Domo, Inc. · NASDAQ:DOMO
Domo — Domopalooza 2020
42 slides from Domo's conference presentation, May 2020
Slide 1: Domopalooza Live and On-Demand
Slide 2: Welcome
WELCOME
Slide 3: Saving Millions with Workforce Optimization
SAVING MILLIONS WITH WORKFORCE OPTIMIZATION APPLIED ARTIFICIAL INTELLIGENCE WITH: BELLE TIRE + DOMO + RXA Don Barnes III, Ben Schein, Jason Harper 3
Slide 4: Ben's Bio: Evolution of a Data Dork
BEN’S BIO: EVOLUTION OF A DATA DORK Philosophy, Politics and Economics (no data) 6 years at database software start up: training, coding, selling (and cleaning the bathrooms) MBA 5 years breaking things in finance at Target (push the envelope of what is given to me) 5 years building analytic solutio
Slide 5: Jason Harper
A Leading applied artificial intelligence company from Ann JASON HARPER Arbor, MI Founder & Chief Executive Officer A LEADING Domo Implementation Partner 5
Slide 6: RXA client logos
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Slide 7: RXA Leadership Team
RXA LEADERSHIP TEAM Jason Harper Jon Prantner Jon Kuznicki Tom Stanek Heather Reed Jeff Doak Founder & CEO Co-Founder & CAO COO President CXO CTO Come say ‘hi’ and get free swag (DELIVERED*) at our virtual booth! www.RXA.io/booth * While supplies last :) 7
Slide 8: Don Barnes, III
DON BARNES, III President & Chief Tire Guy Since 1922 120 Locations 2500 Employees 8
Slide 9: Ben
BEN
Slide 10: Driving the cultural change required for AI and machine learning
Driving the cultural change required to make AI and machine learning initiatives successful
Slide 11: Honor the Human Intelligence
HONOR THE HUMAN INTELLIGENCE • Empower users to engage with the detailed output of machine learning/AI algorithms • Ask for feedback based on the human experience and business context • Help humans understand their continued role in improving the business • Avoid creating a “black box” 11
Slide 12: Avoid the Black Box
AVOID THE BLACK BOX • Humans hate a ”black box” so make your work accessible • Show your work so that they understand what you are doing • Do not over summarize • Allow for human inputs and tuning • Include flexibility to handle different business contexts (new customers, new lines of business, etc)
Slide 13: Jason
JASON
Slide 14: What Really Is an AI Solution?
WHAT REALLY IS AN AI SOLUTION? 14
Slide 15: What Really Is an AI Solution?
WHAT REALLY IS AN AI SOLUTION? Everything from a simple customer churn forecast to an autonomous pizza delivery vehicle. It could be as fancy as you like, but it really boils down to using data to make better decisions, faster. 15
Slide 16: Identifying When You're Ready for AI
IDENTIFYING WHEN YOU’RE READY FOR AI One of the most frequent reasons for not diving into AI that we hear today is: “we’re just not ready yet” If you are watching this video today, you are ready for AI. Get started by identifying your business objectives and mapping them to KPIs that we can measure.
Slide 17: How to Implement and Drive Adoption of an AI Solution
HOW TO IMPLEMENT AND DRIVE ADOPTION OF AN AI SOLUTION 17
Slide 18: Drive Adoption: Analytics Plan
DRIVE ADOPTION : ANALYTICS PLAN Download here: www.Rxa.Io/domo 18
Slide 19: What Skill Sets Does My Team Need for AI
WHAT SKILL SETS DOES MY TEAM NEED FOR AI The dirty little secret of data science is that it’s very much an art. When you are building out your teams it’s important to recognize that successful teams balance technical skills with creative ability. And the glue that will hold your team together is tru
Slide 20: Data Scientist Job Description
DATA SCIENTIST JOB DESCRIPTION The ideal candidate's favorite words are learning, data, scale, and agility. You will leverage your strong collaboration skills and ability to extract valuable insights from highly complex data sets to ask the right questions and find the right answers. Responsibilitie
Slide 21: What Do I Need to Do to Maintain an AI Solution
WHAT DO I NEED TO DO TO MAINTAIN AN AI SOLUTION The skill set shifts a bit. Obviously, you need to drive adoption throughout the AI deployment Lifecycle, and that comes with trust. Domo provides the systems to monitor when data inflows and outflows break and can be set up to monitor when algorithms
Slide 22: Case Study: Workforce Optimization
CASE STUDY: WORKFORCE OPTIMIZATION 22
Slide 23: Don
DON
Slide 24: 2017 Labor Costs Felt Too High
2017 Labor Costs “Felt” Too High. 24
Slide 25: This promotion is always the best of the year
“THIS PROMOTION IS ALWAYS THE BEST OF THE YEAR...I HAVE TO MAKE SURE I HAVE LOTS OF PEOPLE WORKING.” 25
Slide 26: The Problem
THE PROBLEM: Staffing issues on the clock and underutilized -or- short staffed resulting in longer wait times and lost revenue 26
Slide 27: The Solution
THE SOLUTION: WE PARTNERED WITH RXA & DOMO TO DELIVER THEIR LABOR OPTIMIZATION PLATFORM 27
Slide 28: Step 1
STEP 1 The focus was on deploying the pre-built solution, but customizing it to exactly fit our business. Using the Workforce Optimization algorithms as the foundation, we built a plan to optimize our labor that was focused exactly on our needs. 28
Slide 29: Analytics Plan Highlights
ANALYTICS PLAN HIGHLIGHTS 1 BUSINESS INITIATIVE 2 BUSINESS PROBLEM Reduce Bottom Employees are on Line Expenses the clock and underutilized 3 BUSINESS MEASURE 4 BUSINESS OUTCOME Hourly labor Reduction in labor utilization expense 29
Slide 30: Assess
ASSESS ● Store Attributes ○ Date Opened ○ # of Bays ○ # of Customers Served ○ # of each employee type ○ Part Delivery Schedule ● Historic Timeclock data ○ Employees on clock by job code on a 30 minute basis ● Historic Order Track data ○ Employees actively working on an RO by job code on a 30 minute
Slide 31: Assess
ASSESS ● Store Attributes ○ Date Opened ○ # of Bays ○ # of Customers Served ○ # of each employee type ○ Part Delivery Schedule ● Historic Timeclock data ○ Employees on clock by job code on a 30 minute basis ● Historic Order Track data ○ Employees actively working on an RO by job code on a 30 minute
Slide 32: Other Factors
OTHER FACTORS. 32
Slide 33: Initial Assessment: Customers Waiting and Unnecessary Labor Costs
INITIAL ASSESSMENT CUSTOMERS WAITING & UNNECESSARY LABOR COSTS Optimal Staffing Opportunity 46% 46% of hourly shifts are not properly staffed. Longer customer wait times, or staff standing around. Over Staffing 29% 29% of hourly shifts are overstaffed, directly resulting in unnecessary labor expense
Slide 34: Modeling
MODELING Distributed Random Forest (DRF) is a powerful classification and regression tool. When given a set of data, DRF generates a forest of classification or regression trees, rather than a single classification or regression tree. Sample Model Description: •500 Trees in Each Forest •Maximum Dept
Slide 35: Implementation
IMPLEMENTATION Via Domo Dashboards à It’s CRITICAL to meet your consumers where they are. Understand their process, and hand feed them the information they need to make their decisions, when they need it. Pushed right to your manager’s inboxà 35
Slide 36: Adoption & Maintenance
ADOPTION & MAINTENANCE MBE;
Slide 37: Driving Adoption and Maintaining
DRIVING ADOPTION AND MAINTAINING Once we had adoption and results, we monitored use through Domo. RXA to uses Domo to alert the data science team when the predicted schedules are falling out of tolerance and the models need to be adjusted outside the automated process. 37
Slide 38: Results
RESULTS ne MBE;
Slide 39: Store Manager Feedback
STORE MANAGER FEEDBACK “I never have extra bodies or am short staffed.” “We haven’t been running behind.” “I’m trying to follow it as much as I can.” “I think it’s spot on.” 39
Slide 40: Business Results
BUSINESS 14% reduction in hourly labor costs Increased Same Store Sales 2.1% Maintained Net Promoter Score of 75 Increased Same Store EBITDA by 8.4% 40
Slide 41: Thank You
THANK YOU :) 41
Slide 42: Thank You
THANK YOU zoos