, short sentences.

• If you speak another language, try using it– The client may understand the other language better than English

• Develop a list of employee who speak foreign languages– Use this as a resource for helping non-English speaking customers.

• Listen to foreign language tape

Tips for Long-Winded Caller• People will monopolize another’s time on the telephone

– Don’t think silent or giving short answer will work– don’t ask questions– Refocus the attention

• Stating a relevant point

– Using “PRC” technique • (Paraphrase, Reflect, Close)

– Budget time to listen• Budget what you can afford—but don’t tell the caller you are doing this

– Establish mutual time limit• take control of the conversation before it gets too far

– Patience: Give extra minute or two• Let the other party go gracefully with statements such as:“I know you are busy. I

appreciate your help.” .“Thanks for your time. The information you have provided is very helpful. I’ll be back in touch as soon as. …”

Strategies to Handle Argumentative Customer

• Speak softly– the customer must be quiet in order to hear

you.

• Ask for their opinion– If you give them some control by asking a

question, they are liable to ease up.

• Take a break, don’t get drawn in– excuse yourself briefly, count to 10, or get a

drink of water

• Concentrate on the points of the argument– Deal with these points one at a time.

Strategies to Handle Verbally Abusive Customer

• Remember, Customer isn’t angry with you– but at the agency, the situation, or something

else completely unrelated

• Talk quietly– talk quietly so that he or she has to be quieter

to hear you.

• Talk at normal pace– If you begin to talk quickly, it will only make

matters worse

• Let the consumer know the consequences– When you use this language, it makes it

impossible for me or anyone to assist you.

Strategies to Handle Threatening Customer

• Threat can be an attempt to intimidate you

• Keep calm and keep your responses focused on the issue at hand

Strategies to Handle Threatening Customer

• Try to avoid getting into discussion of the threat– Lead the conversation back to the fundamental issue in

dispute

• Evaluate customer ability to make good on threat and decide what to do from there– Don’t overreact– Look for signs of drug or alcohol use

• Advice consumer of the repercussion– Before the threats escalate, calmly advise the customer of

the repercussions of the threats,

• Terminate the interview– document the threat, warn/alert the appropriate people

(supervisor, reception staff, etc.)

Strategies to Handle Hostile/Angry Customer

1. An angry customer is most likely not angry with you

• Don’t– Take the anger personally– Blame the customer– Avoid blame– Dominate the conversation

Strategies to Handle Hostile/Angry Customer

2. Detach yourself from the Customer’s Hostility– Maintain self control

3. Hostility curve– Let’s wait, hear him/her out

Strategies to Handle Hostile/Angry Customer• Listen

– When the customer stops talking, start giving feedback to indicate you heard his or her key points

• Empathize– you understand the situation from the customer’s perspective. Express

empathy for the feelings expressed or demonstrated.

• Apologize– Apologize when the agency is at fault

• Service– S =Say you’re sorry. E = Expedite solutions. R = Respond to the customer. V = Victory to

the customer. I = Implement improvements. C = Communicate results. E = Extend the outcome.

• Summarize– Clearly communicate what you will do and when you will do it

Saying “No”

• Sometimes you have to say no, but if you do it right, you can still get a “thank you” for your service

Saying “No”• Explain why it cant be done• Don’t quote policy

– Don’t say, “Because it’s the law.”

• Don’t be patronizing– Don’t talk down to the customer.

• Offer alternatives when u can– Try to help the customer find

solutions to the problem.

• Avoid making excuses– “I’m sorry your case hasn’t been

processed yet

• Eliminate negative phrases• Don’t mention other/similar

complaints

Group Activity

Price for Handling Difficult Customer

Any Question?

• Methods for diffusing the anger and hostility of customers.

• Strategies for handling difficult customers

SUMMARY & WRAP UPModule 6

Date post:	12-Nov-2014
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