CRM Data Enrichment Best Practices


  • Customer data enrichment delivers measurable value if the data is applied to marketing, sales or customer services processes that improve business outcomes.
  • The value of customer data enrichment is determined by the improvements to marketing, sales and customer service processes that leverage that data.
  • Data must be transformed to action and measurable results to realize a return on investment (ROI) from a data enrichment program.
Johnny Grow Revenue Growth Consulting

CRM data enrichment is customer data enrichment, which is the process of replacing or appending customer data in the lead, account or contact records. This normally occurs in the CRM system but could possibly occur in other customer data management systems such as the Marketing Automation Platform or the Master Data Management (MDM) system.

The CRM data augmentation process reviews lead, contact and account data in the CRM system and flags data inconsistencies, identifies bogus data, merges duplicate data, replaces inaccurate data, removes obsolete data and appends incomplete records.

CRM Data Enrichment Process

This process is needed to maintain customer data accuracy and apply customer data to customer facing processes.

The goal to enrich CRM data is not to get as much customer data as possible, but to get as much accurate customer data as can be used for processes to acquire, grow and retain customers.

The Customer Data Problem

You need to enrich CRM data to offset data decay. Customers move, merge or change names. Employees get promoted, relocated or change jobs. Zip codes and telephone area codes get updated. Customer data depreciates at about 2 percent per month.

So even if your customer data starts at 90 percent accurate, after one year of 2 percent monthly decay it is 71 percent accurate (.9 * (1 – .02)12). After two years of decay is it 55 percent accurate (.9 * (1 – .02)24).

Among other things, inaccurate customer data decreases marketing campaign conversions, lowers sales productivity and erodes trust among users of the CRM system. No marketing department or sales organization can effectively operate when a large portion of customer data is inaccurate.

Customer Data Enrichment Use Cases

Appending CRM software lead, contact and account records with enriched data that goes unused only increases technology cost, system administration overhead and user complexity. To avoid this common trap, it's important to put data into context and identify how customer data will be used before making investments.

Here are some examples where more complete customer data profiles can be used to acquire, grow and retain customers.

  • Improve the ICP. The Ideal Customer Profile (ICP) identifies the best fit target audiences. For marketing, these target audiences incur the highest conversions. For sales, these customer segments incur the fastest sales cycles and highest win rates. Acquiring data that brings more specificity to the ICP drives improvements to many marketing and sales processes.
  • Improve marketing conversions. When acquiring digital leads every marketer knows the more fields on a form the lower the conversion rate. Acquiring easily available prospect or customer firmographic data eliminates the need for online prospects to enter that data and increases online conversions.
  • More precise customer segmentation. Demographic and firmographic data can be used for more specific customer segmentation. Behavioral and psychographic data can deliver buyer insights and context and can be used for dynamic customer segmentation. Data driven marketing is dependent upon both data quantity and quality.
  • Deliver personalization at scale. Personalized digital communications are much more than inserting the contact's first name at the beginning of a message. To achieve customer engagement that resonates and contributes to a dialogue, the content should be personalized by customer type, interest and journey. There are significant differences between what's important to a manufacturing company or a healthcare company, a big company or a small company, or the CEO and the IT director. Demographic data (i.e., role, title), firmographic data (i.e., industry, company size) and behavioral data (i.e., buyer interests) are a few of the customer data types that can be used to deliver more personalized and relevant communications.
  • Improve sales processes. Data can save time and improve lead scoring, qualification and prioritization. When accounts are fully populated with the data salespeople need, those salespeople spend much less time searching for and entering data. The right data also calculates more accurate lead and sale opportunity scores.
  • Derive calculated and inferred results. Acquired data can become symbiotic with existing data. A type of data transformation occurs when two data elements create a third. For example, a software or technology company could use the combination of firmographic and technographic data to identify prospects in a target market with or without a particular technology and thereby compute a new data field value of buyer fit or non-fit. This is a simple example but illustrates with more data values the derived field can become very powerful.
  • Elevated customer experiences. Data builds more comprehensive customer intelligence and provides the capability to deliver differentiated customer experiences.

See the CRM software data enrichment use cases to acquire, grow and retain more customers.

Click to Tweet

Remember, the goal to enrich CRM data is less about data quantity and more about data purpose. More data by itself just accumulates and creates cost without benefit. Unused data creates more complexity to the customer data model, requires additional investment to maintain, creates additional data privacy and regulatory risk (i.e., GDPR, California Consumer Privacy Act) and makes the CRM software more complex and less user friendly.

If for some reason more data without an identified purpose is needed, it's better to store that data in a data lake rather than turn your CRM system into a dumping ground.

Third Party Data Providers

Third party data providers are a common path to clean and appended customer data. Here's a few best practices if you go this route.

  • Start with an analytics data strategy that begins with a well-defined customer data model. This means the lead, account and contact data models should be normalized before appending with new datasets. Normalization separates unrelated table data (i.e., there is no need for Campaign and Case data to be in same table) for improved query and reporting performance. Normalization can also make the CRM system easier to use.
  • Know what customer data you want before approaching third party providers. Otherwise, you are likely to over invest in data that looks interesting, but you will never use.
  • Not all data is equal in value, so create a data value indicator for each data element. This will greatly help you prioritize data acquisition and processes. Similarly, it may be a better idea to only procure customer data that contributes to your Ideal Customer Profile (ICP) or specific customer segments.
  • It's a good idea to validate the data you have before appending it with more. It doesn't make much sense to append bad data. Third party data providers can examine your baseline customer data and advise what's accurate and not.
  • To identify the best fit data provider, start with the match rate. This is the calculation that shows how much of your customer data can be augmented with the data providers database. Poor data quality in your CRM system will lower the match rate, which is why starting with as clean data as possible lowers your cost and improves the result. Vendors may also report a statistic called coverage, which calculates the alignment between your customer data and their dataset.
  • Once the customer data profiles are updated, they should be synchronized with other systems that share customer data. For example, if the updated customer data is in the CRM system, it should sync with the marketing automation system, MDM system, ERP software or other applications that store customer data.
  • CRM data maintenance and data enrichment are a journey. An update cadence or periodic refresh is necessary to mitigate data decay and keep your customer data accurate. Most third-party data vendors offer programs to semi-automatically update customer data in real-time or on an ongoing basis.