recommendation eval framework v2!09!22 2011
TRANSCRIPT
-
8/3/2019 Recommendation Eval Framework V2!09!22 2011
1/15
Customer Scenario and Customers.com are registered trademarks and Customer Flight Deck and Quality of Customer Experience (QCE) are service marks of the
Patricia Seybold Group Inc. P.O. Box 783, Needham, MA 02494 USA www.customers.com Unauthorized redistribution of this report is a violation of copyright law.
Patricia Seybold Group / Evaluation Framework
Recommendation Evaluation Framework, Version 2Evaluating Solutions for Personalization and Recommendations
By Susan E. Aldrich, Sr. VP and Sr. Consultant, Patricia Seybold Group September 22, 2011
NETTING IT OUT
Recommendations are hitting their stride. A decade ago merchants and publishers saw therecommendations on Amazons site and wantedit. A decade ago they had to spend afortune to get it. Today, recommendation engines can be had for some thousands of dollarsa month and can be implemented in a few weeks. My guess is that recommendations will
be ubiquitous within the next two to three years. Im always optimistic on these guesses, butsince recommendations are widely available as a service, rollout can be very swift.
SaaS offerings greatly simplify the technology aspect of implementation and virtuallyeliminate up-front costs, making it relatively easy for customers to sign up with a vendor.Nevertheless, choosing a vendor wisely is always better than choosing often. To aid in thatchoice, I offer a set of requirements and evaluation criteria set forth in this evaluationframework. This framework updates and replaces the recommendation evaluationframework published in January, 2010. I will be using the framework to evaluate a numberof the leading products using the framework during 2011-12, leading to a detailedcomparison.
WHATS INTERESTING ABOUT RECOMMENDATIONS
What Are Targeting and Personalization
Targeted marketing selects content (including products and offers) for consumers based on traits
such as consumer context or behavior. The consumer is likely anonymous. Personalization requires a
consumer profile that includes traits such as demographics, purchase history, and behavior, and this
profile is used by the algorithms that select the content to be presented. While registering and log-
ging in increases the customer profile data, the consumer is most often identified by web browser
cookies and an associated identification number. Without registration, it is likely the profile does not
contain user information such as name and address, so consumer anonymity is retained.
What Is Recommendation Technology
Web site owners worldwide have yearned for recommendations ever since Amazon started tell-
ing 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. Dont you just love
SaaS?
Direct link: http://dx.doi.org/10.1571/fw09-22-11cc
http://www.customers.com/http://dx.doi.org/10.1571/fw09-22-11cchttp://dx.doi.org/10.1571/fw09-22-11cchttp://www.customers.com/http://dx.doi.org/10.1571/fw09-22-11cc -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
2/15
2 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
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 se-
lecting content, whether its 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 ownerwhether merchant selling products, publisher presenting articles, or
marketer presenting offersrecommendation technology means delivering the most attractive itemin those few seconds before you lose your audiences attention.
A recommendation engine can double or quadruple the click-through rate as compared with the
recommendations selected by the expertsthe merchandisers or researchers or support specialists. It
typically has significant impact on revenue, time on site, employee productivity, and customer satis-
faction.
Where Are Recommendations Used
Because recommendations had their most visible debut in ecommerce, people tend to associate
recommendations with shopping. In the ecommerce arena, recommendations are used most often on
product pages, shopping cart pages, category pages, and order confirmation pages. Recommenda-
tions are also used to tailor the content of those emails that entice you to stop work for a moment andshop. As a marketing team becomes more familiar and more confident with using recommendations,
they expand recommendations to cover more of the interactions across the customer lifecycle.
But I think product recommendations are the tip of the iceberg for recommendations. They be-
long everywhere content must be winnowed for a user, or everywhere that personalization improves
a users productivity or experience. For example:
A news site that knows I love football and dont care about rugby, and always shows methe most interesting world news
A personalized view of the corporate intranet that highlights my department, my division,and my projects
My view of corporate research knowledge bases, weighted to what Im working on
Web sites that deliver coaching, e.g., for runners, dieters, investors, tailored to the mystyle and goals
My personalized support portal to the corporate help desk
My companys portal to a key supplier, e.g., Cisco, personalized by role or person
My dashboard with my KPIs and corporate reports, with the hottest items on the firstpage
A personalized investment site, e.g., Fidelity, tailored to the kind of investor I am
A personalized commerce site tailored to my relationship, e.g., the car I drive, the sports Iplay
Billing inquiry that always shows the disputed bills first
Order history inquiry that always shows the most referenced order first
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
3/15
Evaluation Framework for Personalization/Recommendations Solutions 3
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
My guess is that recommendations will be ubiquitous within the next two to three years. Im al-
ways optimistic on these guesses, but since recommendations are widely available as a service, roll-
out can be very swift.
The technology effort, and the up-front costs, for deploying recommendations are relatively
small, which makes it reasonably easy for customers to sign up for a recommendation service. The
greatest effort for recommendation customers seems to be in learning how to use them, where to usethem, and how to use them effectivelyskills which will be portable to any vendors solution. Nev-
ertheless, choosing a vendor wisely is always better than choosing often. To aid in that choice, I have
compiled a set of requirements and evaluation criteria set forth in this evaluation framework. And I
will be evaluating a number of the leading products using the framework during 2011 and 2012,
leading to a detailed comparison.
REQUIREMENTS
The requirements for recommendation services are derived from customer requirements. Cus-
tomers for recommendation services cover several roles, including the end-consumers of recom-
mendations, the business people managing recommendations, and technical staff. Their requirements
generate the evaluation criteria which are listed in the Table.
Recommendation Consumer Requirements
People consuming recommendationsthe visitors, shoppers, readers, researchers to whom con-
tent is being recommendedof course, need relevant and enticing recommendations, thats the
whole point. But they also need privacy and may wish they had some control over what personal in-
formation is used and how it is used. As recommendations are increasingly used to personalize ex-
periences, consumers may also want a mechanism to indicate the persona they represent. Dont
recommend for me, recommend for my [boss, niece, colleague].
Recommendation Manager Requirements
The marketers, merchandisers, editors, business analysts and other business people who are us-
ing recommendations as a tool to improve user experience have a broad range of requirements, start-
ing from the initial deployment.
The business people who are responsible for recommendations need help making great ones, in-
cluding:
Guidance on how to deploy recommendations effectively
Advice on how to increase recommendation effectiveness
Training and tools to track and analyze recommendation effectiveness
Business people need tools to deploy, test, analyze, and optimize recommendations:
GUI or wizards for specifying rules for how recommendations are selected and presented
Consistent interfaces designed for their business processes, not for the structure of therecommendation product
Granularity in controlling the content selection process, e.g., using customer history toselect sports content but crowd wisdom in selecting fashion
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
4/15
4 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
Aggregation in controlling the content selection process, e.g., global rules that apply tomultiple sites, countries, and pages
Automation of deployment, analysis, and optimization
Testing and reporting that will compare different recommendation deployments
Integration with other marketing tools
Business people need to manage the recommendation service, and therefore need capabilities
that include:
Reporting on recommendation results, e.g., click through, conversion, revenue
Reporting on recommendation engine service level, e.g., response time and availability ofservice
Security of their content and user information; privacy of consumer information
Reliable, high performance
Controlled, role-based access to recommendation management functionality
IT Requirements
IT personnel are potentially involved in deploying and managing recommendation technology,
either on premise or as part of the SaaS implementation. They need:
APIs or Web Services for requesting recommendations from other applications
Ease of adding and testing recommendation data gathering to Web pages
Ease of providing a content data feed to recommendation service provider or enabling acrawl
Ease of creating a zone on Web pages, emails, phones, or other venues, where therecommendations will be displayed
Capability to incorporate management of the recommendation engine or service intoenterprise management
Security for data provided to the recommendation service provider
Integration of recommendation inputs and outputs with other segmentation, behavioralanalytics, Web statistics, and data modeling applications
Importance, Visibility, and Differentiation
As I prepare my requirements and evaluation criteria, I confess to taking certain shortcuts. If all
products offer a capability, I will drop it from my criteria regardless of its importance in order to
simplify the evaluation process. For example, it is unarguably critical that you be able to start and
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
5/15
Evaluation Framework for Personalization/Recommendations Solutions 5
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
stop the recommendation service. All products offer this capability, so its not a feature cluttering up
my evaluation matrix.
More problematical are the capabilities that are very important but cant be observed or meas-
ured. At the very top of this list is, for a specific web site, do Vendor As algorithms deliver better
recommendations than Vendor Bs? Algorithm quality will be very important to the recommendation
manager, but perhaps not in the first months or even years of deployment, when she is developingher recommendation expertise. During that time, vendor expertise and guidance are far more critical.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
6/15
6 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
2011 Patricia Seybold Group Inc.
Recommendation Platform Requirements
Category Recommendation Platform Evaluation Criteria
Solution Positioning What solutions are offered by the vendor?
o If the vendor has multiple product l ines, list the major categories of products.
o Briefly describe the solutions offered in the personalization, recommendations,
and targeted marketing arena. Include the unique value proposition for each
solution.
Guidance and Advice What are the vendors target markets, by industry and company size?
o What is the vendors distribution of clients across industries: retail, B2B
ecommerce, media, travel, online services? See Table B.
o What is the vendors distribution of clients across retail categories: apparel +
accessories, books/film/music, computers/electronics, flowers/gifts/jewelry,
food/drug, hardware/home improvement, housewares/home furnishings, mass
merchants/wholesale clubs/department stores, specialty/non-apparel, sporting
goods? See Table B.
o What geographies are supported? What languages do the vendors customers
work in? In what languages are they deploying recommendations? What is the
distribution of the vendors clients, by continent: North America, South America,
Europe, Asia, Africa? See Table C.
o What percentage of clients are multi-site? Multi-country? Multi-currency?
o Does the vendor leverage its client base by offering collaborative or syndicated
recommendations? Syndicated recommendations include product catalogs of
other merchants in a set of recommendations; collaborative recommendationsrecommend products at other retailers that match the customers interest, based
on cross-matching activities on both sites.
How successful are this vendors clients?
o For ecommerce clients, what percent of lift (e.g., revenue increase for
ecommerce sites, ad impressions for media sites) is provided by direct action on
recommendations (shopper clicks on a recommendation and immediately buys
the item), and what percent by delayed action on recommendations (shopper
buys an item previously recommended)?
o What is the average annual growth rate in number of recommendations
consumed per client?
o What is the retention rate of clients?
o What is the average number of recommendations touchpoints deployed per
client?
o What is the distribution of usage of key solution capabilities per client: e.g., web
recommendations, email recommendations, mobile recommendations, ads,
APIs? See Table D.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
7/15
Evaluation Framework for Personalization/Recommendations Solutions 7
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
2011 Patricia Seybold Group Inc.
Recommendation Platform Requirements (continued)
Category Recommendation Platform Evaluation Criteria
Guidance and Advice
(continued)
What types of coaching and training are provided and at what points in the
relationship? E.g., Implementation Classes for IT, 1:1 coaching for merchandisers
new to recommendations, self-training for advanced merchandisers, user
conferences, videos, blogs. What services are provided on site?
What type of account management and relationship management services are
provided (business guidance, best practices, integration support)? What is the
frequency of business, planning or strategy review? What is the experience level of
the people performing the reviews?
Recommendation
Structure
What are the sources of content that can be presented in recommendations? For
example, catalog/databases, content management repositories, CRM systems.
What types of recommendations are supported? For example, people who
bought/viewed this bought that; most popular; your-friends-liked; a dynamic
bundle with a dynamic price; questions that collect and analyze answers; search
results, etc. See Table E.
If algorithms can be customized, what factors can be included, and who performs
the customization?
If behavioral recommendations are supported, what behaviors are observed and
used? E.g., incoming site, search terms, views, time spent viewing, navigation
selections, cart adds, cart abandons. What behavior-based recommendation types
are provided?
Personalization: If customer profiles are supported, what data is collected? How are
customers recognized across sessions and touchpoints? What customer profile-based recommendations are provided?
How does the solution use social media? For Facebook and for LinkedIn, what data
is used, and what algorithms (strategies) are enabled? For ratings and reviews,
what data is used, from what sources, for what algorithms?
Describe the scientific principles, algorithms and data models used for each
recommendation pattern. What associations do the vendors algorithms support?
E.g., item-item; item(s)-item(s), person-item, person-person, other.
What recommendation types are supported for each touchpoint: web, email, kiosk,
mobile, business application, iOs app? When are the recommendations generated
for each touchpoint: batch, at send time (for email), at open time? See Table E.
Can the recommendation systems insights/analysis be used by other systems,
such as search, customer segmentation, data warehouse? Describe.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
8/15
8 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
Recommendation Platform Requirements (continued)
Category Recommendation Platform Evaluation Criteria
2011 Patricia Seybold Group Inc.
Managing
Recommendations
Testing
Describe the approach to testing recommendations: Is it A/B or multivariate; what is
the limit for simultaneous recommendation tests?
How does the solution measure success of the recommendations? Can it measure
based on client-specified KPIs?
In what ways can the solution automatically optimize recommendation approaches
to reach client-specified business goals?
Interface for managing recommendations:
Describe the interface. How is it organized? What is the set of symbols, tabs, and
other interface elements that is consistent (in appearance and meaning) from task
to task? Does it include a preview of changes being made, and provide a workflowfor managing review and approval of changes? Is access controlled based on
roles? What controls are in place that enable groups of people to manage
recommendations without getting in each others way?
What tasks can be handled by business people via the interface without calling on
technical staff for help? Or which tasks require technical (IT) staff?
Describe the principles by which business people can control recommendations.
For example, do they create (or use) templates, what does the template control.
How can rules or filters be applied, and to what objects (e.g., pages, templates,
segments, time periods)? What data can be specified in rules and filters, e.g.,
inventory, margin, segment, advertiser, combinations of factors?
What support is provided for managing multi-site, multi-country, and multi-currency
clients? For example, global and local rules; copy and edit templates; copy and edit
configuration.
Reporting:
Does reporting and analysis provide suggestions on how to improve
recommendations to achieve KPIs? Does the vendors staff?
How does the reporting include targets, make forecasts, provide alerts, and analyze
contribution to KPIs?
What is the most recent period that can be viewed in reports? I.e., how near real
time? What is the mechanism for scheduling automatic report creation and distribution?
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
9/15
Evaluation Framework for Personalization/Recommendations Solutions 9
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
Recommendation Platform Requirements (continued)
Category Recommendation Platform Evaluation Criteria
2011 Patricia Seybold Group Inc.
Integration and
Ecosystem
How does the solution fit into the marketing ecosystem? What information is made
available to other marketing tools? What information from other marketing tools is
used by the recommendation solution? How would recommendations contribute to
a marketing campaign? How would recommendations that are part of campaign be
monitored and managed? How would the results of a campaign be reflected in
recommendations?
How does the recommendation solution fit into the web site ecosystem, e.g., site
search, content management, SEO, SEM, advertising, and other applications?
Describe what data is required by the recommendation engine, and how it is
collected, including the format of feeds, language of collectors, and testing process.
Does the solution have auto-discovery capability for content and items?
Describe the mechanisms for ensuring data privacy and security, for customer anduser data.
Describe APIs provided. What languages/methods are provided? What services are
available via API?
Describe partner programs. What support is provided partners who seek to provide
more services to clients? What is the metric for partner value to the ecosystem?
Describe the typical implementation cycle, including activities, milestones,
timeframes, and project management tools. What role/skill typically manages the
project (for vendor and for client)?
Operations For on-premise software managed by the customer: What is the mechanism for availability and scalability? If multiple machines are
involved, what is the mechanism for managing multiple machines, both day-to-day
and during software upgrades?
Are SNMP alerts provided for integration with enterprise management?
What are the scalability limits, in terms of content and users?
For Hosted Software as a Service (SaaS):
Describe your SLA, including guarantees, penalties, and limits; include a summary
of SLA performance for past 12 months.
What is the typical response time for the recommendation service? How many recommendations were served in the past three months? Please define
what you mean by recommendation: is it a query, or the number of items returned,
or something else?
How many data centers support the recommendation engine? Where are they
located?
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
10/15
10 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
Recommendation Platform Requirements (continued)
Category Recommendation Platform Evaluation Criteria
2011 Patricia Seybold Group Inc.
Table A. The evaluation criteria for recommendation engines or platforms are based on customer requirements.We will use the criteria to analyze leading recommendation solutions.
Vendors Development
and Maintenance
Do you package named releases? If so, describe your release frequency for new
functionality and for bug fixes.
What methodology or standards are followed in support, bug correction, and
testing?
Describe the escalation path for performance or quality issues.
Company and Product
Viability
Product background and release history: month/year of major releases, beginning
with the initial release
Product plans: frequency of major/minor releases; enhancements planned for the
next three months
Partner and OEM strategy for recommendation solution
Number of clients and sites
Pricing for recommendation solutions:
o What is the basis for pricing? If recommendation success is a factor, please
describe how recommendation success is defined.
o What is the average low end and high end?
Company history: date founded, founders, investors, # employees
Financial performance as available
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
11/15
Evaluation Framework for Personalization/Recommendations Solutions 11
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
2011 Patricia Seybold Group Inc.
Table B. Each vendor targets specific industries with its marketing, sales, technology, and customer care.
Industry Focus
Industry Sub category Percent ofclients
Total for all retail
Apparel and accessories
Books/film/music
Computers/electronics
Flowers/gifts/jewelry
Hardware/home improvement
Housewares/home furnishings
Mass merchants/wholesale clubs/department stores
Specialty/non-apparel
Sporting goods
Retail
Other
B2B
Media
Travel
Telecom
Online Services
Advertising
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
12/15
12 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
Geographic Focus
Percent of
Clients
List of Clients Countries Languages Supported for Console
North
America
South
America
Europe
Asia + Pacific
Africa
2011 Patricia Seybold Group Inc.
Table C. Each solution is deployed on multiple continents and on Web sites in many languages. The consoleprovides the interfaces that marketers and merchandisers use to manage and optimize recommendations.
Use of Services
Services provided by this vendor (add or remove services as
appropriate)
Percent of clients using the service
Web recommendations
Email recommendations
Mobile recommendations
Kiosk recommendations
Display Ads
APIs
2011 Patricia Seybold Group Inc.
Table D. Successful clients expand their use of vendors services. This table reflects the level of adoption of keyservices.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
13/15
Recommendation Evaluation Framework, Version 2 13
Unauthorized redistribution of this report is a violation of copyright law. Customers.com Research Service
For Reprints/Redistribution rights, contact: [email protected] 2011 Patricia Seybold Group
Recommendation Types and Structures
Recommendation Structures Supported
Item-item(s) associations X
Many items to item(s)
Person-person associations
Item(s)-Person(s) associations
Recommendation Types (list here)
Behavior-based
Profile-based
Social-Media
Web Email Mobile Kiosk Business
Appl.
Generated as batch; at send; or at open
Rules driven (e.g., top
sellers, lists such as
staff picks)
X Open Send Batch Open
Rules modified (e.g.,
white and black list)
X
People who viewed
this, viewed that
People like you whoviewed this, viewed
that
People who viewed
this, bought that
People like you who
viewed this, bought
that
People who bought
this, bought that
People like you who
bought this, bought
that
2011 Patricia Seybold Group Inc.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
14/15
14 Evaluation Framework
Customers.com Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 Patricia Seybold Group For Reprints/Redistribution rights, contact: [email protected]
Recommendation Types and Structures (continued)
Recommendation Types (list here)
Behavior-
based
Profile-
based
Social-
Media
Web Email Mobile Kiosk Business
Appl.
Generated as batch; at send; or at open
Search results
selected and ranked
Landing page items
selected and ranked
..etc
Syndicatedrecommendations
Collaborative
recommendations
2011 Patricia Seybold Group Inc.
Table E. Each solution supports a variety of recommendation types. This is due in part to the recommendationstructures that are supported. Each recommendation type may be available to multiple touchpoints. Sometouchpoints allow for the most dynamic recommendations, that is, recommendations that are selected at themoment the page is accessed (such as opening an email). Other touchpoints call for the least dynamicrecommendations, produced in a batch. A kiosk in offline mode would require batch recommendations.
CONCLUSION
This evaluation framework will be applied to the leading recommendation solutions during the
coming months. After we have evaluated a number of products, we will compare the leaders and
analyze which solutions best suit which applications and markets. You are welcome to use our
framework for your own evaluation. We welcome your comments.
mailto:[email protected]:[email protected]:[email protected] -
8/3/2019 Recommendation Eval Framework V2!09!22 2011
15/15
About Susan E. Aldrich and Patricia Seybold Group
Customers.com
Research Service Unauthorized redistribution of this report is a violation of copyright law.
2011 P t i i S b ld G F R i t /R di t ib ti i ht t t l @ t
ABOUT THE AUTHOR
SUSAN E. ALDRICH is a Senior Vice President and Senior Consultant at thePatricia Seybold Group.
Aldrich is a senior analyst for the firms Advisory Service. As leading authorityon worldwide technologies, custom er requirements, practices, and
governance for finda bility, she manages the Sear ch, Navigation, and
Discovery Research Practice. Her research foc uses on customer self-service,information management an d technologies and practi ces for mo nitoring,
measuring, and managing the Quality of Customer ExperienceSM (QCE).
Aldrichs experience includes commercial a pplications development, deployment, and
implementation and operating systems deve lopment. She has provided informationmanagement, customer relationship, and distributed systems management consulting
worldwide.
Patricia Seybold GroupTrusted Advisors to Customer-Centric Executives
If you're a visionary customer-focused executive, thePatricia Seybold Group should be your firstchoice for ongoing strategic advice, business and technology guidance, customer experience best
practices, and help with customer-centric initiatives.
Founded in 1978 a nd based in Boston, we provide consulting, research and advisory services,
peer groups, and in teractive workshops. We h elp clients to design an d continuously improve
their customer-focused business strategies and processes using our pro ven consultingmethodology,Customer Scenario Design.
The CEO and founder, Patricia Seybold, is t he New York Times best-selling author of
Customers.com and The Customer Revolution. Patty's latest book, Outside Innovation, isavailable now.
Patricia Seybold Group
P.O. Box 783Needham, MA 02494
Phone: (617) 742-5200
Fax: (617) 742-1028
Email: [email protected]: http://www.customers.com
mailto:[email protected]:[email protected]://www.customers.com/http://www.customers.com/http://www.customers.com/http://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.psgroup.com/books_oi.aspxhttp://www.psgroup.com/books_oi.aspxmailto:[email protected]:[email protected]://www.customers.com/http://www.customers.com/http://www.psgroup.com/signup.aspxhttp://www.psgroup.com/books_oi.aspxhttp://www.psgroup.com/consulting_csmcert.aspxhttp://www.customers.com/mailto:[email protected]://www.customers.com/mailto:[email protected]