Comparative Rating of Five Recommendations Solutions

Analysis of Relative Merits of Avail, Baynote, Certona, Omniture, and RichRelevance

March 31, 2011

Marketers use Recommendation Engines to help customers select and buy their products. This relative ranking summarizes our detailed evaluation and comparison of five leading recommendation solutions: Avail Intelligence, Baynote, Certona, Omniture, and RichRelevance. Although the efficacy of recommendation algorithms may ultimately be the most important aspect of a solution, buying decisions are more often based on vendors’ geographic coverage, target markets, client care, industry expertise, scalability, and the interfaces provided to control recommendations.

NETTING IT OUT

We have completed a detailed evaluation of five leading recommendation solutions: Avail Intelligence, Baynote, Certona, Omniture, and RichRelevance.

Among these and other vendors in the recommendations space, the software-as-a-service (SaaS) model predominates. SaaS has shaped the solutions by driving the focus towards business benefits and support for business goals.

Although the efficacy of recommendation algorithms may ultimately be the most important aspect of a solution, buying decisions are more often based on vendors’ geographic coverage, target markets, client care, industry expertise, scalability, and the interfaces provided to control recommendations. In these areas, the vendors we evaluated exhibit strong similarities and important differences.

WHAT’S INTERESTING ABOUT RECOMMENDATIONS

Benefits of Using Recommendation Technology

Web site owners worldwide have yearned for recommendations ever since Amazon started telling us that “people who bought this also bought that,” and today tell us “52 percent of people who looked at this bought that; 26 percent bought this other thing.” A decade ago, marketers had to spend a great deal to implement recommendations on their sites. Today, recommendation engines can be had for some hundreds of dollars a month and can be implemented in a few days. God, don’t you just love SaaS?

For the user, recommendations on a site can mean pretty darn good search results much of the time; emails with interesting offers; banner ads that catch your eye; useful guidance when you are selecting content, whether it’s a digital camera, a news article, a problem resolution, or a research document; clicks that take you from Google right to the content you need.

For the content owner—whether merchant selling products, publisher presenting articles, or marketer presenting offers—recommendation technology means delivering the most attractive item in those few seconds before you lose your audience’s attention.

What Is Recommendation Technology?

A recommendation engine can double or quadruple your click through rate as compared with handcrafted recommendations selected by your experts—the merchandisers or researchers or support specialists. Automated recommendations typically have significant impact on revenue, time on site, employee productivity, and customer satisfaction.

Recommendation technologies, at a high level, all operate like this:

  1. Gather information about content items, typically via a data feed or crawl.
  2. Gather information about all users’ interactions with all content.
  3. Observe the current user’s activity.
  4. Apply any of a variety of algorithms to select content.
  5. Display the content.
  6. Track and analyze the effectiveness of the recommendations.
  7. Charge the client for the uplift.

Within this general outline, there are big variations, and the variations at each step are, in fact, important. It would seem that variations in step 4, the algorithms, would be the most important. Indeed, in the long term (several years), algorithms may prove to be the biggest enabler or limiter. In the first few years you deploy recommendations, however, methods of gathering information and displaying recommendations seem to have at least as big an impact.

OVERVIEW OF THE RATINGS

Solutions Reviewed

When we began this project in 1Q2010, we selected leading recommendation solutions in ecommerce and online media markets. We chose firms that we felt were leaders in terms of technology or reach. Here is our evaluation list:

  • Avail Intelligence
  • Baynote
  • Certona
  • Omniture (Adobe)
  • RichRelevance

Loomia was on our original evaluation list, but didn’t survive 2010. Its clients are now looking for new solutions.

Bottom Line on Each Solution

As we evaluated each solution, we assigned a summary rating for each category of evaluation criteria. The results are presented in Table A.

Summary Ratings

Please download the PDF to see the table.

© 2011 Patricia Seybold Group Inc.

Table A. As each product evaluation was completed, a summary rating was established. The evaluations were performed from February through September of 2010. All of the solutions have been enhanced since we evaluated them; several are enhanced monthly.


We can conclude from the table that the highest overall ratings were earned (at the time of evaluation) by Avail Intelligence, Baynote, and Omniture. But Certona and RichRelevance were close behind. From the reader’s standpoint, there is perhaps a disappointing sameness in these scores. No one gets a D or even a C. I score on an absolute scale, not a sliding scale. I chose the leaders, and the leaders are quite accomplished at taking care of their clients, offering a range of recommendation types, providing an interface for controlling and optimizing recommendations, opening APIs for connecting recommendations to your applications, and conducting their operations.

But these bottom-line ratings are pretty useless when it comes to making a decision about what solution will work for your situation. In fact, we can categorically state that our ratings are not accurate for your specific situation, since we created generalized criteria to span our broad audience...


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We analyzed each solution using the evaluation framework; the individual reports are published on our Web site:


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