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Data Enrichment Guide – How to Enrich and Optimize CRM Data for Accurate Insights

44% of companies lose over 10% of annual revenue due to poor-quality CRM data.

This shows how many CRM users’ trust in their data is unfounded.

Bad CRM data – whether it’s due to missing, incorrect, incomplete, or outdated information – can lead to missed opportunities, incorrect sales forecasts, and ineffective marketing efforts. To avoid these pitfalls, businesses must enrich their internal data with external, up-to-date sources. Data enrichment ensures that your CRM is not just a repository of information but a powerful tool for driving smarter decisions and building better customer relationships. It transforms scattered, disconnected data points into complete, cohesive, actionable customer profiles.

What is Data Enrichment?

At its core, data enrichment is the process of enhancing your existing CRM data by supplementing it with additional information from external sources. Often performed as part of data quality management, the purpose is to fill the gaps in the CRM.

Data enrichment turns incomplete or basic customer records into rich, more detailed, actionable profiles that drive smarter decisions and personalized interactions.

Let’s consider an example to gain a better understanding:

First Name Last Name Phone Email Address
Randy Hanscom 231-821-8063 rh@groupbuff.com 4085  Bee Street, Holton MI
Mark Watkins 605-857-1883 watkins@syncamore.com 2911  Hartway Street, Yankton

Most CRMs hold basic information, such as names, and contact data – email addresses, phone numbers, and addresses. While this information is valuable, it’s rarely enough for businesses to deeply understand their customers or target them effectively. For example, it doesn’t tell where does a customer work, what is their job title, or what are their buying preferences or income levels. Data enrichment augments these with demographic and firmographic details to help companies create a more comprehensive picture of their customers.

First Name Last Name Phone Email Address Profession Company Salary(avg)
Randy Hanscom 231-821-8063 rh@groupbuff.com 4085  Bee Street, Holton MI Marketing Manager Group Buff $4000
Mark Watkins 605-857-1883 watkins@syncamore.com 2911  Hartway Street, Yankton Sales Director Syncamore $8300

Data enrichment doesn’t end here. As organizations expand their personalization or customer service goals, it can help further enrich customer records with information like marital status, family size, and buying behaviors to build a 360-degree customer view.

Data enrichment, therefore, can be summarized as an activity that takes a large amount of disparate, structured, and unstructured data from various sources and turning it into something of value.

As enterprises step up to benefit from big data, machine learning and AI, they need to enrich their CRM with lifestyle and behavioral data to deliver personalized services. For this to be possible though, these organizations will need to clean, update, and manage both structured and unstructured data.

Challenges of Data Enrichment

While data enrichment offers significant benefits, it comes with its own set of challenges. They include:

1.      Fragmented Data Sources

Many organizations store customer data in separate databases or systems, which leads to fragmented and inconsistent records. Data stored in disparate systems is also difficult to consolidate and enrich, making it difficult for businesses to create comprehensive customer records and offer personalized services.

2.      Data Quality Issues

Enriching data without first addressing quality issues can amplify inaccuracies. Duplicate, outdated, or inconsistent records undermine the effectiveness of enrichment efforts and result in flawed insights.

3.      Privacy and Compliance

As organizations incorporate external data sources to gather more information about customers, it becomes all the more critical – and difficult – to ensure privacy and regulatory compliance.

4.      Cost and Resource Intensiveness

Enrichment processes, especially at an enterprise scale, require significant investments in technology, time, and expertise. Organizations need to evaluate whether the potential ROI justifies these costs.

5.      Leveraging Enriched Data for Actionable Insights

Even with enriched records, businesses may struggle to leverage the data effectively and extract meaningful insights. It requires advanced analytics, integration with existing CRM systems, and alignment with business objectives to ensure enriched data drives actionable insights or tangible outcomes.

By addressing these challenges, organizations can maximize the impact of data enrichment and turn raw data into a strategic asset that fuels growth, innovation, and customer satisfaction.

The Process – How is Data Enriched?

Data enrichment is a systematic process designed to transform raw, fragmented, or outdated information into actionable insights. There are two ways this can be done:

1.      Building On Existing Datasets

Businesses collect a wealth of information every day through sales chats, social media interactions, sign-ups, and more. All this data is recorded in CRMs, ERPs, and/or spreadsheets; however, it’s often isolated, which creates data silos and, as a result, a fragmented view of customers. This is a roadblock to good customer experience and the organization’s personalization goals. Therefore, it’s important for data stored in internal systems to undergo:

  • Integration: Involves consolidating data from disparate systems into a single source.
  • Cleaning: Fixing typos, errors, and inconsistencies; remove outdated or irrelevant data.
  • Deduplication: Eliminating duplicate records to ensure data accuracy.
  • Merge and Purge: Combining data sources and retain only the fields critical to your business goals.

For instance, if you’re using ZOHO, your information could be lost in the dozens of modules and fields that may have been set up by team members over the years. Data enrichment in this context, would mean reviewing the CRM data, cleaning up inconsistencies, merging and purging fields or unnecessary data and creating a “Golden Record” – a single, most accurate version – of each customer profile.

2.      Using Third-Party Data Services

When internal data isn’t enough, external data sources can fill in the gaps. Third-party data services enrich customer records. This additional data could include:

  • Firmographics: Company size, industry, and job roles.
  • Demographics: Age, income level, and marital status.
  • Technographics: Technology preferences and usage patterns.

These services gather data from public sources like social media, Google, and online directories and provide you lists with added verticals that enrich your data, such as job titles and functions, buyer personas, and even behavior-based insights to help you gain a 36-degree view of your customers and prospects.

Whichever of these data enrichment techniques you choose to follow, you must be careful of the quality. If you’re extracting data from internal systems, you must check them for duplication and data hygiene issues (spelling errors, typos, unstructured formats etc.). If it’s third-party data, it must be clean, verified, and validated.

The Data Enrichment Workflow

For data enrichment efforts to be successful, you must follow a well-defined process. Here’s the framework provided by DataMatch Enterprise (DME), a self-service data preparation and data enrichment software by Data Ladder:

Step 1 – Establish Data Enrichment Goals

Customer personalization is usually the key motivator for data enrichment. However, the narrower and more focused the goal is, the easier it is to implement the data enrichment process. Therefore, it’s best to always start with a clean objective.

For instance, we worked with an insurance company that wanted to initiate a health insurance plan for college-going students. The firm first had to identify the kind of data and level of segmentation they needed to achieve that goal. At a very basic level, they needed genealogy records, family records, property and income records, electronic health records, and behavioral and lifestyle records. Most of the records were already available in the insurance company’s repository. However, they obtained behavioral and lifestyle records from third-party data. When the collection and segmentation were done, the firm began the cleaning, merging, and purging process in order to consolidate internal data with third-party data.

For data enrichment to work, it’s imperative that goals, audience segmentation, and data sets are clearly defined.

Step 2 – Prepare Your Data Using Data Enrichment Tools

This is the most important part of the process.

If you don’t already have a data quality plan in place, you will need to prepare your data before any enrichment takes place.

Using data enrichment tools like DataMatch Enterprise, you can clean, dedupe, and consolidate disparate data sources to make the data good enough for merging with a new data set.

The data preparation process involves:

Data Integration

Integrate your data source such as a Salesforce or HubSpot CRM directly on to the platform. You won’t have to waste time with manual extractions of data. Simply plug in the data source and start preparing the data.

Data Profiling

This is a crucial step to data cleaning. Data profiling helps you assess the quality of your data and identify problems within the data source at a row level. For instance, you can see which of your data rows have missing email addresses or ZIP codes. You won’t know what to fix if you can’t see what’s wrong and data quality issues are so deeply ingrained, that it simply misses common observation. You might not even know some of your fields have non-printable characters or punctuation marks within your phone or address data. These invisible problems later become a bottleneck and make it difficult to ensure a smooth enrichment process.

data profiling
Profiling dirty data

Data Cleaning

Raw data is inherently flawed. It needs to be cleansed of typos, spelling errors, and format inconsistencies. Consider data cleaning as much-needed data transformation (transforming all low-case names into upper-case or removing all unnecessary punctuation marks in a field). Once you clean the data of these inconsistencies, it will be usable and valuable.

Data Deduping

It’s quite probable your data has three to four copies of a single entity. If you’re consolidating data from multiple sources, then you’ll definitely end up with duplicates. So before adding more data, match existing data to remove duplicates and make sure there is a unique record for each entity. Duplicate data is dangerous. It leads to skewed data analysis and makes it difficult to get the complete picture of your audience.

cleaning and deduping
Cleaning and deduping lists

Data Merge Purge

If you extracted data from multiple sources – say from a CRM and an ERP, you’ll need to merge them into a single record. Remember though that both sources need to go through the cleaning and deduping process above before they can be merged. Once you merge these records, you can purge the unnecessary fields and only keep the fields you need.

This merge and purge process was historically performed via Excel or via complex data management solutions that would take months to get done. A self-service data enrichment tool like DataMatch Enterprise lets you accomplish this in just a matter of minutes.

Use Merge Purge Software to clean data across the enterprise
Use Merge Purge Software to clean data across the enterprise

Data Survivorship

Data survivorship is the final stage of the enrichment process. Assuming your third-party data is clean (if not, you’ll need to run those through the process above too), you can now merge that record with your final record and create a master record. This master record can be exported to your database of choice, and you now have reliable data to use in your campaigns!

It’s necessary to reiterate a key point – any data source, whether third-party or your own data must be clean, unique & updated before it can be used for any enrichment purpose.

If you’re unsure of the quality of third-party data, check the first 100 or 200 samples to see if it has quality issues. If it has duplicates, formatting, or structural issues, you’ll need to clean and dedupe it. This is especially the case with data obtained from social media sites and online listings.

A word of caution: Any attempt to match two first-party datasets, must have a common factor that links the two datasets together. This could be a common name, a phone number, an ID or an email address. Missing this common factor will void the whole activity because you won’t know what factor refers to the same customers in two different datasets.

Step 3 – Keeping Your Data Updated

Once you’ve created clean records, you’ll need to keep your data updated. Data enrichment is not a one-time effort. Customer data, no matter how detailed, is fundamentally a snapshot in time. Income levels rise and fall, marital status may change, and the type of car and physical address can alter. Even names may change, especially if there is a change in one’s marital status.

Also, as you progress through your campaign or goals, you’ll realize that you need to augment it further. You don’t want to have to go through the whole cleansing, deduping process every single time. To avoid unnecessary complications, it’s recommended to keep a data cleaning schedule. If your first-party data is clean and optimized, you’ll just spend a few minutes cleaning up third-party data and appending it to your database.

Almost every Fortune 500 company we’ve worked with is striving to accomplish data enrichment objectives, but only a few get the process right. Others collect data and dump it in a data lake where it lies dormant until it is decayed. With a data decay rate of 2.1% per month and 22.5% per year, you cannot afford to lose data you’re painstakingly collecting. If you’re not updating your data at regular intervals, you’re losing value. 

Why Data Quality Matters?

You don’t have to wait for grand goals like personalization to maintain the quality of your records. If you implement a strong data quality policy now, you’ll be preventing costly mistakes later. In fact, keeping your records up-to-date will directly impact your customer service and experience. Take a small instance – if you’re sending out a launch email and your list consists of 5,000 obsolete records, you’re missing out on millions in potential sales revenue. Data quality practically saves you on revenue loss!

Data Quality Issue Example
Invalid value Valid value can be “1” or “2”, but current value is “3”
Cultural rule conformity Date = 1 Feb 2018 or 1-1-18 or 2-1-2018
Value out of required range Customer age = 204
Format inconsistency Phone = +135432524 or (001)02325355

Regardless of structure, type, or format, source data intended for enrichment should be validated in terms of the following key attributes:

  • Relevance: Is it relevant to its intended purpose?
  • Accuracy: Is it correct and objective, and can it be validated?
  • Integrity:  Does it have a coherent, logical structure?
  • Consistency: Is it consistent and easy to understand?
  • Completeness. Does it provide all the information required?
  • Validity: Is it within acceptable parameters for the business?
  • Timeliness: Is it up to date and available whenever required?
  • Accessibility: Can it be easily accessed and exported to the target application?
  • Compliance: Does it comply with regulatory standards?

Apply these quality check metrics to both first-party and third-party data and ensure the quality of your data is fit for its intended purpose.

To Conclude, Enrich Your Data, But Don’t Forget Data Quality

Missing, incomplete, and outdated records are the primary detractors to customer data quality. If you truly want your data enrichment to succeed, you’ll need to ensure that quality issues are taken care of first. Data enrichment is not a one-time process. Like everything else, it will require you to maintain an updated version of the data to be useful and effective.

Want to know how we can help you kickstart the process? Talk to our solution architect today and get a free demo of our self-service data enrichment tool.

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