Domo, Inc. · NASDAQ:DOMO
Domo — Domopalooza (May 2020)
34 slides from Domo's conference presentation, May 2020
Slide 1: Domopalooza Live and On-Demand
Slide 2: Welcome
WELCOME
Slide 3: Data Science Strategy: Are we ready for it?
DATA SCIENCE STRATEGY Are we ready for it? Asure / DOMO 3
Slide 4: Data Science Strategy – Asure's Experience
DATA SCIENCE STRATEGY – ASURE’S EXPERIENCE Bruce Harris Ulises Gonzalez-Guerra Director IT and Enterprise Business Applications, Pricing Specialist / AWS Cost Guest Lecturer Graduate School of Accounting Optimization Project Manager University of Texas at Austin Asure Asure 4
Slide 5: Topics We Will Cover
TOPICS WE WILL COVER • What does the Data Science Strategy Consulting Project consist of • Obstacles Asure faced on its way to Data Science Readiness • Building the Asure Analytical Model • Lessons provided and applied working with DOMO Data Science Consultants 5
Slide 6: Key Takeaways
KEY TAKEAWAYS • Don’t let data constraints define your Data Science Vision • Develop your Conceptual Model • Design and create your Master Data Set • Short Term Wins Long Term Vision 6
Slide 7: Data Science Strategy Consulting Project
DATA SCIENCE STRATEGY CONSULTING PROJECT • The Readiness – Developing the capacity to execute a Data Science Strategy • The Modeling – Leveraging the tools you have created during readiness to build and test a Machine Learning Model • The Production – Going live with your machine learning model 7
Slide 8: Building the Asure Model
Building the Asure Model
Slide 9: Application Ecosystem
APPLICATION ECOSYSTEM Customer Support Tax CPM Expenses CRM Integrations Marketing Gamification of Travel Expenses Quote to Cash Analytics ERP
Slide 10: Goals
GOALS • Introduce Domo and analytics to a wider audience • Use analytics to drive decision making and create revenue opportunities • Customer Health, Customer 360 – know our customer better • Improve forecast accuracy • Churn reduction • Prospect identification and conversion 10
Slide 11: Obstacles
OBSTACLES • No analytics solution (before Domo) • Disparate, disconnected data • Lack of In-depth knowledge of steps needed to create analytical models 11
Slide 12: Scenario
SCENARIO • Work with the Domo Data Science team to learn foundations of analytics and models • Create Master Data Set in Domo • Work with Domo Data Science team to turn master data set into working analytical model 12
Slide 13: Outcomes
OUTCOMES • In-depth knowledge of our customer • Master Data Set that can be used by the whole company – no more looking for data wondering if it is correct • Prospect Monetization • In-house knowledge of Data Science best practices 13
Slide 14: Future
FUTURE • Use feedback loop to further fine tune analytical model • Find more uses for Master Data Set • Continue to improve our knowledge of Data Science best practices 14
Slide 15: Foundation {Philosophy}
FOUNDATION {PHILOSOPHY} Customer Customer Renewals CaPDB 360 Health Master Data Set Analytics Driven Root Cause Analysis Master Data Management No Connected Scalable Cloud Code{Configuration}
Slide 16: Components of a Data Science Model
COMPONENTS OF A DATA SCIENCE MODEL • Outcome – Behavior you want to predict (churn, revenue, prospect conversion): dependent variable • Predictors – what affects the outcome: independent variables (AR balance, payment history, product count) • Controls – other factors that are related to the outcome
Slide 17: Building the Asure Master Data Set
BUILDING THE ASURE MASTER DATA SET
Slide 18: Analytical Unit of Measurement
ANALYTICAL UNIT OF MEASUREMENT • What unit of analysis will all your variables be recorded across? • Think rows, not columns • Necessary to define which format your data will revolve around
Slide 19: Domo Master Data Set
Domo Master Data Set Accounts Customers 370+ Data Points Financial Risk/Credit Risk/Payment Risk # Employees SF Account ID # Locations NS Customer ID Address Industry Codes Annual Revenue Primary Key Sales Orders/ NS SO ID Opportunities Invoices SF OPP ID Domo Domo Macro Economic Data Connector Conn
Slide 20: Build the Customer Profile
Build the Customer Profile Master Data Set Salesforce Account NetSuite Customer Salesforce Opportunity NetSuite Sales Order/Invoice Customer Profile Dun & Bradstreet Data Asure Transactional Data Macro Economic Data Zendesk Cases 1 Record Per Customer Per Month Unit of Analysis
Slide 21: Domo Data Science Models Generate Customer Churn & Revenue Forecast
Domo Data Science Models Generate Customer Churn & Revenue Forecast Customer Churn Customer Profile Revenue Forecast
Slide 22: Prospect Conversion
Prospect Conversion Prospect data cannot be loaded Into HubSpot for 370+ Data Points Campaigns due to Financial Risk/Credit restrictions. Risk/Payment Risk # Employees # Locations Create Prospect Address Account Records in Industry Codes Annual Revenue Salesforce Marketing Prospect Campaign Accounts
Slide 23: Process Prospects in Prospect Analytical Model
Process Prospects in Prospect Analytical Model Using Domo Data Science Prospect Accounts Opportunities Customer Master Data Set Prospect Master Data Set Feedback Salesforce Account Salesforce Account Loop NetSuite Customer Dun & Bradstreet Data Domo Salesforce Opportunity Connector NetSuite Sales Or
Slide 24: ROI – Targeted Prospect Conversion
ROI – TARGETED PROSPECT CONVERSION * Typical prospect conversion rates for SaaS Software companies are 0.5% to 7.5% Number of Targeted Prospects Conversion Rate Prospects Converted ROI Requested 25,000 0.1% 25 -45% 25,000 0.5% 125 173% 25,000 1% 250 446% 25,000 2% 500 993% 25,000 5% 1,250 2,632% 50,
Slide 25: ROI – Churn Mitigation
ROI – CHURN MITIGATION Churn Reduction ROI 1% -76% 2% -52% 5% 20% 10% 140% 12% 189% 15% 261% 20% 381% Prospect Conversion Analytics would also contribute to Churn reduction by limiting prospects to companies with strong financial scores. 25
Slide 26: The Data Is Everywhere
THE DATA IS EVERYWHERE • Where is the data? • What data do we need to accomplish a specific goal? • What process do we need to implement to start generating the necessary data? 26
Slide 27: Churn – Conceptual Model
CHURN – CONCEPTUAL MODEL • Credit Score • Billing Terms • Number of Checks Processed • Customer Life - Bill • Last Processed Check • When they are Up for Price Increase • Number of Payrolls Processed (Contract Renewal) • Industry • System Up-time/Downtime • Number of Employees at Company - feature •
Slide 28: Domo Scripts Automatically Create Detailed Data Profile: Summary Statistics
DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE Summary Statistics 28
Slide 29: Domo Scripts Automatically Create Detailed Data Profile: Correlation Matrix
DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE Correlation Matrix 29
Slide 30: Domo Scripts Automatically Create Detailed Data Profile: Histogram
DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE Histogram Data Profile 30
Slide 31: Key Takeaways
KEY TAKEAWAYS • Don’t let data constraints define your Data Science Vision • Develop your Conceptual Model • Design and create your Master Data Set • Short Term Wins Long Term Vision 31
Slide 32: And One More Thing…
AND ONE MORE THING… 32
Slide 33: You Get a Script
YOU GET A SCRIPT, oY OU.GETA SCRIPT ——
Slide 34: Thank You
THANK YOU zoos