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Strategic Marketing Plan created as final project for the Stanford course "Strategic Marketing for High Tech Products and Innovations", by Tony Seba.

Strategic Marketing Plan created as final project for the Stanford course "Strategic Marketing for High Tech Products and Innovations", by Tony Seba.

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  • 1. DATAFORGOOD Marketing Plan Target: social foundations, VCs Justo N. Hidalgo
  • 2. Executive Summary Pain
    • A growing number of NGOs and Social Entrepreneurs relentlessly work on social projects.
    • A critical challenge in every step of the way is to have enough social, political, regulatory and technical information so that their social networks, lobbying and services can be optimized.
    • Public Data is:
        • Hard to find
        • Hard to access
        • Hard to process
        • Hard to understand
    DATAFORGOOD
  • 3. Executive Summary Opportunity
    • The growth of NGOs and Social Entrepreneurs in the Health area is exponential. In the last 20 years the number of healthcare-related non-profits has increased from 50,000 to 140,000 only in the United States.
    Sources: NCCS , FCOnline , How Much Information , NetCraft , 2008
    • The number and heterogeneity of public data sources has more than tripled since 2003.
    DATAFORGOOD
  • 4. Executive Summary Service Description
    • Web-based reporting and alert site where Health-related NGOs can access, combine, enrich and publish public information from patents, news, regulatory data, and social sites.
    • Focused on helping them achieve a level of information understanding that allows them to take the most appropriate decisions: DATA FOR GOOD.
    DATAFORGOOD
  • 5. Agenda
    • What is DataForGood: Service Description
    • Segmentation
    • Targeting
    • Positioning
    • Competitive Analysis
    • Opportunity Size
    • Marketing Mix
  • 6. What is DataForGood
    • Web-based service that enables a non-profit or social entrepreneur to extract, transform and combine data from heterogeneous sources, and expose it as reports and alerts.
      • Data Sources: Web sites, Web Services, Databases, RSS feeds, MS docs, …
    DATAFORGOOD Ventas Patents News Demographics Combine and Transform Internal Regulatory Non-Profits Foundations Alerts Reports
  • 7. Segmentation DATAFORGOOD Understand what is technically being done in the area at work Understand specific issues related to his/her task Technical and scientific information about related issues Technical and scientific information about related issues Research reports Understand what other companies, NGOs, … are doing in the same areas and projects Understand what other companies, NGOs, … are doing in the same areas and projects “ Competitive” intelligence Have a clear vision of what the NGO/project is providing (e.g. inform Kiva lenders about political/social/economic conditions) Generate a complete and up-to-date view of every project involved. Generate a complete and up-to-date view of every project involved. Generate a complete and up-to-date view of every project involved. Generate a complete and up-to-date view of every project involved. Single View of a Project Minimize effort. Provide detailed information about requirements for specific funds Provide an overview of funding possibilities Provide an overview of funding possibilities Funding info Improve introductory reasons for grant Deep and wide knowledge of the project Deep and wide knowledge of the project Improve and decrease time to choose projects Improve and decrease time to choose projects Country´s historical and political background Improve strategic performance of the organization Strategic reports Final User NGO Grant Seeker NGO Expatriate NGO Volunteer Small NGO Leader / Social Entrepreneur INGO Leader (HealthCare – Social – Education) Use,application/ Customer (segment)
  • 8. Segmentation Customer Segment Description (1/3)
    • Small NGO/Social Entrepreneur:
      • Specialized, but working in different projects.
      • Impossible to know everything about each one.
      • Has not a broad network.
      • Can even feed from other NGOs, associations.
      • No lobby capabilities, but needs to know about new policies so to act.
    • INGO (International Non-Profit ):
      • More professional, but still with the same problem.
      • Has a broad network. Information may come from many sources. Have people to “inhale” it, but it might get to the right person already biased.
      • Lobbying. Need for accurate information.
      • Three main areas: Health, “Social” (human needs) and Education
    DATAFORGOOD
  • 9. Segmentation Customer Segment Description (2/3)
    • Volunteer :
      • Not-on-the-field NGO workers. Management, organization, local activities.
      • Need both specific and holistic view of the project and its target area
      • Need a more efficient way of working
    • Expatriate :
      • On-the-field NGO volunteers/employees. First-hand contact with the situation.
      • Critical: information about the target area. No mistakes here.
    DATAFORGOOD
  • 10. Segmentation Customer Segment Description (3/3)
    • Grant Seeker :
      • Learns about opportunities, writes grant proposals.
      • Need complete information of funding opportunities, laws involved, target area information, policies, and so on.
    • Funder :
      • Social Foundations, VCs. Read proposals, proactively find social actions.
      • Find the best targets
      • Once found and funded, up-to-date information about their grantees´ actions
    DATAFORGOOD
  • 11. Segmentation Use Case Description
    • Strategic Reports : ability to create high-level reports for management
    • Historical and political background : report or alert about specific aspects of the target country, area or political target
    • Funding : aggregate information required for grant proposal generation
    • Single View of a Project : up-to-date view of how a project is going. Expectation-meeting reports
    • Competitive Intelligence : reports about other associations´actions in specific areas
    • Research : reports and alerts about the research/technical area of interest
    DATAFORGOOD
  • 12. Targeting Factors DATAFORGOOD % DESCRIPTION 100 TOTAL 5 Need for combination and transformation of data (not just an unrelated aggregation of data”) 5 Profitability potential (not so important for a Social Enterprise) 6 Capital Availability (NGOs: funding elsewhere, but it can be important at the beginning) 8 Customer growth 8 Leverage into other segments 8 Competitive advantage 10 Need for changeable transformations of different reports (a “once and for all” report is just not valid) 10 Lack of dominant player 10 Complexity of required data sources (i.e. something that can not be easily solved by other means) 15 Heterogeneity of required data content (i.e. content that can not be easily provided by a single source/limited number of sources) 15 Strategic value of reports
  • 13. Targeting Single View of a Project DATAFORGOOD 3.97 2.95 3.79 3.34 4 3.405 3.79 3.7 100 TOTAL AVERAGED 43 31 40 36 41.5 37 39.5 40 100 TOTAL 5 2 5 5 2 3 3 3 5 Need for combination and transformation 3 2 2 2 4 3 2 3 5 Profitability potential 4 2 2 2 2.5 3 2 4 6 Capital Availability 4 3.5 2 2.5 3 4 4 4 8 Customer growth 5 4.5 3 2.5 3 4 4.5 4 8 Leverage into other segments 2 3 4 4 4 2 3 4 8 Competitive advantage 4 2 5 4.5 4 3.5 4 3 10 Need for changeable transformations of different reports 2 3 4 3 5 4 4 4 10 Lack of dominant player 5 2 5 3.5 5 4 5 3 10 Complexity of required data sources 5 4 4.5 3.5 4 4 4 4 15 Heterogeneity of required data content 4 3 3.5 3.5 5 2.5 4 4 15 Strategic value of reports FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER INGO LEADER-EDUCATION INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE Ratings (1 <= x <= 5) Weight
  • 14. Targeting Research Reports DATAFORGOOD 3.57 2.975 3.39 3.315 3.29 3.43 3.47 3.73 100 TOTAL AVERAGED 40 31.5 36 35.5 35.5 37 36 41 100 TOTAL 5 2 5 5 3 3 3 4 5 Need for combination and transformation 3 2 2 2 4 3 2 3 5 Profitability potential 4 2 2 2 2.5 3 2 4 6 Capital Availability 4 3.5 2 2.5 3 4 4 4 8 Customer growth 5 4.5 3 2.5 3 4 3 5 8 Leverage into other segments 2 3 4 4 2 2 3 4 8 Competitive advantage 3 1 3 3 3 3 3 3 10 Need for changeable transformations of different reports 3 3 3 3 4 4 4 2 10 Lack of dominant player 4 4 4 4 4 4 4 4 10 Complexity of required data sources 4 3.5 4 3.5 4 4 4 4 15 Heterogeneity of required data content 3 3 4 4 3 3 4 4 15 Strategic value of reports FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER INGO LEADER-EDUCATION INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE Ratings (1 <= x <= 5) Weight
  • 15. Targeting Competitive Intelligence DATAFORGOOD 3.87 3.275 3.01 2.935 3.24 3.61 3.49 3.75 100 TOTAL AVERAGED 42 33.5 33 32.5 35.5 39 36 41 100 TOTAL 5 2 5 5 3 3 3 4 5 Need for combination and transformation 3 2 2 2 4 3 2 3 5 Profitability potential 4 2 2 2 2.5 3 2 4 6 Capital Availability 4 3.5 2 2.5 3 4 4 4 8 Customer growth 5 4.5 3 2.5 3 4 3 5 8 Leverage into other segments 2 3 3 3 2 3 2 3 8 Competitive advantage 3 1 3 3 4 4 4 4 10 Need for changeable transformations of different reports 3 3 3 3 4 4 4 2 10 Lack of dominant player 4 4 4 4 4 4 4 4 10 Complexity of required data sources 4 3.5 4 3.5 4 4 4 4 15 Heterogeneity of required data content 5 5 2 2 2 3 4 4 15 Strategic value of reports FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER INGO LEADER-EDUCATION INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE Ratings (1 <= x <= 5) Weight
  • 16. Targeting Background DATAFORGOOD 2.98 2.735 3.57 2.835 2.835 2.925 2.735 3.135 100 TOTAL AVERAGED 33 29.5 39 32 31.5 32.5 29.5 34.5 100 TOTAL 3 3 3 3 3 3 3 3 5 Need for combination and transformation 3 2 3.5 2 4 3 2 3 5 Profitability potential 4 2 4 3.5 2 3 2 4 6 Capital Availability 3 2 5 4 3 3 3 3 8 Customer growth 3 3 3 3 3 3 3 3 8 Leverage into other segments 2 3 3 3 2 3 2 3 8 Competitive advantage 3 3 4 3 3 3 3 3 10 Need for changeable transformations of different reports 2 2 2 2 2 2 2 2 10 Lack of dominant player 4 4 4 4 4 4 4 4 10 Complexity of required data sources 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 15 Heterogeneity of required data content 3.5 3 5 2 3 3 3 4 15 Strategic value of reports FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER INGO LEADER-EDUCATION INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE Ratings (1 <= x <= 5) Weight
  • 17. Targeting Funding DATAFORGOOD 3.57 3.73 2.11 2.11 3.75 2.87 2.68 2.93 100 TOTAL AVERAGED 38 38.5 21 21 38.5 30 27 31 100 TOTAL 3 3 1 1 2 1 1 1 5 Need for combination and transformation 3 2 1 1 3 3 2 3 5 Profitability potential 4 2 1 1 2 3 2 4 6 Capital Availability 3.5 4 1 1 3 3 3 3 8 Customer growth 3.5 4 1 1 4 2 2 2 8 Leverage into other segments 4 4 3 3 4 3 2 3 8 Competitive advantage 2 3.5 2 2 3.5 2 2 2 10 Need for changeable transformations of different reports 2 3 2 2 4 2 2 2 10 Lack of dominant player 4 4 4 4 4 4 4 4 10 Complexity of required data sources 4 4 4 4 4 4 4 4 15 Heterogeneity of required data content 5 5 1 1 5 3 3 3 15 Strategic value of reports FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER INGO LEADER-EDUCATION INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE Ratings (1 <= x <= 5) Weight
  • 18. Targeting Summary Matrix
    • The Initial Target Segment will be Social Foundation, on the SCV, Research and CI areas.
    • Later, Health-related INGOs.
    • Future service lifecycles could include Social INGOs or new features that benefit the Funding Use Cases.
    DATAFORGOOD 2.925 2.87 3.61 3.43 3.405 INGO LEADER- EDUCATION 2.735 3.135 2.98 2.735 3.57 2.835 2.835 Background 2.68 2.93 3.57 3.73 2.11 2.11 3.75 Funding 3.49 3.75 3.87 3.275 3.01 2.935 3.24 Competitive Intelligence 3.47 3.73 3.57 2.975 3.39 3.315 3.29 Research Reports 3.79 3.7 3.97 2.95 3.79 3.34 4 Single Customer View INGO LEADER-SOCIAL INGO LEADER-HEALTH CARE FUNDER NGO GRANT SEEKER NGO EXPATRIATE NGO VOLUNTEER SMALL NGO LEADER
  • 19. Positioning Per Market Segment: Social Foundations
    • For Social Enterprise Funding Companies
    • Who need to decide carefully which NGO and which area to fund and manage
    • The Social Reporting tool is a Social Intelligence Reporting and Alert Service
    • That shows integrated competitive intelligence and single customer view information (report or alert-based) from a vast amount of data sources
    • Unlike Data Providers and EII (Enterprise Information Integration) tools
    • Our service allows easy customization and integration, and the possibility to easily add new sources in a single service.
    DATAFORGOOD
  • 20. Competitive Analysis Competition Areas (1/4): reporting
    • To our knowledge, no current public service specifically meant for non-profits, but there is a huge amount of competition in related areas.
    • Business Intelligence and Reporting tools
      • Definition: tools for advanced analysis of data
      • Pros: powerful reporting capabilities, some are no-cost
      • Cons: basic combination capabilities, no access to public web, client/server infrastructure
      • Main vendors: Business Objects (SAP) , Microstrategy , Eclipse BIRT
    DATAFORGOOD Ventas Patents News Demographics Combine and Transform Internal Regulatory Non-Profits Foundations Alerts Reports
  • 21. Competitive Analysis Competition Areas (2/4): combination
    • EII (Enterprise Information Integration)
      • Definition: capability for combining information from heterogeneous datasources
      • Pros: powerful tools, complex combination
      • Cons: no access to public web, server infrastructure, cost
    • Mashup tools for Web 2.0 sources:
      • Definition: tools to retrieve and combine web-based data
      • Pros: APIful web sites access, easy combination, non-cost, reporting capabilities
      • Cons: combinations can not be very complex, only Webful sources
    DATAFORGOOD Ventas Patents News Demographics Combine and Transform Internal Regulatory Non-Profits Foundations Alerts Reports
  • 22. Competitive Analysis Competition Areas (3/4): data
    • Web Automation tools:
      • Definition: automate H2M and M2M interaction with web applications and services
      • Pros: access to APIless web sources
      • Cons: lack of combination and reporting capabilities, costly
    • Data Providers:
      • Definition: provision of accurate data sets
      • Pros: clean data, organized
      • Cons: lack of combination, lack of detailed or proprietary info, lack of reporting, too many to choose from (analysis paralysis)
    Ventas Patents News Demographics Combine and Transform Internal Regulatory Non-Profits Foundations Alerts Reports DATAFORGOOD
  • 23. Competitive Analysis Competition Areas (4/4)
    • Custom-based
      • Definition: in-house implementation of reports and alerts
      • Pros: cheaper for simple cases, customized
      • Cons: prone to problems with changes – monolithic-
    • No change
      • Definition: “everything is just fine as it is”
      • Pros: things go well (specially for small NGOs)
      • Cons: minor increases in work, project means problems; might be missing critical information
    DATAFORGOOD
  • 24. Opportunity Size
    • Total Addressable Market:
      • Total # of potential customers in the target market:
        • Total number of Social Foundations worldwide: alfa
        • Estimated number of Social Foundations with non-local projects: 30%
      • Total # of units potentially sold by all companies:
        • No publicly available information available, so we executed a set of interviews to potential customers plus extrapolation
        • Estimated # units:
        • […]
          • X% of foundations with more than $5M /year for grants: N units
          • Y% of foundations with less than $5M/year for grants: M units
          • TOTAL: Z units
      • Average Revenue per User: $A (see Pricing Section)
      • TAM = Z * A
    DATAFORGOOD
  • 25. Marketing Mix Agenda
    • Whole Product
    • Distribution Channels
    • Partners
    • Pricing
    • Branding
    • Promotion
  • 26. Whole Product Data integration Reports alerts Training Online Help Tutorials Training courses
      • Software Value Addition
      • PDA access for nomadic management
      • PDF/Excel export
      • Data cache
      • Professional Services
      • Experts in creating sources and reports on demand
      • Support
      • Hotline
      • Systems Integration
      • Data Integration partners
      • Reporting tools partners
      • Content Provision partners
      • Law firm
      • Issues in access and extraction of public data
      • Immediate kickoff
      • Free basic service (predefined reports)
      • Bundles (predefined set of sources and reports)
      • Sharing/social network
      • Data source catalog by category
      • Options for customers to meet and send news to each other (even combined rows).
      • Limited to “n” sharings per free user.
      • Use other customer´s sources and reports
    Installation SaaS and on-site to access internal sources
      • Technology
      • Relational databases for cache
      • machines for processing and computing (initial: Amazon Services)
      • Data mashup and web automation
      • Reporting
      • Standards&Procedures
      • Best practices for report creation
      • Administration and generation wizards
    DATAFORGOOD
  • 27. Distribution Channel TALC: early adopters stage * Will use the Data Integration partnership as another channel. DATAFORGOOD Data Integration Vendor * Service too complex VADs Not for this stage Not for this stage VARs Not for this stage OEMs Service too complex Internet Catalog Web-based service Retail Stores Web-based service Distributors Training Not for this stage Systems Integrators Direct Sales Economic Performance Good fit with DataForGood Strategic fit Use of the channel Channel Candidates Sequence Decision
  • 28. Partnerships Technological
    • Technological Partners
      • Data Integration Partner (relational)
        • […]
      • Web Automation (relational)
        • […]
      • Content Provider (transactional)
        • […]
      • Reporting: (relational)
        • […]
    DATAFORGOOD
  • 29. Partnerships Training and Systems
    • Training Partners
      • […]
    • Systems Integration
      • Integration Partner and DataForGood (transactional)
      • […]
    DATAFORGOOD
  • 30. Partnerships Law
    • Law
      • (undisclosed) (relational)
        • Discount of law services to customers
    DATAFORGOOD
  • 31. Pricing Estimated Value to the Customer
    • From a Social Foundation standpoint, revenue increase has both a financial and an emotional value
      • Revenue increase : more projects, better knowledge of the possibilities
        • […]
      • Cost Savings :
        • less people looking for information on the web; savings on specific data content providers
        • […]
      • Emotional Values :
        • Giving better service to more people
        • […]
    • GRAND TOTAL : […]
    • NEW MARKET: […] PER SERVICE.
    DATAFORGOOD
  • 32. Pricing Strategy
    • Skimming
      • Reasons:
        • early adoption: current TALC stage
        • high barriers to entry: very cautious organizations
        • product is easy to deploy, but the advanced services are complex to offer
        • Challenge: other lower-level services can cause disruption
        • Strength: knowledge of the market, free services that gives confidence to the customer.
    • Switching costs:
      • Not at the beginning. Interaction with the customer is by reports/alerts, easy to replace
      • But when customized, change is much more difficult since it requires infrastructure change
    DATAFORGOOD
  • 33. Pricing International
    • International pricing strategies:
      • Standard services have standard rates per region (from the website)
        • Each region has its own interests (different patent pages, news pages, …)
        • For advanced services, regional pricing since we depend on our partners pricing policy (usually per region). Initially from central office, then different offices in main regions.
    DATAFORGOOD
  • 34. Pricing Competition
    • Companies are paying around $X for the same kind of service when purchasing it from commercial vendors.
    • Companies are spending around $Y for the same kind of service by in-house development
      • Without adding maintenance costs, which can be more than 4x the initial cost.
    • Our cost for STANDARD SERVICES is much lower
      • Customized ones will have a similar cost than other vendors
      • But we offer specialized knowledge of the sector.
    • Backlash:
      • Vendor specialization: 1 year advantage. Leader in the area, plus move to adjacent areas.
      • Price lowering: not really, data integration costs are high.
    DATAFORGOOD
  • 35. Branding
    • Data Integration must not be only for “greedy” corporations. The story is about how leading complex but successful data integration projects fed the founder´s brain, but creating small solutions for non-profits and volunteers meant something much more important!
    • Founder:
      • Data Integration expert with 10+ years of experience in real-time access to public and internal sources. Academic and professional background (Ph.D in Data Integration, VP of Engineering and Product Management in previous multinational company)
    • Spokespeople: use of blog and social techniques to narrate the day-to-day of the company… before it even starts!
      • Attractive to: Entrepreneurs, NGO agents, data integration influencers.
      • It helps create the story.
    • Messages:
      • People: DataForGood is “data integration and reporting experts with a heart”
      • Product: Fetching the RIGHT information to do the RIGHT thing
    DATAFORGOOD
  • 36. Promotion Main Steps (1/2)
    • Positioning:
      • Helping Social Foundations choose and manage the most efficient projects
    • Personal selling:
      • Corporate blog: reporting and data integration in the NGO world.
        • Invite volunteers and NGO leaders to write about their expectations (before they even think of purchasing the product)
        • Invite influencers from CI and Social Foundation institutions (list from Foundation Center)
    • Create a web site:
      • Goals: inform, SEO, access to services
    Source: Foundation Center DATAFORGOOD
  • 37. Promotion Main Steps (2/2)
    • Collateral on the web:
      • description of the service: benefits
      • technical whitepaper: the behind-the-scenes architecture
      • free access to limited set of pre-defined reports on major areas
    • SEM: position the web site in Google, Yahoo!, MSN, Ask (in that order)
    • Not yet:
      • Participation in CI and Social Entrepreneurship conferences (e.g. http://www.ciinpharma-events.com/usa / , http:// www.pharmabiotechci.com / )
      • AR: not yet. Gartner does not have any specific category for NGOs, neither does Forrester. We don´t yet need “press releases” since we need customers first.
    DATAFORGOOD
  • 38. Conclusions
    • Web-based reporting and alert site where Health-related NGOs can access, combine, enrich and publish public information from patents, news, regulatory data, and social sites.
    • Helping Social Foundations choose and manage the most efficient projects
    • Positioned in a $90M market, in an early-adopter stage, and growing.
    • Unique blend of technology-savvy and knowledge of the environment.
    • There´s a compelling story behind and a potential story in front.
    • The RIGHT data to do the RIGHT thing.
    DATAFORGOOD
  • 39. Assumptions
    • Only one customer segment: Social Foundations
    • Only one product: DataForGood
    • TALC stage: early-adopter
    DATAFORGOOD
  • 40. DATAFORGOOD The RIGHT information to do the RIGHT thing Justo Hidalgo