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  Attack of the Amazons: Data Mining at

by Cliff Kurtzman
Chief Executive Officer, ADASTRO Incorporated.

September 15, 2004

"In the end, you're measured not by how much you undertake but by what you finally accomplish."

-- Donald Trump, The Art of the Deal

Over this past summer, I had the chance to stop by the third annual Emetrics Summit held in Santa Barbara, California. This event, organized in California and London each year by Target Marketing's Jim Sterne, discusses the subject of how to measure and improve online marketing efforts. In this issue of the Apogee, I provide a review of one of the sessions given by Ronny Kohavi, who is Director of Data Mining and Personalization at is an organization that is able to bring tremendous resources to address issues such as online personalization and process automation. The stakes here are HUGE... small improvements to the site result in increases in revenues measured in tens of millions of dollars. Ronny's session provided a fascinating opportunity to glimpse the kind of "Rocket Science" practiced by one of the largest scale online marketplaces in the world, and I'm pleased to be able to relate some of the details to our readers.

Ronny's team at has 70 people in it, and everything they do is focused on automation. They take things that people have done by hand and that have been shown to provide value, and they automate them, so that they can continue to receive value without having to reinvent the wheel each time.

Amazon is a Fortune 500 company, with over 41 million active customers within the last year. They fulfill to over 200 countries, and were listed as the 74th most valuable brand according to a recent Business Week survey. Last year they booked 5.2 billion dollars in revenue and were profitable. Amazon has six global sites: The U.S. site, a U.K. site, a Canada site, a Germany site, a France site, and a Japan site, all running on the same platform in a distributed development and deployment environment.

What is Amazon's vision? It consists of two things. One is to offer the Earth's biggest selection, consisting of many millions of products, and they are continually expanding the depth and breadth of their offerings. The second part of their vision is to be the Earth's most customer-centric company, focusing on how they can continually improve their ability to help customers find what they want, perform research, and make purchases.

Amazon is organized into small, cross-functional teams of both business and technology people that are able to execute end-to-end. Teams are given qualitative performance goals to meet, and then they are allowed to figure out how to best meet those goals.

Amazon's business strategy focuses on price, convenience, and selection:

  • Price - At the end of the day, this is the thing that people will continue to care about. They don't necessarily need to be the very lowest price, but they do need to be cost competitive at all times, while giving customers an experience that is reliable. They try to leverage fixed costs across a very large number of customers and inventory to have a competitive advantage.

    They believe that price is such an important factor in getting people to their site that they enable third parties to sell directly competitive products through their site even if it means that they undercut Amazon's own price.

  • Convenience - This includes features such as recommendations, wish lists, registries, and search inside the book. Delivery the same day as ordered service is being tested in New York City.

  • Selection - They offer a spectrum of offerings that ranges from a half ton Jointer that ships free with Super Saver Shipping to a Nirvana Necklace for $390,000. Specialty food items include caviar and Ben & Jerry's Ice cream. Jeff Bezos challenged them to sell milk from all mammals... so they sell Yak cheese from Tibet -- the highest altitude made cheese in the world. Books range from 22,000 pages in 20 volumes (the Oxford Dictionary) to Bhutan: A Visual Odyssey, 60" x 44" in size, $10,000 in price.

They do not consider themselves just a retailer... they consider themselves to be a technology shop, building a platform for other vendors. For example, Target Stores ( run on their platform. In Q1 2004, 23% of world-wide items sold over were sold from retailers other than

They do 19-20 inventory turns a year, with a gross margin of 24%. This compares with Barnes & Noble (3 turns/year, 27% gross margin), Costco (11 turns/year, 12% gross margin), Home Depot (5 turns/year, 32% gross margin), Best Buy (7 turns/year, 25% gross margin), and Wal-Mart (7 turns/year, 22% gross margin).

This is important because Amazon has a negative operating cycle, and they know of no other retailer that has one. It means that, on average, if they receive a product from their supplier on Day 0, then they will ship the product to a customer on Day 20, receive the customer's payment on Day 23, and finally pay their supplier 21 days later on Day 44. So as they grow, they don't need more money to build inventory.

They work to provide an extremely high level of site availability, and each service they provide must be operating at all times. They need to have graceful failures if some things should break.

They have revenue projections for every minute of every day, with upper and lower bounds. Alarms will go off when revenues go out of limit. People will get called out of meetings or paged to immediately diagnose and fix their problems.

They also sign internal performance service-level-agreements... for example, if someone responsible for a section of the site wants to have their offering featured on the home page, they might have to guarantee that their offering will be available 99.99% of the time with pages returned within 2 seconds.

They do a lot of A/B testing, where one segment of their audience is given one version of their site, while another segment of their audience is given another version. "This is the ONLY way we know to do honest experiments." They have other tracking mechanisms within the site... for example, monitoring how many people click on links, but A/B testing is the most reliable. The ability to do these tests easily is built into their platform, and every new feature that they introduce goes through these tests.

Every day at Amazon, there are probably 4-6 tests ongoing. The software allows them to tweak a feature in an experiment, and quickly have simple but detailed reports that assess changes in revenues, changes in order sizes, and how it might, for example, have increased revenues for books but decreased revenues for electronics. An experiment might have had a negative overall impact, but it might have a positive impact for a certain audience or a certain product segment. In those cases, Amazon tries to learn from what happened and design a new experiment that will have a positive overall impact.

Challenges they face in running A/B tests include:

  • Test conflicts. When two experiments touch the same feature, if they are not careful, it can make it hard to assess which experiment is responsible for an observed effect.

  • Long term effects. Some features work well at first, but then die out. Other features, such as "search inside a book," may not be greatly appreciated at first, but gain value in the eyes of users with time and experience.

  • Primacy effects. Some changes (such as site navigation changes) may not produce good early results because people are used to the site being the old way, and it takes time for them to become comfortable with the new layout, even if it offers advantages.

  • Consistency. Because of A/B testing, individuals may see different versions of the site if they access it from different physical locations.

  • Statistical tests. Distributions are far from normalized, which makes it hard to use standard statistical tests that would tell you if a change that is seen is significant. They have a large mass at zero (no purchase).

A fundamental tenet of Amazon's culture is that "Data Trumps Intuition." Over and over again they have found that people, even experienced employees, will advocate changes and new features for the site that they "are absolutely sure" will produce strong results. But many ideas fail to show significant improvement. But if Ronny's team does 50 experiments, and 4 of them produce good results (1% improvement apiece), and if each percent increase means $50 million in revenues, then the impact of running those experiments is truly significant to Amazon's bottom line!

Ronny talked about conflicts between focus groups and A/B testing. Every focus group they conduct consistently tell them that the site is too complicated, and there are too many features. Yet in testing, they consistently find that the site performs poorer when they remove features or otherwise design to try to make the site simpler. They have NEVER been able to take a feature out and show that it has a positive impact on site performance.

Adding the "we have recommendations for you" feature has proven to be quite statistically significant for them in terms of increasing sales.

Stating that "data is king at Amazon," Ronny talked about a number of examples of data driven automation. These included:

  • Management of real estate on the Amazon home page. For their home page, use of the space is highly contentious. Every category VP wants top-center placement for their offerings. Friday meetings about placements for the next week were getting too long, too loud, and lacked performance data. Now they have entirely done away with these meetings and arguments, letting automation replace intuition. The staff members that used to spend their time arguing about placements on the site now spend their time in more productive endeavors.

    Anyone in the company can submit content for the slots on the home page, and the content is run for several thousand impressions. Those campaigns that perform best get run the most, as determined by real-time experimentation with real customers. You will often see a credit card offer in the prime real estate on their home page, because it consistently brings them the greatest revenue return of anything they have tried to run in that slot.

  • Automated email measurement and optimization. For email management, they have 41 million active customers that they can target with their email campaigns. Their email campaign calender used to be manually managed, and results were difficult to measure. Their new system does automatic testing between different creative alternatives, allowing those that perform best with test audiences to be distributed to wider audiences. They also automatically run about 1000 different campaigns now per day that no human ever sees. Some of these campaigns may only target three customers that meet the targeting criteria, and it would be impossible to segment their audience that finely unless the system was automated. Their system also avoids sending out campaigns that have low clickthrough rates or high unsubscribe rates. It manages customer inboxes to prevent them from receiving too many promotions, even if they are in the target audience for many of them. One problem they are dealing with is that sometimes promotions are more successful than their current inventory will allow them to fulfill, so eventually they will want to have a feedback loop between their promotions and their inventory.

  • Tieing in behaviors of customers that have made same purchases or product searches. It is very helpful to site users to know, for example, that 38% of the people that searched for DVD X ended up buying DVD Y. This feature relies on having and crunching massive data. But there can be problems in this kind of a feature when search key words turn up results that are not really substitutable products. For example, some people that search and look at big concrete vibrator machines also may look at vibrators of an adult nature. So they need a sensitivity filter to not inadvertently show inappropriate results, even if there is a high correlation. They also need to take into account that in some product categories the products run their life cycle very quickly, and it doesn't help to show a product that has correlated well in the past, only for the customer to find out that the product is no longer available.

  • Making custom recommendations. They use a relatively simple algorithm fed off of a massive dataset. With millions of customers and millions of items, making recommendations in real time is a challenge. They want to make new recommendations for each customer based on their purchases immediately on check out.

  • Goldbox. The purpose of this feature is to introduce people to items that they are not aware that Amazon sells, and it makes a real difference in sales. They give away their profit margin on items that they think customers are not likely to purchase from them, in order to get them used to buying new categories of purchases from Amazon. Customers complain that the offerings are not targeted to their behavior, but it is entirely by design... the purpose of this feature is to encourage new behaviors.

  • Sponsored links. These are short, text-only ads, purchased on a cost-per-click basis on sites like Google and Yahoo. They built a system that generates keywords automatically, writes the creative, determines the landing page, and supports bid management based on all their product names. Their system adjusts bids based on measured conversion rates and profit per converted visitor. The system needs to be able to adjust quickly, because some keywords will produce large click rates but small sales conversion rates, and if they are unable to quickly react to those situations they could incur significant losses. They estimate that their ads now make up approximately 2% of Google click-throughs, bidding on millions of words.

All in all, it is clear that while has made tremendous progress, they still struggle with finding satisfactory solutions to many of the same challenges that the rest of us do (but on a larger scale!).

The above session summary barely touches the surface of what was covered at the Emetrics Summit. Jared Spool of User Interface Engineering gave a truly outstanding session covering the kinds of things that organizations don't realize they are doing that cause them significant lost revenues from their online marketing practices. (You can find our review of that session using this link.) We also heard case studies presented by InterContinental Hotels, SAP, Hewlett Packard, Avaya and SmartDraw. Jim Novo gave a great presentation on determining customer lifetime value, Terry Lund covered the topic of how to evaluate vendors of Emetrics software tools, and Eric Peterson of Jupiter Research talked about key performance indicators for web analytics. A panel of Emetrics software vendors gave briefings on the strengths of their products, and answered audience questions as conference attendees tried to sort through the various offerings.

If you have an interest in learning more, you can get a copy of the full handouts from the Summit along with audio recordings of the full sessions at:

Will next year find the Summits answering the same questions with new answers, or will it address entirely new challenges? Some of both, most likely. The 2005 Summits are already being planned. They will be held in Santa Barbara June 1-3, 2005 and in London June 8-10, 2005.

Details are at:

"The measure of success is not whether you have a tough problem to deal with, but whether it is the same problem you had last year."

-- John Foster Dulles

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