The EOI framework for big data analytics to drive business impact at scale
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Analytics is a core function in today’s businesses and corporations, enabling companies and organizations to get insights from raw data and make better business decisions and operations. In the era of big data and AI, it is increasingly important to analyze the huge amount of data in an effective and scalable manner. LinkedIn has recently formed a company-wide analytics team with 150+ talented analysts and data scientists to work together to drive business impact at scale.
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The EOI framework for big data analytics to drive business impact at scale
- 1. The EOI framework for big data analytics to drive business impact at scale Analytics @ LinkedIn Strata NY, Sep 2017
- 2. From “Big data” to “AI”? Financial Social network (LNKD, FB, etc) CRM Weblog (GOOG, YAHOO, EBAY, etc) +∞ +∞ Volume Complexity of Processing and Application 0 Shared economy, IoT, Instant Messaging (Uber, Didi, Airbnb, WeChat, Snapchat, etc) Health care? Environmental science? Education? Entertainment?
- 3. Intelligence (Prediction/Optimization/Automation) What will happen? Information/Knowledge Why did it happen? Data What happened? Business ROI Analytics evolution: from data to impact Insights What is the best that could happen? Custom Analyses => Scalable automated solutions (AI) Change & Impact Recommend, Transform, Implement & monitor
- 5. schoolscompanies knowledgeskillsMembers jobs The Economic Graph
- 6. Member value propositions CONNECT with your professional world STAY INFORMED through professional news and knowledge GET HIRED and build your career
- 7. Customer value propositions HIRE MARKET SELL LEARN
- 8. 8 11B Endorsements for 45K+ standardized skills 10M open jobs 9M companies 29K schools on member profiles members on LinkedIn across the globe 500+ M
- 9. Data 500M+ Members Worldwide Broad Detailed Breakdown By location, function, skills…etc Trends Tracking data over time like talent movement HistoricalReal-Time Updated Everyday, members update their profiles
- 10. Critical mass of data Relevant and valuable products and services Technology platform Member growth and engagement LinkedIn’s business model & why analytics is important
- 11. Analytics Data Science drives business value through the EOI framework leveraging big data
- 12. Empower business partners to have access to the data and insights they need when they need them Empower • Information - Performing ad-hoc analyses • Scalability- Automating and simplifying processes • Democratization - Surfacing self- service data insights via tools and products
- 13. Talent flows help discover dynamic trends on where companies are winning talents from and losing talents to Company B Company C Company D Company E Company F Example: Talent flows for Company A Company G Company H Company I Company J Company K Company L Company M Company N
- 15. Discovering the best start-ups in the Silicon Valley 2012 2013 2014
- 16. External Products LinkedIn Data Intelligence Platform Data Apps Platform Data Storage Platform Insights Service (Metrics, Metadata, Standardized data) Data Analysis Platform Governanc e Offline Online Users, Customers, Members Internal Products DWH Data Products Interaction Service (Discover, Analyze, Predict, Experiment)
- 17. Analytics Intelligence at Scale
- 18. Optimize business performance by leveraging the powerful and unique LinkedIn data we have Optimize • Advanced Analytics – Focusing on predictive data science • Leverage– Developing modeling tools accessible to data scientists • Optimization – Creating greater return on investment (ROI)
- 19. LinkedIn has a unique mix of B2C and B2B business B2C Business to Consumer B2B Business to Business Analytics is the key to bridge the gap
- 20. Drive features from economic graph
- 21. Drive features from economic graph
- 22. B2B Data Science Landscape B2B Predictive Modeling Market Segmentation Marketing Prioritization Propensity Score Acquisition Channel Onboarding Expansion Renewal Nurture
- 24. Model Performance & Business Impact Model numerical validation •B2C Propensity model: now accounts for 56% of total LinkedIn consumer marketing acquisitions •B2B Account Propensity Score: High APS accounts had 3x higher win rate vs. Low APS accounts, providing for a effective segmentation strategy Business Impact B2C Propensity Model - Marketing Total Prediction PowerPropensity Model 56% Signal Based Marketing 34% Profile Based Marketing 10%10% 34% 56% 100% Transforming business with analytics intelligence engine Low HighMed Avg 3x Q1’17 APS Win Rate by Tier B2B Account Propensity Model - Sales
- 25. Innovate the way analytics can help our business grow by leveraging both internal and external data & create new economic opportunity Innovate • Exploration – Taking intelligent risk to identify new opportunities • Execution – Developing MVP type of solutions to help solve global challenges • Game Changer – Changing the way to better serve 3B global workforce
- 26. Government Organizations Nonprofits NGOs Aligned Private Sector Partners Academia Media ECONOMIC GRAPH TEAM Applying LinkedIn’s data and insights to help solve labor market challenges around the world
- 27. What kind of tech jobs and skills are in demand by employers in LA?
- 28. Economic Graph Team 5.2million members in Greater Los Angeles Area on LinkedIn Economic Graph Team
- 29. Economic Graph Team 5.2million members in Greater Los Angeles Area on LinkedIn Tech talent 5% We identify tech talent based on their job experience
- 30. Tech workers are not always employed by tech companies Economic Graph Team Tech workers are not always employed by tech companies
- 31. Economic Graph Team Programming and web development skills are topping the list of most in demand tech skills by employers in LA Most in demand tech skills
- 32. Economic Graph Team Most in demand tech skills Most hired job titles Programming and web development skills are topping the list of most in demand tech skills by employers in LA
- 33. Economic Graph Team Most in demand tech skills Most hired job titles By analyzing the relationship between every skill and job, we were able to identify the specific jobs driving this high demand.
- 34. Software Developer is still the most common job title, accounting for approximately 36% of LA’s tech hires in 2016-2017 and is associated with a broad range of skill sets. Economic Graph Team
- 35. 2017 tech talent retention by region Percentage of local jobs hired by tech talent Economic Graph Team
- 36. Retaining L.A.’s Tech Talent: L.A.’s tech talent pool competes for tech jobs in LA and beyond. Only 37% of the jobs LA tech talent hired are in LA. Percentage of local jobs hired by tech talent Economic Graph Team
- 37. Domestically, LA imports tech talent from the East, and exports to the West Economic Graph Team LA tech talent net migration
- 38. Tech Workforce Pipeline Apply LinkedIn’s data and insights to help solve labor market challenges around the world Innovate how the world understands and creates economic opportunity 3. Innovate Identify the right segment for marketing campaigns or sales targeting, and offer the right product at right timing Optimize business performance thru analysis and propensity models Propensity Model (B2C / B2B) 2. Optimize Talent Flows 1. Empower Dynamic tools to enable discovery on business insights Empower people to have accesss to data thru interactive tools Analytics deliver results to business in 3 progressive ways
- 39. We are hiring! Michael Li linkedin.com/in/limichael Chi-Yi Kuan linkedin.com/in/chiyikuan
- 40. Thank you
- 41. External Products LinkedIn Data Intelligence Platform Stzd. DataMetrics Metadata Discover Analyze ExperimentPredict Data Apps Platform Data Storage Platform Insights ServiceData Analysis Platform Governance Offline (Gobblin, Azkaban, HDFS, Hive, Presto, Pinot, Spark, TensorFlow) Online (Kafka, Samza, Waterloo) Users, Customers, Members Internal Products DWH (TD) Data Products Interaction Service
- 42. We optimize everyday business decisions Track and monitor product launches to transform member experiences Identify B2C & B2B challenges to drive conversion, retention, and upsell opportunities Deliver actionable member and customer intelligence to improve user experience Identify business opportunities to grow intelligently
- 43. Which companies are driving demand for tech talent in LA? Economic Graph Team
Editor's Notes
- ERP data: transactional data, information （Financial services companies, Airlines: Capital One, American express, United Airlines, AA, etc) CRM: Marketing, campaigns, usage, engagement (Oracle, Salesforce, SAP, etc) Web: Engagement, pathing (Tech companies, Google, Yahoo, eBay, Alibaba, Tencent, Baidu, etc) Social Data (LinkedIn, Facebook, etc) Shared economy, O2O, Communication (Uber, Airbnb, WeChat, Didi, P2P lending, Snapchat, etc) What happened? (BI and Reporting) Analyzes Real time monitoring what the key business trends. Predictive analyzes Teradata to small
- Our dream is to help people more easily navigate this increasingly challenging 21st century global economy by developing the world's first economic graph, i.e. we want to digitally map the global economy and in doing so, create economic opportunity for every one of the 3B people in the global workforce. We want to create a digital profile for every member of the global workforce We would like there to be a digital profile for every company in the world, and who you know at those companies up to three degrees to help you get your foot in the door. We would like to have a digital representation of every job offered by these companies, full-time and part-time for profit and volunteer. We would also like there to be a digital representation of every skill required to obtain these jobs. And a presence for every higher educational organization and learning and development tool that would enable the members to obtain those skills. And lastly, we want to be in a position where we can overlay the professionally relevant knowledge for every one of those individual members, companies and universities to the extent they want to share it. Then we want to step back and allow capital, all forms of capital, intellectual capital, working capital and human capital to flow, to where it can best be leveraged and in doing so, help lift and transform the global economy.
- Our dream is to help people more easily navigate this increasingly challenging 21st century global economy by developing the world's first economic graph, i.e. we want to digitally map the global economy and in doing so, create economic opportunity for every one of the 3B people in the global workforce. We want to create a digital profile for every member of the global workforce We would like there to be a digital profile for every company in the world, and who you know at those companies up to three degrees to help you get your foot in the door. We would like to have a digital representation of every job offered by these companies, full-time and part-time for profit and volunteer. We would also like there to be a digital representation of every skill required to obtain these jobs. And a presence for every higher educational organization and learning and development tool that would enable the members to obtain those skills. And lastly, we want to be in a position where we can overlay the professionally relevant knowledge for every one of those individual members, companies and universities to the extent they want to share it. Then we want to step back and allow capital, all forms of capital, intellectual capital, working capital and human capital to flow, to where it can best be leveraged and in doing so, help lift and transform the global economy.
- With regard to our customers, we’re changing the way companies: 1. Hire 2. Market 3. Sell 4. All built on a scalable sub/payments platform
- So to summarize, our data is different because: It’s broad, with over 500 million members worldwide It’s detailed, we can break it down by location, function, seniority and so many other dimensions It’s real-time, members are updating their profiles everyday And it’s historical, we can track it over time to see how things are changing -- Advance to the next slide
- Hire, Market, Sell For our enterprise customers, we focus on hire, marketing and sale Hire, help enterprise to find and attract great talent, target the right person with the right job Marketing, Engage members with relevant and meaningful content at scale. Sell, find and engage buyers, use your company's connections to get warm introductions.
- For our enterprise customers, we focus on hire, marketing and sale Hire, help enterprise to find and attract great talent, target the right person with the right job Marketing, Engage members with relevant and meaningful content at scale. Sell, find and engage buyers, use your company's connections to get warm introductions.
- For data scientists, Everything starts from Data. Data is the fundamental thing for all tasks so we pay a lot of attention to enrich our feature capability. On the member level of data, not only cover 500+M member’s profile, but also consider the signals from member social networking, and member engagement activity on the site.
- [unique place eco-graph: m2m, m2c, c2m: ] On company wise of data, in general it is very limited to the feature development for any B2B related tasks. Fortunately, as Michael mentioned earlier, LinkedIn is a very unique place because we have a very large economic graph. On the site, we have more than 9 MM companies and 500 M members. Furthermore, we also have many different relationship data, including member to member, member to company, company to company. So think about this different connection, we can derive many insights as valuable features. For example, increase or decrease number of employees within a company can reflect a company growth feature; on the other hand, any social activity of recruiter can reflect usage of LinkedIn product We have combined all those derived information at large scale to our centralized data mart.
- First, let me introduce some common problems we are solving in B2B analytics world. From business point of view, there are two main tasks in B2B sales: (1) acquire new customers and (2) empower existing customers. If you look at the entire sales lifecycle, different stages have different focuses. At early stage, we care about identifying new opportunities and prioritizing marketing effort. Once prospects get into the sales funnel, then our sales will care about how likely they can convert this potential client. they also want to understand the business impact as well, e.g. how much they will spend once become our client? Once a sales successfully closed a deal, a new journey just begins. then in the later stage of the sales life cycle, we going to continuously nurture your client and try to understand whether there is a attrition risk? if so, what action we need to take? Or, is that possible to sale more to this client? To effectively address those questions, we need to build predictive models for different problems Those are not the only problems we solve. At Linkedin, we have several business lines: Talent solution, Sales solution, Marketing solution, Learning solution. Each one will ask similar questions. If you break down by product, there could be more. All together, we are talking about over tens or even hundreds models.. We understand the increasing demands, we also aware of challenges behind. add animations Challenges of B2B Data & Application Data quality issues: (1) Incomplete, sparse, noisy and dynamic over time (2) Missing historical data Lack of centralized data covering various needs Unclear source of truth From score to actionable insights Unify existing solutions (e..g, Multiple owners, Multiple predictive scores exist for similar purposes, Inconsistent quality) Scale model building
- At a high level, our system is composed of 3 layers of solutions. > Data layer provides data foundation of the whole system. > Above the data layer, we have our intelligence layer that provides data science solutions, supporting various of supervised and unsupervised learning. > On the top is our application layer where we deploy our models and integrate the results with our client-facing tools. Here we also highlight tools we used to build such system. As you can see, we benefit a lot from the open source community and hadoop eco-system. We use pig/hive for our data ETL, spark & mlib for modeling. Azkaban for scheduling and workflow management, Dr. Elephant for performance monitoring and tuning. Just name a few
- Regarding business impact, we have leverage our end-2-end solution for both marketing and prioritization. Overall, we have observed direct and significant monetized impact. For example, for aps, we have 5.3% lift of win-rate as compared to oppos with no prioritization. For upsell, we have 3.3x CTR compare to baseline, and generate 22%-46% more oppos for smb sales.
- Our LinkedIn vision is to create economic opportunity for every member of the global workforce. That vision happens to align tremendously with the goals of policymakers around the world who are very focused on creating economic opportunity for their citizens. The reason we do this is because now is the time. It's no secret to any of us that we are living in very turbulent time. If we scratch the surface of that, we see a lot of anxiety about youth unemployment, the future of work, the future of work in the context of automation and the corresponding impact. This is why it’s important for us to apply LinkedIn’s data and insights to help solve labor market challenges around the world
- One recent example includes our work in Los Angeles where we partnered with Mayor Garcetti to essentially understand what kind of tech jobs and tech skills are in demand across industries and how can they best train for those skills. The ideas include > aligning the curriculum with educational institutions, boot camps, and accelerators, and > building tech talent pipeline. https://data.lacity.org/stories/s/Investing-in-a-thriving-Tech-workforce-pipeline-fo/x899-9vc5
- There are 5.2 million workers in the greater Los Angeles region who have LinkedIn profiles.
- Of THIS TOTAL, 244,000 (5%) self-identify as part of the tech workforce, based on the type of job title/function at the company they currently work for. The methodology is limited to software and information technology related job (regardless of the industry of the company they work for).
- Let’s zoom in to see the tech industry landscape: Software and Information technology accounts for 20% of tech workers in LA. In other words, 4 out of 5 LA tech members are not in tech companies. with the largest concentrations in the manufacturing, healthcare, education, and entertainment industry sectors. While Tech has historically been thought of as a singular industry, we are finding more and more that the digital revolution has changed the way in which technology skills are integrated across all sectors of our economy. In Los Angeles, 80% of tech occupations can be found outside of Software and IT, with the largest concentrations in the manufacturing, healthcare, education, and entertainment industry sectors. Tech-roles in non-technology companies in LA are plentiful and suitable for tech-talent looking beyond traditional roles within technology companies. As such, we need to work collaboratively to build private and public partnerships that connect the Pipeline to this growing demand.
- Let’s continue to zoom in to see the TOP [tech skills] and [tech job titles] landscape:
- LinkedIn defines job titles by grouping similar titles listed on member profiles. For instance, Associate Software Developer, Senior Software Developer, and similar titles would be aggregated under Software Developer
- While Tech jobs are spread across industries, it is notable that Software Developer is still the most common job title, accounting for approximately 36% of L.A.’s tech hires — in 2016-2017 and is associated with a broad range of skill sets.
- Retaining LA’s Tech Talent - CALL TO ACTION: L.A.’s tech talent pool competes for tech roles in L.A. and beyond. Only 37% of the jobs L.A. tech members applied for, are in LA.
- Retaining LA’s Tech Talent - CALL TO ACTION: L.A.’s tech talent pool competes for tech roles in L.A. and beyond. Only 37% of the jobs L.A. tech members applied for, are in LA.
- how can they best train for those skills. The ideas include > aligning the curriculum with educational institutions, boot camps, and accelerators, and Investing in Los Angeles’s Tech Talent Pipeline. This is why it’s important for us to apply LinkedIn’s data and insights to help solve labor market challenges around the world The ideas include > aligning the curriculum with educational institutions, boot camps, and accelerators, and > building tech talent pipeline.
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