FORECASTING VISITATIONMarch 06, 2014

GOALS FOR TODAY’S PRESENTATION

Overview of predictive analytics and modeling process

Share a use case that illustrates PA

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THE MODELING PROCESS

USE CASE PROFILE

Science center in the Midwest

Approx. 800,000 visitors a year

Approx. 20,000 member households

The Raiser’s Edge for fundraising

Ticketmaster VISTA for ticketing

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How do we make more money?

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How do we make more money?

What are the factors that affect visitation?

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What do we think the factors are? Exhibits Day of the week Seasonality Holidays

These are the “predictors” – use these to create the modeling database

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MONITOREXPLORING THE DATA

Generally become familiar with the data

Where are the outliers?

Are you finding evidence of bad data?

Do you have the data you need?

Transform the data so it is ready to be modeled

EXPLORE THE DATADEFINE

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EXPLORE THE DATADEFINE

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EXPLORE THE DATADEFINE

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10.07.55.02.50.0-2.5-5.0

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MODELING: FIRST PASSDEFINE

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MODELING: FIRST PASS = 44%Predictor Coef P

Constant 1085.08 0Mon -651.48 0Tue -650.91 0Wed -266.8 0Thur -308.87 0Fri -56.84 0.388Sat 507.88 0Apr -128.2 0.412May -253.93 0.011June 370 0.001July 1019.8 0Aug 843.4 0Sept -392.99 0Oct -398.2 0Nov -179.2 0.014Holiday -214.8 0.053Holiday Wkn 355.26 0EXH2 578.5 0.01EXH3 448.9 0.069EXH4 62.6 0.908EXH5 629.3 0.01Active Exh+ -3.2 0.995

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EVALUATE AND IMPROVESECOND PASS = 66%

7.55.02.50.0-2.5-5.0

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Normal Probability Plot(response is ADM)

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EVALUATE AND IMPROVETHIRD PASS = 85%

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43210-1-2-3-4

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ADM = 1124 + 354 DW_SUN - 448 DW_MON - 339 DW_TUE + 113 DW_WED + 297 DW_FRI

+ 1295 DW_SAT - 189 M_JAN + 102 M_MAR + 25.1 M_APR - 454 M_MAY

+ 360 M_JUN + 1349 M_JUL + 972 M_AUG - 515 M_SEP - 565 M_OCT

- 426 M_NOV - 541 M_DEC + 426 EXH_26 + 450 HOL_WKN + 144 AE

- 17.2 AE_01 + 917 SPR_BRK + 1064 CXNY_WKS + 4952 P_COH + 202 FAMFRI

- 54 FTH_WKS - 3689 RWB - 2576 MKT_EV - 416 NH_COL - 1971 NH_FTH

+ 2798 NH_MLK + 3058 NH_LBD + 1196 NH_MEM - 1273 NH_NYD + 3009 NH_PRES

+ 309 NH_VET + 633 CH_ESTRS - 2201 CH_ESTRM + 1776 CH_GDFRI + 3332 NYL

+ 3938 PKD - 2169 OLOW + 2838 OHIGH + 1738 STDH - 1421 STDL + 1275 S99

- 1155 S01

THE FINAL MODELDEFINE

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THE FINAL MODEL

Constant 1124

Predictor (top) Effect Predictor (bottom) EffectCommunity Open House 4952 September -515New Year's Week 3332 December -541Labor Day Weekend 3058 October -565President's Day 3009 New Year's Day -1273Martin Luther King Day 2798 Fourth of July -1971Good Friday 1776 Easter Monday -2201July 1349 Red White and Boom -3689

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COMPILE DATA TO PREDICT ADMISSIONS

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COMPILE DATA TO PREDICTDEFINE

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PREDICTION LINE FIT PLOT

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July Admissions Line Fit Plot

July 2012

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COMPARING TO REALITY

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July Admissions Line Fit Plot

July 2012

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DEFINE QUESTI

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The model translates the your strategy into numbers

Business decisions could include… Adding or reducing staffing and volunteers more strategically Open the right amount of ticket windows Opening an auxiliary room to handle lunch overflow Planning for shuttle parking and security Leveling visitation - if you know a day will likely be low attendance, you

could move events or group outings

Using a visitation model, you can… Invest resources more efficiently Improve the visitor experience