Bringing Your CRM to Life: 3 Pillars of a Data-Backed Go-to-Market Strategy

Nearly 25 years after the introduction of cloud-based customer relationship management systems, go-to-market teams are equipped with more tools and more signals than their predecessors could dream of. 

But access to more data than ever still doesn’t mean better outcomes. 

Data warehouses, CRMs, and marketing automation tools that rely solely on first-party data still face unacceptable rates of decay — in one industry survey, data professionals estimated their CRM data degrades 34% a year without intervention. 

Fortunately, this is a solvable problem. Artificial intelligence and machine learning, guided by human reasoning, are revolutionizing data maintenance for GTM professionals. Whether it’s demographic, firmographic, or intent data, teams today don’t have to make tradeoffs between data coverage and accuracy because of technology limitations. 

The result? A living data-powered CRM that can take your GTM strategy to unmatched heights. Here’s what it takes to make that possibility a reality.

1. A Defined Total Addressable Market of Companies and Contacts

To effectively manage data, you need to define your TAM. A proper calculation — ideally including business and business professional information — will build trust with stakeholders and position your sales and marketing teams to realize their GTM goals. 

Without your TAM as a guardrail, your business might chase after every potential lead and dead-end opportunity — wasting precious time and money. To improve your market opportunities, you want to know what you’re working with. 

2. A Third-Party Data Provider with Coverage in the Total Addressable Market

Choosing the right data provider is similar to choosing a house. You understand that no house is perfect for everyone, but one can still be perfect for you. 

Look for a data provider proficient in integrating first- and third-party data to create a reliable database for your sales and marketing teams. Once you commit to a data provider, you can work with them to mitigate data hygiene challenges that stem from data degradation. The outcome will be a dynamic, living data asset to fuel your entire go-to-market strategy.

3. First- and Third-Party Data Integration

Here’s where things get detailed — and where you need an expert partner to handle the essential components of a living CRM: matching, field mapping, survivorship, and assignment.


Matching is the process of identifying and correlating similar or duplicate data across multiple sources, to represent the same real-world entity consistently. It’s important for maintaining data quality, eliminating redundancy, and integrating data from various systems. 

This process is the foundation for integrating first- and third–party data, allowing users to combine previously disparate data populations. Matching unlocks use cases like data deduplication, data enrichment, whitespace identification, TAM analysis, and much more. 

Key aspects of matching include: 

  • Duplicate identification: Matching aims to identify duplicate or similar records within a dataset. For example, in a customer database, it finds and merges multiple entries for the same customer.
  • Data quality improvement: Eliminating redundancy and ensuring consistency improves data quality. Duplicate records can lead to errors and confusion in data analysis and decision-making.
  • Data integration: Matching supports data integration projects by aligning records from different sources. This is especially important when consolidating data from various systems into a single, unified database or data warehouse.
  • Rule-based matching: Rules or criteria can be simple — like matching records with the same name — or complex, involving fuzzy matching algorithms to account for variations in data.
  • Fuzzy matching: Fuzzy matching applies to records that are similar, but not identical. It considers spelling variations, typos, abbreviations, and other data discrepancies. This is particularly useful for dealing with messy or incomplete data.
  • Blocking: Sorting records into subsets or “blocks” based on specific attributes. Matching is performed within each block to reduce the computational load.
  • Scoring or weighting: Assessing different criteria to determine the strength of a match. For instance, exact matches might receive a higher score than partial matches.
  • Manual review: In cases where matching processes may be uncertain, a manual review step can be included to resolve ambiguities. Human intervention makes final matching decisions.
  • Record linkage: Matching is also called “record linkage,” especially when historical or longitudinal data needs to be linked to a single entity over time.

Field mapping

Field mapping aligns data fields from one source to another to ensure accurate data transfer, integration, and synchronization between several systems or databases. 

This process maintains consistent data across different applications. Proper field mapping enables optimal matching algorithms and survivorship rules. Without field mapping, everything breaks. 

Key aspects of field mapping include:

  • Data integration: Field mapping is used in data integration processes where data from one source is brought into another system or database. This is necessary when companies use multiple software applications or databases that need to share data.
  • Correlation: Field mapping involves identifying which fields in the source data correspond to or match with fields in the target system. This includes specifying how data types, formats, and values should be transformed or converted during the mapping process.
  • Data transformation: In some cases, data transformation or manipulation will ensure that data from the source system aligns with the requirements of the target system. This can involve data cleansing, formatting changes, or calculations.
  • Data validation: Validation processes ensure accuracy — data may be checked for completeness, consistency, and conformity to standards.
  • Automated mapping: Many data integration tools and platforms offer automated field mapping features, which significantly simplifies the process. Automated mapping matches fields based on similar names, data types, or other characteristics.
  • Manual mapping: Manual field mapping may be required to define the relationships between fields when data sources have complex or unique structures.
  • Mapping Rules: Mapping rules (or scripts) are used to define how data is transferred or transformed between fields. These rules specify how source data should be mapped to the target fields.
  • Documentation: Documentation is essential to maintain data integration processes. It provides a clear record of how data is being transferred, making it easier to troubleshoot issues and understand the integration logic.

Field survivorship

Field survivorship is a specialized technique for deciding the “final” or “surviving” value for a specific field when data comes from multiple sources. This is necessary in situations where conflicting or redundant data needs to be consolidated. 

Specifically, field survivorship is vital  for customer data management, where conflicting records for the same entity may exist across different databases. Companies can establish a master record that holds the most accurate and reliable data, enhancing data consistency and decision-making quality.

Without optimal field survivorship rules, accurate data could be overwritten with inaccurate data — defeating the purpose of integrating first- and third-party data.

Field survivorship is particularly important for fields in CRM and marketing automation applications where fields are limited and only one record and one data value can survive. 

Key aspects of field survivorship include:

  • Conflict resolution: Conflicts can arise when data is collected or stored in different systems, leading to variations or inconsistencies.
  • Data quality: Selecting the most accurate, up-to-date, or reliable value from among the competing options ensures that consolidated data is high-quality.
  • Rule-based selection: Predefined rules or criteria can determine which value should “survive.” These rules can be simple, such as selecting the most recent date, or complex, involving data quality scoring and priority ranking.
  • Data source priority: Field survivorship considers the priority of data sources, such as giving precedence to data from a trusted and authoritative source.
  • Data transformation: Data values may need to be transformed or standardized before field survivorship decisions are made, such as converting to a consistent date format.
  • Conflict resolution methods: Common approaches include selecting the most recent value, the highest-rated source, or the most complete data. Custom algorithms and business logic can also be applied.
  • Historical data: Maintaining a record of changes over time, such as when a system stores both old and new values to keep a data change history.


Data assignment is the practice of allocating data to specific categories or destinations based on set rules. It’s a cornerstone for data management and processing that ensures data is well-organized and efficiently used. The process often uses software systems and algorithms for automation, particularly when handling large data sets.

Assignment is particularly important for routing leads and accounts to the appropriate owner and can be based on highly complex rules. 

Assigning leads and accounts in a CRM application typically requires enriching first-party data (web form data, as an example) with third-party data. 

A best practice would be to enrich the first-party data with third-party data before running the record through a matching exercise to prevent duplicates and ultimately assign the record. 

Here are some common applications and examples:

  • Categorization: Assigning data to different groups or classes based on specific attributes, including demographics, geographic regions, or purchase behavior.
  • Record assignment: Associating or assigning multiple records to a single, consolidated record for the same entity. This is commonly used in master data management.
  • Data routing: For example, in a call center, incoming customer calls are assigned to agents or departments based on criteria such as the nature of the inquiry or the customer’s account type.
  • Data distribution: Sensor data from different locations, for example, can be assigned to specific data repositories for storage and analysis.
  • Task assignment: Assigning specific tasks or responsibilities to individuals or teams ensures the right people are responsible for specific actions or decisions.
  • Role-based assignment: Specific permissions or roles can be assigned to users based on their level of access and authority.
  • Data mapping: Data can be mapped from one format or structure to another, such as from a CSV file to a relational database.
  • Auto-assignment: Some systems use automated algorithms or business rules to determine data assignment, such as routing customer service requests to the appropriate agent based on the nature of the query.
  • Geospatial assignment: In geospatial applications, data assignment associates geographic data with specific locations, boundaries, or regions.
  • Data ownership: Indicates who is responsible for maintaining, updating, or using specific datasets or elements.

Data Decay Never Stops. Neither Should Your Business

In a world where the complexity, volume, and sourcing of business data continues to expand at an exponential rate, addressing data decay problems head-on is vital for businesses. 

Few things in life are certain, and 100% data accuracy is simply not realistic in a messy world of human-to-system interactions. 

But solving the solvable problems today will put the most advanced GTM teams on a path to sustainable growth that less-equipped competitors will find difficult to equal.