WELCOME DATA SCIENCE STRATEGY Are we ready for it? Asure / DOMO 3 - - PowerPoint PPT Presentation

WELCOME

DATA SCIENCE STRATEGY

Asure / DOMO

3

Are we ready for it?

DATA SCIENCE STRATEGY – ASURE’S EXPERIENCE

Bruce Harris

Director IT and Enterprise Business Applications, Guest Lecturer Graduate School of Accounting University of Texas at Austin

Asure

Ulises Gonzalez-Guerra

Pricing Specialist / AWS Cost Optimization Project Manager

Asure

4

5

• 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

TOPICS WE WILL COVER

6

• 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

KEY TAKEAWAYS

7

• 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

DATA SCIENCE STRATEGY CONSULTING PROJECT

Building the Asure Model

CRM Analytics Integrations CPM ERP Tax Gamification of Travel Expenses Customer Support Quote to Cash Marketing Expenses

APPLICATION ECOSYSTEM

10

• 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

GOALS

11

• No analytics solution (before Domo)
• Disparate, disconnected data
• Lack of In-depth knowledge of steps needed to create analytical models

OBSTACLES

12

• 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

SCENARIO

13

• 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

OUTCOMES

14

• 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

FUTURE

Master Data Management

Connected No Code{Configuration} Cloud

Analytics Driven Root Cause Analysis

Customer 360 Customer Health Renewals CaPDB

Scalable Master Data Set

FOUNDATION {PHILOSOPHY}

• 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 (industry, geography,

credit score, number of employees)

• Errors – affect the outcome but cannot be accounted for in the model

COMPONENTS OF A DATA SCIENCE MODEL

BUILDING THE ASURE MASTER DATA SET

• 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

ANALYTICAL UNIT OF MEASUREMENT

Accounts Customers Opportunities Sales Orders/ Invoices Primary Key

NS SO ID SF OPP ID SF Account ID NS Customer ID

Master Data Set Salesforce Account NetSuite Customer Salesforce Opportunity NetSuite Sales Order/Invoice Dun & Bradstreet Data Asure Transactional Data Macro Economic Data Zendesk Cases Domo Connector Domo Connector

370+ Data Points Financial Risk/Credit Risk/Payment Risk # Employees # Locations Address Industry Codes Annual Revenue

c

Transactional Data

Domo Connector

c

Employment Data

Macro Economic Data

Cases c

Domo Connector

Domo Master Data Set

Master Data Set

Salesforce Account NetSuite Customer Salesforce Opportunity NetSuite Sales Order/Invoice Dun & Bradstreet Data Asure Transactional Data Macro Economic Data Zendesk Cases

Customer Profile Build the Customer Profile 1 Record Per Customer Per Month Unit of Analysis

Customer Profile Domo Data Science Models Generate Customer Churn & Revenue Forecast Customer Churn Revenue Forecast

Prospect Accounts Prospect Conversion

Master Data Set Salesforce Account NetSuite Customer Salesforce Opportunity NetSuite Sales Order/Invoice Dun & Bradstreet Data Asure Transactional Data Macro Economic Data Zendesk Cases

370+ Data Points Financial Risk/Credit Risk/Payment Risk # Employees # Locations Address Industry Codes Annual Revenue

Using Domo Generated Customer Profile, Send D&B Request for Prospects Matching Our Standard Profile Create Prospect Account Records in Salesforce Current process is Reactive (pulls credit report after deal is closed. Proposed process is Proactive (credit score is known before prospect is contacted), limiting churn risk due to financial condition. Marketing Campaign Prospect data cannot be loaded Into HubSpot for Campaigns due to restrictions.

Prospect Accounts Process Prospects in Prospect Analytical Model Using Domo Data Science

Customer Master Data Set Salesforce Account NetSuite Customer Salesforce Opportunity NetSuite Sales Order/Invoice Dun & Bradstreet Data Asure Transactional Data Macro Economic Data Zendesk Cases Domo Connector Prospect Master Data Set Salesforce Account Dun & Bradstreet Data

Generate Prospect Conversion %

Domo Writeback

Send Closed/Won Results to Domo

Opportunities

Feedback Loop

ROI – TARGETED PROSPECT CONVERSION

Number of Targeted Prospects Requested Conversion Rate Prospects Converted ROI 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% 50,000 0.1% 50 3% 50,000 0.5% 250 417% 50,000 1% 500 934% 50,000 2% 1,000 1,967% 50,000 5% 2,500 5,068%

* Typical prospect conversion rates for SaaS Software companies are 0.5% to 7.5%

ROI – CHURN MITIGATION

25

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.

THE DATA IS EVERYWHERE

26

• 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?

CHURN – CONCEPTUAL MODEL

27

• Billing Terms
• Customer Life - Bill
• When they are Up for Price Increase

(Contract Renewal)

• System Up-time/Downtime
• Classic vs Web Customers (Platform Type)
• Last Upgrade Version
• Time since last Up-sell
• Number of Products/Product Mix
• Macro-variable (Employment, GDP)
• Time to live
• Customer Contacts (weak maybe) -time

since last support case submitted

• Credit Score
• Number of Checks Processed
• Last Processed Check
• Number of Payrolls Processed
• Industry
• Number of Employees at Company - feature

engineering (turn employee count into factor)

• Date/Time Pricing
• Geography
• Customer Service Contacts
• Customer Payment History
• Payment Terms

DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE

28

Summary Statistics

Correlation Matrix

DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE

29

Histogram Data Profile

DOMO SCRIPTS AUTOMATICALLY CREATE DETAILED DATA PROFILE

30

KEY TAKEAWAYS

31

• 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

AND ONE MORE THING…

32

33

THANK YOU