Enhancing the digital experience at the Smithsonian MCDP Non-Profit Challenge Alexa Arboleda, Natasha Koduri, Ruta patel, and Rachel Snyder

LANDSCAPE ANALYSIS Alexa Presents

SMALL DONATION BUTTON ON WEBSITE Donations At National museum of natural history (NMNH) SMALL DONATION BUTTON ON WEBSITE Alexa Presents Source: https://www.si.edu/museums/natural-history-museum

successful case study MET APP LAUNCHED IN SEPTEMBER 2014 USED 1.9 MILLION TIMES IN FIRST 21 MONTHS APP FEATURES INCLUDE INTERACTIVE MAPS, EXHIBIT RECOMMEDATIONS, AND DONATION LINKS Alexa Presents Source: http://www.metmuseum.org/visit/met-app

Donor and Non-Donor Demographics at NMnH Alexa Presents

Nmnh demographic snapshot Analysis based on a sample of 5,000 visitors in 2016 surveyed as they visited the museum desk. Sample dataset Shows nmnh museum attendees, on average, are:  Middle age AVERAGE AGE: 38.3 YEARS OLD White 74% ARE WHITE Educated 88% ARE AT LEAST COLLEGE EDUCATED Live in US 90% live Domestically  Alexa Presents

Demographic Analysis: donation patterns donation amounts by age group and method of donation for past donors only are broken out below. Ages 30 – 59 donate the most ($) overall Most of these donations are occurring on non-mobile platforms Teens and 20-29 year olds have larger % of donations via mobile Alexa Presents

Non-Donor attitudes toward technology out of the 5,000 visitors, 60 Non-Donor attitudes toward technology out of the 5,000 visitors, 60.6% (3030) responded they have not donated to the museum in the past. Out of the 3,030 non-donors: 70% used phone in interactive museum exhibit 56% would like supplemental info on museum exhibits  63% used phone to make payment in past year Alexa Presents POTENTIAL OPPORTUNITY FOR INCREASED USE OF MOBILE APP

SI Museum Attendance NMNH makes up ALMOST 25% of total SI visits over the past 6 years Ideal Pilot site for click to donate application Alexa Presents

Viability of click-to-donate app Findings reveal success of click-to-donate-app at the nmnh: Opportunity to pilot to a critical mass of people and make a significant difference in the Smithsonian’s Volume of donations Patterns of mobile phone usage among non-donors suggesting an increased user base for the app Trends across age groups regarding donation amount and method highlight potential for increased donations with mobile platform Alexa Presents

Data Limitations SAMPLE MAY NOT BE REPRESENTATIVE OF POPULATION BASED ON SURVEY METHODOLOGY BIASED SURVEY METHODOLOGY: CONVENIENCE SAMPLING AND NOT RANDOM MONTH TO MONTH DATA NOT AVAILABLE FOR MUSEUM TRAFFIC Alexa Presents

COST MODEL Alexa Presents

Model the cost projection model simulates various payback scenarios to determine the most likely payback period. Project Cost Breakdown Total project cost: $417,418 At month 7: SI owes $229,580 Remaining cost*: $247,469 *Includes month 8 labor charges Simulated Model Inputs # of monthly visitors Average donation amount % of donations Accenture receives Rachel Presents

assumptions SAMPLE IS REpRESENTATIVE OF THE POPULATION ANNUAL Historical data is used to determine the expected number of annual visitors Donations STAY CONSTANT ACROSS THE ENTIRE PROJECT PERIOD and do not account for seasonality. Average donation is used as an input as donations vary by age distribution 77% OF si VISITORS OWN SMARTPHONES POPULARITY OF APP Stays CONSTANT Rachel Presents

demo Rachel Presents

Projections The most likely scenario: After generating 1,000 different scenarios: The likelihood that the payback period is less than 3 months is 62.8%. The likelihood that the payback period is less than 12 months is 75.9%. The most likely scenario: after 8.98 months, 100% of the donations collected via the mobile app will go to the Smithsonian institute. Factors that must be present: - 638k visitors per month - $5 average donation among donors - Accenture receives 3.0% of donations Rachel Presents

Next steps: analysis Conduct a Controlled survey analysis for a more representative sample and refined data analysis. Collect monthly museum attendance data to enable advanced forecasting techniques that include seasonality components. Utilize advanced analytical modeling to determine the likelihood of an individual to donate.

Sources Lee W. Wilson, Natural history Museum background picture https://www.si.edu/museums/natural-history-museum http://www.metmuseum.org/visit/met-app