What should Yahoo! do?

Yahoo!'s CEO, Carol Bartz, has stated firmly that Yahoo! is not a search company. In fact, Yahoo!, she states, is much closer to a portal. The goal is not search, but editorial comments and a local feel. A portal, if you will, from a major media/technology firm. Unfortunately, in the coming years, portals will be less and less necessary as people will get their news from a swath of algorithmically mined sources across the social media landscape. I'm afraid the era of Yahoo! dominance has ended and will not return.

Like IBM in the 90's, Yahoo! must redefine itself. Yahoo! must sell the mills.

Again, like IBM, who has become a major integrator, and Kimberly-Clark, who has become a consumer goods powerhouse, Yahoo! must set out on the next phase of it's life - it must enter and dominate a new area or be relegated to a technological side show until it dies. But what should it pursue? To answer that question, we need to look at its strengths and products, especially those that are hard to mimic, such as those related to its core technology.

Yahoo! has world class data centers, meaning they can store and process data cheaper than anyone else (except, perhaps, google). Yahoo! also has a data processing platform that allows it to analyze web scale data quickly and efficiently. It's also beginning to build a dataflow language which allows developers to be productive. Finally, Yahoo! has a respected research organization which keeps it at the forefront of areas such as machine learning and information retrieval.

The question then becomes: "What business needs massive computational power, data storage and processing ability, and makes heavy use of machine learning and information retrieval?" The biggest one I can think of is data marketing. Analyzing billions of consumer and business records as quickly as possible and making decisions on the fly is what data marketing is all about.

Imagine a person walking into an electronics store. Currently, if the person buys something he may get a coupon to promote his buying something more, later. However, this is suboptimal for two reasons. The simplest reason is that the coupon may be unnecessary. The patron may have been planning to return even without the coupon and so the coupon represents wasted revenue. The second reason is that the coupon may be too late. It could be that if the patron would have been given the coupon on the way in then he might have bought more or upgraded or any other number of things that could have produced more revenue. It could even be that a customer doesn't buy anything at all, but would have purchased if a coupon was given beforehand. The holy grail of marketing is to sell an item to each person at an individualized cost. You want that person to pay as much as possible for the item. If person A will pay $100 for a camera and person B will pay $300 for the same camera then you want to issue the $200 off coupon to person A and no coupon to person B.

Now, imagine a system where a customer is recognized by an image recognition system the moment they walk in. Their data is retrieved instantly and a machine learning algorithm is run to determine what the person is shopping for and what the person is willing to pay. From their online profile and twitter account, the algorithm is able to determine that the person recently broke their camera and is looking to replace one. Using data from a company such as this one the algorithm can tell that they are struggling economically and will want to buy a cheaper camera. However, it also knows from statistics that a coupon for the higher priced camera has a good probability to make the consumer stretch his budget and go for the "prestige" item. A few more checks against the data center reveals that the current store location has excess inventory of the higher priced camera and therefore the algorithm decides to dispatch a bigger discount coupon for the better model. The sales attendant walks to the customer and presents the coupon, escorting the customer to the camera aisle. The sale is made and the computer gets an extra volt in its bedtime snack ;-)

More seriously, Yahoo! could perform those computations more efficiently and with better accuracy than an enterprise. Enterprise data centers are not going to reach the efficiency of Yahoo!'s data center. Moreover, enterprise IT programmers are not going to have the time or penchant to do the necessary IR and ML research. Yahoo! is uniquely positioned to do those things; furthermore, since it is dangling on the precipice of disaster now is a good time to bite the bullet and make the change. Not that I expect them to. Change is something that is hard to accept and Ms Bartz doesn't seem like the type to change their core business...if she ever figures out what that is. More than likely the model I express above will be adopted by a new comer...perhaps by these guys.