Fluent Essays

Uber: Applying Machine Learning to Improve the Customer Pickup Experience Case Author: Mohanbir Sawhney, Birju Shah, Ryan Yu, Evgeny Rubtsov & Pallavi Goodman Online Pub Date: January 04, 2021 | Original Pub. Date: 2020 Subject: Product Management, Creativity & Innovation in Business Level: | Type: Direct case | Length: 6233 Copyright: © 2020 Kellogg School of Management, Northwestern University Organization: Uber | Organization size: Large Region: Eastern Africa, Northern America, Southern Asia | State: Industry: Land transport and transport via pipelines Originally Published in: Sawhney, M. , Shah, B. , Yu, R. , Rubtsov, E. , & Goodman, P. ( 2020). Uber: Applying Machine Learning to Improve the Customer Pickup Experience. 5-419-752. Evanston, IL: Kellogg School of Management, Northwestern University. Publisher: Kellogg School of Management DOI: http://dx.doi.org/10.4135/9781529741728 | Online ISBN: 9781529741728 javascript: void(0); javascript: void(0); javascript: void(0); javascript: void(0); javascript: void(0); javascript: void(0); http://dx.doi.org/10.4135/9781529741728 © 2020 Kellogg School of Management, Northwestern University This case was prepared for inclusion in SAGE Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes. 2021 SAGE Publications Ltd. All Rights Reserved. The case studies on SAGE Business Cases are designed and optimized for online learning. Please refer to the online version of this case to fully experience any video, data embeds, spreadsheets, slides, or other resources that may be included. This content may only be distributed for use within University of South Florida. http://dx.doi.org/10.4135/9781529741728 SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 2 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience http://dx.doi.org/10.4135/9781529741728 Abstract Uber had pioneered the growth and delivery of modern ridesharing services by leveraging the explosive growth of technology, GPS navigation, and smartphones. Ridesharing services had expanded across the world, growing rapidly in the United States, China, India, Europe, and Southeast Asia. Even as these services expanded and gained popularity, however, the pickup experience for drivers and riders did not always meet the expectations of either party. Pickups were complicated by traffic congestion, faulty GPS signals, and crowded pickup venues. Flawed pickups resulted in rider dissatisfaction and in lost revenues for drivers. Uber had identified the pickup experience as a top strategic priority, and a team at Uber, led by group product manager Birju Shah, was tasked with designing an automated solution to improve the pickup experience. This involved three steps. First, the team needed to analyze the pickup experience for various rider personas to identify problems at different stages in the pickup process. Next, it needed to create a model for predicting the best rider location for a pickup. The team also needed to develop a quantitative metric that would determine the quality of the pickup experience. These models and metrics would be used as inputs for a machine learning (ML) model that would automate the pickup experience. Case In 2018, Birju Shah, group product manager of maps and sensors, Ryan Yu, senior product manager of pickup experience, and Evgeny Rubtsov, product analyst of maps at Uber Technologies, were working on the best way to measure and improve the quality of the pickup experience for riders and drivers. Ensuring that the pickup experience went flawlessly was a top priority at Uber. Flawed pickups could lead to rider and driver dissatisfaction, reduce driver productivity, and increase the frequency of canceled rides. Picking up a rider sounded like a simple task. Pulling off a flawless pickup experience was very challenging in practice, however. Finding the precise location of the rider and navigating the driver to the best rendezvous location in an efficient manner was not easy, especially in crowded locations like airports and concert venues. GPS navigation signals could be flawed in urban areas with tall buildings. One-way streets and parking restrictions could also create problems for drivers. Further, drivers and riders across the world had different definitions of what constituted a good experience. The Uber team members had structured the initiative as three steps. First, they needed to analyze the pickup experience so they could identify potential problems faced by riders, as well as drivers, at each step in the pickup experience. Next, they needed to create a model for pinpointing the best location for picking up a rider. Finally, they needed to develop a quantitative metric for the quality of the pickup experience. The eventual goal of the Uber team was to create an automated system that would use machine learning (ML) technologies to improve the pickup experience for all Uber riders and drivers. History of Uber Uber was founded in March 2009 in San Francisco by Travis Kalanick and Garrett Camp as a luxury car service through which users could request a ride via an application on their phone. Over the years, Uber transformed into a mobility platform, offering its users various ways to get from place to place, including various ridesharing services from personal cars (UberX, Uber Black for luxury cars) to shared carpool rides (UberPool), as well as electric bike and scooter services. Uber also entered the food delivery market with Uber Eats and was developing shared air transportation with Uber Elevate and investing in autonomous vehicles. Uber services could be used via the company’s websites or mobile apps. By 2018, Uber had a worldwide presence and operated in more than 785 metropolitan areas around the globe. That year, the company reported top-line gross bookings of $50 billion. SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 3 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience The Ridesharing Industry in 2018 Ridesharing had become a popular idea, but it was not a new phenomenon. Evidence of ridesharing could be found as far back as World War II, when acute gasoline shortages led to shared rides. The same idea reappeared during the oil and energy crisis in the 1970s. The arrival of high-end technology, GPS navigation devices, and smartphones gave birth to the modern ridesharing market, in which drivers who owned mostly cars teamed up with companies such as Lyft and Uber to provide rides to potential customers through dispatching platforms. The ridesharing market had seen explosive growth around the world, fueled by aggressive expansion of global players like Uber, as well as local competitors like Didi Chuxing in China, Ola in India, Yandex in Russia, and Grab in Southeast Asia. In 2017, the size of the worldwide ridesharing market was estimated to be $36 billion and was projected to grow to $285 billion by 2030. In the United States, ridesharing companies transported 2.61 billion passengers in 2017, a 37\% increase from 1.90 billion in 2016. 1 Ridesharing trips were highly concentrated in large cities. The nine largest cities (Boston, Chicago, Los Angeles, Miami, New York, Philadelphia, San Francisco, Seattle, and Washington, DC) accounted for 70\% of all ridesharing trips, totaling 5.7 billion miles. In December 2018, Uber’s share of rideshare spending was 68.8\%, whereas Lyft accounted for 28.8\%. Ridesharing was rapidly becoming an integral part of everyday life in urban areas, particularly for younger riders who preferred ridesharing to owning a car. Overview of Pickups The quality of pickups was a crucial factor for the success of ridesharing companies. To keep riders and drivers happy, Uber needed to ensure that riders were picked up on time at the right place and that drivers spent their time efficiently and productively. The quality of pickups was also important to the cities in which Uber operated. Uber needed to collaborate with city and state administrators in the management of road construction, road closures, and traffic. By working with cities, Uber could ensure efficient use of roads and compliance with traffic and parking laws. As a start, Uber partnered with cities to provide anonymized data from more than two billion trips to help urban planning around the world through Uber Movement. Access to such data could inform decisions on adapting existing infrastructure, reducing congestion, and investing in efficient transportation in the future. Challenges in Pickups The quality of a pickup experience could be affected by several factors. To illustrate, consider the examples below: Cultural Differences In the United States, riders preferred to interact with drivers through the Uber application or through text messaging. Riders would only call drivers if a problem occurred with the pickup, such as difficulty finding the right rendezvous location or a delay in a driver’s arrival. The contact rate for rides in the US was less than 20\%. In contrast, riders in India liked to call drivers to give them precise directions or to make sure the driver was on the way. The contact rate between riders and drivers was more than 80\% in India. Such cultural differences needed to be considered in designing a stress-free pickup experience. For instance, using the contact rate between drivers and riders as a signal of friction in the pickup experience would be appropriate for the US market but not for the Indian market. Modalities Uber offered various ridesharing modalities (UberX, UberPool, and Uber Black, as well as scooters, bikes, etc.). Riders could choose among all these options, so Uber needed to make sure that riders were matched to the appropriate type of Uber service. For modalities like UberPool, which involved sharing a car with other riders, Uber would need to choose routes that made sense for all the riders in a car, as well as for the driver. SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 4 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience Venue Pickups In many airports and event venues, Uber was allowed to pick up passengers only in designated areas. These rules needed to be communicated to both riders and drivers in an intuitive way when they opened their app. Being able to operate in such venues was critical to Uber’s business, as venue pickups accounted for a significant percentage of gross bookings and annual trips. Safety Safety was a top priority for riders and drivers. To establish trust with its users, Uber needed to build safety into the trip experience and to be prepared to help when incidents arose. All riders, drivers, and cars were protected by insurance maintained by Uber, which encouraged riders and drivers to participate in creating safe environments. For instance, in Uber’s safety tips, drivers were advised to check for cyclists and pedestrians on the road before pulling over for a pickup and to encourage their riders to sit in the back seat so that they could safely exit either side of the vehicle when they reached their destination. Compliance Cities were an important part of Uber’s ecosystem, and the company wanted to help foster a compliant and safe environment. For example, compliance in the New York City area could be confusing. A New Jersey driver would be able to pick up a passenger in New Jersey and drop him or her off in New York City. However, without an NYC Taxi & Limousine Commission (TLC) license, the driver would not be able to pick up another rider in New York. Uber worked with drivers in NYC to help them obtain TLC licenses and provided assistance and guidance throughout the process, which included a drug test, a background check, and required classes. Rider Expectations Uber catered to a diverse set of users, so riders’ expectations could vary significantly based on the rider persona and the context of the trip. For instance, a commuter who called an Uber to work every day might expect a consistent and reliable pickup from the same spot near her home each morning. In contrast, a suburban rider who used Uber occasionally might be more concerned about the choice and availability of drivers in remote suburbs. Drivers also wanted different things from a pickup. Some drivers might expect riders to be waiting when they arrived, whereas other drivers might not like the inconvenience of shared rides. These examples are just a small illustration of the complexity that Uber needed to consider in understanding and optimizing pickups across its vast global network. The challenge for Uber was to deliver a quality pickup experience that could scale with the company’s growth. History of Pickups With Uber 2014–2016: Pickup-First Paradigm From 2014 to late 2016, Uber’s mobile application centered the rider experience around the pickup experience, positioning pickups as the first step of the flow to requesting a trip (see Exhibit 1 for an illustration of the pickup-first paradigm). The onus of setting a pickup was almost entirely on the rider. Riders needed to take the key decision to set the location of the pickup. All other actions downstream (e.g., selecting the modality, setting the destination) depended on this initial action. If riders made a poor choice of pickup location, the Uber app did very little to deal with downstream problems with the pickup. Essentially, the app merely matched riders and drivers, without offering assistance during pickup. 2016–Present: Destination-First Paradigm In late 2016, Uber launched a new rider application redesigned from the ground up. In the redesigned app, riders were first asked where they wanted to go. They then selected a product and set their pickup. The app marked a radical shift in the request flow, flipping the paradigm from pickup-first to destination-first (see SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 5 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience Exhibit 2 for an illustration of the destination-first paradigm). The destination-first paradigm had an unintended consequence. The action of setting the pickup became a secondary action, so riders treated the pickup location as an afterthought. Uber found it more difficult to focus the riders’ attention on the pickup location, which could increase the possibility of pickup problems. Mapping the Pickup Experience Shortly after the rider app redesign in 2016, Uber developed an end-to-end customer journey map for the customer pickup experience that consisted of six steps (see Exhibit 3 for a visual illustration). These steps were defined as follows: • 1. Contextualize: When a rider opened the application, Uber tried to deduce his or her anchor location, a best guess at where the rider was. The anchor location was determined either using the rider’s GPS data and/or search data entered for their pickup. • 2. Assist: Uber next tried to determine the best rendezvous location (i.e., pickup location) for the anchor location. For instance, the anchor location in Chicago’s O’Hare International Airport might be the arrivals level in Terminal 1, but the rendezvous location for this anchor location would be the rideshare pickup area between Terminal 1 and Terminal 2 at the departures level. To find a good pickup spot, Uber used historical trips and outcomes to suggest a rendezvous location to the rider. • 3. Depict: In this phase, Uber presented riders with suggestions for improving their pickup location. Uber could present suggested rendezvous location candidates to the rider for selection and confirmation. Uber could also show riders a screen to refine their pickup location if it needed more information about where they were. Then, with the user’s input, Uber dispatched and passed along the rendezvous location to a driver. • 4. Navigate: Uber navigated the driver to the rendezvous point. The routing algorithm guided the driver to the end-of-route (EOR) location, which was the closest point on a road segment to the confirmed rendezvous location. The Uber driver app provided a route-line, or suggested navigation path, that the driver could choose to follow. Drivers could also choose to use their own navigation provider to get to the EOR location. • 5. Track: After dispatch, Uber enabled the rider to track the car and take action appropriately. The rider might receive push notifications about the car arriving or the driver waiting. In addition, riders could monitor and manage their pickup in several ways: They could see the real-time GPS location of the car; get an estimate of the time that the driver would take to arrive; contact the driver; or change their pickup location post-dispatch. • 6. Meet: In the final moments of the pickup, Uber did all it could to make sure the rider and driver could find each other. Most of this involved metadata the rider could see, such as rider/driver names and the car model and license plate number. In addition, Uber provided riders with tools to facilitate the finding and meeting process, including the ability to share their real-time GPS location and use their phone as a color spotlight to help their driver see them (see Exhibit 4 for details of the “meet toolkit” for riders). Analyzing Pain Points in the Pickup Experience Uber’s vision was to build an effortless pickup experience for everyone, everywhere, every time. Effortless implied a simple, transparent, and efficient pickup. Everyone included riders, drivers, and city residents; Uber wanted to improve the experience and efficiency of traveling. Everywhere meant all markets where Uber operated around the world. Every time meant that Uber aimed to deliver a consistent experience riders and drivers could rely on. These goals were represented in the vision statement image below ( Figure 1 ): SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 6 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience Figure 1: Uber’s Pickup Experience Vision Source: Uber. The Uber team created a wish list for a perfect pickup experience: • 1. The rider requested a car at the best pickup location possible. • 2. The best available driver was assigned to the rider. • 3. Driver took the most effective route to the pickup location. • 4. Rider knew exactly how to get to the pickup location. • 5. The rider and driver arrived at the pickup location at the exact same time. • 6. The rider got into the car as soon as the driver stopped, and the car was on its way immediately. Pickup experiences in practice often were less than ideal. With the ideal pickup experience in view, the Uber team next started to analyze the potential problems that riders and drivers could experience during the pickup experience. These pain points could be something as simple as an Uber driver who stopped farther down the street from where a rider actually was because the rider’s phone GPS placed the rider in a location significantly off from where they actually were. Drivers could reach the rendezvous point only to find that the rider was on the wrong side of a one-way street. Drivers could reach a pickup location on a busy city street to find that the rider had not yet arrived at the location, forcing the driver to abandon the pickup. Drivers could be stymied by the lack of parking at a pickup location, gated communities with restricted access, and temporary parking restrictions at crowded concert or sporting event venues. Riders could have trouble finding drivers at busy pickup locations like airports. The Uber team created a narrative that illustrated potential pain points at each phase of the pickup experience (see Exhibit 5). Although this illustration of general pain points was useful, the Uber team realized that the pain points, as well as customer expectations, could vary substantially based on the customer persona. To get a better understanding of pain points for specific customer contexts, the team created a set of personas to represent different customer contexts: SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 7 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience • Premium: Riders who wanted the best quality of transportation that Uber could provide. They were typically professionals who were traveling for a business purpose and were able to expense their trip to their companies. Premium customers opted for Uber Select (premium rides in higher-end cars with leather seats) or Uber Black (luxury rides for six in high-end cars with professional drivers). They expected curb-to-curb service and a personalized experience, and they were willing to pay a hefty premium for a high-quality ride. Premium riders enjoyed pickups that included a little extra time to get to the car and features tailored to suit their needs, such as temperature control and luggage service. • High-Frequency Riders (HFR): Riders who used Uber very frequently for short rides in a densely populated urban area like Manhattan or downtown Chicago. These riders could use Uber several times a day, and their rides were typically a few miles or less. They expected quick curbside pickups on busy streets, a challenging task for drivers dealing with dense traffic, one-way streets, and parking restrictions. HFRs qualified for notable rewards, such as discounts on future rides, better customer support, and priority airport pickups. For such frequent riders, Uber introduced Ride Pass, designed to let riders use Uber anywhere in a city or at any time for a low price. Ride Pass began with an introductory rate of $14.99 monthly. • Commuters: Riders who used Uber to get to and back from work on a regular basis. They had the same origin and the same destination for most trips. They expected a consistent pickup and dropoff experience and wanted to make sure that they reached work on time without having to wait a long time for the Uber driver. For these commuters, Uber launched Uber Express Pool, which cost as much as 50\% less than the standard shared UberPool ride. Users could select an area to wait at rather than a pickup point, then wait a maximum of two minutes in which Uber matched them with their rideshare buddies. • Social: Riders who used Uber for social activities like going to restaurants, concerts, sporting events, or bars. These riders tended to use Uber during late evening hours and on weekends. They often traveled in groups, and they were sometimes inebriated. They expected drivers to pick them up at late hours and at busy venues, and they were often frustrated with “surge pricing” on busy weekend nights. • Travelers: Riders who used Uber at an airport or train station as part of a long-distance travel experience. These riders included business travelers, families traveling on vacation, and international tourists visiting the United States. They expected timely and stress-free pickups at airports, and they were frequently frustrated with finding the right pickup location and the right Uber driver at congested rideshare pickup locations at busy airports. These riders also tended to have a lot of luggage, and they appreciated the driver’s assistance in loading and unloading it. The team wondered how to map the six stages in the pickup experience to the pain points for each segment. They would need to create a persona-specific list of pain points for the different phases of the pickup experience. They would also need to define the ideal pickup experience for each persona, as the outcome expectations varied dramatically across personas. The pain points and outcome expectations would be based on the persona profiles, customer interviews, and persona experience. This map of pain points and outcome expectations would help the Uber team to understand common themes in improving the pickup experience that could scale across customer segments and pickup locations. Improving the Pickup Experience With Automation With an understanding of the pain points in the pickup experience, the Uber team turned to the solution approaches to the pain points. They embarked on an initiative to improve the pickup experience using advances in artificial intelligence (AI) and machine learning (ML) technologies. Before an ML-based model could be built, the team needed to create a model to predict the location of the rider and they needed to define a quantitative metric for the quality of a pickup experience. Predicting the Location of the Rider Uber used a sophisticated process to identify and refine the pickup location of the rider: • 1. Sense Signals: Uber collected relevant data about the rider’s anchor location, such as the GPS SAGE © 2020 Kellogg School of Management, Northwestern University SAGE Business Cases Page 8 of 13 Uber: Applying Machine Learning to Improve the Customer Pickup Experience coordinates of the anchor location and manual inputs provided by the rider. • 2. Score the Anchor Location: Uber scored signals on the rider’s location by assigning either a low or a high confidence to the anchor location. If the rider had defined the anchor location through manual text input or by manually locating a pin, Uber assigned a high confidence score to the anchor location. If the rider had not entered anything, Uber used GPS data as the default source of the anchor location. If the anchor source was GPS data, the anchor confidence could be high or low, depending on the context of the pickup. If the confidence was high, Uber would move on in the pickup process. If the confidence was low, Uber would try to leverage other data to improve the confidence by using hypotheses related to the rider’s profile and behavior and the characteristics of the pickup location. These hypotheses would be used to create a confidence score for the rider’s location using a predictive model. • 3. Decide Pickup Location: Once Uber had a score for the anchor location, it would determine what to show to the riders on the app to assist them with their pickup. For low anchor confidence pickup requests, Uber showed no suggested hot spots for pickups. Instead, it showed the rider their predicted location pin and a search bar to prompt the rider to give additional information on their pickup location. For high anchor confidence pickup requests, Uber showed one or more hot- spot suggestions nearby and let the rider choose one. These hot-spot suggestions were based on historical data accumulated from rides on the Uber platform or, for certain pickup venues like airports, curated hot spots for designated pickup areas. For instance, if the rider was at an airport terminal, Uber might have suggested Door 2 on the departures level, where rideshare pickups were allowed. • 4. Adapt Pickup Location: Uber used the rider’s input (either updated pickup location or selected hot- spot pickup) to move on with the pickup process or loop again with the new signal. • 5. Refine Pickup Heuristics: Uber continued to evaluate the data from pickup experience to determine ways to optimize it. For instance, if a hot spot was rarely chosen for a specific location, often resulted in rides that were rated lower, or had led to more cancellations, then Uber might have stopped suggesting it. If Uber saw that riders always adjusted their pin to a certain spot or never adjusted it and were happy with their pickup, then Uber assumed that pickup location with high confidence going forward. To improve the confidence score of the anchor locations when GPS data was the only signal available, the Uber team needed to come up with a model to predict the likely location of the rider. GPS coordinates were an imperfect signal for determining the best rendezvous location (i.e., where the rider should be picked up). First, the GPS location could be erroneous. The GPS system worked well in clear areas under open skies, achieving accuracy to within a 5-meter (16-foot) radius. However, its positioning accuracy could be degraded by tall buildings or walls, which could reflect the GPS signals. In densely populated and highly built-up urban areas, GPS location estimates could have a margin of error of 50 meters (164 feet) or more, which could place the driver on the wrong street. GPS coordinates often could not pinpoint at which entrance of a large building the rider was located. Buildings like train stations had several entrances and exits within the same city block, which opened on different streets. If drivers arrived at the wrong entrance, they could be forced to loop around one-way streets to get to the correct entrance. Drivers could also arrive at … 1 Case Write-Ups Purpose and Guidelines Case write-ups are required for every case discussed in this class. Each will focus on a broad range of marketing problems related to topics we will discuss in class, as well as some that you may experience in your careers. Remember that you are being assessed on the quality of the answers that you provide in this write up and in the corresponding class discussion. Performing these quick case write-ups will help you to properly prepare for discussion. Some of your write ups will be collected for a grade. Others may not be. Be ready! Each write-up is to be submitted on Canvas, no later than the beginning of the class meeting in which they are discussed. The instructor is inclined to NOT accept late assignments because of in class discussion around the case on the date that they are due. The requirements for the write-up are listed below. Instructions The key to a good case write-up is to ground your analyses and recommendations on sound strategic principles: take what you learned from your marketing classes and apply it to the analysis of the case (SWOT, Porter’s 5 Forces, Customer Lifetime Value, etc.). You MUST be able to justify your position by using key information provided in the case, not just simply recommend a position. Cases should be approximately 1.5 pages in length, single-spaced, 11pt font, 1 inch margins. In most organizations, top management has a very short attention span. It is critical for you to be able to make a succinct coherent argument for your ideas to get attention. You may use bullets sparingly to drive home specific points. Your write-up is to be broken into three distinct parts (please provide headings for each part): 1. Problem(s) / Environment / What is the underlying problem (and what symptoms is it Analysis causing)? What information in the case is relevant to your analysis (e.g., trends, current marketing, performance, etc.)? Discuss analysis. Anchor analysis to a framework. 2. Reasonable Alternatives List approximately 2-3. 3. Recommendation Recommend best alternative. Provide a supporting argument using information from the case as rationale (reference analysis). Convince the reader that this is the best course of action. Make sure recommendation addresses identified problem and be explicit. Be specific – think about how such a recommendation may impact the 4 Ps. Discuss the benefits and consequences of such a strategy. Assessment Among the critical aspects that you will be assessed on are the quality and thoroughness of your write up and the clarity of your writing. Remember to use data, where necessary, to support your problem statement, analysis, and recommendations. Avoid “pie in the sky” – make sure your recommendations make sense given the case. When possible, be specific in your recommendation and make sure it addresses the problem that you identified. Clear and concise articulation of your thoughts is paramount. 2 Examples Examples of both good and poor case write-ups appear on the pages that follow. Please note the structure/formatting used, proper grammar, and the general quality of the write-up. In the good example, statements are backed up with specific information from the case. Example of a Great Case Write-Up Squatty Potty Case Problem and Environment The Squatty Potty CEO and investors are looking to determine the success of their digital marketing campaign and how to move forward with these results. Based on their marketing KPIs and financial standing at the end of 2016, I would qualify the campaign as an overall success for the following reasons: • For 2016, promotional costs per Squatty Potty purchase (on SquattyPotty.com and Amazon combined) was $3.39 • The viral video generated 32 million organic views in its first month – and while organic views demonstrated the exponential drop-off typical of viral videos, it still averaged 3.6 million per month from Nov 2015 through the end of 2016 • SquattyPotty.com had a monthly average of 368,641 unique visitors after the launch of the viral video* However, the campaign faced one significant problem: focusing their targeting on mothers. The argument made by Squatty Potty that the “purchase of these [constipation-related] solutions was typically driven by women—specifically mothers—who accounted for 70–80 per cent of all household purchases” makes sense. Traditionally, women hold significant influence over purchase decisions and may be the one physically shopping for household items. Selecting mothers as their main target audience appeared to be the best strategy prior to the campaign’s launch, but several factors support a shift in target audience: • Social: Constipation and bowel movements are topics most people have difficulty discussing or admitting to – and this may be heightened with women. It is possible that women feel less open to discussing private, “embarrassing” health matters compared to men. They may not have wanted to admit to liking the video or may have been less likely to share. The embarrassment or discomfort with the product may make them less likely to shop for it. If they are not comfortable shopping for this kind of product and they do the shopping for their household – the Squatty Potty might not get purchased even if a need exists. • Facebook promotion results: When comparing the number of SquattyPotty.com checkouts and the cost per Squatty Potty (CPA) of the various Facebook targets, many of the male groups outperformed their female counterparts. For example, the company invested an equal $17,260 in targeting each ‘Interest in Yoga, Male’ and ‘Interest in Yoga, Female’ groups – but the male group converted better with 40\% (540) more SquattyPotty.com checkouts and cost $3.75 less per conversion. Similar trends of lower CPAs were found when comparing male vs. female interest in veganism and fans of Howard Stern. While mothers may still be an important audience for Squatty Potty, they are severely underestimating the opportunities that exist for a health-conscious male audience. Reasonable Alternatives 3 1. Test & refine the targeting of males on Facebook with paid advertisements. Determine what other interests besides yoga, veganism, chiropractic medicine etc. are relevant filters to apply to reach men that would be interested in the Squatty Potty. 2. Reactivate Squatty Potty’s earned media campaign using influencers/channels more relevant to a male target audience. Recommendation I recommend alternative 2.) Reactivate Squatty Potty’s earned media campaign using influencers/channels more relevant to a male target audience. Offering the product for influencers to promote worked before the company’s widespread awareness and has the potential to be successful again. First, Squatty Potty would benefit from more in-depth research of what this male audience looks like. It would be helpful to develop a customer persona and narrow down the demographics, psychographics, and behaviors they are interested in. The more they understand this audience, the more specific and targeted campaigns they can run. Highly targeted campaigns create the effect that the product is “meant for them.” With new information on their male target audience, the way Squatty Potty approaches an earned media influencer campaign will change. Different channels may appeal to this new audience. The type of influencers they engage may change as well. They would need to determine what influencer qualities would relate to the target. The earned media approach could be useful in combatting the stigma of talking about digestive issues. It allows for real people to connect with others who have the same problems. It can develop personal credibility for the product. Influencer campaigns are easily trackable through blogs, social media, or other websites. Additionally, the increased funds the company has compared to the first run of their earned media campaign would allow them to layer on incremental tactics. They may benefit from retargeting consumers who interacted with the influencers’ content with a digital display campaign. *More information on web traffic throughout Squatty Potty’s history would help determine the marginal increases as different campaigns were introduced. They averaged 900 total visits per month prior to any major marketing campaigns. It would be important in the company’s full analysis to track traffic following their earned media/influencer campaign, feature on Dr. Oz, feature on Howard Stern, appearance on Shark Tank etc. to determine if the post-viral video campaign traffic increase is statistically significant. 4 Example of a Good Case Write-up: The Upstart’s Assault Case Problem and Environment: Meridicom has to decide on how to react to TelZip’s offer of free broadband service to new business customers who were willing to leave their current provider and enter into a long-term contract with TelZip. Meridicom needs to proceed very carefully because TelZip’s bold move may be a “game changer” within the telecommunications industry. There are 5 aspects of Meridicom that need to be understood and considered carefully before making a decision regarding the strategy that needs to be implemented to counter TelZip’s offer. • Meridicom is separated in 3 independent divisions: Landline, mobile, and broadband. Each division operates independently. Meridicom customers receive a different bill per service, which can be confusing. Also, the fact that the 3 divisions operate independently does not contribute to minimizing Meridicom’s total operational costs. • The landlines division represents 70\% of the company’s revenues, and Meridicom has an 85\% market share in the landlines market. This means that it is important that this division does not lose customers. • Meridicom has a 60\% market share in the broadband market, and the broadband division represents 25\% of the company’s revenues. Offering broadband service for free would negatively affect Meridicom’s revenues and would make the broadband division operate at total loss. • Meridicom’s major business customers complain about the company’s services being expensive. • TelZip has a much larger mobile phone market share than Meridicom’s 5\% market share. Management at Meridicom needs to understand that whatever strategy they implement will have an important impact on the telecommunications market distribution. Reasonable Alternatives: • Quickly develop and implement a strategy to minimize the operational costs, and send individual bills to their business customers instead of one different bill per service. This would decrease the company’s costs and eliminate confusions that customers may have. • Offer free broadband to their customers because TelZip’s move is a “game changer”. Customers will be expecting to get broadband service for free or change providers. • Increase prices of the landlines service by 5\%. This is the reason why: Offering free broadband would help Meridicom acquire a larger market share in the landlines market, which would increase their revenues and partially cover the losses resulting from offering broadband for free. The 5\% price increase in the landlines service is relatively low, and it is very likely that it will not be noticed because it would happen at the same time as the free broadband. Recommendation: I recommend that Meridicom increases the prices of landlines service by 5\%. As stated above this gives Meridicom the power to lower its broadband price to compete with TelZip and maintain its strong market position. This strategy takes advantage of Meridicom’s unique portfolio. However, because each division is run separately, one challenge in implementation is convincing the landline and broadband divisions that this is the best action; broadband may not want to give up its revenues and one division may not wish to subsidize the other. 5 Example of a Poor Case Write-up: Alegre Hotels Problem: The problem with this case was that Alegre Hotels just opened up a brand new flagship hotel at Palma Cay and is struggling with filling the rooms at the peak of the tourist season. Analysis: Beatriz Soto proposed that Alegre pay her $700,000 so that she can create a campaign focused towards generating more revenue and bringing more customers to the hotel. The company is struggling to accept her request because they run their hotels individually of one another. Individual hotel managers are outraged because they feel that they could use the money that Beatriz is asking for better opportunities but the company feels that they need to push their new hotel as much as possible. It seems as if Alegre Hotels is suffering from an internal calamity due to its decentralized business structure and lack of uniform brand recognition. Recommendations: I would recommend that Alegre take the approach of Best Western when trying to create a brand throughout the company. The Best Western model sticks to one initiative that exudes quality and convenience at affordable rates. Alegre should brand itself amongst its different categories because each entity is so distinct from the other. The city hotels should be branded the same as other city hotels and the resorts should be branded just like other resorts but the key to all of them is to have one common identity which is luxury and style. As far as giving Beatriz the money for her campaign my suggestion would be to closely supervise her campaign and to force her to model it off of the entire brand as a whole. The reason that she needs to brand with a companywide focus is because the company needs to maximize revenue across the board and it can essentially fund two different initiatives for the price of one with Beatriz’s campaign. The company needs to develop a rewards program because several other competitors already do that and they have seen great success in offering them. By offering rewards to customers they can drive customers back into the hotels time and time again. Independence between individual hotels is essential in achieving success amongst the brand because individual hotels run and operate in specific ways from one another and help to create a unique brand for the company as a whole. By going back to the Best Western model Alegre needs to be known for its ideals rather than the look of all of its hotels. Rubric for Case Analysis Criteria Excellent (4) Good (3) Needs Improvement (2) Unacceptable (1) Identification of the Main Problem(s) 25\% Clearly identifies the main problem(s), demonstrating ability to prioritize problems. Reports this in a concise (1-2 sentence) manner. Problem identification is clear, but prioritization is not. Reporting is longer than necessary. Symptom is reported in lieu of a problem. Problem identification and prioritization are not clear. Reporting is longer than necessary, or incomplete. Problem identification and prioritization are not clear or misidentified. Reporting is longer than necessary, or incomplete. Section is missing entirely. Application of Marketing Concepts 25\% Clearly identifies several complexities of the main problem(s). The analysis appropriately incorporates strategic marketing management concepts and financial analysis. Identifies complexities of the main problem(s). Some statements are unsupported by analysis/ calculations. Assumptions are stated, but some are not justified. The list of complexities is incomplete or unclear in some aspects, and includes items that are not reasonably linked to the problems and issues. Analysis is too narrow and does not identify a broad strategic direction. Some analysis is included, but it is not very detailed. Many statements are not supported by analysis/calculations. Most of the stated assumptions are not justified. Either the list of complexities is missing or the list is very incomplete or there is no linkage of the alternative actions to the problems/issues. None of the alternatives identifies a broad strategic direction. Shows lack of thorough consideration. Analysis is trivial or missing, lacking any depth. Conclusions & Recommendations 25\% A clear action plan is given, logically derived from complexity analysis, that provides optimal solution for identified problems/issues and that further makes sense; the recommendation is based on only one of the strategic alternative actions. Assumptions, caveats, ongoing considerations concerning recommendation are provided. The likely benefits/disadvantages of each action are clearly identified and supported by the analysis. An action plan is given, which is mostly, but not completely, logically derived from alternative analysis. There may be a better solution to the problems/issues than the one recommended. Most, but not all, assumptions, caveats, and ongoing considerations are provided. Most, but not all, benefits/disadvantages are clearly identified and supported by the analysis. A solution is recommended, but logical derivation from analysis is unclear, and there is clearly a better optimal solution. The recommendation is based on more than one alternative action. No identification of assumptions, caveats, or considerations that might affect the recommendation is provided. Several benefits/ disadvantages are missing and/or not clearly identified or unsupported by the analysis. A solution is recommended, but it is not derived from the alternative analysis at all; or the recommended solution is clearly not viable; or the recommended solution does not address the problems/issues; or there is no recommended solution. No assumptions are stated (and are needed). Likely benefits/disadvantages are not provided at all or are unsupported by the analysis. Organization, Grammar, Spelling, and Formatting 25\% Written work is well organized and easy to understand. There is a brief introduction. Sections of case analysis are marked with appropriate headings. The work has been thoroughly spell- checked and proofread. There are none to almost no grammatical or spelling errors. There are no formatting errors. The organization is generally good. There is a brief introduction and section headings. But some sections seem out of place or mislabeled, diminishing the ease with which the case reads and is understood. There are a few spelling and grammatical errors and one to three formatting errors. The organization is unclear; headings are missing. The introduction is not succinct. There is more than one spelling, grammatical, and/or formatting errors. The case analysis is disorganized to the extent that it prevents understanding of content. There are no headings. There is no introduction. There are frequent misspelled words, serious grammatical errors, and formatting errors, indicating that time was not taken to spell-check and proofread the report.