June 18, 2016

Case Study Analysis: Zipcar

I've decided to share some of the individual work I did to complete my MBA from IE Business School. Below is the paper I submitted for our Entrepreneurial Management class in response to the Harvard Business School Case Study Zipcar: Refining the Business Model.

Zipcar Company Logo

Throughout my analysis I will refer to data and exhibits from the actual case. Please purchase a copy of it from HBS as I will not be providing copies here due to copyright rules. The analysis and thoughts that follow are mine and are by no means to be considered the correct and only analysis for the case.

Read more of my thoughts after the jump.

Please assess the overall validity of the Zipcar business idea.

I like the Zipcar idea because of three main criteria: a) it satisfies a consumer need and has developed proprietary technology to protect its competitive advantage; b) there is scale and scope; c) and strong financial projections to suggest a viable business model.

Zipcar would have a unique position in the market and provide a new, low-cost, convenient alternative to owning an automobile for drivers who logged less than 6,000 miles per year. With monthly cost of maintaining a car estimated at $575, Zipcar would occupy a particular gap in the market by catering to this group. Zipcar would allow these low mileage car users access to a vehicle when they needed it, without having to spend as much in maintenance costs. In addition to occupying this unique position, Zipcar would also be able to protect the position from possible competition because of proprietary technology it was developing that would allow it to have a unique pricing model (lower upfront fees but higher per mile charging) while providing consumers with convenience (card key access to any car in the system, online booking) and security.


Zipcar focused their launch in Boston, the 8th largest city in the US. This gave it a nice test area to see whether the business model would be viable. Once the business model is proven, there is a lot of potential to roll out Zipcar to other larger cities giving Zipcar good growth potential. Boston also provided a good base from which to secure positive public awareness for the company. Launching in a much bigger city would have provided a bigger potential market, but also a larger opportunity for failure and negative publicity.


Looking at the original financial projections (exhibit 3), I have done a simple breakeven analysis and computed (using an average of 18.33 members per car and including initial $200,000 startup costs) that Zipcar needs to have at least 22 vehicles or 408 members to reach breakeven. The breakeven point improves if we use the revised financial projections (see below). I believe that this makes Zipcar an attractive startup venture because Zipcar’s financial data shows potential for profitability at an early stage (as early as year 1-2) and because it has an achievable breakeven point. Because of the above points, I would recommend investing in Zipcar.


Why did the founders revise their financial plans (Exhibit 3 -> Exhibit 5)?

The revision in the financial plans was brought about by necessity. The founders quickly realized that some assumptions made for variable costs were incorrect (parking cost, lease cost, access equipment). They also realized that the upfront annual fee originally charged was too high and would become a significant barrier for new customers. To offset the increased variable costs and lower upfront annual fees, per mile usage rate was increased.


The result is a 40% increase in revenue per member (from $1,347.4 to $1,890.4) while variable costs only increased by 15% per member (from $414 to $474). This translated to an increase of 52% to projected margin per member (from $934 to $1,417), which lowered breakeven point to 15 vehicles or 269 members. However, looking at actual financial data given towards the end of the case, some more adjustments should be made to the costs given the higher than expected leasing (up 9%), fuel (up 10%), parking (up 25%) and overhead costs (up 39%).


What can you learn from the real operation data (Exhibit 8b)?

From a quantitative point of view, analysis of real data shows me that greater percentage of revenue comes from hourly uses (64%) than from daily uses (36%). This is despite the fact that daily uses have 67% share of miles driven and 58% of hours used. Thus, there is potential to further optimize the usage rates so that revenues are more aligned with actual usage rates. Also, there is a higher skew of trips and hours towards night and weekend. This opens up the potential to have an additional higher pricing tier for nighttime and weekend use that could potentially increase revenue.


From a qualitative point of view, attrition rate is only 3%, much lower than the forecasted 15%. To me this is a positive development. Also, there is some membership lost (4%) from number of members approved (105) to actual new members (101). Actual operation data can be used to improve the forecast model, the day-to-day product and overall customer service.

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