Top Reasons Why Customer Data Matching Matters for Marketing Teams (+ Challenges)

With larger volumes and newer data varieties, organizations are beginning to realize how they’re being overwhelmed by this data. How do they connect customer information obtained from multiple sources to create a complete, accurate customer view to execute a personalized marketing campaign, to perform an analysis or to create reliable business intelligence reports? And how do they do this without moving data around or increasing the risks of data loss?

Building a complete customer view requires connecting multiple data sources such as addresses and contact details with big data sources (behavioral, firmographic and demographic data) to get complete information of the entity.  This is where the need for customer data matching technology comes in.

What is customer data matching?

Data stored in multiple sources need to be connected to give complete information about the entity. This includes merging information from both internal and external sources to develop a deeper understanding of the customer. For companies aiming to deliver personalized customer experiences, it’s necessary to get this singular view. Customer data matching is the process of matching one or multiple data columns to accomplish this view.

Data matching isn’t a static activity that occurs at one point in time, it’s a consistent, ongoing activity that needs to be performed at regular intervals to ensure customer data is enriched and remains updated.

In today’s data landscape, data matching has taken on a whole new meaning. It’s no longer just about matching rows and columns to remove duplicates. It’s about creating the Golden Record. It’s about enabling a deeper knowledge of the audience, an objective that lays the foundation for successful customer experience outcomes.

Identifying the three major challenges with customer data

Customer data is one of the most volatile forms of data. It’s plagued with inconsistencies and human errors. Even with the best front-end controls in place, you just can’t avoid poor customer data quality.

Three of the most common challenges enterprises face with customer data involve:

1. Managing disparate data sources

Companies deal with millions of customer records daily – all streaming in from big data sources and external vendors. This data is then stored in multiple systems across multiple departments. This disparity results in disjointed customer views leading to inaccurate analytics and ineffective campaigns.

2. Managing duplicate data

Duplicate data is one of the most challenging problems in data management. For instance, one customer can be recorded several times in the system, each time they use a new name variation, email ID, or phone number. Similarly, a CRM migration, a merging of records during a merger and acquisition, and data entry caused by human error are all causes of duplicate data that become increasingly complex to deal with. This gets worse when big data sources are connected to traditional sources which means the company must match lists to ensure there are no duplicates.

3. Sorting dirty data

If duplicate data is worrisome, dirty data is mind-boggling. Take a look at the image below. See how messy this data is? Unfortunately, this is a universal problem, making data cleansing an ongoing activity – and one that most companies tend to ignore.

The Importance of customer data matching technology

Until a few decades ago, data matching  was done manually via Excel spreadsheets or during ETL processes. Over the last few years, machine learning has enabled the rise of smart data matching technology that uses several matching algorithms in combination with proprietary algorithms to match millions of rows of data, all while enhancing data accuracy.

Most data matching technologies come with additional functionalities to clean, parse, prepare, and standardize data for matching. Data matching tools are equipped with defined business rules and text analytics to extract relevant information and match it to customer profiles to build a comprehensive customer view.

It’s pertinent to have clean data before matching lists. The consequences of disparate, dirty and duplicate data can range from costly to catastrophic – linking the wrong address to the wrong person could mean accidentally sending out confidential information. In worse cases, it could also lead to fines and penalties. In industries like healthcare, mislinking records could be a matter of life and death.

Not all matching solutions are designed for big data. As companies continue to invest in big data, they need technologies that can integrate data sources, process, analyze, clean, and match within a secure on-premise platform. More importantly, these technologies should enable businesses to own and process this data.

Some of the most advanced data matching technologies allow users to:

Govern and standardize data

Internal and external sources of data can either be structured, semi-structured, or unstructured. It’s safe to say internal data is usually structured data that often only needs to be reviewed or run through periodic data cleansing processes. It’s the external, third-party data such as social media data that may need to be cleansed and standardized before they’re connected to internal data. Here, a data matching solution will allow for direct integration with the third-party data and the eventual cleaning, parsing, sorting of this data into structured information that can be matched with the internal database.

Reduce false positives with matching accuracy

False positives and false negatives are the consequences of inaccurate data matching, caused by solutions that don’t have the algorithmic capacity to manage the complex nature of big data sources.

False positives can burden employees with an ever-increasing task of manually validating and verifying information.

False negatives increase the risks of missing critical information. In the case of banks, data matching is critical to ensure compliance with anti-money laundering laws. Missing key matches and an increase in false positives or false negatives not only burdens the institution but also sets it up for penalties and legal troubles. Hence, data matching technologies must deliver accurate results.

Implement and automate a data quality framework

Data matching and data quality go together. It’s impossible to match data without ensuring its quality which is why top-in-line data matching solutions allow users to implement and automate a data quality framework. From data integration abilities to data cleansing, standardization to deduplication, merging and finally enrichment, users can reiterate the match process until they get a consolidated customer view.

Customer-centric initiatives is the step forward. Futuristic companies understand the relationship between customer data and business success and so invest millions in big data technologies. Amidst the excitement though, companies miss out on the very crucial functions that can help them make sense of this data. Matching and cleaning customer data from multiple internal and external sources helps companies better understand their customers. Moreover, you’re in a better position to ensure the quality of your data.