What are the greatest growth opportunities for new corporate teams?

Bike MS has reported that corporate teams with 10+ members are their key to a successful season of fundraising. They also have a goal of increasing active registration by almost 9% from 2017. Last year there were 74,000 riders and a total of 6,150 teams and their goal for 2018 is 80,572 riders (40,000 new who have not participated within the last five years), and 6,489 teams. On the Bike MS Business Questions page⁸, justification for targeting corporate teams:

“Because of participant demographics (mostly male, middle age, higher income earners), we know that Bike MS is an ideal corporate event and that corporate teams of 10 or more cyclists are seven times more valuable than any other kind of team. Companies with a large professional employee base, especially those with a corporate culture of health and wellness – regardless of industry – are key prospects”

Now we have looked at donations from Fortune 1000 and 500 companies and estimate that their contribution is not as significant as they might think. While the summation of donations is large for these companies, but participants employed by these companies are not more likely to donate any more than a participant in the Friends and Family division.

There is no doubt that corporate teams have a large role to play in the success of Bike MS, by bringing characteristics like corporate donation matching, inter-company competition, large teams, and the localization of generally wealthy individuals. However, In 2017 there were a total of 28.8 million registered small businesses in the United States, with anywhere from 1 to 5,000 employees. To find specific corporations that fit the bill of the most successful existing Bike MS corporations might be a perfectly good way of going about answering this question, but in my opinion extremely onerous and potentially yield the same fleeting results each event is experiencing now. Here is where the introductory information can be used to our advantage. Instead, maybe we should be thinking from the perspective of a potential donor. What would make yourself a donor of one fundraiser over another? There is a huge number of illnesses that impact larger populations of the United States than the estimated 1 million individuals living with MS. My best guess is that donors, riders, and volunteers are propelled to Bike MS because of the personal impact it has had on their own life. These are data-driven insights - not just assumptions:

participants$`Participant Connection to MS`[grep("MS", participants$`Participant Connection to MS`)] <- "Direct Connection to MS"
participants$`Participant Connection to MS`[grep("Other|other|Relative: Other", participants$`Participant Connection to MS`)] <- "Other"
participants$`Participant Connection to MS`[grep("Blank|None|No connection|NA", participants$`Participant Connection to MS`)] <- "None"
participants$`Participant Connection to MS` <- as.factor(participants$`Participant Connection to MS`)

bad.states <- c("Dallas", "Alicante", "Ardmore", "Austin", "Canyon Lake", "Columbia Heights", "Edinburg", "Greendale", "Houston",
                "Katy", "Midwest City", "Minneapolis", "Morristown", "Newport", "None", "Ridgefield Park", "west haven")
bad.state.rows <- which(participants$`Address  -  Participant State/Province` %in% bad.states)
participants <- participants[-bad.state.rows, ]

con.ms <- participants %>%
  select(`Participant Connection to MS`) %>%
  group_by(`Participant Connection to MS`) %>%
  tally(!is.na(`Participant Connection to MS`))
con.ms <- con.ms[1:3,]
colnames(con.ms)[2] <- "Count"
kable(con.ms, caption = "More participants have a connection to MS")

More participants have a connection to MS

Participant Connection to MS	Count
Direct Connection to MS	272670
None	52910
Other	22069

connect.ms.state <- participants %>%
  select(`Address  -  Participant State/Province`, `Participant Connection to MS`, `Participant Occupation`) %>%
  filter(`Address  -  Participant State/Province` %in% filt.top.state) %>%
  group_by(`Address  -  Participant State/Province`, `Participant Connection to MS`) %>%
  tally(!is.na(`Participant Connection to MS`))

connect.east <- participants %>%
  select(`Address  -  Participant State/Province`, `Participant Connection to MS`) %>%
  filter(`Address  -  Participant State/Province` %in% east) %>%
  group_by(`Address  -  Participant State/Province`, `Participant Connection to MS`) %>%
  tally(!is.na(`Participant Connection to MS`))

con.east.viz <- ggplot(connect.east, aes(x = reorder(connect.east$`Address  -  Participant State/Province`, -`n`), y = `n`, 
                                               fill = `Participant Connection to MS`)) +
  geom_bar(stat = "identity")
con.east.viz + 
  theme_minimal() +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
  labs(y = "Number of Participants",
       x = "Participant State",
       title = "Participant Connection to MS per State")

connect.ms.state <- connect.ms.state[complete.cases(connect.ms.state), ]

connection.viz <- ggplot(connect.ms.state, aes(x = reorder(connect.ms.state$`Address  -  Participant State/Province`, -n), y = `n`, 
                                               fill = `Participant Connection to MS`)) +
  geom_bar(stat = "identity")
connection.viz +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
  labs(y = "Number of Participants",
       x = "Participant State",
       title = "Participant Connection to MS per State") +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

We already know that on average individuals with the highest risk of developing MS are women in the Eastern Region of the United States. Knowing that on average participants have a personal connection to MS, encouraging populations where prevalence is high can actually make a difference. Understanding and building out a profile for the average participant can prove to be valuable - let’s take a look at some general stats about our riders starting with where the majority of participants are coming from.

tree.city <- ggplot(top.city, aes(area = Count, fill = Count, label = City)) +
  geom_treemap()+
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
                    grow = TRUE)
tree.city

tree.state <- ggplot(top.state, aes(area = Count, fill = Count, label = State)) +
  geom_treemap()+
  geom_treemap_text(fontface = "italic", colour = "white", place = "centre",
                    grow = TRUE)
tree.state

Let’s compare MS prevalence with the tree maps above to quickly see if we are on track with our assumption that where MS prevalence is high, we will see great participation.

Just as we suspected, 3 out of the top 10 participant states are within the highest prevalence region. There is a large gender gap in MS prevalence and participation in Bike MS events, but let’s take a look at the hard numbers.

# make a data frame of gender counts to make visualizing easier
gender.df <- as.data.frame(table(participants$`Participant Gender`))
colnames(gender.df) <- c("Gender", "Count")
gender.overall <- kable(gender.df, caption = 'Participant gender over all years')

# visualize gender counts 
gender.viz <- ggplot(gender.df, aes(x = Gender, y = Count, fill = Gender)) + 
  geom_bar(stat = "identity")
gender.viz + 
  theme_minimal() +
  labs(x = "Gender",
       y = "Number of Participants",
       title = "Gender of Participants") +
  scale_y_continuous(labels = c('0', '100,000', '200,000', '300,000')) +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
gender.overall

Participant gender over all years

Gender	Count
Male	287795
Female	156488

gender.k <- kable(gender.y, caption = 'Participant gender since 2013')
g.year.viz <- ggplot(data = gender.year, 
                     aes(x = Year, y = Count, fill = Gender)) +
  geom_bar(stat = "identity", position = "dodge")
g.year.viz +
  theme_minimal() +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  labs(title = "Annual Participant Gender Count") +
  labs(x = "Event Year") +
  labs(y = "Count")
gender.k

Participant gender since 2013

Gender	2013	2014	2015	2016	2017
Male	67008	63082	58553	51641	47587
Female	36241	34643	32301	28212	25112

Just as Bike MS had briefed us, the average participant is male. Maybe we assume that, in general, more men than women cycle. However, in 2015 PeopleforBikes.org conducted a U.S. Bicycling Participation Survey and found that from a sample of 16,000 citizens 43% of women reported participating in cycling activities. Bike MS is missing out on targeting the female demographic, which also happens to have the highest prevalence and incidence of MS. Is there a particular industry women contribute to most in the workforce?

Top 10 Reported Occupations of Female Participants

Participant Occupation	2013	2014	2015	2016	2017
Healthcare	2251	2074	1888	1525	1305
Education and Training	1212	1160	1092	815	701
Accounting	674	644	604	447	419
Student	516	445	434	287	242
Sales	510	474	429	342	273
Engineering	457	497	504	319	260
Information Technology (IT)	410	383	371	297	267
Legal and Paralegal	397	399	364	248	206
Marketing	385	413	375	266	223
Executive/Management	343	332	299	247	209

It appears women have the highest number of cyclists working in the healthcare industry. There is a common theme that connects each of the first priority business questions, which is generated by addressing greatest growth opportunities for corporate teams. Furthermore, our recommendation is to target corporations located in the East Coast specifically those operating in the healthcare sector. According to Business Insider, in 22 states Walmart is the largest employer, many of which are within easy travel distance from the most successful Bike MS events. More importantly, six out of the ten states defined as Eastern Region (highest overall MS prevalence) have Healthcare listed as its largest employer - Pennsylvania, Vermont, Rhode Island, Massachusetts, Connecticut, and Delaware. Can we tell if healthcare is represented in the Eastern Region?

The data shows us that only three of the six states who have Healthcare listed as their largest employer have large numbers of cyclists signing up for Bike MS events. In past years these numbers were higher, but have been steadily declining. Of the top states that participants record as their home state, all of the major occupational industries are declining.

ggplot(state.occ) +

      geom_point(aes(x = `Fiscal Year`, y = `n`, colour = `Participant Occupation`)) +
      geom_path(aes(x = `Fiscal Year`, y = `n`, colour = `Participant Occupation`)) +
      facet_wrap(~ `Address - Participant State`, scales="free_y", nrow = 2) +
      
      theme_pander() +
      theme(legend.position="right") + 
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
      labs(x = "Year",
           y = "Number of Participants")

occupation.state <- participants
occupation.state$`Address  -  Participant State/Province` <- as.factor(occupation.state$`Address  -  Participant State/Province`)
healthcare <- occupation.state %>%
  select(everything()) %>%
  filter(`Participant Occupation` == "Healthcare") %>%
  group_by(`Address  -  Participant State/Province`) %>%
  tally() %>%
  arrange(desc(n)) %>%
  top_n(10)

Can we apply those opportunities to specific rides/markets, especially our biggest events?

Bike MS has supplied us with budget information for their top 20 events. Three out of the top 20 events are located in the Eastern region where we are recommending Bike MS focus their marketing/advertising efforts. Concentration of events and date of event are of particular interest, because we will saturate the market with too many events in one area especially if they are only a couple months apart.

states <- map_data("state")
gg <- ggplot() 
gg <- gg + geom_map(data=states, map=states,
                    aes(x=long, y=lat, map_id=region),
                    color="white", fill="#cccccc", size=0.5)
gg <- gg + geom_point(data=arrange(topEvents18, desc(topEvents18$Budget)),
                      aes(x=topEvents18$Lon, y=topEvents18$Lat, size=topEvents18$Budget),
                      shape=21, color="white", fill="steelblue")
gg <- gg + geom_map(data=vor_df, map=vor_df,
                    aes(x=long, y=lat, map_id=id),
                    color="#a5a5a5", fill="#FFFFFF00", size=0.5)
gg <- gg + scale_size(range=c(2, 15))
gg <- gg + coord_map("albers", lat0=30, lat1=40)
gg <- gg + theme_map()
gg <- gg + theme(legend.position="none")
gg

The voronoi diagram partitions the United States into regions whose distance to the seed (in our case event) is the shortest. This allows us to see which populations should be going to which event based on distance from the event. Following our central theme, one practical recommendation is to allocate resources to boost participation in the Northeast states and in particular the states where the largest employer is operating in healthcare. Referencing a visual shown above, we would like to highlight the following states and encourage increased advertising in the following states because of the low numbers of cyclists working in healthcare. Below are top priority states with their respective state-wide largest employer.

State	Employer
Connecticut	Yale New Haven Health System
Delaware	Christiana Care Health System
Rhode Island	Lifespan System of Hospitals
Vermont	The University of Vermont Medical Center

Additionally for your reference, here are the largest employers in each state. You will also notice that Walmart is within the target region, which would obviously be a great sponsor.

east.health.viz <- ggplot(east.health, aes(x = reorder(`Participant State`, -Count), 
                                           y = `Count`,
                                           fill = `Participant State`)) + 
  geom_bar(stat = "identity")
east.health.viz + 
  theme_minimal() +
  theme(panel.border = element_blank(), panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
  labs(x = "Number of Participants",
       y = "State",
       title = "Number of Cyclists Working in Healthcare Within the Target Region")

What industries have had the strongest involvement in Bike MS in the last five years and what occupations were responsible for most of our fundraising?

Involvement with Bike MS can take two forms, either you participate as a cyclist or you donate to the fundraiser. We have already seen that the top occupations of participants are Engineering, Healthcare, Sales, IT, and Executive Management. From the donations data, we can discern what industries are the top donors.

The data shows us that the same occupations are responsible for the majority of the fundraising. The darker the color shows that the average donation is Men are responsible for a large sum of money, but mainly due to the significantly higher number of participants. The average donation from women is just as much as men. The national teams are also responsible for a significant amount of involvement - fundraising and participation. Here are some statistics on the top national teams over the years.

Each of these national teams have major offices across the United States and therefore can participate in the most popular Bike MS events. It is reflected

Upon exploration it was seen that the information was consistent with the rest of the data. The National Teams with the highest amount of revenue raised, varied from year to year during the 2013-2017 time period. Regardless of how much it varied these National Teams are responsible for a large portion of the overall revenue raised. Although some National Teams tend to repeat participation a larger portion of them do not, either way their teams tend to have more team members. The information here was consistent with other analyses showing the “Primary Connection to MS” overwhelmingly as “Friend has MS” followed by None/NA, and “Relative has MS”. It is important to recommend that Bike MS definitely prioritize keeping these teams engaged, but highlight the fact there is a lack of healthcare organizations among this list.

Can we tie together these industries and occupations to identify gaps/opportunities?

Again, the top national teams with the most involvement are from male dominated fields. Our central themes all apply to this question - as we stated above there is a lack of healthcare organizations involved in putting together formal teams. Of particular interest, are the corporations that are spread out across the United States and those located on the East coast. Taking a closer look at Deloitte and Salesforce, for example in 2017, Deloitte reported employing almost 85,000 operating in 97 U.S. cities while Salesforce reported employing 25,000. Targeting companies with offices in multiple cities across the U.S. has the potential to produce highly involved national teams.

What is the common denominator for our top performing corporate teams?

Echoing the previous answer, the top performing corporate teams have employ a large population and have offices across the United States. Returning to our central themes, the data tells us that connection to multiple sclerosis drives participants and donors to this massive fundraiser. The purpose of Bike MS is to fund research, advocate for social and political change, provide education, and sponsor services that help people with multiple sclerosis and their families. If the prevalence and incidence numbers are true then there is no reason participation and fundraising efforts should be declining. Let’s take a look at the biggest common denominator for not only corporate teams, but everyone involved.

Of all the responses of reported connection to MS from the corporate teams and participants, most people have a direct connection to a friend afflicted by MS. This finding does not come as a surprise given that the friend category would have the widest reach compared to sibling or spouse relationships. As participation and donations for this fundraiser decline, the one constant is that the overall prevalence and incidence of multiple sclerosis is something that persists. Focusing on the populations, occupations, and geographical locations that are impacted by this disease the most will ensure consistent fundraising and participation. Here is a visualization of the growing MS population in the United States that shows the magnitude of just the lives who are living with the disease.

# growth of MS 
# build dataframe of dates with weekly growth interval 
start <- as.Date("2012-01-01")
end <- as.Date("2017-01-08")
grow.date <- as.data.frame(seq(start, end, by = "1 week"))
grow.ms <- as.data.frame(seq(length = 263, from = 400001, by = 2290.0763))

ms.growth <- as.data.frame(cbind(grow.date, grow.ms))
colnames(ms.growth) <- c("Date", "Weekly Increase")
ms.growth <- ms.growth %>%
  mutate(Country = "United States")
ms.growth <- ms.growth %>%
  mutate(Longitude = 37.0902) %>%
  mutate(Latitude = 95.7129) %>%
  mutate(ID = "main")

ms.growth$`Weekly Increase` <- round(ms.growth$`Weekly Increase`, digits = 0)

usa <- map_data("usa")

usa.map <- ggplot()
usa.map <- usa.map + geom_map(data=usa, map=usa,
                    aes(long, lat, map_id=region),
                    color="#2b2b2b", fill=NA, size=0.5)
usa.map <- usa.map + geom_map(data=ms.growth, map=usa,
                    aes(fill=`Weekly Increase`,
                        map_id=ms.growth$ID, frame = ms.growth$Date),
                    color="white", size=0.5)
usa.map <- usa.map + scale_fill_continuous(high = "#132B43", low = "#56B1F7")
usa.map <- usa.map + coord_map("polyconic")
usa.map <- usa.map + geom_text(aes(x=-93, y=37, label = ms.growth$`Weekly Increase`, frame=ms.growth$Date), cex=10, color = "orange", fontface = "bold")
usa.map <- usa.map + annotate("text", x = -100, y = 40, label = "Total MS Population:", size = 5, color = "orange", fontface = "bold")
usa.map <- usa.map + theme_map()
usa.map <- usa.map + theme(plot.margin=margin(20,20,20,20))
usa.map <- usa.map + theme(legend.position=c(0.85, 0.2))
usa.map <- usa.map + theme(legend.position = "none")
usa.map <- usa.map + labs(title = "MS Prevalence and Incidence in the United States", 
                      subtitle = "2012 - 2017", caption = "Estimated MS population in 2012 was 400,000 with recent estimates projecting around 1 million.")
giffy <- gganimate(usa.map, interval = 0.1)

Can we quantify the effect competing events are having in our top markets?

To quantify the effect of these competing events on our top markets, it is important to understand the cause and objective of each fundraiser. Let’s assume a cyclist only participates in one to two bike fundraisers during peak season (Spring and Summer), we already know that the participants choose Bike MS, or any fundraiser for that matter, because of it’s connection to their life. Here is some key information regarding the purpose of each competing fundraiser and their known markets that they cater to:

Without any knowledge on the amount of donations and registration a competing fundraiser is generating, we took a closer look at the donations and participation that Bike MS generated in the states that the competing events take place.

It’s difficult to attribute the decrease in overall participation and donations in a particular market to the saturation of cycling fundraisers or competing events. Best Buddies is one of the biggest competitors in this arena and their biggest event, the Hyannis Port Challenge, is held in Massachusetts. The most obvious way of quantifying the effect Best Buddies has on Bike MS events is to analyze the surrounding

bestbudsHP <- bestbudsHP %>%
  dplyr::select(everything()) %>%
  dplyr::group_by(`Security Category Name`) %>%
  dplyr::filter(State %in% HPradius)

bestbudsgroup <- bestbudsHP %>%
  dplyr::select(`Fiscal Year`, `Security Category Name`, `Active Registrations`) %>%
  dplyr::group_by(`Security Category Name`, `Fiscal Year`) %>%
  dplyr::summarise(`Active Registrations` = sum(`Active Registrations`))

bestbudsHP.viz <- ggplot(bestbudsHP, aes(x = `Fiscal Year`, y = `Active Registrations`)) +
  geom_bar(aes(fill = `Security Category Name`), stat = "identity", position=position_dodge()) +
  geom_smooth(aes(colour = "lm"), method = "lm", se = FALSE)
bestbudsHP.viz +
  labs(title = "Effect of Best Buddies on Northeast Market")

bestbudsgroup.viz <- ggplot(bestbudsgroup, aes(x = `Fiscal Year`, y = `Active Registrations`, colour = `Security Category Name`)) +
  geom_point() +
  geom_line()
bestbudsgroup.viz

These two visualizations in parallel provide the perfect insight into how Best Buddies is effecting Bike MS metrics. We are able to see that specifically in Massachusetts, where the Hyannis Port Challenge is held, there is no significant change in active registrations from 2014 to 2017. We have fit a line to the average registration and see a slight decline from 2013 to 2017, but we do not believe this to be a function of Best Buddies’ impact.

What are the greatest opportunities for digital marketing investments? Where have we seen the greatest ROI?

Bike MS uses three main channels or platforms to engage their target market - Social, Display Ads, and Search Engine. Using their marketing reports from 2015 to 2017 we were able to see the growth of each platform and conversion rates that were recorded due to the budget increase for each channel. There was a direct correlation between the amount of money spend on social media platforms and total conversions. Registration was the metric used to describe conversion. Here is the breakdown of conversions per channel:

From 2016 to 2017 Bike MS allocated 75% of their digital marketing budget to social media and the remaining 25% display ads and search engine. Judging by the conversion from social media they saw in 2017 their return on investment was positive and we would recommend that they continue prioritizing social media as the primary source of digital marketing.