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Online Personalization 2.0: This Time It's Personal
Copyright (c) 2006-2023 John Black
(See This Article in its Original Format.)
After the flame-out of early business models focused on online personalization – for instance Firefly – it was easy to dismiss this as another over-hyped casualty of the dot-com boom. But there was nothing fundamentally wrong with the technology or the concept. It was primarily ahead of its time. Now personalization is an integral part of leading e-commerce sites such as iTunes, Amazon and NetFlix, and a contributing factor to their success. However, beyond this top tier, use of personalization is not yet widespread online.
All of the macro trends - expanded product selection, consumer-generated content and information overload - suggest that personalization is poised to come back in a big way. What's needed for mass adoption is a new business model rather than new technology.
Trends
First, let's look at the trends that build the needs and the opportunities for personalization
Product selection. The "Long Tail" phenomenon was first coined by Wired in 2004 to describe how, removed from the constraints of the physical world, the economics of retailing and the behavior of consumers have changed radically. Where a traditional retail store could only dedicate shelf space to high volume products, an e-commerce site can stock literally millions of products.
Consumer-generated content: Partly in response to distrust of marketers and professional critics, partly in response to the ease of personal publishing/blogging, consumers are posting their views, profiles and opinions online en masse. An estimated 33 million Americans have rated or reviewed products online. Social networks like MySpace and Facebook have become a cultural phenomenon. The combined voice of consumers is a powerful force. Study after study shows that word of mouth by far carries the most influence on purchase decisions.
Information Overload: Unfortunately, consumer-generated content is often lacking in relevance. A consumer reading conflicting reviews of the same product is often left asking: what do people like me think of this? Was the one-star book review from an English professor, or from a high school dropout? Further, it is often difficult to separate objective feedback from self-promotion.
While online retailing offers consumers unlimited choice, this choice can be paralyzing. While word of mouth often provides objective peer opinions, just as often it creates more confusion without any filter on relevance.
Existing Solutions
New online services and technologies are starting to emerge to solve these problems. The site Trendwatching.com has coined the term "Twinsumer" to describe matching consumers up with "their taste twins; fellow consumers somewhere in the world who think, react, enjoy and consume the way they do." These solutions address real and growing consumer concerns:
Tell me what's right for me
Help me explore beyond the mainstream, or in the words of Wired, push people down the long tail
Personalized recommendations are typically driven by statistics, in the form of "collaborative filtering", or by the user's own network of contact. In collaborative filtering, "like users" (or "like items") are matched based on their statistical similarity. So it Bob and James liked 10 of the same books, the 11th book that James rated 5-stars would be recommended to Bob. Or if customers who buy the Godfather Part 1 also buy the Godfather Part 2,... well you get the idea.
In the social network approach, recommendations are driven by your friends, or by people you have chosen to bring into your online circle of trust. This operates more like traditional word of mouth, but on a much larger scale.
These personalization solutions tend to be tied to either e-commerce or affiliate marketing business models:
e-commerce merchants: iTunes, Amazon, NetFlix, eMusic
online communities: listal.com, nextfavorite.com, librarything.com. ratingzone.com
music applications: Pandora.com, last.fm, MusicIP. Yahoo Music
In most cases, personalized recommendations have focused on product categories with a) broad selection and b) subjective tastes. Hence, books, music and movies.
Challenges
With all of the promise of personalization to increase sales and improve customer loyalty, you'd think its use would be more widespread. However, every personalization application faces the dual, and opposing, challenges of critical mass and data quality. The best recommender technology is worthless without enough data to populate the recommendations. In categories with a broad selection, such as books, recommendations are not very effective beyond the most mainstream titles until the number of ratings/purchases reach the hundreds of thousands.
So how to get hundreds of thousands of data points from customers before you can offer effective recommendations? Most e-commerce sites use observed customer behavior – clicks, searches, carted items and purchases – to infer product feedback. While this is the quicker and easier path to critical mass, it sacrifices data quality. Just because a user clicked on or even bought an item does not mean they liked it. Often the customer purchased a gift, did not enjoy the product, or had a one-off need for the product. I suspect other people have a similar mish-mash of recommendations at Amazon as I do: from gardening tools to lullaby CDs to Accounting books.
These data challenges – not technology limitations - have kept personalized product recommendations out of all but the very largest, most sophisticated e-commerce sites. And let's not forgot about traditional brick and mortar retailing, which still accounts for 90%+ of book and music sales. When was the last time you got "personalized" service at a big box retailer or chain store?
A New Approach
There's no good reason why every retailer shouldn't be able to implement personalization as well or better than Amazon or iTunes. At least in books, music, movies, video games and probably consumer electronics and travel. In this new world of ASPs, Web 2.0, APIs and web-services, the technical barriers have been all but removed.
Which leaves the data. A new business model that can successfully aggregate anonymous customer data and product reviews across multiple retailers could be far larger, and more predictive, than any database within a single merchant. And literally any retailer, down to a single-store independent bookseller, could tap into the benefits by also contributing to this uber-database. If this sounds farfetched, note that Abacus Direct grew a similar cooperative database model into a $100 million business in the offline catalog market.
The benefits are clear for those sites who have successfully implemented personalized product recommendations: dramatic improvements in sales, conversion rates and customer loyalty.
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John Black has a long experience with personalization and predictive modeling. John was the product manager for the first one-to-one online banner ad targeting product at DoubleClick, and managed market research and new product development at Abacus, the leading predictive modeling company in the catalog market. John is currently the founder and CEO of NextFavorite.com (http://www.nextfavorite.com), a personalization service provider.
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