Effectiveness of Advertising on Lottery Ticket Sales...x5 $17.34 Bill Proposal: Do Good to Do Well...
Transcript of Effectiveness of Advertising on Lottery Ticket Sales...x5 $17.34 Bill Proposal: Do Good to Do Well...
March 8, 2013
Effectiveness of Advertising on Lottery Ticket Sales OR: Auditing Lottery’s Oddities
Peter Heineccius, JLARC Staff
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Sales
$500 M
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Media Buys $6.4 M
Production & PR
Vendor Fees
Advertising
Revenue $124 M
Revenue $136 M
Revenue $136 M 36
30 25 20 15 05 00
Revenue
Admin & Operating
Costs $63 M
Advertising
Prizes $301 M
Lotteries Are Oddities in State Government
selling a product instead of
providing a service
$12.1 Million Advertising Budget
(FY 2011)
x5
$17.34
Bill Proposal: Do Good to Do Well
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“Buy a ticket, get rich!” Advertises Greed
“Buy a ticket, send a kid to college!” Advertises Altruism
$89.24
Per Capita Revenue
WA GA
WA Beneficiary: GA Beneficiary: College Scholarships
Washington: Georgia:
School Construction College Scholarships
“Buy a ticket, send a kid to college!” Advertises Altruism
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Lottery Sales Higher in Eastern U.S.
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$17.34
$89.24 $-
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Per
Cap
ita
Sale
s
Longitude
Washington’s Lottery Beneficiaries
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Revenue $124 M
School Construction 80%
Other 20%
Sports Stadiums Economic Development
Problem Gambling
Scholarships/Early Learning 80%
45% of players knew education was a beneficiary
A third of those (15% of all players) claimed the beneficiary influenced their decision to buy a ticket
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Beneficiary Awareness
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6% 36%
6% of Players Distinguished New Beneficiary from Old
36% of Players Claim Identity of Beneficiary
Impacts Their Purchase 2% of Players Both
Knew and Cared About the New Beneficiary
$480
$490
$500
$510
$520
$530
$540
2009 2010 2011 2012
Mill
ion
s
JLARC Directed to Study Advertising Impact
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Beneficiary Change
Lottery spent $2.25 Million on new beneficiary ad campaign
Sales increased by $19 Million the next year
JLARC concluded that advertising had an 8.4 return on investment
Sale
s (
Mill
ion
s)
Linear Regression
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Average Impact of Longitude on Sales
Per
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Sale
s
Longitude
JLARC’s Approach
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Collected weekly ticket sales data for each of Lottery’s games
Collected weekly expenditure data for media buys (television, radio, internet, etc.) Since advertising is valued based on expected
audience, using expenditure dollars worked as a proxy value for reach
Only available for two years (104 observations)
No apparent relationship between weekly ticket sales and weekly advertising dollars
JLARC’s Approach
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Rounded up other data on likely factors that might influence ticket sales in Washington: Jackpot sizes Employment Consumer Price Index Stock Market (DOW) Gas Prices Weather Holidays Seasons
Iterations After Iterations
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Each variable added to the analysis can affect the significance of all the other variables
JLARC tried a myriad of different combinations to discover which variables were consistently significant, regardless of what other variables were in the analysis (i.e., stepwise regression the hard way)
Eventually came up with predictive models based on consistently significant variables
Model 0
Dependent variable: Sales from all games combined
Time period: 2 years; 4.5 years; 1 year Adjusted R2: 0.872; 0.754; 0.945 Advertising broken out into several variables,
tried in several combinations; none were significant EXCEPT: Advertising not related to the
beneficiary change showed a significant negative impact in the 1 year analysis
Too few observations to make a conclusion March 8, 2013 PNIAF: Effectiveness of Lottery Advertising in Washington 15
Model 1
Dependent variables: Sales from each game separately (since each game is driven by different incentives to play)
Advertising did not appear to be a significant factor in weekly ticket sales (even if lagged)
Instead, model was highly predictive of sales just based on jackpot amount, economic indicators, and holidays
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Model 1
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$-
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kly
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Actual Sales Predicted Sales
Adjusted R2: 0.912 for all games recombined
Paranoia Sets In
Model 1 was predictive…maybe too predictive… However, it would not be able to recognize the
impact of advertising if it was gradual or irregular over time – it assumed that advertising would spur more sales in the week(s) following exposure What if the cumulative effect of advertising
slowly altered people’s behavior? What if a variable that more accurately captured
advertising’s impact was omitted?
Another model was needed! March 8, 2013 PNIAF: Effectiveness of Lottery Advertising in Washington 18
Model 2
Dependent variables: Sales from each game separately
Time Period: Developed from the years prior to FY 2011 to create a baseline expected value for the year after the new beneficiary
Found actual sales in FY2011 to be $19.3 Million higher than expected But on further analysis, increase was limited to
Scratch tickets – other games were as expected Determined that the increase was due to a new
Scratch game launched in FY 2011 March 8, 2013 PNIAF: Effectiveness of Lottery Advertising in Washington 19
Model 2
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We
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Actual Sales Predicted Sales
Beneficiary Change
Adjusted R2: 0.893 for all games recombined
Natural Experiment: Advertising Hiatus
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Lottery stopped almost all media advertising during a three month period
During this hiatus, ticket sales were slightly lower than the models would have predicted However, the drop in sales occurs during the
first half of the hiatus Sales in the second half of the hiatus are slightly
higher than expected
So even if drop in sales caused by hiatus, sales returned to “normal” a month before advertising resumed
Findings
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Couldn’t find any direct impact weekly advertising might have had on weekly sales
Couldn’t find any gradual impact beneficiary advertising might have had on sales
Stopping advertising for three months didn’t appear to have any impact on sales
Limitations and Caveats
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Could not conclude why we found no impact: Poor quality advertising? Non-responsive public? Not enough advertising? Too much advertising?
Could not definitively rule out that advertising had some impact: Possible that impact is diffuse and cumulative Possible that impact is “hidden” in other factors Possible that impact needs more time before
becoming noticeable
Contact Information
Peter Heineccius, Project Lead [email protected] 360-786-5123
Stephanie Hoffman, Research Analyst [email protected] 360-786-5297
John Woolley, Project Supervisor [email protected] 360-786-5184
www.jlarc.leg.wa.gov