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Salesforce Einstein: Use Cases and Product Features

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Salesforce Einstein: Use Cases and Product Features
#ImpactSalesforceSaturday Session
by @newdelhisfdcdug

Date and Time
Saturday, June 29, 2019
11:30 AM – 12:30 pm

Location: Online

A warm welcome to all the members and Guests in the meetup.

Join us for #ImpactSalesforceSaturday a series of online Salesforce Saturday sessions.

We invite all – Developers – Administrators – Group Leaders – Consultants with advanced, intermediate or beginner level knowledge on Salesforce(Sales Cloud, Service Cloud, Pardot, Marketing Cloud, IOT, CPQ, Einstein, etc).

Speaker: Jayant Joshi
Jayant is a Salesforce Technology Delivery Architect and Manager (Multi-Cloud) working on Large and Mid Size CRM Implementation Projects. Overall, He has around 14 years of CRM delivery experience with 9+ years in Salesforce.com Projects and Opportunities. He is very passionate about emerging technologies and has a deep interest in major innovations around the world. He has 19 Salesforce Certifications and accreditations currently and is passionate about various Salesforce Products including Salesforce Einstein.
Agenda

a. Introduction
b. Salesforce Einstein – Products and Use Cases
c. Q & A

Published in: Software
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Salesforce Einstein: Use Cases and Product Features

  1. 1. New Delhi Salesforce Developer Group #ImpactSalesforceSaturday Einstein – Use cases and Products By: Jayant Joshi LEARN . SHARE . CELEBRATE . SALESFORCE
  2. 2. About New Delhi Salesforce DG • First Revival Meetup in February 2016 • Twitter: https://twitter.com/newdelhisfdcdug • New Delhi Salesforce DG Trailblazer Community Group: http://bit.ly/NewDelhiCommunity • Facebook: https://www.facebook.com/newdelhisfdcdug #ImpactSalesforceSaturday
  3. 3. What is #SalesforceSaturday • Started by Stephanie Herrera in Austin, Texas • Meetup every Saturday in a Coffee Shop or anywhere to share and learn about Salesforce • It’s now a global phenomena with more than 25 SalesforceSaturday Group over 5 Continents • For India, it comprises of Online Knowledge Sharing sessions and Trailhead Challenges #ImpactSalesforceSaturday
  4. 4. TIME Topic ‘15 What is AI and Machine Learning? AGENDA ’15 Demo ‘25 Salesforce Einstein – Use Case and Products ’05 Q & A and Wrap-Up
  5. 5. About Me: Jayant Joshi #ImpactSalesforceSaturday Professional: - SFDC Technology Architecture Managing Delivery Architect - Around 14 Years of overall experience and 9+ years in SFDC - Working in CG but have worked in Accenture from the last 6 years. Have worked with Deloitte Consulting and IBM earlier. - Have worked in India, US, Canada, and Germany. - Enterprise Architecture/TOGAF - Passionate about SFDC - Among Top SFDC Certified People in World - Regularly contribute to Salesforce related articles on Social Media - Mentoring around SFDC Topics - Upcoming Public Sessions on SFDC Topics in India and Germany - Additional Skills: SFDC Commerce Cloud, Machine Learning, IOT, TOGAF Hobbies include Travelling (Have visited 23+ countries so far), Sky Diving and Astronomy. 1 Min
  6. 6. What is AI and Machine Learning? 29.06.2019 15 Min
  7. 7. WHAT IS AI? As per Wiki, Artificial intelligence (AI), sometimes called machine intelligence, "is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals". 1 Min
  8. 8. AI – Current Use Cases Let us look at some examples of AI already used currently (as of 2019):  Google Assistant for engaging in a two-way conversations  A powerful and a very successful recommendation engine is used by Amazon for Products Search and Recommendations. They use machine learning behind the scenes  Image recognition by companies like Google (Google Photos), Facebook (Facebook App) etc. They use the Image recognition algorithms for this purpose  Navigation Apps like Apple Maps, Google Maps, Waze etc.  Cab sharing Apps like Uber, Ola, Lyft are using advanced Machine learning techniques to provide best user experience to their customers  Harvard scientists used Deep Learning to teach a computer to perform viscoelastic computations, these are the computations used in predictions of earthquakes 1 Min
  9. 9. AI – Facts and Figures • By 2025, the global AI market is expected to be almost $60 billion; in 2016 it was $1.4 billion • Global GDP will grow by $15.7 trillion by 2030 thanks to AI • AI can increase business productivity by 40% • AI startups grew 14 times over the last two decades • Investment in AI startups grew 6 times since 2000 • Already 77% of the devices we use feature one form of AI or another • Cyborg technology will help us overcame physical and cognitive impairments • Google analysts believe that next year, 2020, robots will be smart enough to mimic complex human behavior like jokes and flirting 30 Seconds Source: https://techjury.net/stats-about/ai/
  10. 10. How do we describe 'Intelligent Behavior'?  Learn from experience  Determine what is important (Prioritize)  Solve Problems  Handle complex situations  Apply knowledge acquired from experience (Very Important trait for being human)  … 2 Min
  11. 11. How can machine acquire Intelligence? 2 Min  Learn from experience (As human do)  Determine what is important (Prioritize)  Solve Problems (as human do)  Handle complex situations (As human do)  Apply knowledge acquired from experience
  12. 12. And what is Machine Learning (ML)? “Machine learning (ML) is the ability for computers to learn and act without being explicitly programmed”. The key idea behind ML is that it is possible to create algorithms that learn from and make predictions on data. Some of the examples of ML from real life are:  Amazon and Netflix Recommendations for products you buy and movie recommendations respectively.  Spam Filters of your favorite email account (e.g. Gmail) keep on learning as the new data (spam messages) comes on. 2 Min
  13. 13. Machine Learning - Process 2-3 Min
  14. 14. Types of Machine Learning • Supervised learning is when you have knowledge of the input (X) and the output (Y), then you “supervise” the program in predicting the right outcome via trial and error. This means mapping input data to known labels, which humans have provided. • Unsupervised learning is when you have zero knowledge of the output and you want to try to find patterns or groupings within the data. 2-3 Min
  15. 15. Good to Learn… • Deep Learning • Machine Learning Algorithms (at least a few) • Machine Learning Frameworks and Tools • Application of AI across various industries • … 1 Min
  16. 16. Now, talking about Salesforce Einstein…  Einstein products are based on Machine Learning models.  Every machine learning model needs a lot of data to make better predictions. Of course, This is the same with Einstein.  Einstein Products (well, most of them including Einstein Discovery) identify patterns with the data and make predictions. e.g. Identify potential late payments)  Most of the time, as an admin or even a developer you will not create models in Salesforce but use the Salesforce Einstein features (which are based on models). e.g. When you create a Story in Einstein Discovery, the analytical model building takes place automatically.  With Einstein Products, many decisions can be made automatically (of course in many cases, human intervention is required). 1 Min
  17. 17. Salesforce Einstein – Use Case and Products 29.06.2019 Total Time: 25 Min
  18. 18. What is Salesforce Einstein?  Salesforce has embedded a lot of Intelligence into the existing Clouds. e.g. there are many Einstein features in Sales Cloud itself (e.g. Intelligent Lead Scoring, Opportunity insights, Automated Contacts, etc.).  All (well, almost), Salesforce Clouds have Einstein features (e.g. Service Cloud, Marketing Cloud, Commerce Cloud, etc.).  Additionally, Salesforce provides a lot of other Einstein Products. 2-3 Min Einstein Out-of-the-Box Applications and Einstein Platform are the two categories of Einstein.
  19. 19. What are typical AI Use Cases for CRM?  How can Leads be Priortized for the Sales Reps?  How can a Sales Rep identify which deals are likely to be won?  How to handle Customer Service request in a best possible way? How can a company reduce service request handle time?  Why company’s Margins are Shrinking?  How to upsell/cross sell opportunities?  How to take notes while taking with Voice Assistent?  How to predict customer sentiment?  How to add recommendations in the CRM data?  How to predict customer attritions?  How to predict customers with Late Payments?  ... 5 Min
  20. 20. Use Cases and Einstein Products 2-3 Min CRM AI Use Case • How can Leads be prioritized for the Sales Reps? • How can a Sales Rep identify which deals are likely to be won? • How to handle Customer Service request in a best possible way? E.g. How can a company reduce service request handle time? • How can service requests be deflected? E.g. Reduce the number of cases Einstein Product / Feature Sales Cloud Einstein: • Automated Lead Scoring • Opportunity Insights • Opportunity Scoring Service Cloud Einstein: • Case Classification • Einstein Bots 1
  21. 21. Use Cases and Einstein Products 2-3 Min CRM AI Use Case • How can you automate image classification to resolve issues faster? • How to understand customer buying behaviour and take actions? Einstein Product / Feature Field Service Lightning Einstein: • Einstein Vision for Field Service Pardot Einstein: • Einstein Behavior Scoring 2
  22. 22. Use Cases and Einstein Products 2-3 Min CRM AI Use Case • How to help shoppers find exactly the products they’re looking for? • How to have highly relevant product recommendations tailored to every shopper? Einstein Product / Feature B2C Commerce: • Einstein Predictive Sort B2C Commerce: • Einstein Product Recommendations 3
  23. 23. Einstein Bots  Bots can handle routine requests and free your agents to handle more complex issues.  They can even prevent them from being opened in the first place!  Bots can also gather pre-chat information to save your agents time.  Bots can be trained to understand human language—and respond intelligently— through Natural-Language Processing (NLP). 2 Min #Einstein Tools “Chatbots will power 85% of all customer service interactions by 2020.” - Gartner
  24. 24. DEMO (Einstein Chatbots) 10-15 Min 1. Obtain a Service Cloud license and a Chat or Messaging license. Each org is provided 25 Einstein Bots conversations per month for each user with an active subscription. To make full use of the Einstein Bots Performance page, obtain the Service Analytics App. 2. Enable Lightning Experience. 3. Run the Chat guided setup flow. 4. Enable Salesforce Knowledge if your bot serves Knowledge articles to customers. 5. Publish a Salesforce community (preferable) or a Salesforce Site. 6. Provide an Embedded Chat button for your customers on your community or site.
  25. 25. Einstein Prediction Builder  Point & Click Wizard  Make custom predictions on your non- encrypted Salesforce data  Enrich the prediction by creating other special fields  Use rules-based and predictive models to provide anyone in your business with intelligent, contextual recommendations and offer 2 Min #Einstein Tools
  26. 26. Einstein Analytics (EA)  Explore your organization’s data and get interesting insights.  Get great visualizations using charts and dashboards  Easily import the data to Einstein Analytics  Export data from EA  Use Apex to pull data in real time to your Einstein Analytics dashboard.  Query data with Salesforce Analytics Query Language (SAQL) 2-3 Min Note: Topic for Second Einstein Session (20-July)
  27. 27. Einstein Analytics – How it work? 2-3 Min Note: Topic for Second Einstein Session (20-July)
  28. 28. Einstein Discovery Einstein Discovery provides answers to key business questions: • What happened? What was significant or unusual? • Why did it happen? What are the factors that possibly contributed to the observed outcome? • How do some factors compare with other factors? • What might happen in the future, based on a statistical analysis of the data? Is there a trend, or does this data represent an isolated incident? • What are some possible actions that could improve the outcome? 2 Min Note: Topic for Second Einstein Session (20-July)
  29. 29. DEMO 29.06.2019
  30. 30. DEMO  Einstein Products Setup  Sales Cloud Einstein Features  Lead Scoring  Einstein Chatbots  Einstein Prediction Builder  (If Time Permits) 10-15 Min
  31. 31. Next Sessions 29.06.2019
  32. 32. Einstein Sessions  Einstein – Use Cases and Products, 29.June  Einstein Analytics and Discovery, 20.July  Einstein Platform, TBD (Sometime in August) 1 Min
  33. 33. How to Learn Einstein? 29.06.2019
  34. 34. Einstein Resources  Salesforce Trailhead  Einstein Trailmixes  Salesforce Einstein Help 1 Min
  35. 35. Q & A 29.06.2019 Total Time: 05 Min
  36. 36. Follow & Join New Delhi Salesforce DG • Join to know about future events and to RSVP: https://trailblazercommunitygroups.com/delhi-in-developers-group/ • Let’s start conversations on Success Community: http://bit.ly/NewDelhiCommunity • Follow us on Twitter: https://twitter.com/newdelhisfdcdug • Follow us on Facebook: https://www.facebook.com/newdelhisfdcdug • For all the content: https://newdelhisfdcdug.com #ImpactSalesforceSaturday
  37. 37. #ImpactSalesforceSaturday

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