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

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.

“ 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.

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

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

Erica Stowe CRM Process Manager [email_address]

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

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

Why is data quality important? Poor Data Quality Turns Into: Inaccurate Reporting Time Wasted Lost Money Pursuing Bad Information Kills Users Adoption

Poor Data Quality Turns Into:

Inaccurate Reporting

Time Wasted

Lost Money Pursuing Bad Information

Kills Users Adoption

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

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!

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

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

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

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

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

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

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

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

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

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

How to Assess a CRM Implementation’s Data Quality Reports and Dashboards De-Duplication Tools Survey Your Users Profile the Data Analyze the Results

Reports and Dashboards

De-Duplication Tools

Survey Your Users

Profile the Data

Analyze the Results

Data Quality Reports and Dashboards Understand the Important Fields of Every Entity Create Exceptions Reports for Each Entity Create Data Quality Dashboards by Role

Understand the Important Fields of Every Entity

Create Exceptions Reports for Each Entity

Create Data Quality Dashboards by Role

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

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

Demo Data Quality Scoring and Dashboards

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

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

Matthew Evans CRM Project Manager [email_address] The AdminExchange www.adminexchange.wordpress.com

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.

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

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

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Additionally, please score each individual speaker on:

Overall delivery of session

Question and Answers