I typically focus on a generic, one-size-fits-all approach to many of my posts. This week, I’ll be focusing more on the smaller mountains and sharing some insights and ideas specific to them. It doesn’t mean you big guys can’t benefit, but I’ve got a soft spot in my heart for the little guys.
Marketing is a lot of things. It’s creativity, it’s human behavior, it’s business. But the one thing we often forget is that marketing is math. Knowing whether a guest is worth $1,000 of $10,000 over their lifetime is critical to know when buying advertising. In other industries, it’s a massive part of a marketing department’s time and budget. In the ski industry, not so much.
As that slowly starts to change, and resorts see the true power in what many call “big data”, I want to make sure smaller resorts don’t get left behind.
So, consider this a crash course on database marketing the way I see it. It’s not perfect, but this is the kind of data I’d start to think about gathering if I were on the marketing staff at small ski resort.
Criterion 1: Unique Identifier
The first thing you need to build your database is a unique identifier for guests. Best bet? Probably an email address. Unlike names, these are completely unique. And unlike physical addresses, there is only one way to write them. For that reason, my customer info in the past has usually started with my email list.
Criterion 2: Contact Methods
Knowing who a guest is is one thing. Knowing how to reach them again is another. Email, phone, physical address, social media usernames, etc. all fall into this category. The two I’d focus on are email and physical. Email serves as a unique ID, and physical can be used for direct mail and something we’ll talk about in a sec.
Criterion 3: Transactions
When you know a guest’s history and behavior, you can segment them based on the revenue they generate. This may be as simple as having a list of “pass holders” and another of “potential pass holders”. A step above would be using transaction data to figure out the value of a new passholder vs. a new lodging guest so you know how much to spend on advertising to acquire new guests.
Criterion 4: Details with Positive Correlations
Next, you’ll want to capture data that correlates to significant changes in guest behavior. For example, older guests typically lodge much longer than younger guests, so age would be great to have. Out-of-state guests stay longer than locals, so a location would be awesome as well. Family, days skied last year (for pass holders), etc. are more examples of this type of data.
Criterion 5: Can Be Used to Get More
Some information is simply a gateway to more details about that person. Social media is a perfect example. When you know a specific guests Twitter handle, you’ve just been granted public access into their daily lives. Same goes for an address. There are plenty of 3rd-party appends with deep guest data, but even the Zillow API can give you basic income levels and family sizes based on location.
So, to recap, my bare-bones list would be:
Notice that many of these of the types of gateway data I mentioned in #5. From transactions you can figure lifetime value. Physical address can be used for income range. Age can be used to estimate other attributes that correlate to stages in life (married, job, kids, retirement, etc.).
Guest data is the key to smarter marketing decisions and more effective marketing campaigns. If you can’t gather it all now, start piece-by-piece. Segment your email list into lodging vs season pass holders. Then, see if you can add a zip code. Add in basic transaction groups and go from there.
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