NETTING IT OUT

Recommendation engines are a way for content owners—such as merchants, marketers, and publishers—to present the most interesting content to each customer at each step in the interaction. Recommendations were popularized a decade ago by Amazon’s famous “other people who looked at this bought that” style of recommendation. Today, recommendation solutions are available from a variety of sources: we’ve analyzed those from Avail, Baynote, Certona, Loomia, Omniture, and RichRelevance.

Our side-by-side comparison of the six solutions is presented in the accompanying Excel spreadsheet. You can add your own rows to reflect your own requirements and your own columns to include solutions that interest you.

OVERVIEW OF THE COMPARISON

How You Can Use It

We present the comparison in a spreadsheet so you can readily add and delete rows, reflecting your requirements, and you can add and delete columns, reflecting the solutions that interest you. You may wish to add a scoring mechanism, reflecting the pri-ority of given features.

How It Was Created

The side-by-side comparison is a summary of the recommendations research conducted during the first three quarters of 2010. The research began with preparing an evaluation framework . The framework synthesizes the requirements and evaluation criteria we identified through customer interviews and research. The framework describes requirements in seven categories: guidance and advice, recommendation structure, managing recommendations, integration, operations, vendor’s development and maintenance, and product and company viability.

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

How We Use It

We use the side-by-side comparison matrix when helping our clients to select recommendation solutions. The comparison matrix is also the primary source for our upcoming report on the comparative ranking of the six solutions.

TIPS FOR EFFICIENT USE

Home In on Your Industry

All of the solutions we reviewed had strong client care components, with the objective of helping clients optimize the use of recommendations. You will get the most value from client care if you ally with a supplier that understands your industry. Some of the vendors were focused only on retail; others were broader. See Table A for a summary of industry focus by vendor.

Get Past the On Premise Debate

I love SaaS solutions: they are quick to implement, with low up-front cost and good on-going support. Others hate SaaS because of the ongoing cost, or because data has to “leave” the enterprise, or because they feel less control. Recommendations are particularly suited to the SaaS model because of their compute- and data-intensive nature. For this reason, the SaaS model is the most common. If your team’s feelings are strong, perhaps you should narrow your choices upfront. Table B summarizes the vendors’ delivery models.