CUSTOMERS.COM® RESEARCH FROM THE PATRICIA SEYBOLD GROUP
Baynote Collective Intelligence Platform and Recommendation Applications
Automated Recommendations, Search, and Navigation for Multiple Industries
By Susan E. Aldrich, Sr. VP and Sr. Consultant, July 8, 2010
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.
•
E-mail 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
This report continues...
**ENDNOTE**
1) “Recommendation Evaluation Framework,
Version 1,” January 7,
2010, by Susan E. Aldrich, http://dx.doi.org/10.1571/fw01-07-10cc
**ENDNOTE**
(Back to top)
|