COMPANY OVERVIEW

1. COMPANY OVERVIEW

2. ONLINE INSIGHT INC. • Founded July 1998 by President/CEO Ken Forster • Raised $250K in seed capital with COO Paul Krebs • Developed Alpha products w/leading academics • Raised $4.5M in first round venture from Greystone Capital • Launched Precision Choice 1.0 • Put senior management and core team in place ~50 FTEs. • Landed six customers, four financial services, two outside • Partnerships with key integration, R&D, technology players • Launched Precision Choice 2.2, Precision Insights 2.0

3. Online Insight’s solutions: i) Provide the foundation for online guided sellingenvironments WHILE AT THE SAME TIME ii) Capture, analyze, and allow companies to act on quantitative insight into customers’ buying needs, preferences and motivations.

4. What is guided selling and why is it critical to take e-commerce forward? • Commerce sites are seeking to: • Raise close rates • Provide high-touch, one-to-one selling to self-service customers (especially mainstream segments) • Offer products that are complex, multi-feature, require greater consideration, involve trade-offs • Current selling tools: • Fall short of delivering these capabilities • Cater to experienced buyers • Focus on marketing, not selling • The answer is “guided selling” • Consultative on-line sales processes that enable buyers to efficiently define needs and make decisions in a self-service environment.

5. Financial Services Guided Selling Product Complexity Parametric, Collaborative Filtering, AI Content Pub- lishing and Management Commerce Transaction Capabilities Books Sales Assistance Required Low High Where guided selling fits…

6. Guided selling’s main elements…

7. Sites must also monitor and interpret the their online selling interactions. • Why? • Effectively manage the sales process itself • Assess how buyers’ needs are met by current products, pricing, marketing campaigns, etc. • Tracking buyer clickstream/mining sales transactions (i.e., ‘who bought what’) gives little understanding of motivations behind sales interactions. • ‘Dumb’ data plus black-box analytics • Knowing how to ask the right, intelligent questions to build intelligent profiles is the key.

8. GUIDED SELLING CUSTOMER PROFILING CUSTOMER INSIGHT • Generate higher sales • Lower service cost • Create higher satisfaction through individualized customer service • Discover and compile customer motivations, preferences and values • Better setup Precision Choice • Develop marketing campaigns • Test new product concepts • Develop new segmentation Marketing • Answer who is/isn’t buying what, how and why? • What price points? • What products/features? • What marketing messages to which segments? • What if…? Offline Product Dev. Campaign Mgt. Online Content Mgt. Precision Solutions is an integrated sales environment management platform.

9. We are expanding our capabilities to fill a larger market footprint. • Facilitating purchase B2B/net marketplaces and strategic sourcing platforms • Driving targeted marketing systems • Incorporating additional algorithms and customer interaction approaches in Precision Choice • Integrating with configurators for ‘mass customization’

10. Management team • Ken Forster, President/CEO – Company founder, 6+ years e-commerce/online fin. svcs. strategy development experience, Dartmouth graduate • Paul Krebs, COO – 5+ years e-commerce /online fin. svcs. strategy development experience, Williams graduate • James Sandry, CFO – Founding officer of iXL Enterprises. During tenure at iXL he held the positions of CFO, Treasurer, and Executive Vice President • Daryl Wehmeyer, Director of Analytics – 10+ years leading teams to develop user profiling and data mining models at IRI and Kellogg’s • Charles Flowers, VP Software Development – 8+ years experience. Led development organizations at Mapics. Recognized Java thought-leader • John Grendi, VP Sales & Business Development – Former Senior Category Marketing Manager at Dell Computer and VP Sales at Chase Manhattan • Duane Cunningham, VP Professional Services – 20+ years software implementation experience. Former Director at USWeb/CKS • Scott Sanders, Director of Product Development – 6+ years e-commerce experience. Former Director at iXL. Strategic Planning IBM.

11. Vertical market focus CURRENT • Financial Services - Mutual funds, Mortgages, Credit Cards, Insurance • ADDITIONAL VERTICALS • Computers - Laptops, Desktops, Servers, Palm, Software, ISP services • Consumer Electronics - DVD, Stereos, TVs, Camcorders, Cellular Services • B2B – Net Marketplaces, Small Business, Services • Real Estate - Homes, Apartments, Condos, Timeshares • Automotive - Cars, Trucks, SUVs, New/Used, Tires, Automotive Audio • Travel - Airline Packages, Hotels, Destinations, Cruises • Career Services - First Job, Career Change, Offer Negotiation • Outdoor - Tents, Backpacks, Hiking Boots • Lifestyle - Cities, Pets, Colleges, Health Clubs, Charities, Restaurants, Wine

12. Overview of Online Insight Business Issues • Overview of Research Questions Two Fundamental Business Questions: • Usability of online recommendation technologies • Credibility of online recommendation technologies • Overview of Recommendation Technologies • Six Online Guided-Selling Applications

13. Overview of Research Questions Online Insight is interested in gauging consumer feedback regarding our Precision Choice recommendation technology and several competitive decision tools. Question 1 How do consumers view the usability of the various decision tools to be studied? • What are consumers’ overall perceptions of the interaction with each technology? • Was the technology easy to use? • Did it require a significant time and effort investment? • Was the tradeoff between the effort required and the accuracy obtained worthwhile? • Do some tools require more knowledge on the part of the consumer to be used effectively?

14. Overview of Research Questions Question 2 How do consumers characterize the credibility of the various decision tools? • Consumer-perceived “accuracy” of recommendation? • Did the tool respond effectively to consumers by accurately identifying their needs? • How much confidence do consumers place in each tools’ feedback? • What factors influence consumers’ trust and confidence? • What issues does each tool raise in terms of consumers’ willingness to provide the needed data? For example, are there privacy concerns associated with any of the tools?

15. Overview of Research Questions Compare and contrast the strengths and weaknesses of the various technologies on two dimensions. • What types of product categories lend themselves to the various recommendation approaches? • For example, how do consumers’ information needs differ for Computers, Financial Services, Autos, CD’s, etc.? • How does the traditional, offline sales process influence consumers’ perceptions of the online sales process for a product category? • How does consumer feedback vary for different types of consumers? • Price-sensitive or brand loyal shoppers versus shoppers focusing on other product features? • How does a consumer’s experience-level with a particular category affect their feedback?

16. Overview of Recommendation Technologies

17. Conjoint-based • Conjoint analysis takes into account the fact that consumers make complex decisions based on several factors jointly rather than one factor at a time. • Products or services are constructed in the form of product "profiles.” Each profile is a combination of one selected level from each of the attributes. • Attributes represent the key features of products or services • Levels represent specific points along the key attribute dimensions. Attribute Levels Example Profile Brand IBM, Compaq, Dell IBM 600 mHz $2,000 Speed 500, 600, 700 mHz Price $1,000, $1,500, $2,000

18. Conjoint-based • Respondents are led through a two step process to obtain their preferences on key product attributes. • First, respondents are asked to rate the importance and desirability of various product attributes. • Second, they are presented with a series of product profile pairs. They are then asked to indicate a scaled preference to one of the profiles. • Data produced by this process allows us to quantify respondents’ likes and dislikes, and determine the strength of preferences. Customer Profile 1) User Rates Product Feature Importance and Desirability 2) User Selects Most Preferred Products From Hypothetical Pairs Result: Quantified Customer Profile

19. Constraint-based • Constraint-based recommendation technologies utilize a database filtering methodology. • The user’s set of potential product recommendations initially includes the entire set of products in the database. • The user then sets certain specifications which enable products to be removed from the recommendation set (e.g. ‘eliminate all products that cost more than $500’ or ‘only include products with a quality rating of at least 4 stars.’) • As the user creates constraints, the set of available products diminishes. At any point, the user can view all products that are still available, and then choose to change previously set constraints or add new ones. • Constraint tools are most appropriate for databases of fixed products, rather than build-to-order products.

20. Multi-Attribute Utility Theory/Stereotyping • Multi-Attribute Utility Theory defines products by their features. Features are in turn defined by a number of attributes. • For example, laptops might be defined by the following features: Manufacturer, Memory, Operating System, Price, Processor, Screen Size, and Weight. • The user specifies the range of acceptable values for each attribute within each feature. • For example, for a numeric attribute such as Screen Size, the user could specify a range of 13.3” to 15.1”. • The user then ranks the importance of each feature. Based on this information, the software scores each product in the product database.

21. Multi-Attribute Utility Theory/Stereotyping • Stereotyping is a methodology used to expedite the preference profiling process. • Users are given the ability to choose from one of several pre-established ‘stereotypes’ which sets default preference ratings for each attribute. For example, for laptops, stereotypes might include: business traveler, family user, and budget buyer. • The preset default preference ratings would differ for a business traveler versus budget buyer and would emphasize different product attributes. • Priority Features • Business TravelerBudget Buyer • Laptop weight Price • Battery life Financing terms

22. Configurator • Configurators enable customers to ‘build’ made-to-order products while ensuring that incompatible features are not paired together. • Configurators contain the rules and logic that dictate product assembly, ensuring that no product is ‘configured’ that can’t actually be assembled. • To the user, configurators often appear very similar to constraint tools.

23. Collaborative Filtering • Collaborative filtering is a methodology used for predicting a customer’s preferences for particular products based on the preferences of other users who have exhibited similar characteristics. • It can rely either on explicitly entered preferences, observed click-stream behavior, or transaction history. • Users are placed in segments and are served recommendations based on the observed preferences of others in the segment. • From the user’s perspective, collaborative filtering is typically not an explicit process; rather the user simply receives a recommendation without providing any explicit input.

24. Artificial Intelligence • Artificial Intelligence-based recommendation technologies engage customers in a dynamic question-answer dialogue in order to uncover their needs and preferences. • Questions can be in natural language form or in multiple choice form with radial buttons and check boxes. • Typically, the questions pertain to how the user plans to use the product. • Also, some constraint-type questions are usually included. Based on the answers to the questions, the technology assigns weights to various attributes and scores the products in the database.