7 Best Practices For Successful Data Enrichment


7 Best Practices For Successful Data Enrichment

The best way businesses can gain a competitive edge is to rely on valuable data. A company can only implement changes that drive growth with a comprehensive understanding of its strengths, weaknesses, and opportunities.

Gathering valuable and accurate data is only possible with data enrichment. An essential marketing and sales practice, data enrichment aims to improve the quality of a business’s current datasets.

As a result, companies can get valuable insights about their business and customers, allowing them to accomplish multiple goals. They can improve product and service development, operational efficiency, customer service, and revenue growth through effective marketing.

This article discusses a few best practices to ensure optimal results from your data enrichment efforts.

  1. Set your goals

When setting goals, make them measurable by establishing key performance indicators (KPIs). It can be as simple as ‘filling out 90% of 200 incomplete data entries’ or complex like ‘targeting 100 potential leads through data segmentation.’

Setting goals lets you prioritize your data enrichment process and update datasets that add value to your customer analysis. A recent survey shows up to 68% of enterprise data remains unused. Your data enrichment can help optimize high-value information to scale your business.

  1. Identify your requirements

You can adopt different data enrichment types and techniques depending on your marketing and sales needs. For instance, behavioral data enrichment, which records customer habits and patterns, helps marketers and salespersons determine a lead’s position in the sales funnel. Similarly, account enrichment helps boost account-based marketing, a highly targeted tactic for high-value clients.

Several software tools are available on the market. Because of demand growth, experts predict the need to grow to USD$ 3.5 billion by 2030, more than twice its USD$ 1.7 billion value in 2021.

Some tools can accommodate multiple industries. At the same time, some products are specific to either business-to-consumer (B2C) or business-to-business (B2B) companies. It’s crucial to choose the right enrichment tools and ensure they work well with your current customer relationship management (CRM) software.

  1. Keep your data complete and accurate

The data enrichment activity must have complete data to produce consistent results. As such, your tools and processes must supply accurate data. This can be challenging, especially since third-party data is one external data enrichment source. Information from customers, websites, and prospects is considered third-party data. They’re not always accurate for various reasons, including human error.

At the same time, some unstructured data may need categorization to prepare it for analysis. Such is the case for sentiment analysis, which extracts a customer’s emotions from a specific input. Similarly,  you can also perform cross-checks and schedule regular data cleansing to improve data analytics results.

  1. Establish clear workflows

Once you’ve chosen the best data enrichment method, identify the datasets that need continuous enrichment. Your company must be able to run the enrichment process smoothly and repeatedly with the same positive outcomes.

When you’ve identified the datasets, the next step is developing a workflow supporting these activities, including the tools and the necessary integrations. This step becomes easy as you automate and make it part of your standard operating procedures.

  1. Put a premium on scalability

Any manual data enrichment activity can take time and increase costs. Fortunately, there are available tools to automate the task. Using a data enrichment software tool can make the process free from errors and fast.

This automated tool lets you upscale your data enrichment task to meet additional business requirements. In the case of expansion, you must process big data in the same amount of time and resources as before. Similarly, you must adjust your workflows and data enrichment rules to accommodate other processes according to your growing needs.

  1. Integrate data enrichment across all channels

Data enrichment must be universally applicable to different datasets. Hence, your data rules must run on different data sets and are recyclable for other tasks with the same purpose. For instance, using data parsing, you can extract a potential customer’s name from their company website, email address, and other public online sources.

On a larger scale, you must also apply your data enrichment process to all potential sources, whether online or offline and regardless of source type. Make sure to integrate this in apps, lead forms, physical and electronic mail, and other online or in-store campaigns and transactions to improve operational efficiency.

  1. Schedule frequent checks and updates

Besides data cleaning, regular updating must be a priority to maintain accuracy. According to a survey, up to 30% of customer data is inaccurate, while up to 70% becomes obsolete in a year. As such, data enrichment must be a continuous process to ensure the data you have can help you make accurate decisions.

Review your data regularly and check for anomalies. Correct them immediately so your marketing and sales team can work flawlessly to increase your bottom line.


Collecting, keeping, and processing data entails costs, time, and resources. But it’s necessary to understand your clients better and make data-driven business decisions that lead to growth.

Review your data regularly and look for ways to develop your workflows to ensure you’re getting the most from your enrichment processes. Once you’ve implemented these best practices, it’ll be easier to achieve your desired outcomes regularly.