How to choose the right Martech stack and Data for your organization
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The document discusses essential considerations for vendor selection in performance measurement, emphasizing the importance of data quality and unification in marketing technology. It outlines common mistakes such as data hoarding and using the wrong tools, and provides a framework for effective segmentation and integration of data sources. Best practices for data management and methodology, including normalization and automation, are highlighted to improve campaign execution and analysis.
How to choose the right Martech stack and Data for your organization
- 10. Key Questions In The Vendor Selection Process
- 11. Performance Measurement Training & Adoption Systems & Data Integration System Deployment Project Management Resourcing Alignment & Expectations
- 12. Resourcing Considerations Internal –vs – Vendor – vs – Agency
- 13. 5. Unify Data Sources 4. Monitor & Refine 3. Clean & Segment 2. Define Data Standards 6. Append & Validate 1. Assess Data Quality MarTech Data Management Methodology
- 14. Common Mistakes #1: Data Hoarding
- 15. Common Mistakes #2: Boil The Ocean Paralysis
- 16. Common Mistakes #3: Wrong Tool for the Job
- 17. • Establish a baseline • Input to the maturity model / roadmap • Know areas of weakness • Know where low-hanging fruits are • Monitor quality metrics Start With Data Quality Assessment
- 18. Clean, Accurate, & Updated Data != Quality Data Do you have the RIGHT leads? Do you have USEFUL segmentation?
- 19. • Profile based scoring • Buyer persona • Account based marketing • Personalization • Campaign execution & ROI analysis Segmentation Starts With Why?
- 20. Common B2B Dimensions Custom Dimensions • Job function / sub-function • Job level • Buyer persona • Industry • Company size • Region / territory / metro • Language • Number of job openings • Size of vehicle fleet • Number of mobile devices • Number of stores • Competitive products • Complimentary products • Channel Which Dimensions & How Many Segments?
- 21. Common Dimension != Standard Segments = = Vending
- 22. • Difficult to correct false positives and overlaps • Even more difficult to identify false negatives • Can’t analyze and report on segmentation results • Can’t be used by other systems But I’m Already Doing Segmentation With Filters The Problem With Filters You Can’t Do Analysis Like This Add Segmentation As Part of Your Data!
- 23. What You Will Need To Do Segmentation Right Segmentation Mapping Data A Data Automation Platform
- 24. Lists & Databases Predictive Solutions Help Desk Marketing Automation CRM Social, Sales Advertising The Big Marketing Data Challenge Is Unification
- 25. This even assumes data from the different sources are clean. CA vs. California Normalize data standards +1 415-555-1212 vs. (415) 555-1212 Normalize data format Toyota Motors U.S.A. vs. Toyota Motor Sales USA Correlate non-exact data values Normalize segmentation 11-50 employees vs. 20-100 employees Unification Challenges
- 26. Unification Tips You don’t have to have a central data warehouse Leverage the systems you already have Avoid a master data schema at all cost Translate and map what you need as required There is no “perfect” or even “best” data vendor Know which data parts you want from which vendor(s) Have a unification strategy before buying any data Digest data immediately after acquisition
- 29. Data Automation for Marketing & Sales Analyze Clean Enhance Unify
Editor's Notes
- #27: Data unification is not easy, but the companies that can make it happen has an absolute unfair advantage in terms of gaining new insights, creating better engagements, and execute with high efficiency and speed. Here are 4 tips I have for marketers who are embarking on data unification projects: The knee jerk reaction when people talk about data unification is you need a central data warehouse to pull all the data in one place. You actually don’t have to. Depending on the scope and targeted users for the project, you can very will use your CRM or marketing automaton platform as the system of record for the reconciled data. The second knee jerk reaction is that you need a master data schema that can unify all the data you have from difference sources. It is extremely difficult to come up with such a schema that can be maintain with reasonable effort. This is probably the number one reason why master data management projects fail and they do fail more often than they succeed. It is just too rigid. Instead, develop the ability to map and translate whatever subset of data you need for each applications as you need it. There is no perfect data set that will fill all your needs. As your data management program gets more mature, you will most likely come to the conclusion that you need multiple data vendors to assemble the data you need. Get to know which data vendor excels at what type of data and which data parts you need from each data provider. It’s up to you to assemble the data that is perfect for you. As we mentioned when introducing the data management framework, we recommend you do not buy any data until you have a unification strategy on how you will digest new data. If you don’t have a unification strategy, you won’t be able to digest the acquired data immediately, and if you do not digest it immediately, it will accumulate and lead to the data hording problem.