Personalization companies are finally realizing they can't bank their entire company on one magic technology. A smart collection of algorithms working together to deliver product recommendations and other personalized experiences is a much better approach for marketers.
In 2002, I wrote an academic paper called "GAAPP: A Generic Adaptive Architecture for Profiling and Personalization." In it, I described a personalization platform that used multiple technologies to solve any number of problems in the personalization space. Today, I'll talk a little about that architecture and emerging companies that are taking similar approaches.
GAAPP had several specific goals:
The system's core architecture is too complicated to describe here, but the basic premise is easy to understand: Instead of using one core technology (as most companies' platforms do), the platform consists of a bus architecture on which many different algorithms and technologies sit. Each algorithm registers itself with the platform and tells the platform what kind of input it can receive and what output it gives.
For example, a collaborative filtering algorithm might take user ratings as an input and give product correlations as an output. A statistical algorithm based on purchases would take sales data as input and give product correlations as output. An algorithm responsible for recommending articles to someone might take meta data as an input and either related meta data or references to related articles as output.
The point of this system is to allow a company to make the best use of available data. By feeding the data into the algorithms that are best suited for that data, the platform can give more accurate results. Plus, as different types of data become available, more sophisticated algorithms can be used.
For example, product recommendations for someone who has just started using the site can't be based on that person's purchase history, so the platform would choose an algorithm that's based broadly on click-through behavior. But as the user buys products, a more finely tuned algorithm that takes purchase behavior into account could be used.
Additionally, if multiple algorithms solve a similar problem, the system can pit them against each other and monitor the success rate of each algorithm (determined by sales or some other metric).
Fast-forward to 2008. Personalization companies are finally realizing they can't bank their entire company on one magic algorithm. This is what the collaborative filtering companies of the '90s did.
Companies like MyBuys and richrelevance are taking the multi-algorithm approach. I don't know what their architectures look like and the above describes my GAAPP architecture, not the architecture of any company in the marketplace. But these companies offer several similar features to the platform I describe. They use multiple algorithms to solve different problems. Plus, they can run tests pitting algorithms against each other or weight the results of different algorithms based on how each user responds to the recommendations.
Personalization has come a long way. In the early days, everyone thought collaborative filtering was the same thing as personalization. Back in 1997 when I was at Open Sesame (a competing personalization company not using collaborative filtering), we tried to tell the world that personalization was not one technology. It's a number of enabling technologies that solve specific problems.
Personalization is a mindset, an ethos, and a way of crafting a user experience. Any number of technologies must work in concert to achieve that user experience. I developed GAAPP with that philosophy, and I'm excited that companies have come to the same conclusions independently and are now taking a multi-algorithm approach.
Personalization will never solve the world's problems, but a smart collection of algorithms working together to deliver product recommendations and other personalized experiences is a much better approach than first-generation personalization companies took.
Questions, thoughts, comments? Let me know.
Until next time...
Join the Industry's Leading eCommerce & Direct Marketing Experts in Chicago
ClickZ Live Chicago (Nov 3-6) will deliver over 50 sessions across 4 days and 10 individual tracks, including Data-Driven Marketing, Social, Mobile, Display, Search and Email. Check out the full agenda and register by Friday, Oct 3 to take advantage of Early Bird Rates!
Jack Aaronson, CEO of The Aaronson Group and corporate lecturer, is a sought-after expert on enhanced user experiences, customer conversion, retention, and loyalty. If only a small percentage of people who arrive at your home page transact with your company (and even fewer return to transact again), Jack and his company can help. He also publishes a newsletter about multichannel marketing, personalization, user experience, and other related issues. He has keynoted most major marketing conferences around the world and regularly speaks at Shop.org and other major industry shows. You can learn more about Jack through his LinkedIn profile.
IBM Social Analytics: The Science Behind Social Media Marketing
80% of internet users say they prefer to connect with brands via Facebook. 65% of social media users say they use it to learn more about brands, products and services. Learn about how to find more about customers' attitudes, preferences and buying habits from what they say on social media channels.
The Multiplier Effect of Integrating Search & Social Advertising
Latest research reveals 68% higher revenue per conversion for marketers who integrate their search & social advertising. In addition to the research results, this whitepaper also outlines 5 strategies and 15 tactics you can use to better integrate your search and social campaigns.
September 17, 2014
September 23, 2014
September 30, 2014
1:00pm ET/10:00am PT