Customers.com Technologies

Need Personalized Recommendations? Peerius May Be Your Solution

By Sue Aldrich

If you sell products and/or deliver content online or via mobile phone, there’s usually a big benefit to being able to provide personalized recommendations tailored to each site visitor. Automated personalized recommendations can double or quadruple the click-through rate on your site, increase the number of products each customer buys, and increase the frequency with which they return to buy again. For the content owner—whether a merchant selling products, a publisher presenting articles, or a marketer presenting offers—recommendation technology means delivering the most attractive item in those few seconds before you lose your audience’s attention.

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. Don’t you just love SaaS?

Good Recommendation Solution for Retailers

By Patty Seybold

If you are an online or multi-channel retailer in search of a good, state-of-the-art recommendation solution, you should consider PredictiveIntent’s Personal Merchant. Your prospects and customers will receive targeted, personalized recommendations on your web site, on Facebook, via email, and on their mobile devices. These personalized recommendations are based on their behavior across channels and visits. The recommendations are personalized in terms of images, navigation, search results, and content selection.

The fact that this solution includes personalized search along with recommendations makes PredictiveIntent attractive to companies seeking both. Only a handful of vendors in the recommendation space also offer a search solution, including Adobe, Baynote, Locayta, and Peerius.

PredictiveIntent’s founders, Neil Hamilton and Stuart Swift, have deep mobile roots. So the solution is designed natively to deliver recommendations to mobile devices.
The company does not offer separate email and mobile products: these capabilities are embedded in the core product. Cross-channel customer profiles further enhance the value of PredictiveIntent’s algorithms in the mobile channel.

Results. The company reports that its recommendation solutions positively impact 27 percent of its clients’ revenues. On average, 14 percent of clients’ revenue comes directly from customers clicking on recommendations shown to them.

Speed of Implementa

tion. The typical timefr

ame for a launch is less than three weeks.

Lets Your Customers Opt Out of Behavioral Tracking. According to Sue Aldrich, “PredictiveIntent has the only recommendation solution we’ve yet seen that allows customers (end-users, visitors) to opt-out of behavioral tracking and have their behavior deleted. Social data is collected and used when visitors have logged in to Facebook. These two features increase the likelihood that PredictiveIntent’s clients will be in compliance with the emerging ‘cookie laws,’ which require user permission before placing a cookie and collecting user information. PredictiveIntent’s blog post, “Cookie Law Solutions” provides an excellent review of cookie laws and compliance.”

PredictiveIntent Recommendations Evaluation
PersonalMerchant’s Strong Architecture and Industry Focus Deliver Revenue for Retailers
By Susan E. Aldrich, Sr. VP and Sr. Consultant, Patricia Seybold Group, May 17, 2012

Revisiting the Requirements for Evaluating Recommendation Technology

Once again, our expert on merchandising and recommendations, Sue Aldrich, looks at the decisions and criteria you should consider before implementing a recommendations solution. As she advises, choosing a vendor wisely is always better than choosing often. And to aid you in that effort, she offers insights into how recommendations are crucial to effective targeting and personalization, how recommendations are best used, and what the requirements are for the marketers, merchandisers, editors, business analysts and other business people managing the recommendations, as well as for the end-customers who receive the recommendations.

As we move forward, Sue will use this updated framework for evaluating recommendation solutions< as she analyzes the major offerings in the market.

You can use this framework to gain an overview of the functionality that makes a difference in the quality of the personalized and targeted recommendations you deliver by email, on your web site, and on mobile devices. It’s also an extremely valuable tool for those of you evaluating recommendation solutions on your own so that you do choose wisely.

Recommendation Evaluation Framework, Version 2
Evaluating Solutions for Personalization and Recommendations
By Susan E. Aldrich, Sr. VP and Sr. Consultant, September 22, 2011

Recommendations: Avail Behavioral Merchandising

By Susan E. Aldrich
Sr. VP and Sr. Consultant, Patricia Seybold Group

Our analysis of Avail Behavioral Merchandising is our third review of a recommendation solution using our Recommendation Evaluation Framework, Version 1. The target market for Avail Behavioral Merchandising, as for RichRelevance Recommendations and Omniture Recommendations, is ecommerce, especially retail ecommerce. As is often the case, retailers are in the lead in adopting a technology that has so much impact on the customer experience—and on their bottom lines. Recommendations not only deliver the most interesting content in general, they can deliver the most interesting content specific to this visitor in this context. And for a merchant, delivering the most interesting content means enticing customers to buy more now, rather than get frustrated or bored and leave. Avail’s clients typically see revenue increases ranging from 5 to 30 percent per visitor. Not surprisingly, Avail has had very strong growth in the ecommerce market.

Avail Behavioral Merchandising is among the more mature recommendation offerings, on the market since 2001. This maturity is apparent in its UI, which has advanced features such as preview and test capabilities. Avail Behavioral Merchandising also has some unique capabilities that open new possibilities for merchants to work together, including collaborative merchandising and syndicated merchandising. Avail Intelligence is based in Sweden and has mostly European clients, a demonstration of its multinational and multicurrency capabilities.