Group Product Manager at Levi's Talks: Data for Beginners
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The document discusses data and data analytics for product managers. It defines data as facts and statistics collected for reference or analysis. It identifies common data types like quantitative and qualitative data. It provides examples of potential data sources for a company like customer support, payment processors, analytics tools. It outlines a process for working with data that includes defining goals, gathering data, analyzing results. It discusses considerations for working with data like roles, deliverables, timelines, capacity, and prioritization. It provides real world examples of how companies have used data analytics to address issues like friendly fraud, customer support costs, and payment failures. It also discusses technologies used for working with data like Hadoop, SQL, Python, and data visualization tools.
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Group Product Manager at Levi's Talks: Data for Beginners
- 1. Group Product Manager at Levi's Talks: Data for Beginners www.productschool.com
- 2. FREE INVITE Join 10,000+ Product Managers on
- 6. Include @productschool and #prodmgmt at the end of your tweet Tweet to get a free ticket for our next Event!
- 8. What is data? Facts and statistics collected together for reference or analysis
- 9. Data Types 99 Quantitative Information about quantities. Examples: Number of Sales, Refunds, Chargeback Rate, Email Sign-ups, Authorization Attempts, Site Visits, Conversion Qualitative Information about qualities. Examples: Site Feedback, Social Media Comments, Customer Support Reports, Voice of Customer, User Testing
- 10. Customer Support These could be reported issues, both quantitative and qualitative information Payment Processor Transactional information Segment Events, Tagging Google Analytics/Omniture Web Traffic Order Management System Order status, customer information eCommerce Platform Information in your system Session Recordings Inspectlet, Tealeaf record users on the site Customer Feedback Customer Surveys, site feedback form 10 A Few Data Sources
- 11. Create A Process 1111 Meeting Second Meeting Seek Clarity Gathering Data (YOU + TEAM) Share Results with Stakeholders More Research (You + TEAM) Close out analysis & Retrospective Define the Goal (YOU + Stakeholder )
- 12. Ways of Working 1212 • Who does what? • Defining the problem phase Roles & Responsibilities • What will be the results of the effort? Clear Deliverables • This must be completed by Friday… • Create a timeline Setting Expectations • Is the data analysis you want Ad Hoc or is this reporting that needs to be scheduled? • Is this need monthly or quarterly? Planning Ahead • How much work can the data team handle? Capacity • What is the priority of the ask? • How does this data request rank against competing requests? Prioritization
- 13. Real World Examples 1313 • Analyzed millions of transactions • Segmentation analysis gave true customer LTV by card type • Identified high fraud, risk bank cards as foreign • Reduced chargeback risk Friendly Fraud High chargeback rates are not good • Poured over two years of CS data • Discovered the cost of each phone call, email, chat • Created customer FAQ, changed call center hours, made chat more prominent and personalized • Saved company over $1.25 M in operating expenses annually Customer Support Understanding the cost of doing business can inform product decisions. • Payment reporting showed decrease in sales • Analytics Tools pointed towards mobile issue • Investigation showed deficit in QA capabilities and oversight by Product • Conversion restored to site Payment Failures Low conversion reported for South American website
- 14. 14 Cluster of machines that collectively crunch data very quickly Hadoop Great introductory language, easy to learn SQL Language Statistical analysis R Programming In memory processing, very fast, expensive HANA Programming language used by some data analysts Python The ecosystem for data is growing quickly. This is a small portion of the many players in the space. How data is stored, how it is processed, the languages used to analyze data and the reporting that is built on top of all the data are changing quickly.
- 16. 16 There are a wide variety of visualization tools on the market that will help you visualize your data. Google Analytics is a good first step (and FREE!). However depending on your needs, funding, resources there are other tools like Looker, Tableau, MicroStrategy that can also give you in-depth reporting.
- 17. Part-time Product Management Courses in San Francisco www.productschool.com