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Einstein Analytics


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Einstein Analytics: AI + Analytics for Every Business Process

AI solutions at Salesforce promise to make companies more productive by augmenting the decisions they make with relevant information. But in practice, how can you deliver algorithmic insights at scale for your business at the moment of action? Join this session to see some real-world examples of data and analytics intelligence in action and hear from our customers to find out how they're optimizing their business across industries, including financial services, to drive growth through data and analytics.

Published in: Business
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Einstein Analytics

  1. 1. Einstein Analytics Peter Newall Master Solution Engineer, EMEA Analytics Kevin Sweeney IT Director, CPL Resources AI + Analytics For Every Business Process Alicia Stone KeyBank Jennifer Higgins McKesson
  2. 2. Salesforce and Tableau FAQ “With the acquisition of Tableau, Einstein Analytics and Datorama will continue to be core to our analytics strategy. We will be working over the next few months to jointly build a product roadmap that takes advantage of all the unique capabilities of each technology and brings to market a solution or set of solutions that enhances all three products.”
  3. 3. The Analytics Market is at an Inflection Point 90% of BI platforms will have AI features by 2020 Through 2020, the number of citizen data scientists will grow 5X faster than the number of expert data scientists 74% of customers who don’t have AI are planning to buy over the next 2 years Citizen data scientists and end users are emerging as key personas Our customers are ready for AI but need support AI & BI are converging “Seed-and-grow and combined AI/BI is the way the market is headed” - Forrester
  4. 4. Business Apps
  5. 5. Analytics Business Apps
  6. 6. AIAnalytics Business Apps
  7. 7. Intelligent Experience
  8. 8. AIAnalytics Business Apps Intelligent Experience Optimised data storage 1B rows default Simple Drag & drop visual queries, No-code AI Actionable Embedded in Business Processes Complete Descriptive, Diagnostic, Predictive, Prescriptive Outcomes Focused Recommendations & Explanations Trusted & Transparent Accountable AI
  9. 9. Einstein Analytics Plus (Summer ‘19) Discover insights, predict outcomes, find recommendations, and act quickly.
  10. 10. - Kevin Sweeney, IT Director, CPL Resources “By embracing artificial intelligence, we can deliver better outcomes for our candidates, clients, and the business. It’s transforming how we think and informing how we act.”
  11. 11. CPL uncovers key insights into invoice payment timing with Einstein Prediction Builder Extremely competitive industry requires recruiters to quickly identify best candidates A lot of manual exploration required around job applications and financial data No consistent process to determine which clients would be tardy on invoice payments Seeking insights into what factors cause a job order to complete Unifying operations across locations and automated workflows increase productivity Centralizing and analyzing external financial transaction data in Einstein Analytics Einstein Prediction Builder uncovers true impact of Client POs and its correlation with on time payments Ranking jobs by score allows consultants to prioritize efforts based on AI driven insights Challenge Solution “By embracing artificial intelligence, we can deliver better outcomes for our candidates, clients, and the business.” Kevin Sweeney, IT Director 1.5M Candidates processed via Salesforce Sales Cloud Einstein Analytics
  12. 12. Capture Data Automate amap Present Results No hands Understand Data Identify impacts Predict Future Results Action Prediction • Implemented in 2010 as a Platform Customer • 2019 -- 30 teams, 500 recruiters in 10 countries • First Cloud Implementation – 1st Job Science international customer • Common Data Across all Divisions and Countries • Use Platform Integration and App Exchange to eliminate manual keying • Let System capture and report the data (OoaS) • Build base analysis model and reuse/ extend it
  13. 13. One Day on Salesforce in CPL 985 new CVs 1116 updated candidates 1260 applications 370 submitals 140 interviews 120 secure docs 2664 Candidate phone calls 30 perm placements / invoices 55 jobs added
  14. 14. Job Fulfilment Can we predict which job orders will achieve successful placement ? Invoice Payment Can we identify which invoices will / wont be paid on time ? The Einstein Challenge
  15. 15. Present Results - Many Sources
  16. 16. Understand Data and the resultant relationships
  17. 17. Confirmed Interview Slots No 12% YES 26% Applications 8.55% 4.08% Interviews 34.21% 38.78% New 6.91% 1.02% Offers 1.64% 8.16% Placements 10.86% 27.55% Submittals 37.83% 20.41% Grand Total 100.00% 100.00% Salaries agreed No 8 % Yes 20% Applications 7.69% 7.43% Interviews 26.92% 36.57% New 7.69% 5.14% Offers 1.92% 3.43% Placements 5.77% 16.29% Submittals 50.00% 31.14% Grand Total 100.00% 100.00% Met Hiring Manager No 13% Yes 21% Applications 6.50% 8.42% Interviews 36.00% 34.65% New 5.00% 5.94% Offers 1.50% 4.95% Placements 13.00% 16.83% Submittals 38.00% 29.21% Grand Total 100.00% 100.00% ???? NO 14% YES 20% Applications 9.60% 5.39% Interviews 33.84% 36.76% New 4.55% 6.37% Offers 2.53% 3.92% Placements 12.12% 17.65% Submittals 37.37% 29.90% Grand Total 100.00% 100.00% Predicting Future Job Success based on History
  18. 18. Key Take-aways 1. Focus on your data quality and the results will come 2. Know your business operation x10 2. Let someone else worry about the AI engine 3. Start the journey before you know final destination. No perfect answer Thank you
  19. 19. DEMO Demo #1 –
  20. 20. Field Sales Minimise Days to Close Area of Interest Goal/Business Outcome Predictors (actionable?) Industry Region Product Family Promotion Discount Defining a Use Case for Analytics and AI
  21. 21. Blaze Trails Together Trailblazer Community Peter Lyons Senior Associate, Huron Consulting Group Learning Map Salesforce MVP 30K Members 200+ MVPs