Earlier this week, Nintendo Europe's senior marketing director, Laurent Fischer,
weighed in:
"For me, you are a gamer or non-gamer," he said. "I think most of you
know that you can spend ten or twenty hours on an Internet flash
game... The guy who plays these games regularly - he's a core gamer."
And then the next
day, Electronic Arts Mobile's European Marketing Director, Tim
Harrison,
followed suit:
"I think the big difference in terminology here is that when people say
'casual games' they assume a certain type of game, or a certain kind of
person," he told GamesIndustry.biz. "The reality is that it's a
lot
more complex than that - there are certain types of people in certain
types of need states, and a gamer in one environment will have a very
different set of criteria to a gamer in another environment."
There's definitely some truth in what both execs say. A lot of
marketers make assumptions about casual gamers based on the data that's
available. While casual gamers are, on average, older and female, that
certainly doesn't mean that
they're all older and female -- and it absolutely doesn't mean all
casual games should be designed for that audience.
But despite the odd bad assumption, there's still a use for the "casual" game distinction that shouldn't be lost.
One part of the definition that Fischer and Harrison both attack is the
idea that casual games should be quick and easy, and this is where I
personally take issue. The key trait of a casual game is that it's
simple to learn its primary
mechanics -- that while it may be possible to play for hours upon
hours, such intense play is not required to make progress. These
qualities are what attract a broad audience, and what convert nongamers
into gamers -- the essence of casual
gaming.
But that's just my take. Leave a comment and tell us what you think
makes a casual game casual, or if you think the whole classification is
just bunk.
Former MediaPost reporter Shankar Gupta is now an Online Communications Strategist at 360i. |
the Online Customer’s Next Want
By ERIC A. TAUB
Marketers have always tried to predict what people want, and then get them to buy it.
Among online retailers, pushing customers toward other products they might want is a common practice. Both Amazon and Netflix, two of the best-known practitioners of targeted upselling, have long recommended products or movie titles to their clientele. They do so using a technique called collaborative filtering, basing suggestions on customers’ previous purchases and on how they rate products compared to other consumers.
Figuring that out is not so easy. For one thing, people do not always buy what they like. Someone may buy a sweater for their grandmother even though they dislike it and would never get it again. Similarly, a person who rents a movie may actually detest it but knows her child likes it. Or a film that was seen on a small airplane screen may garner a lower rating than if it were seen at a large multiplex.
The search for a better recommendation continues with numerous companies selling algorithms that promise a retailer more of an edge. For instance, Barneys New York, the upscale clothing store chain, says it got at least a 10 percent increase in online revenue by using data mining software that finds links between certain online behavior and a greater propensity to buy.
Using a system developed by Proclivity Systems, Barneys used data about where and when a customer visited its site and other demographic information to determine on whom it should focus its e-mail messages.
For instance, an e-mail message announcing sales might go to those Web site visitors who had purchased certain products or types of products in the past, but who had done so only when the items were on sale. In the simplest terms, if someone buys only when something is on sale, but never buys anything in December, then the e-mail sale flier might not be sent to that customer in December. “There is a digital trail of interest left by customers,” said Sheldon Gilbert, Proclivity’s chief executive and founder.
The observation about sales could be integrated with other behavior. Does the customer buy only when an item reaches a certain price? Is the customer more likely to buy on a weekend or during the week? Must it be organic material? An algorithm would weigh those behaviors to determine the likelihood that someone will open the e-mail message, and once opened, decide to click through to the site and buy the product. The more data, the better it gets at predicting, says Proclivity, which is based in New York.
“One customer found that 10 percent of its population accounted for 60 percent of bargain sales. So on the day of the sale, you can send a full-price ad to everyone else,” said Mr. Gilbert.
Barneys experienced at least a 10 percent increase in online revenue, as compared to control groups, said Larry Promisel, Barneys’ vice president of e-commerce. It found 20 percent more customers would purchase once sent the targeted e-mail messages. The company has saved money by not sending e-mail letters to customers unlikely to buy.
Not only are sales increasing, Mr. Promisel said, but with the store focusing on customers with items they are likely to buy, its clientele feels that it understands their interests, which increases good will.
Still, the problem of knowing what people want is hardly solved. While Netflix has persuaded almost five million subscribers to provide two billion movie ratings to its site, the company still has trouble figuring out exactly what somebody will like.
“I wish I could tell you that our recommendations system was reliable, but it’s not perfect,” said Reed Hastings, Netflix’s chief executive.
At best, Netflix knows that if someone rates a particular drama highly, it can predict what other drama they might like by correlating one’s rating of that film with others. “But if I know your taste in drama, I do not know your taste in horror,” Mr. Hastings said.
As customers value selection and rapid delivery more than recommendations, the company is not that worried about its prediction system. Even a 10 percent improvement of its ratings system has not been possible. Netflix has offered a $1 million prize to anyone who can do that, but to date, only slightly better than a 9 percent improvement has been achieved.
“Using as much information as you can is very important,” said Yehuda Koren, an AT&T Labs researcher, who was part of the group that achieved the results. To do even better, Mr. Koren would “track all clicks, the movies that people searched for, the pages they jumped to, their mouse movements,” information that Netflix does not now collect.
Doing this type of analysis, Mr. Gilbert of Proclivity believes, would stop retailers from sending out buying recommendations based on outdated information.
“I still get e-mails from Amazon recommending books based on the Jared Diamond titles I bought three years ago,” he said. “But I get nothing about my interest in gardening.”
July 2005 August 2005 September 2005 October 2005 December 2005 January 2006 May 2006 June 2006 July 2006 August 2006 September 2006 October 2006 November 2006 January 2007 March 2007 April 2007 June 2007 August 2007 October 2007 November 2007 December 2007 January 2008 February 2008 March 2008 April 2008 May 2008 June 2008 July 2008