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Data-Driven Product Development Case Study: GradeGuru

Learn how GradeGuru applied data-driven insights to develop new products for college students, uncovering latent needs and market opportunities through ethnographic research and iterative testing.

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Data-Driven Product Development Case Study: GradeGuru

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  1. Data-Driven Marketing and Product Development

  2. Overview The process: Creating & marketing new businesses and products based on data Case Studies: how to apply the theory GradeGuru: data-driven product development Kaplan eBooks: data-driven marketing

  3. The Process: Developing successful new products and businesses takes research, experimentation & data-driven analysis and iteration Research-Based Approach to New Product Development Research & Analyze Prepare • Determine objectives • Set scope • Determine appropriate research plan – top down and bottom up • Gather data • Synthesize • Analyze Sales & Service\ “Ideate” • Fulfill and service demand • Gather customer feedback • Identify latent needs/ opportunities • Brainstorm 100s product/ service ideas • Prioritize ideas DATA AND INSIGHTS Prototype & Assess Launch & Market • Push/ pull to get the product into customers’ hands • Refine/ leverage our deep understanding of the segments of demand • Build cheap models customers can respond to • Test and reject or refine rapidly • Build out the vision and feature set • Does it pass the jobs and business model tests? Design & Develop • Refine the design through testing • Full product implementation/ manufacture

  4. Practical Application: GradeGuru.com, a case study in data-driven product development Objectives: Identify latent needs in the college student market to identify new product/ service opportunities Approach: ethnographic research – uncover customer motivations, behaviors and beliefs: • Video ethnography with “think-alouds” • Journals • Observations and focus groups Findings: • Anxiety over the unknown: unclear rules for success • Stress arising from a lack of clarity on what is expected • The freshman struggle: transitioning to tertiary-level studies • Less prescriptive environment/ less direction, far greater volume and complexity of concepts • New methods/ skills required, but no knowledge of how to acquire them or improve • Limited access to iterative feedback – either constructive or positive reinforcement • I am not alone: students turning to their peers for support • Emotional/ psychological support • Academic support: when they are struggling and/ or to practice for assessments • Technology as a toy: unsophisticated academic technology use • Extensive use of technology for social/ recreational purposes: online shopping, games, Facebook, music, sharing photos, SMS, etc • Unsophisticated use of research tools, e.g. simple Google search and Wikipedia • Grades are key: assessment outcomes are everything • Grade-related activities are the most important • THE BOTTOM LINE: Students ONLY want to study what is relevant for their class with their professor PREPARE RESEARCH & ANALYZE

  5. Practical Application: GradeGuru.com, a case study in data-driven product development Product brainstorming: • Students want class-specific, highly customized content  How can we deliver that? • The supply exists! Students themselves are creating it, but is not productized • How can we offer feedback and positive encouragement?  macro web 2.0 growth: • Reputation and status a la web development communities • Ratings a la Amazon • Moderation of content a la forums •  Product concept:: a college study network of class communities where students can share materials, rate, review and get feedback & build up status/ be recognised as “gurus” Test the concept: Test if this product does a “job” and refine the design and road-map • Stage 1 - rapidly paper prototype and refine, test with students & determine feature set • Stage 2 – test “live” designs with students Build out the product: Release the site in stages, setting realistic expectations • Stage 3 - Develop a BETA with basic functionality and usability test it with students • Stage 4 – Build out the full and stable set of functions for release Roll-out: attract content, then attract users. Deeply understand the student segments to tailor the messaging and value proposition • More research, more data… social media data mining to identify student behavioural segments • Results: • Clear picture of our likely contributors and users for use in developing messaging and collateral • Clear user stories to sell the business case “IDEATE” PROTOTYPE & ASSESS DESIGN & BUILD LAUNCH & MARKET

  6. Practical Application: GradeGuru.com, a case study in data-driven product development Refine and expand: build out the product road-map and expand • More research and data: contextual inquiry to understand the full student “workflow” • Map out the study flows • Look for commonality and differences across simple segments – disciplines, year-level, school-type etc – a comprehensive tools needs to allow for these variations • Look for break downs in the process/ flow where we can add value Extend the product vision: • Overlay top-down inspiration on our ideas to fix the break-downs and improve the current workflow • Build prototypes based on the workflow insights • Test and refine with students – paper, then online demos • Output: two year product road map, complete with wireframes PREPARE RESEARCH & ANALYZE “IDEATE” PROTOTYPE & ASSESS

  7. Practical Application: Case Study 2 - Setting up a data-driven marketing campaign • Define specs to implement • Define metrics/analytics you want to collect • How/when/to whom will metrics be delivered? • Specify success metrics Phase 1: Planning (Ideate) Phase 2: Design (Design & Prototype) • Visual design review • Copywriting • Design/copy approvals • Build properties based on specs • Apply visual design/branding across channels • Copywriting completed (web, email, SEO keywords, etc.) Phase 3: Execute (Develop) Phase 4: Launch (Launch & Market) • Monitor performance • Respond when appropriate (social media, email) • Engage with customers • Daily SEO checks Phase 5: Post (Sales & Service) • Collect and compile data from all channels • Analyze data based on success metrics • Apply findings to next campaign/product

  8. Data to collect

  9. Case study: Free eBook promotions Campaign 1: August | Back to School Campaign 2: January | New Year 2 weeks 95 eBooks 500k downloads Facebook base Social media, email, PR 2 weeks 140 eBooks 2MM downloads Website base Social media, email, PR Low lead generation Usability Issues (How do I download books?) Low SEO performance (FB) Good download numbers Positive email metrics Standard publicity pickup Medium post-campaign sales (second life for some titles) High lead generation (2000% increase) Usability addressed in design High SEO performance (FB, AdWords) Heightened download numbers Positive email metrics Standard publicity pickup High post-campaign sales

  10. Campaign 1: August | Back to School

  11. Campaign 2: January | New Year, New Possibilities

  12. Questions? • Our contact details: • Brett Sandusky, Director of Product Innovation, Kaplan Publishing Brett.Sandusky@kaplan.com Linked In: Brett Sandusky Twitter: bsandusky • Emily Sawtell, Senior Director, Student Innovations, McGraw-Hill Higher Education Emily_Sawtell@mcgraw-hill.com Linked In: Emily Sawtell Twitter: emilysawtell

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