It is 4am in the vast but increasingly tatty Harrah’s Casino, just off the famous Strip in Las Vegas. Betty, a retired insurance clerk from Los Angeles, sits at one of the tens of thousands of slot machines, vacantly pulling the bandit’s arm and watching the wheels spin. Every now and again, she quietly cheers a small win, but really, she’s waiting for the big one. From her waist, a spiral cord stretches up to a plug hole in the machine, as if she is joined to some vital medical apparatus. She hasn’t moved for hours.
In other industries, her presence might be an unwelcome embarrassment. But to Gary Loveman, the CEO of Harrah’s Entertainment, she is just the kind of customer he has strived to attract. Casinos attract a lot of “seniors”, explained Loveman at the recent SAS Better Management Live! Conference in Las Vegas, people “who have oxygen tanks” – and they love to gamble.
“They might not be pretty, but I can show you places where there are a lot of young men chasing young women, and they make no money,” says Loveman. Families, he adds, are not only poor, but “have these destructive little people with them – their grandparents, on the other hand, have lots of money.”
Customers like Betty are critical to Harrah’s: the cord that stretches from her to the machine is not keeping Betty alive, but it is sustaining Harrah’s in its struggle to keep the most profitable, frequent gamblers from de-camping to nearby rival casinos. The cord helps to prevent forgetful customers like Betty from misplacing their Total Reward loyalty cards, which log every transaction in every Harrah’s property, from slot machines to hotel rooms and restaurants.
The cards, first introduced in 1997, are enormously popular with gamblers, who can earn substantial and valuable rewards for spending at Harrah’s. The reward programme now has 41 million members, 15% of the US population. Harrah’s encourages members to aspire to the ultimate ‘Diamond’ class of membership, where the rewards are greatest. A recent newspaper obituary, which Loveman likes to cite, read: “Grandmother of eight, a member of the choir and the Harrah’s Diamond programme”.
“When your membership scheme shows up in obituaries, you’ve got traction,” says Loveman.
The power of data
The Gold, Platinum and Diamond card names reflect the programme’s value to Harrah’s more than they do to the customers. Every time a customer makes a transaction, they are helping to dig the mine on which Harrah’s success is based – a vast database of customer preferences that can be read and reacted to in real time.
Harrah’s innovative use of this data has enabled the company to grow dramatically and earned it recognition as one of the most analytics driven companies in the world. “We’ve come out top in the casino wars by mining our customer data deeply…and using the results to develop and implement finely tuned marketing and service delivery strategies,” says Loveman.
Its success is all the more remarkable given the state of Harrah’s at the turn of the millennium, when the underperforming, low margin company was losing ground against glitzy rivals that had invested in huge new properties. In 2000, it had revenues of $3.3 billion, and was valued at around $3 billion. In 2006, helped by acquisitions, sales are forecast to reach $9.5 billion, and it has just agreed to be bought by private equity firms Apollo Management and Texas Pacific Group for $17 billion. As such, it is the largest gaming company in the world, operating four of the biggest global brands – Harrah’s, Caesars Palace, Bally’s and Horseshoe.
According to Loveman, Harrah’s uses analytics to compete at every turn. That requires a lot of IT and a lot of “bright people” – specialists in marketing, customer behaviour and analytics. It has invested heavily in NCR’s Teradata technology (building a massive data warehouse), SAS (for rapid or real-time analysis and customer modelling) and Tibco technology, for real-time event-processing (allowing insights to be acted upon while the customer is still in the building). Reflecting that, the company commits an annual budget of more than $100 million to IT.
The success of this technology has been spectacular. A report by analyst group Nuclear Research into Harrah’s Teradata investment – just one part of the programme – found that an investment of $22 million in people, training, software and hardware over three years produced direct, measurable benefits of $208 million over the same period.
Necessity the mother
Harrah’s passionate use of analytics data began shortly after Loveman, a Harvard Business School professor, was hired as the chief operating officer in 1998 with the brief to turn Harrah’s from an operating company that owned casinos into a “marketing-driven company that built customer loyalty”.
At the time, says Loveman, he had no choice but to focus on the value of the data and build on customer service. Harrah’s didn’t have the capital to rival the approach of other casinos such as Bellagio’s, which were huge, opulent casinos and entertainment centres in Las Vegas, Atlantic City and elsewhere.
“We had to compete with the kind of place that God would build if he had the money,” says Loveman. “The only thing we had was data.”
So began an extraordinary journey into advanced analytics, an activity which now consumes – but clearly justifies – hundreds of millions in expenditure every year.
Because it had a couple of year’s worth of loyalty card data, Harrah’s already knew a lot about its customers. But focus groups revealed what the management suspected – they might have cards, but they weren’t loyal. Nearly 65% of their gambling expenditure went elsewhere.
Harrah’s decided to implement a game-changing plan to persuade gamblers to spend a greater share with them. Loveman stressed that if a strategy like this is to work, it has to be fully embraced. “If you are going to do this, it has to be something that will really change behaviour. Some businesses are not able to cross that threshold,” he notes.
The first step was to find out who exactly its customers were. Two big facts emerged: 82% of revenues came from 26% of customers; and they were not the “gold cuff-link-wearing, limousine-riding high rollers” everyone imagined they were. They were Mr and Mrs Average, middle-aged and senior.
Rolling the die
Loveman might be in the gambling business, but he doesn’t believe in leaving things to chance. Hunches, folklore and instinct are out, control groups and heavy analysis are in. To get fired from Harrah’s, “you can steal, you can get arrested…or not use a control group,” says Loveman. There is no place for the headstrong manager who doesn’t use a control group because “he knows”.
Executives are exemplars. As Tom Davenport, the information management guru observes, “senior executives [at Harrah’s] set a consistent example with their own behaviour, exhibiting a hunger for and confidence in fact and analysis”.
Loveman realised that if Harrah’s was to establish loyalty, it had to give the customers “things of consequence…things of [their] own choosing”. Controlled experiments revealed that expensive gift shop discounts were of little value, but discounts on hotel rooms were “very effective”. But at the same time, analysis showed many customers didn’t stay in a hotel, but actually lived near the casino. They valued free casino chips above all else.
Further delving using empirically-based models enabled Harrah’s to understand a customer’s value over time. Those who enjoyed their visits to Harrah’s increased their spending in the following year, those who didn’t were seen to spend much less. Harrah’s responded by incentivising staff not on financial performance, but on customer satisfaction.
Harrah’s also decided, since it could track individual preferences and their value to the company, to offer different incentives to different customers, breaking a long-standing policy. In fact, it made a point of making sure those who spent less could see the benefits they were missing out on.
Now the analysts were in full swing. On New Year’s Eve, for example, the Harrah’s hotel is full, booked up long in advance. That is not necessarily a good thing: the databases showed that the most valuable gamblers tend to book late. The answer: reserve places for the high revenue generators. “You don’t leave people who are more valuable on the street,” says Loveman.
And what of the 60-year-old woman who turns up, bets for a while on a $10 slot machine, and then leaves to go elsewhere, lured by a free deal? She appears to be little value – but analysis reveals that the most frequent players of $10 machines are not typically casual gamblers. Controlled experiments show that if she is offered an extra incentive, she stays and it pays.
Into the future
The use of predictive technology is one of the latest and most effective additions to the Harrah’s armoury. Analysis of gambling patterns in the Teradata database showed that most customers have a “pain threshold” beyond which they will not bet. When losses reach that point, they may become disillusioned and leave the casino. Harrah’s now uses software from SAS to calculate each individual’s threshold, while Tibco provides an event-processing system that reports each event – such as a gambling loss – and holds the running scores in a database, which is checked in real time against a loyalty index.
This gives it the power to anticipate dissatisfaction and intervene before it happens. If the computer flags that Betty is about to reach a level of losses at which she usually quits, for example, she will be offered a free meal or a ticket to a show to keep her happy and keep her in the casino. Pandering to her superstitions, the casino will even offer to lock the machine, so that she can try to recoup her losses once refreshed.
Vivek Ranadivé, the CEO of event-processing supplier Tibco, uses Harrah’s as an example of a new wave of predictive analytics that is about to sweep through business. He praises his old Harvard room mate’s foresight: “In the years since he [Gary Loveman] took over, Harrah’s has become the most successful entertainment company on the planet. And it has done that largely through applying these [analytic] concepts and through the concept of predictive business”.
Others businesses are now following its examples by making the similar large scale bets on analytics.