There’s more and more buzz growing around the next generation of Google Analytics, Universal Analytics. As it slowly rolls out, more and more people are talking about some really interesting applications.
I thought I would take a moment to write another piece, grounded in a bit of reality, about how Universal Analytics is an analytics platform for business, not just a single digital channel. Remember the goal of Universal Analytics is to help drive strategic customer insights.
Just a quick disclaimer: This is a personal post on my personal blog. These are my personal ideas and do not reflect those of my employer.
Let’s get started!
Those of you that know my know that I love skiing. So I thought I would combine my two passions, analytics and skiing, as the subject matter for this post.
Technology is helping the ski industry change. Resorts and equipment manufacturers are embracing new technologies, like mobile, to make it easier for the resort to connect with customers and streamline business processes.
I thought I would work through a hypothetical Universal Analytics implementation plan for a ski resort then discuss some of the strategic insights we could get from the data.
Like every business, all of this data can be silod. Measuring across all of the data sources is a functional and technical challenge. That’s where Universal Analytics comes in. It can be the platform that spans all customer touch points – both online and offline.
Measuring the Website
The easiest thing to measure is probably the website. Heck, we’ve been measuring websites for 15+ years.
The most common customization is using Custom Dimensions (previously called Custom Variables) I can think of a few custom dimensions that we should implement on a ski resort site. First I’d want to know if this is an existing customer or prospect. If it is a customer I would want to know the type (individual, family, etc.).
Now let’s talk about measuring the conversions that happen on the site. Users are able to purchase many different types of lift tickets. From single-day tickets to season passes, weekend-only passes to full passes. All of these transactions need to be measured with the ecommerce tracking code.
I also want to measure other micro conversions, like social connections (Twitter, Facebook, Email newsletter, etc.)
We also need to measure marketing, things like marketing channels, ad variations, etc. . We do this using campaign tracking. This will be critical when we measure the ROI of various channels.
Finally, and this is something new to website measurement, we want to join data from the website to other data, like the resort’s CRM. The key is literally a key – some type of user ID that the CRM uses. When someone logs into the website we need to add that key to the Google Analytics data. If a user converts offline we’ll be able to join that data to online usage, and vice versa. This feature has not been released yet but will be soon.
Now let’s move to the offline world.
Measuring the Resort
We can also integrate Universal Analytics into the POS (point of sale) systems at resort. This includes the cafetaria and other retail shops.
We’d want to collect all the transactional data using the Measurement Protocol. This would include revenue, product names, categories, etc.
Many reorts allow customers to add money directly to their ski pass. They can then use the ski pass like a credit card and purchase food in the cafeteria or equipment in the ski shop. We could join the transactional data from the POS system to other data (like website) via the User ID associated with the ski pass.
Let’s jump back to the inline world.
Measuring the Mobile App
Today most ski resorts have a mobile app. Heck, it seems every business has a mobile app!
The app provides all sorts of information about the mountain and the conditions. It can even record a skiers route around the mountain and measure how many vertical feet a person skied in a day. We skiers love to brag about how many runs we do in a day :)
This is another spot where a Universal Analytics integration can happen.
Using the Android and iOS SDKs the resort can measure all the standard app metrics, like sessions, screen views, feature usage, etc. These metrics can help the resort fine-tune the app and develop features that are popular.
There’s also a wealth of error reporting to help the dev team fix features those that may not be working right.
Again, all of the data from the app can integrated into a user’s profile via a user ID – as long as there is some mechanism for users to log in. Nothing personally identifiable.
Back to the offline world.
Measuring Skier Traffic on the Chair Lift
Here’s where things get fun! Almost all modern ski resorts have some type of technology-enabled ticket checking.
For those that don’t ski, anyone that wants to ride a ski lift needs a pass. In the old days your pass would be manually checked by someone at the bottom of the lift.
Today lift operators use bar-code scanners to verify a skier’s pass.
More modern lift tickets have a small RFID chip embedded in the ticket. The skier walks through a scanner and it automatically validates their pass.
This is another place where Universal Analytics can be applied. With some additional programming the measurement protocol can be integrated into the ticket checking system.
When a customer get’s scanned Universal Analytics can record this unique skier. It can measure which chair lift they boarded and the difficulty of the ski runs serviced by the lift.
Remember, the data would need to be collected in a web-based framework. So a session or visit would be equivalent to a single ski run. And the average time on site would be equivalent to the time it takes between skie runs.
Importing Other Data
Another key feautre of Universal Analytics is the ability to import other data into Google Analytics. This will help all businesses add more context to their data. For a ski resort I’ll want to import a few different types of data.
First, cost data. I want to import all the cost information associated with my advertising. This helps me better calculate ROI on advertising. This is something that is active now. So you should already be doing this :)
Next, I would want to import snowfall data. I want to see snowfall by the day of the week. This will help me better understand a lot of other data in my system. I would expect to see more skiiers skiing on days when it snows. I would also expect to see more traffic to the website and more people recording their ski runs using the app.
Less snow means less people skiing. That’s great context for my analysis.
Drawing Strategic Insights from the Data
Now it’s time to use all of this data!
Obviously we can measure each specific silo and understand what’s happening to our customers. That’s what we do today. But we want to take a more strategic view and segment the data and a whole. Here are some of the questions I would want to answer:
Question: Do customers spend a lot of time on a certain type of terrain?
Action: If they do then perhaps I can offer them a more appealing ski package. For example, if I notice someone skiing on the Extreme section of the mountain perhaps I offer them a discounted lesson with one of our extreme skiers.
Question: Do customers spend a lot of time in the cafeteria?
Action: Could I offer this customer a better food-service package? Perhaps some type of discount? I could segment based on those that buy family passes and look for more insights. Perhaps I find that people like Justin, who buy a family pass, don’t spend much money in the cafeteria (primarily because Justin is cheap!). Maybe I create an ‘Hot Chocolate Treat’ package to incentivise Justin to get hot chocolate for his kids.
Question: Do customers buy a lot of equipment in the rental shop?
Answer: For those that do, perhaps we can offer them a yearly gear-rental package to help save them money. Or at least add some coupons for use in the store.
Question: Are skiers getting a good value for their investment?
Answer: We can use the data from the pass-scanners to better answer this question. Just segment based on the various customer typess (individual, family, unlimited, weekends only, etc) and view actual number of runs completed. This could be awesome marketing information and help the resort taylor their marketing to each individual customer, giving the right customer the right ticket offer.
Now that’s #smartdata.
When you think about it, this is the same thing that we currently do with website or app data. We segment based on behavior and look for opportunities.The difference is that now we’re looking at a larger data set that includes many different types of behavior.
Another key analysis opportunity is measuring the value of each customer. I can get total value, based on all transactions, in one system, which is a huge benefit.
As I thought about this analysis I tried to think about the value of mobile. In this particular instance I don’t see a lot of value from the app. Sure, I can measure the customer value of those that use the app. But I don’t think there are as many strategic insights from this data vs. other data sources (like chairlift of cafeteria).
There you have it. How I might architect Universal Analytics solution for a ski resort. Remember, not all of these features have been activated yet. There is no cross device tracking, and data import is currently limited to cost data. But we’re moving towards the future. If you create a new web property in Google Analytics is will be a Universal profile.
I’d love to hear what excited you about Universal Analytics and how you plan to merge your online and offline data!
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