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Fueling your growth with accurate data and smart data management

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As businesses, we know the importance of correct, complete, and integrated data to drive our marketing performance. Learn what the experts recommend, what a high-performing data management program looks like, and three steps to get there.

The basics of data ownership, rights, and who’s in charge of maintenance

The fundamentals of data governance and essential tools for data hygiene

Real-world examples of common data integration and data quality pitfalls that we see every day, and tips for how to avoid them

How CRM integration and high-quality data powers your marketing automation

Published in: Data & Analytics
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Fueling your growth with accurate data and smart data management

  1. 1. FUELING YOUR GROWTH WITH ACCURATE DATA AND SMART DATA MANAGEMENT: LEARN HOW TO COLLECT, CLEAN, ENGAGE AND LEVERAGE YOUR DATA
  2. 2. FUN FACTS 90% of all data has been created in the last two years. https://public.dhe.ibm.com/common/ssi/ecm/wr/en/wrl12345usen/watson- customer-engagement-watson-marketing-wr-other-papers-and-reports- wrl12345usen-20170719.pdf Today it would take a person approximately 181 million years to download all the data from the internet with an average download speed of 44Mbps. http://www.physics.org/thankphysics/internet/ 2012 only 22% of all the data had the potential for analysis from sources such as surveillance, entertainment and social media, etc. By 2020, the percentage of useful data, i.e., the information that has the potential for analysis, is predicted to jump to 37%.
  3. 3. THERE IS A LOT OF DATA OUT THERE https://www.visualcapitalist.com/what-happens-in-an-internet-minute-
  4. 4. AGENDA • Basics of Data Ownership and Data Governance • Importance of Data Quality • Tools for Maintaining Your Data • Tips for CRM Integration; Using Data to power Your Marketing Automation • Bringing it All Together
  5. 5. DATA OWNERSHIP What does Data Ownership mean? • Definition - Data ownership is the act of having legal rights and complete control over a single piece or set of data elements. It defines and provides information about the rightful owner of data assets and the acquisition, use and distribution policy implemented by the data owner. • Data ownership also defines the data owner’s ability to assign, share or surrender all of these privileges to a third party. This concept is generally implemented in medium to large enterprises with huge repositories of
  6. 6. DATA SOURCES Where does it come from? • Your customer database – Automatic Ownership • Third Party data (Vendor/ Partner) – Licensed Ownership
  7. 7. UNDERSTAND YOUR DATA SOURCES• Identify your data sources – do you know where your data is from? • If using third party provider – do you know the frequency that they update their data? Are you scheduled to receive data refreshes? • Is all data created equal? A one-time conference list should be ranked differently than a trusted data partner In an enterprise setting, the term ‘ownership’ generally assigns a level of accountability and responsibility for specific datasets.
  8. 8. FUNDAMENTALS OF DATA GOVERNANCE DEFINITION - DATA GOVERNANCE REFERS TO THE GENERAL MANAGEMENT OF THE INTEGRITY, USABILITY, SECURITY, AND AVAILABILITY CAN BE A CROSS- DEPARTMENTAL TEAM INCLUDING VOICES FROM VARIED USERS CREATE FUNCTIONAL DATA FLOWS BETWEEN APPLICATIONS
  9. 9. COMMON PITFALLS IN MAINTAININ G YOUR DATA • Lack of data ownership • Focusing only on new data and/or Fear of removing or inactivating data • Data overlay – when data differs, how do you know what data to keep • Not understanding all data sources • Duplication
  10. 10. DATA QUALITY ISSUES • For example, if an employee hits the A key instead of the I key when entering an address in Washington, IL, you might end up sending marketing material to an address in the ghost town of Washington, AL instead of the livelier town of Washington, IL. Inaccurate Address Data • If you operate internationally, having numbers that are as complete as possible is likely to be important. Incomplete Phone Numbers • While you can’t (typically) force users to enter data that they don’t want to enter, you can at least use data- quality tools to identify fields within a database that are missing, or appear likely to be inaccurate. Missing Data Entries
  11. 11. WHAT DOES YOUR DATA LOOK LIKE?
  12. 12. SOLUTION S • Accuracy • Completeness • Consistency • Cost • Security • Sustainability
  13. 13. HOW DO WE ENSURE DATA QUALITY? •Data Hygiene •Email Hygiene •Data Append
  14. 14. DATA HYGIENE • Merge Purge: Utilize partners to merge records from one or multiple data sources to eliminate duplicate records. The result is one unique record containing all of the valuable data. • Data Refresh: Updating records with current information; Adds, changes, deletes
  15. 15. EMAIL HYGIENE What is email hygiene? Email hygiene/ verification is defined as the process of verifying or removing invalid email addresses from an email list • Increase deliverability • Saves money • Increased conversion rates • Increased email ROI
  16. 16. DATA APPENDS Data append is taking known customer data (first name, last name, and postal address) and matching it against a database to obtain new information Demographic/ Firmographic append: Appending variable(s) to a customer file Email append: Utilizes a postal address to provide customers with an email address Reverse append: Uses an email address to provide customers with a postal address What is a Data Append?
  17. 17. DATA INTEGRATION
  18. 18. IMPORTANT CHALLENGES OF DATA INTEGRATION Defining Data Integration - What are you integrating? 1 Formatting of data sources – How are you integrating it? 2 Defining KPIs of the Data – Why are you integrating it? 3 Do you have the infrastructure to support the integration? 4 What are the costs involved in the integration? 5 Data integration can pose multiple challenges during the implementation process if you do not approach it the right way. Successful data integration requires knowledge and thorough planning.
  19. 19. DATA INTEGRATIO N SOLUTIONS Understand all your data sources and repositories and the workflows between them Understand Identify key fields that can link source data to your existing data Identify Seek outside assistance with data mapping tools to update existing and new records Seek
  20. 20. Email Landing Pages Display Ads Qualified Prospects Marketing Qualified Leads Sales Qualified Leads Sales Accepted Leads Sales Updated CRM CAMPAIGN TO SALES PROCESS
  21. 21. 200,32 0 4,560 359 8 $700,000 + DELIVERED EMAILS UNIQUE EMAIL OPENS OPPORTUNIE S LEADS PASSED TO SALES WON MEANINGFUL CONVERSATIONS 35 36,900 UNIQUE CONTACTS Lead Scoring Criteria Explicit Title, Location, Industry, Revenue, Lead Source Implicit Website Visits, Phone Calls, Downloads, Form Fills Negative Lack of response, No budget, Inactivity, Lack of authority It Actually Works!
  22. 22. ANY QUESTION S?

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