CHALLENGE

I am CEO of a intercity passenger transportation busline in Mexico. Historically, our industry has  not engaged in price-competitive dynamics, so busline tickets are sold typically at a fixed prices  with few discounts available for the elderly and students.  We had the hypothesis that offering our  passengers access to lower price tickets would créate value for the customers and drive demand  and sales up, as well as increase bus occupation rates. Creating a dynamic pricing system was not  easy and would demand a substantial amount of investment and time, so we decided to take a  design thinking approach.

SELECTION

We chose the Learning Launch tool, since we wanted to test a hypothesis, we had a bunch of  assumptions (e.g., customers would be willing to buy round-trip instead of one-way if a discount  was offered, or people would be inclined to plan their trips earlier if tickets were sold cheaper for  future travel dates instead of paying full-price tickets buying the same date). We had some data  from similar industries such as the airline industry but no data for our industry, so we wanted to  test the hypothesis and assumptions fast and cheap before engaging in any system development.

APPLICATION

As professor Essen explained in his Learning Launch tool video, we followed a similar approach to  testing our hypothesis.  We formulated the hypothesis (i.e., dynamic pricing will increase number  of passengers in a route (occupation) and yield higher revenue (average price ticket will decrease  but overall total revenue will be higher).

The assumptions were: there are different type of price-sensitive customers that will take  advantage of discounts for buying earlier or buying round-trip tickets. Other assumption was that  cannibalization for current prices would be offset by the additional passengers gained.

We selected 10 different routes to serve as pilots to target different type of customers (leisure,  work-related, college students, etc). We created a baseline using historic data from previous  months to compare occupation, revenues and average price ticket sales.

Finally we also incorporated some in-depth interviews to customers in those routes to gain some  additional insights and to corroborate or contrast with the hard data obtained from the pilots.

INSIGHT & APPROACH

We were able to gain many insights on what our customers valued and expected.

1.-We learned that the customers valued the discounts and they were willing to plan ahead in  order to get the discount. We also discover interesting insights from the in-depth interviews, for  example, we discover there was also a potential improvement in brand value since they felt our  company was the first one in the market to care for them offering these lower price alternatives.

2.-We also learned that heavy-users valued the discounts the most, so that created a whole set of  new hypothesis on how to maximize the value for these customers, including the potential

creation of a loyalty program and other new initiatives to test specifically with these type of  customers.

3.-We also gained insights into optimal levels of discount based on anticipation to reduce  cannibalization and achieve income growth through incremental passengers. It required a lot of  iteration and constantly testing and adjusting prices to capture different responses from our  customers.

4.-One of the things we would do differently next time is reduce the number of pilots. It was  probably too much information and complexity to handle, so it was hard and time consuming to  analyze and did not add any additional insights to the learning process.

