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Lessons from the casino industry on engagement metrics and lifetime value

Great book covering the modern casino industry
I recently
stumbled on "Winner Takes All," which is a great overview of the modern
casino industry starting with Steve Wynn, Kerk Kerkorian, and Gary
Loveman. It starts out mainly talking about the amazing vision of Steve
Wynn, and how he was able to create some of the world’s more expensive
and opulent casinos, including the Mirage and the Bellagio.
In it, they also talk about a bunch of techniques that the casinos use
to maximize on revenue, including vertical integration, in which they
build "cities within cities" at a casino, so that you can eat, sleep,
shop, entertain, and gamble all without leaving a single complex.

(scroll below for more)

Harrah’s, the casino run by quants
The big story for me was the formation and operations of Harrah’s, which mostly constituted lower-tier casino boats for much of their history. They were decidedly unglamorous, and seemed uncompetitive to the entire high-touch Vegas scene. Think of them as Wal-Mart of casinos, versus Wynn’s Prada of casinos. Whereas the Vegas casino scene was very focused on "art" and the creating massive experiences, Harrah’s was run by the numbers and very methodical about how they grew their business.

Harrah’s eventually became the largest casino company in the world, and is led by Gary Loveman, who got his PhD in Economics from MIT. And they grew their business like a business run by a quant. Here were the major steps they took, as outlined from the book:

  • First, they created a loyalty card to centralize identities and create consistent experiences
  • They created a granular calculation of LTV for their customers
  • Then, Harrah’s segmented their key clients based on usage, and then based on "lifecycle"

All in all, a very interesting approach – I jotted down a couple notes as I was reading the book, and wanted to share some of these thoughts below:

Building a single identiy – the loyalty card
Unlike on the Web, it’s not easy for businesses to keep track of their customers – this becomes a big problem in an industry like gambling, where a small % of your customers make up a big % of the profits (aka, these guys are the "whales"). So if a gambler went to their regular Harrah’s casino, people would recognize them and they’d get differentiated service – but if they went to a different Harrah’s, for example on vacation, their history didn’t follow them. That way they couldn’t differentiate between the $100k spender versus the casual looky-loo, which was bad for business.

So Harrah’s introduced a series of loyalty cards called Total Rewards, which were used for "comps" and other free stuff. For the games folks out there, notice that you can "level up" as a member from Gold, Platinum, Diamond, Seven Stars, and for one member – Harrah’s "best" customer – there’s a Chairman’s Club card. They go so far as to fly you around, give you free hotel and accomodations, and other great perks.

This loyalty card gave them the underlying data which they could now use to drive the other parts of their data strategy.

LTV on a per-user basis
The next step once they had all this data was to create models against the lifetime value (LTV) of their customers. This was done in two ways – first, you can imagine a visit to a casino, where a customer comes in, plays cards/slots/whatever, and then leaves. Based on their actions, a "theoretical win $" is calculated, which is an expression of what the casino should expect to get from that person. Combining this number and other services consumed and comps, you end up with a net profit calculation. You can imagine that this number is a rolled up view of:

  • How much money that person brought with them
  • What games they played, in what mix
  • How long did they play for
  • What other services did they consume
  • etc.

Once you can value an individual session, then you can also chain together multiple visits to calculate an aggregate value. This means that you can now tell the approximate difference between a rich customer that visits every July 4th, once a year, versus someone who plays frequently but also spends less money.

Targeting based on customer lifecycle
Josh Kopelman from FirstRound Capital recent wrote a great blogpost called Lifecycle Messaging that I’d encourage you guys to read. It basically talks about the lifecycle of a customer, and how you want to send them differentiated messaging based on what stage they’re in.

Harrah’s did exactly this – once they had the ability to model out a customer’s LTV, then when new customers arrive, you can start to put them into buckets of profiles that are already "like" them, in order to predict future LTV. Then based on LTV and their stage in the lifecycle, you can start to do some very interesting things: For new high-value customers, they can try to engage them quickly and get them highly personalized service right away, so that they’ll stick. For low-value customers who don’t fit the Harrah customer profile, it may be better to ignore than group than spend too much cash chasing it.

One of Harrah’s most profitable customer segments turned out to be older, retired gamblers who came by very often, and mostly played slots. They called these guys Avid Experienced Players (AEPs) and targeted this group for both new customer acquisition as well as retention. This group was not the "whales" of the Vegas casinos, but had a similar financial heft to the company.

There’s a ton to learn from external industries, and I’d like to add casinos as an interesting place to extract lessons for Web entrepreneurs. It has an interesting blend of quantitative data, in gambling transactions, as well as the qualitative, which drive the emotions behind why people prefer the Bellagio to other hotels. It’s one of the industries that is at a fascinating intersection of both, and like the social web, you need both perspectives in order to thrive.

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