User Research Findings

1. User Research Findings Beth Goldman Director of User Experience July/August 2011

2. Agenda • Goals & Research Methodology • Participants • Key Insights • High-Level Business Process • Design Implications • Next Steps • Other Insights

3. Goals & Research Methodology

4. Goals & Methodology • Goals • Learn how we can improve engagement and ensure successful adoption of our product. • Understand the difference in behavior, motivation and background of power vs. light users. • Note: Power users do not necessarily use intelligence features (only one used agents and connections). • Methods • Site visits with users at FinancialForce.com and Innotas. • Webex sessions with users at BigMachines, Merchants Leasing and Silkroad Technologies. • Reviewed notes, identified recurring insights.

5. Participants

6. Participants • Companies • FinancialForce.com (6) – 200-px mash-up • Innotas (4) – Only using Contact mash-up • BigMachines(4) – 200-px mash-up • Merchants Leasing (2) • Silkroad Technologies (1) • 17 Participants • 6 Lead Generation (1 Lead Gen, 3 Business Analysts, 1 Enterprise Bus Rep, 1 Sales Support Admin) • 5 Inside Sales Representatives • 1 Field Sales Representative • 2 Corporate Sales Reps • 1 Regional Sales Manager • 1 Account Executive • 1 Marketing Coordinator

7. Key Insights

8. Key Insights • Minimal use of our intelligence features • Everyone prospects & qualifies • “I don’t have time” • “I was trained but forgot about that” • “What’s a smart agent?” • “I need to figure out how to start the conversation” • “Linkedin is for business, Facebook is personal” • “Where does your data come from?” • Agent alerts are less useful when sales cycle is short and reps own many accounts • We have a product champion

9. Min usage of our intelligence features

10. Everyone prospects and qualifies

11. “I don’t have time”

12. “I was trained but forgot about that”

13. “What’s a Smart Agent?”

14. “I need to figure out how to start the conversation”

15. “LinkedIn is for business, Facebook is personal”

16. “Where does your data come from?”

17. Agent alerts are less useful when sales cycle is short and reps own many accounts

18. We have a Product Champion

19. Key Insights - Recap • Minimal use of our intelligence features • Everyone prospects & qualifies • “I don’t have time” • “I was trained but forgot about that” • “What’s a smart agent?” • “I need to figure out how to start the conversation” • “Linkedin is for business, Facebook is personal” • “Where does your data come from?” • Agent alerts are less useful when sales cycle is short and reps own many accounts • We have a product champion

20. High-Level Sales Process Sales process • Find Prospects • Marketing. Lead Gen/Inbound Sales, Outbound Sales • Use Marketing lists, Inbound leads, Linkedin, List Building • Lead Qualification, Sales • Review Company and Contact details, web site, some news • Identify decision makers • Identify conversation starters • Understand company’s needs and product fit • Qualify Leads or Prospects • Sales • Create proposals • Speak to decision makers as necessary • Give or assign demos to services team • Negotiate Deal • Sales or Account Management • Upsell / Renew Account

21. Design Implications

22. Design Implications | 1 of 2 • Improve discoverability and use of intelligence features • Improve discoverability of key intelligence features • Demonstrate benefits of key intelligence features on first use (or soon after) • Provide training or access to training when necessary, in context, where they need it, when they need it, short and sweet • Ensure it’s clear that: • Smart Agent benefits and differentiate from news or google alerts • Benefits of connections and how to leverage for referrals • Agent results and connections are conversation starters • Require little or no set-up to use (intelligence) features • Use data we have (or ask for) when possible (e.g. If user logs in via LinkedIn, previous employments can populate Smart Connections. • Ensure users understand our data differentiators • Clarify how we get our data, the sources, accuracy, etc.

23. Design Implications | 2 of 2 • Let process learnings impact UI and training • Allow users to identify their tasks and process to help identify what features they would benefit from. • Ensure UI and training recognize that everyone needs to use prospecting and qualification features. • Focus training more on Intelligence. Users understand how to use the “data”. • Leverage our product champions • Leverage our product champion to help train and engage other users, impact the sales process, etc. • Involve them in the community, CABs, other research efforts. • Beware of mixing business with pleasure • Currently Facebook is not seen as helpful with business sales.

24. Next Steps

25. Next Steps • Brainstorm Concepts • Research with more “intelligence feature” users • Understand motivations, how they learned, etc. • Concept Development • FUE Overhaul V1 • Concept Refinement • FUE Overhaul V1 • Design Validation • Post-launch with users • Concept Development • FUE Overhaul V2

26. Additional Insights • Have issues with breadth, depth, and accuracy of data but manage around it • Like photos – puts face to name • Look for decision makers based on set info, will go lower or call front desk if required • Browse, don’t search for people • Often don’t notice “search” • Also, “don’t want to miss anything” • Want contact alerts • No one calls our mash-up a “mash-up” • Some search for other company details in CRM mash-up using “not the right company” • Mentions of wanting to know how to better leverage product

27. We all own the user experience of our product. Let’s make it rock!