Personalization & Recommendations: Telling E-Tail Customers What They Really Want

By Rachelle Crum
E-Commerce Times
06/20/08 10:22 AM PT

Personalization is one way e-tailers hope to bolster their cross-selling and upselling efforts and increase customer loyalty. Online consumers are embracing the idea of having tailored suggestions served to them during various stages of their shopping experience.

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E-tailers are taking a new look at personalization these days as they hunt for more ways to cross-sell, upsell and encourage repeat business. E-commerce solutions providers and research firms are advocating the integration of personalization into online strategies as one way companies can optimize cross-channel marketing and keep pace with the industry's changing dynamics.

Personalization tools let e-tailers make tailored, real-time recommendations for additional purchases at various stages of the shopping cycle -- from the initial view of product details to the shopping cart and final order confirmation pages. They offer e-commerce firms opportunities to maximize the value of each customer interaction and promote customer loyalty .

The leading book retailers appear to have mastered this strategy: Amazon (Nasdaq: AMZN) has its "Recommended for You" items and "Customers Who Bought ... Also Bought"; Borders' (NYSE: BGP) new online storefront includes a Magic Shelf -- a feature that lets customers view as many as 20 shelves of book, movie and music titles matched to their tastes; and Barnes & Noble suggests items "You May Also Like" during checkout, in addition to listing "also bought" information.

A Top Priority

E-tailers are turning to personalization for an assortment of reasons -- to increase sales, to keep up with competitors, and to differentiate themselves in a crowded field.

Penetration of U.S. households is leveling off, and "the trick now ... is to get more out of existing customers," says Forrester Vice President and Research Director Carrie Johnson.

Experienced online shoppers like recommendations, she notes; they are much more interested in engaging with a customizable tool than others, and they're willing to spend more money.

"The bigger the spender, the more interested they are in personalized recommendations," says Lori Trahan, executive director of marketing for ChoiceStream.

Seventy-six percent of consumers polled for ChoiceStream's 2007 personalization survey said they wanted personalized recommendations, and 56 percent said they were more likely to return to Web sites that offer them.

Research and Solutions

Other researchers and e-commerce solutions providers -- including the Aberdeen Group, Sitebrand, ATG and Loomia -- are also pushing personalization with renewed gusto.

For example, Aberdeen reports that a whopping 91 percent of the Best-in-Class organizations that participated in a 2007 survey saw improvement in online conversion rates after introducing personalization.

"More and more retailers are turning to it," Sitebrand President and CEO Justin Shimoon told the E-Commerce Times. "The technology has gotten much better, and it's a proven methodology."

Countless retailers have been running their online storefronts for several years now, and are ready to take their operations to the next level, Brian Collins, vice president of product management for ATG, told the E-Commerce Times.

Companies used to view their online stores as just another outlet, but "I think there's just a certain level of maturity that retailers are reaching online," he said. "More recently, they've gotten more focused on optimizing e-commerce and taking operations that they have and increasing sales."

Weighing Options, Measuring Results

However, piecing together a personalization strategy can't be done overnight. Among the numerous factors e-tailers need to consider are whether to use manual or automated personalization tools (or a combination of both); how to measure results; and how to match products to customers -- including new, anonymous customers they don't yet know anything about.

E-tailers can use clickstream or popularity-based data to serve new customers with recommendations, suggests David Block, ChoiceStream's senior vice president of product marketing and product management. Within a few clicks by a new customer, a personalization engine can begin to make timely recommendations.

Alternatively, a retailer can suggest popular or traditional items instead of going out on a limb with scant data from a few recent clicks. Of course, that's not very personal.

"Traditional recommendations are valuable but limited," Block acknowledges. "They don't maximize the real estate [but] they're certainly far better than doing nothing."

E-tailers can also piece together suggestions by analyzing users' historical data -- that is, accessing cookies, Sitebrand's Shimoon notes.

However, while retailers are collecting these piles of data, they aren't necessarily being studious about measuring the results, adds Block. "Most retailers put solutions in place and actually don't measure them, [but] measurability is critical. ... Without the measurability, you don't know if you're succeeding."

What is the Recommender Industry?

GUEST COLUMN: What is the Recommender Industry?
Author: MSG Staff
By Dr. Rick Hangartner

Dr. Rick Hangartner, Chief Scientist, MyStrands, has nearly 30 years experience developing computing hardware and software in the aerospace, data communications, heavy-trucking, and supercomputing industries. Prior to joining MyStrands, he worked for seven years at Cray, Inc. developing high performance computing hardware and software.

No, the headline on this entry is not a careless grammatical error. Nor is the question really “What is the recommender market?” That would imply that “recommenders” are mature, well-defined technologies that deliver specific features and value to the online world. Emerging recommendation technologies are currently setting the standards for discovery and personalization in today’s social networking-dominated Web 2.0 environment-and the future of online social networking is all about discovery and personalization. While search engines help you find things you know you are looking for, discovery helps you find the rest.

If we accept that every business must make its case in 10 to 20 seconds on its Web site, then we are all but forced to admit that recommenders, more than anything else, represent the conceptual answer to the question, “How can I get that visitor/user/customer to realize that I offer something of value to him or her?”

Although venture capitalists and Web 2.0 users may find that claim to be just the tiresome excuse they need for hitting the “Back” button, the point is that a good argument can be made that unlike search engines, the recommender idea is a formal concept that has as many different concrete examples as there are separate market applications.

The recommender industry really is the business of pulling three components together into a system that helps a user-driven business convince their potential customers that they should stay for a while. These three elements include,

1) An effective model that relates the needs visitors have to what the business offers,

2) Quality data to build a model instance that relates specific needs to specific offerings, and

3) Unobtrusive means for easily and quickly determining an individual user’s needs.

Note that these three components are not quite as simple as “good (statistical) algorithms,” “a lot of data,” or “simple user interfaces.” In the coming years, defining an effective model will increasingly involve a scientific approach to understanding user needs and the market strategy of the business. Gathering quality data will require more sophisticated understanding of which data are actually relevant to the model. Devising means for characterizing an individual user’s needs will depend on a refined understanding of how people implicitly and explicitly signal needs that they themselves may not even fully understand.

In short, the recommender industry is the evolving business of building and deploying systems that reify some of the psychology of human economic transactions. What this means for the marketplace seems relatively clear: Search engines as we know them will never disappear. In the near term, search engines will increasingly incorporate simple recommender technologies to handle approximate queries (e.g., “You asked for this, and based on similar queries/behavior by others, you might be looking for this.”). But in the long term, the recommender industry will be larger, and recommender technologies will be more pervasive than the search industry and search technology as we know it.

Beyond that, some general themes about the future of the recommender industry that seem to be worth watching for include,

Multiple revenue models: Unlike search engines, which primarily are monetized through contextual ads of some form, recommender systems will be monetized in multiple ways. Recommender technology suppliers will continue to partner with customer businesses to derive revenue as a share of explicit sales increases directly accredited to the recommender system. In the longer term, recommender technology will increasingly enable business models, including advertising schemes, which could not exist without it. An implicit valuation for a specific application of a recommender system will be derived from the enabled economic activity.

Increasing focus on how users require change over time: In that recommender systems reify aspects of the psychology of economic transactions, there is an increasing appreciation for the probable value of responding to how economic behavior changes over time. This includes how an individual’s needs change over time and how the needs of the community evolve. The former can, in part, be accommodated by simply taking care to build a recommender system instance using data that is an adequate sampling of individuals whose needs are changing. Adapting to the latter may require recommender system models that explicitly incorporate features of how community needs to evolve.

New concepts of personalization: One of the recent trends in personalization is using information about an individual’s social network to better characterize that individual’s needs and interests. This may be just one aspect of a new concept of personalization that puts the focus not on delivering an isolating, customized experience to a person, but rather on connecting an individual with affinity communities who can provide information of value to that individual. Few people really want to be out there all alone. And for those explorers who do, they might, in reality, be hoping to build a community of like-minded souls or be waiting for others to catch up with them.

More than anything, the future of the recommender industry is a business that will continue to grow and become more sophisticated as the science of recommenders greatly develops to increasingly encompasses computer science, psychology, economics and cognitive science.

Recommendation Engine MyStrands Expands War Chest to $55m to Go Beyond Music

Written by Marshall Kirkpatrick / December 4, 2007 / 4 comments

How do you navigate a nearly infinite world of digital data to find the best content for your tastes and needs? Our collective answer to this question is in its infancy, but Oregon based recommendation service MyStrands has now raised a whopping $55 million to build on the existing science of recommendation.

In a world at risk of information overload, where the line between content producers and consumers is no longer clear and where the pace of everyday life is increasing rapidly - I'd say the recommendation engine business is a very smart one to be in. There is ample precedent and this startup is moving into a relatively established field. Richard MacManus lauded the company's previous multi-million dollar investment and our enthusiasm here for this project continues.

The Money

The company announced this morning that it has raised a $24 million B round to take its recommendation system far beyond music. The company says it intends to "lead the social recommendation industry." The round was lead by Spanish bank BBVA and with the participating of existing investors from their June round of $25m. That's an insane amount of money and some people are bewildered why MyStrands has been given it. I'm not one of those people.

The Initial Product and Sales

The company started with an iTunes plug-in that recommends songs similar to what you're listening to and quickly expanded its offerings to include a full multi-media juke-box platform that lets people personalize public playlists with their mobile phones and profiles from home. With offices in Oregon, New York and Barcellona, Spain - this is a web 2.0 company with brisk sales already. The company says it will bank $12 million in 2007. That's no mean feat.

Why This is Very Smart

Music, however, is just the first of many areas of engagement for MyStrands. A huge part of the Amazon.com story is its product recommendation process. Netflix has some of the world's top scientists racing each other to outdo its in-house recommendation engine for a million dollar prize. Last.fm's music recommendation community went to CBS for $280 million. StumbleUpon built web page recommendation into a tasty morsel for eBay to scoop up.

Now MyStrands has a war chest to hire top scientists and bring try and take recommendation to the next level for any type of data. If you can't see the value in that, then you're probably not paying attention.

MyStrands would be a great place to see attention data made real, be it in APML or some other open data standard. This is the kind of company that could create piles of that data or make great use of inbound Attention Data for superior recommendations. MyStrands' recent hire of Scott Kveton, Chair of the OpenID Foundation, to be the company's Director of Open Platforms makes me think that something like that is probably in the works. Kveton says the company is "looking closely at APML, as well as working on some other 'open formats' for describing user taste data. The gist is, the users own this data and we want to give them as much control over it as possible."

When I first saw MyStrands several years ago I thought it looked like a trivial and akward little iTunes plug-in. Like so many startups, though, this company had a much bigger vision all along. Now that vision has $55m in backing, is making big hires and is will record $12m in sales already this year. I'd say this is one company you've got to keep an eye on.

Rethinking the recommendation engine:Click here for the upsell

Web sales pitches are getting better, thanks to new programs designed to sell you stuff you didn't even know you wanted, reports Business 2.0 Magazine.

By Erick Schonfeld, Business 2.0 Magazine editor-at-large

July 11 2007: 6:38 AM EDT

(Business 2.0 Magazine) -- Online shopping recommendations are the Internet's answer to the old-fashioned upsell. "You like that red Prada hobo bag? You'll love this black canvas number from Dolce & Gabbana."

And apparently they work. Consumers last year spent $220 billion online, according to Forrester Research analyst Sucharita Mulpuru, who estimates that recommendation systems can account for 10 to 30 percent of an online retailer's sales.

Up to 30 percent of an online retailer's sales may be driven by product suggestions.

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This, naturally, suggests that there's yet more money to be made by offering even better product suggestions. Amazon.com (Charts, Fortune 500) popularized the practice a decade ago with a system that suggested items to customers based on what they and others like them had previously bought -- or even just window-shopped for.

As online shopping scales up, however, the limitations of Amazon's approach are starting to become all too apparent. One reporter who purchased two backyard animal books as a gift -- the only books on the topic he's ever bought -- complains that three years later Amazon is still recommending that he buy Squirrel Wars: Backyard Wildlife Battles & How to Win Them.

Amazon CEO Jeff Bezos says he is not exactly sure why this happened but views personalized recommendations as a "key differentiating factor" for his company. Amazon's engineers are constantly working to make the system better, he says. "If we have 66 million different customers, we want to have 66 million different stores."

The 50 Who Matter Now

But even as Bezos fine-tunes his existing system, several new companies are determined to beat him at his own game. Moreover, they have already started to hire out their sophisticated sales tools to other online retailers.

ChoiceStream is one of the largest newcomers, with more than $10 million in revenue. But scrappy startups like Aggregate Knowledge and CleverSet are also gaining customers. All three are focused on "discovery" -- selling people goods they didn't know they wanted. And all three work on the premise that you need more than a customer's old shopping list to get him to buy new stuff.

Take CleverSet, currently being tested by big-name retailers like Drugstore.com, Sephora, and WineEnthusiast.com.

CleverSet's engine analyzes consumer purchases by product descriptions, prices, ratings, and dozens of other attributes. The software organizes the information into a relational database and then offers products with similar attributes, even if they're not big sellers. Buy a book about camping in Alaska, for instance, and it might suggest a subzero sleeping bag.

CleverSet also tracks how visitors click through a site and makes educated guesses about whether they're browsing, researching, or buying -- knowledge it uses to close the sale. So far, the techniques seem to be paying off: CleverSet CEO Todd Humphrey claims that the 75 online retailers using his engine are averaging a 22 percent increase in revenue per visitor.

Beating Oprah at the book club game

ChoiceStream uses a similar approach, especially for movies, music, and the like. The company has painstakingly categorized 40,000 films by more than 50 attributes; these allow its system to match movies by genre, actors, plot type, and more.

For instance, if you like the movie Babel, it might suggest that you check out Do the Right Thing. At first glance, the Brad Pitt vehicle set in Morocco and the Spike Lee movie set in Brooklyn might not seem to have much in common. But ChoiceStream's engine knows that both are complex, unpredictable, suspenseful, and based on sociopolitical themes; each plot is heavy on character transformation and delivers a twist.

That matchmaking logic can help lead viewers to niche titles they otherwise wouldn't have considered, something that's helped Blockbuster (Charts, Fortune 500) compete with Netflix (Charts) in online DVD rentals. Since Blockbuster adopted ChoiceStream, cancellation rates have fallen and subscribers have nearly doubled the number of movies on their order lists.

"We think the technology is more accurate than anything we have looked at," says Shane Evangelist, general manager of Blockbuster Online.

The discovery market has turned out to be lucrative; many industry insiders think it will eventually be as important as basic search. "Discovery is when you don't know what you are looking for to start, but you know it when you see it," says Paul Martino, CEO of Aggregate Knowledge, which is backed by $25 million from Kleiner Perkins Caufield & Byers and DAG Ventures.

Aggregate Knowledge soon will offer cross-site recommendations. Searching a newspaper site for stories about the Valerie Plame CIA leak, for example, can lead you to a site devoted to the old TV series MASH, a send-up of bumbling buffoonery perpetrated in the name of national defense.

In the fall, Martino plans to launch a discovery network that will make connections between online media consumption and online buying. "We can make use of your news browsing to make better product recommendations," he says.

If that blurs the line between recommendations and advertising, that's fine with Martino. It's one of the ways he plans to make money from the service; product suggestions will become highly targeted ads that consumers will discover without ever typing a search term.

Erick Schonfeld is an editor-at-large at Business 2.0.