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A G S004 Smith 091707

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Full session information and videos available at successforce.com.

Full session information and videos available at successforce.com.

Published in: Economy & Finance, Technology

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  • Transcript

    • 1. Data Data Everywhere! Matthew Evans, Tribune Media Services Erica Stowe, R. L. Polk Deborah Sanford, salesforce.com Kevin Smith, salesforce.com Admin I: Getting Started
    • 2. Safe Harbor Statement
      • “ Safe harbor” statement under the Private Securities Litigation Reform Act of 1995: This presentation may contain forward-looking statements including but not limited to statements concerning the potential market for our existing service offerings and future offerings. All of our forward looking statements involve risks, uncertainties and assumptions. If any such risks or uncertainties materialize or if any of the assumptions proves incorrect, our results could differ materially from the results expressed or implied by the forward-looking statements we make.
      • The risks and uncertainties referred to above include - but are not limited to - risks associated with possible fluctuations in our operating results and cash flows, rate of growth and anticipated revenue run rate, errors, interruptions or delays in our service or our Web hosting, our new business model, our history of operating losses, the possibility that we will not remain profitable, breach of our security measures, the emerging market in which we operate, our relatively limited operating history, our ability to hire, retain and motivate our employees and manage our growth, competition, our ability to continue to release and gain customer acceptance of new and improved versions of our service, customer and partner acceptance of the AppExchange, successful customer deployment and utilization of our services, unanticipated changes in our effective tax rate, fluctuations in the number of shares outstanding, the price of such shares, foreign currency exchange rates and interest rates.
      • Further information on these and other factors that could affect our financial results is included in the reports on Forms 10-K, 10-Q and 8-K and in other filings we make with the Securities and Exchange Commission from time to time. These documents are available on the SEC Filings section of the Investor Information section of our website at www.salesforce.com /investor . Salesforce.com, inc. assumes no obligation and does not intend to update these forward-looking statements, except as required by law.
    • 3. Data Data Everywhere
      • Why is Data Quality important?
      • How to assess CRM Data Quality
      • Standardizing and Cleansing Data
      • Improving and Protecting Data
      • How to Get Started
      • Q&A
    • 4. Erica Stowe CRM Process Manager [email_address]
    • 5. We gather automotive data, compile it, analyze it and provide intelligence to the automotive market.
        • INDUSTRY : Automotive
        • EMPLOYEES : 1,300
        • GEOGRAPHY : Global
        • # USERS : 415
        • Login 85%
        • PRODUCT(S) USED : Salesforce SFA & Service & Support, 16 Custom Objects, 4 Custom Apps, 4 S-Controls, 2 AppExchange Applications
    • 6. Why is data quality important?
      • Poor Data Quality Turns Into:
      • Inaccurate Reporting
      • Time Wasted
      • Lost Money Pursuing Bad Information
      • Kills Users Adoption
    • 7. Why is Data Quality Important?
      • R. L. Polk did not have a data quality strategy and decided to run one direct mail campaign using the data in their CRM tool. Here was the impact to R. L. Polk’s bottom line…
      • The total cost of poor data quality to any marketing program cannot be fully quantified but can easily run 25% or more!
      4,000 Fulfillment Packages $80,000 500 Bad Addresses $10,000 1,000 Duplicates Time spent 500 Pricing Errors Due to Account Hierarchy Issues $ 10,000 Minimum Cost of Poor Data $20,000
    • 8. Data Quality – Key Challenges
      • People Challenges
        • All users had the ability to add data to every object
        • No standards or guidelines on how data was to be entered
      • Process Challenges
        • No required fields
        • No field dependencies
        • No validation rules
        • No address validation
        • No account hierarchy verification
      • Technology Challenges
        • No integration between systems or processes
    • 9. Data Quality – The Solution
      • How did we address these challenges?
        • Required Fields
        • Field Dependencies
        • Validation Rules
        • Visibility into the data
      • R. L. Polk has over a million records in Salesforce. Here are some highlights:
        • 200,000 - Opportunities
        • 100,000 - Contacts
        • 85,000 - Cases
        • 60,000 - Accounts
        • 25,000 - Leads
        • 3,500 - CARS
        • 1,000 - Forecasts
        • External Data Validation
        • CRMfusion Data Therapist
        • Processes & Guidelines
        • Training
    • 10. Data Quality – Results
      • What were the results?
        • Cost of poor data dropped from 25% to 3% for most campaigns
        • Improved forecast turnaround time from 1 week to real-time
        • Increased sales pipeline visibility from 60% to 95%
        • Increased management visibility into competitive losses
        • Gathering data earlier within our process reduced the need to re-key data down stream
        • Year over year customer satisfaction increase
        • CAR Process
    • 11. R. L. Polk’s Data Strategy Today
      • Our Forecast is distributed every Monday
      • Reports, Dashboards, Escalation Rules
      • De-Duplication Tools; Demand Tools by CRM Fusion
      • Data is shared with every level of management within our company
    • 12. Data Data Everywhere
      • Why is Data Quality important?
      • How to assess CRM Data Quality
      • Standardizing and Cleansing Data
      • Improving and Protecting Data
      • How to Get Started
      • Q&A
    • 13. How to Assess a CRM Implementation’s Data Quality
      • Reports and Dashboards
      • De-Duplication Tools
      • Survey Your Users
      • Profile the Data
      • Analyze the Results
    • 14. Data Quality Reports and Dashboards
      • Understand the Important Fields of Every Entity
      • Create Exceptions Reports for Each Entity
      • Create Data Quality Dashboards by Role
    • 15. Scoring the Data Quality of Each Record
      • Leverage custom formula fields to score each record for data completeness.
        • Sample Formula :
        • “ IF( ISPICKVAL(Industry,""), 0,20) + IF( ISPICKVAL(Rating,""), 0,20) + IF( LEN(BillingCity) = 0, 0,20) + IF(LEN(Phone) = 0, 0,20) + IF( ISPICKVAL(Type,""), 0,20)”
      • Use the Data Quality Dashboard on the AppExchange as a starting point:
      • Data Quality Analysis Dashboards 1.0
    • 16. Demo Data Quality Scoring and Dashboards
    • 17. Data Data Everywhere
      • Why is Data Quality important?
      • How to assess CRM Data Quality
      • Standardizing and Cleansing Data
      • Improving and Protecting Data
      • How to Get Started
      • Q&A
    • 18. Matthew Evans CRM Project Manager [email_address] The AdminExchange www.adminexchange.wordpress.com
    • 19.
        • INDUSTRY : Media
        • EMPLOYEES : 610
        • GEOGRAPHY : Global
        • # USERS : 40 Currently, 200+ by 2007 year end
        • Login 82%
        • PRODUCT(S) USED : Salesforce SFA & Service & Support, 7 Custom Objects, 2 downloaded AppExchange applications
      Tribune Media Services, a subsidiary of Tribune Company, is a leading provider of information and entertainment products for print, electronic and on-air media.
    • 20. Standardizing & Cleansing Data Names Company Name & Address Identify, Match & Score Load to Sandbox Find & Replace 2 4 5 Standardize Cleanse Enrich (Optional) De-dupe Validate US, U.S, U.S.A -> USA Acme-Widgets-453 Acme Inc HQ Acme UK J. Smith, John Smith – 80% Hot  High Cold  Low Data Transformation Hierarchy Data Demographics Re-parent Child Records acme incorp.-> Acme Inc Account: Division, Opportunity, Contact Naming Conventions Addresses Merge Mergers, acquisitions, spin-offs 3 Postal Standards J. Smith, John Smith -> John Smith Archiving & Filtering Validate & Modify Load to Production 1
    • 21. Tribune Problem – Bad Data!
      • Too many records
        • 23,000 Accounts for client base of 3,000
      • Duplicates
      • Unused fields
        • Asking for redundant data
        • Fields never used
      • Bad naming of records
        • Multiple opportunities and accounts with the same name
      • No standardization of field inputs
        • Billing State: CA, Calif., California, Cal.
    • 22. Pre-Define Data Management
      • Standardize and Cleanse
        • Correct inaccuracies and inconsistencies in data
        • Make sure data ownership and sharing is accurate
        • Define your CRUD rights on each profile
      • Augment
        • Add missing information from 3 rd party DB’s
        • Understand what data would provide additional value
        • Add behavioral specific data
      • Integrate
        • Understand your masters
        • Avoid stale information, misinformation from spreading
        • Create a true “360” view of your customer
        • Make some information read only
    • 23. Tribune Game Plan
      • Cleanse
        • Delete old/unused Accounts
        • Use Demand Tools to clean duplicates
        • Survey Users to remove bad fields
      • Standardize and Enforce
        • Create naming standards for Accounts and Opportunities
        • Enforce naming standards and field inputs with validation rules and workflow rules
        • Train Users on new standardizations
      • Monitor
        • Use the Adoptions Dashboards from AppExchange to track data quality
    • 24. Tribune Results
      • Reduced number of workable
      • accounts to 8,000
        • 3,000 Clients; 5,000 Prospects
      • Eliminated duplicates
        • 2,500 duplicate accounts
        • 1,500 duplicate contacts
      • Removed 15 unused fields from 4 standard objects
      • Trained users on cleaner Salesforce and introduced naming standards
      • Created 16 validation rules and 6 field update workflows to standardize field inputs
      • Customized data quality reports and dashboards to meet our needs
    • 25. Data Data Everywhere
      • Why is Data Quality important?
      • How to assess CRM Data Quality
      • Standardizing and Cleansing Data
      • Improving and Protecting Data
      • How to Get Started
      • Q&A
    • 26. Data Protect Data quality decays rapidly & enterprises should follow a methodology that includes regular measurement of data quality with goals for improvement & deployment of process improvements & technology ” “ Safeguard your cleansed data and prevent future deterioration Train
      • User Training
      • Naming Conventions
      • Address Conventions
      • Dupe. Prevention Process
      • Data Importing Policies
      • Required Fields
      • Default Values
      • Data Validation Rules
      • Workflow Field Updates
      • Web-to-Lead Restrictions
      • Data Quality Dashboards
      • Data Quality Reassessment
      • AppExchange Tools
      Enforce Monitor
    • 27. Data Data Everywhere
      • Why is Data Quality important?
      • How to assess CRM Data Quality
      • Standardizing and Cleansing Data
      • Improving and Protecting Data
      • How to Get Started
      • Q&A
    • 28. How to get Started
      • How to apply what you’ve learned when you get home
        • Get Data Quality Dashboards from AppExchange
          • (and/or User Adoption Dashboards)
        • Survey your users
        • Review available Data Quality tools and offerings
        • Create Data Quality project plan
        • Review this presentation
        • Utilize Salesforce Data Quality Assessment and Cleansing Solutions
    • 29. Session Feedback Let us know how we’re doing!
      • Please score the session from 5 to 1 (5=excellent,1=needs improvement) in the following categories:
        • Overall rating of the session
        • Quality of content
        • Strength of presentation delivery
        • Relevance of the session to your organization
      We strive to improve, t hank you for filling out our survey.
      • Additionally, please score each individual speaker on:
        • Overall delivery of session
    • 30. Question and Answers