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, including software-as-a-service providers such as Baynote.

If you are in retail, telecommunications, travel, media, healthcare, high-tech, consumer packaged goods or manufacturing and are looking for a recommendation solution, Baynote should be on your short list.

Baynote’s focus is recommendations and personalization for ecommerce, customer service and support, marketing, intranets, and extranets. Baynote’s customers use its services not only to generate recommendations, but also as a means to personalize interactions, a method of improving site search, or a better mash-up of disparate customer service knowledge.

The key strengths and differentiators of Baynote are its target market and supported applications, UseRank’s impressive engagement-measuring heuristics, and the ability to preview the impact of changes to rules controlling recommendations.

OVERVIEW OF BAYNOTE

Baynote

Baynote was founded in 2005. It is headquartered in San Jose, California, with offices in the UK and Germany; it has 73 employees and growing, with job postings on its Web site for 13 different positions covering sales, marketing, and development. In its four and half years in business, Baynote has accumulated more than 130 customers and is deployed on more than 300 sites, supporting ecommerce, customer support, marketing, intranets, and extranets. Roughly half of Baynote’s customers are in retail; the rest span telecommunications, travel, media, healthcare, high-tech, consumer packaged goods, and manufacturing. Customers include ATT, Bluefly, BT, Campbell Soup, Debenhams, Dell, Expedia, Fox News, General Mills, Intuit, The Knot, Motorola, NASA, Optus, Texas Instruments, Urban Outfitters, and VEVO.

Baynote Products

Baynote’s repertoire supports applications beyond the expected cross-sell: additional applications include dynamic navigation, search recommendations, email, and mobile recommendations. These services are provided by Baynote’s family of applications, as follows:

• Content Recommendations. Recommends similar, multimedia, and next-step content; automatically generates FAQs.
• Product Recommendations. Recommends similar, popular, and accessory products.
• Social Search for Content. Optimizes search results by harnessing visitor behavior to identify the content visitors found most relevant and reliable.
• Social Search for Product. Optimizes search results by observing how visitors interact with products they discover, regardless of terminology used.
• Email Recommendations. Integrates product and content recommendations into email campaigns.
• SEO/SEM. Using context, intent, and engagement analytics, automatically detects content gaps; dynamically creates landing pages for visitor keywords; and optimizes existing pages with visitor metadata.
• Mobile Recommendations. Selects content, links and search results based on what visitors have found to be most engaging.
• Video Recommendations. Supports discovery and viewing by selecting the most engaging video; integrated with ad networks.

All deployments include the Collective Intelligence Platform and the Insights Console. Clients add any combination of applications to create their solution.

• Collective Intelligence Platform. The underlying platform supporting all applications; source of UseRank, Baynote’s recommendation algorithm based on engagement, context and like-minded peers.
• Insights Console. Used by all customers; provides the interfaces for controlling and tuning recommendations; provides analysis and reporting.

EXAMPLE: TURBOTAX.INTUIT.COM

We consult with many organizations about customer experience. A common refrain from our clients’ frustrated customers is, “please give us customer support that brings in the community’s wisdom – and makes it searchable.” That is a seemingly simple request, but with the standard technologies supporting threaded discussions and site search, it has been a dream never realized.

This is why TurboTax’s use of Baynote Social Search for Content is so dazzling. Intuit has deployed Social Search for Content in the customer support area for TurboTax, and suddenly customers can get Intuit’s carefully crafted content as well as –miracle!—the riches of every relevant question asked and answered. Paragraphs about taxes can be impenetrable. Even when you actually have the answer in front of you, you might not realize it, or you might not understand what it means. The questions and answers from like-minded people – your peers—help you over the hump of translating from your initial fuzzy question to the final step-by-step action.

In this example, a query about “health insurance deduction” delivers a set of very clear answers from the community. Maybe the answers from Intuit are just as clear; for me, the community was all I wanted. See Illustration 1.

Recommendations at TurboTax

© 2010 Patricia Seybold Group and Intuit Inc.

Illustration 1. At TurboTax.Intuit.com, searching for “health insurance deduction” brings up great content from the community. The community—people like me—asks questions in my kind of terms and answers them in terms that I can connect with. Baynote Social Search for Content selects the community content that is most relevant for me, based on behavior of other visitors.

SUMMARIZING THE RECOMMENDATIONS EVALUATION FRAMEWORK

Recommendations are hot, solving problems from order size to search to personalization. My guess is that recommendations will be ubiquitous within the next three to four years. I’m always optimistic on these guesses, but since recommendations are widely available as a service, rollout can be very swift. Through customer interviews and research, we have identified the requirements and evaluation criteria. These criteria are set forth in our evaluation framework¹, which we will be using in 2010 to analyze a handful of the leading recommendation solutions, culminating in a detailed comparison.

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. The evaluation requirements are presented in Table B...

Contact Info:
Baynote
Kathleen Wiersch, Corporate Communications
Email: kathleen@baynote.com
Internet: http://www.baynote.com

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1) “Recommendation Evaluation Framework, Version 2,” September 22, 2011, by Susan E. Aldrich.

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