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Nov 30, 2018 – Liftopia we work hard to help our partners run their businesses more effectively. We do this often through our focus on improving the e-commerce funnel and increasing return on marketing spend (usability, pricing strategy, customer communication, etc.). We also spend much of our time providing our partners with data to try to help aid in their decision making.

As our software becomes more broadly used (not to mention as the years pass), we’re able to provide more information to understand performance of a ski area’s e-commerce system – using relative data to normalize performance across the industry as a whole.

With that we’re taking a small step to unearth some market-level data (for those of you who remember we did this first after the 2016/17 season in our Market Overview Report) on a more regular basis – such that our partners and the industry as a whole can get a sense of demand and booking patterns, and ultimately we aim for this information to be helpful for any ski area looking to improve their e-commerce execution.

This is very much a “beta” report, but we’ll start releasing it weekly and at some point (hopefully) in real time. This is imperfect, is not “all the information”, and certainly could be improved – but that is the point. You have to start somewhere.

Example: Change in Bookings by City: Week over Week

What are we looking at?

In this report you’ll find…

  1. Week in Review: Take a look at metrics like searches / visit, demand capture, revenue / searches, booking window, and average order size. We’ll show you how these numbers vary between “Partner Direct” vs. Liftopia.com bookings over the past 7 days. This section also highlights booking patterns in geographies across North America week over week.
  2. Search Data: Understand demand patterns over the last 15 order dates and ski dates, and what to expect from the next 30 days ahead.
  3. Demand Capture: Gain insight into the percentage of customers that are looking vs. shopping over the last 15 order dates and ski dates, and what to expect from the next 30 days ahead.
  4. Guest Days: Find out how many ski dates were booked for the last 15 order dates, last 15 ski dates, and upcoming 30 ski dates.
  5. Yield Ratios: Lastly, in this section you can see how yield ratios change over time, and the spread of prices vs. window for ski dates looking back 15 days and ahead 30.

Why are we looking at it?
We want you to be able to better understand e-commerce trends in the North American ski industry, and be able to compare your own e-commerce trends to the market. While this data certainly does not represent the entire industry, we believe it provides a helpful snapshot of trends we’re seeing across our dataset that brings value back to you.

What can you expect from us going forward?
Lots more data! We’re going to send this report weekly to you, so you can see how trends change from week to week. As always, we’re open to your feedback as to how we can improve the data we provide.

Autor: Kathryn Quinn

2 Replies to “Beta: North American Ski Market Pulse Report”

  1. Our ski season is over and my data collection is not near as sophisticated as yours but heres our recap of the season : Liftopia sales were down about 50% over the past year…factor – we cut the discount from around 40% to about 20%….our yield went up and dollars went down. So this year we have to look at raising the base price so we can give a bigger % discount with a lower apparent yield but a greater revenue capture. Saw a similar thing in our season pass sales as we raised the base price by 50% for last winter. Result was ouch as we dropped from our normal 80% renewal rate down to the industry average. Caused us to revamp our season pass approach where we now offer three types of passes….schools still out on this. Still dont like it that the entire Midwest is lumped into one pot rather than a state by state packaging as is done in other parts of the country. Groupon seems to be very strong in Wisconsin though we didnt use them last year

    1. Hey Gren – thanks for your comment! What you describe is pretty common in the data that we see – that is if you focus too hard on yield growth, you forego revenue. We have many times seen within our dataset that consumers disproportionately punish overpricing, and your results are very much an outlier from the rest of the network (average $ growth on Liftopia.com YOY was 23% this year – that data will be in this year’s market overview, coming in a few weeks.

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