Data Matching MS 03

Imagine this: You are creating a loyalty program campaign for which you need to see your customers’ history, buying patterns, responses to previous campaigns, spending limits, and their communication history with your company (support tickets or chats).

The problem? All this data is stored in different departments, possibly in different formats. Worse, if you have multiple third-party data sources (such as apps, service providers, etc), you are far from likely to get the holistic view you need to create this campaign. You need to extract, import, and link these different data sources to get the information you need.

You need a customer 360 view – which you can only get if you’re able to match and link multiple data sources at a time.

Here’s a quick guide on how you can achieve this task.


A customer 360 view is an overarching, comprehensive view of a customer, based on the aggregation of data from multiple internal and external sources. Put simply, it is a combined record of customer records collected from data points such as personal data, geographic data, marketing data, and so on.

Like a crystal ball, a 360-degree customer view gives excellent insights into the present, past, and future.

The Past:

The client 360-view shows his or her interactions with your services or products in the past. You will be able to see a customer’s buying history, including:

  • Service or product activity
  • All past interactions
  • Product views
  • Process history
  • Marketing and sales campaign activity

The Present

Present data gives a customer a 360-degree view that gauges where a client stands in the buying cycle. Organizations get information such as:

  • The background of the buyer
  • Current or pending queries or issues related to the customer service
  • The context behind customer-business interactions
  • The customer’s involvement with the organization
  • Current or pending customer orders

The Future

A client’s 360-degree view, comprised of past and present data, offers insights into the future. When you start a business, you want to grow it, using data from a client 360 view for the long haul.

A company uses the customer data they have gathered to build long-term relationships with clients. Moreover, the business finds tools and information to cross-sell and upsell opportunities.

customer 360 process


All this data is scattered across systems. For example, marketing teams may use HubSpot as a CRM, but customer service teams may be using Zen Desk as a service platform. The same data may even be recorded in different formats making it difficult for your organization to get a holistic view of your customer.

Why does it matter?

Because having a consolidated record of your customers will help you with:

👉 Better understand your customers.
👉 Create more personalized experiences.
👉 Increase sales and profits.
👉 Improve customer satisfaction and loyalty.
👉 Reduce costs and improve efficiency.

With this information, you can make better decisions about how to market to your customers, what products and services to offer them, and how to provide them with the best possible customer experience.


Creating a customer 360 view relies on three key elements:

Data integration:

This involves bringing data from multiple sources, such as CRM systems, marketing automation platforms, and customer support systems, into a single repository. Generally, business teams do this by extracting the data from different sources and combining it into an Excel file.

While this seems simple, it gets real complicated, real fast. Before you know it, you’re flipping between different sheets trying to make sense of all this data.

Ideally, you need a single platform that can allow you to merge these different files onto one dashboard so you can easily process the data.

Data matching:

This involves identifying and linking records that refer to the same customer across different data sources.

Data matching can help with customer 360 views in the following ways:

  • Improved data accuracy and completeness: Data matching can help to identify and correct duplicate records, as well as fill in missing data. This can lead to a more accurate and complete view of the customer.
  • Better understanding of the customer: Data matching can help to identify relationships between different data sources, such as the customer’s purchase history, loyalty program membership, and social media activity. This can lead to a better understanding of the customer’s needs and preferences.
  • More personalized customer experiences: Data matching can help to create more personalized customer experiences by enabling businesses to target customers with relevant offers and messaging. For example, a business could use data matching to identify customers who have recently abandoned their shopping carts and send them a reminder email.

Data quality:

Data quality is essential for getting a customer 360 view. Without clean, reliable, and accurate data, it is impossible to get a complete and accurate picture of your customers.

Here are some specific ways in which data quality impacts customer 360 views:

  • Duplicate records: Duplicate records can lead to inaccurate insights and recommendations. For example, if a customer has two profiles in your CRM system, you may end up sending the same offer on two different addresses, causing confusion and annoyance to the customer.
  • Missing data: Missing data can also lead to inaccurate insights and recommendations. For example, if customers’ address data are missing, you won’t be able to create location-specific campaigns, nor gain demographic insights.
  • Inaccurate data: Inaccurate data can lead to incorrect decisions and actions. For example, if a customer’s phone number is incorrect, you may not be able to contact them about an important issue.

To ensure that you are getting an accurate customer 360 view, it is important to invest in data quality management. This involves implementing processes and tools to clean, match, and enrich your data.

Here are some tips for improving data quality for customer 360 views:

 Identify and eliminate duplicate records with the help of a data match solution

 Use address validation tools to validate data

 Remove obsolete or incorrect data

 Implement data quality standards and procedures.

 Use data quality monitoring tools to identify and address data quality issues on an ongoing basis.

By investing in data quality management, you can ensure that you are getting an accurate and complete customer 360 view. This will enable you to better understand your customers, create more personalized experiences and increase sales and profits.


Having a customer 360 view is like having superpowers! Here are some specific examples of what businesses can achieve if they have holistic customer views:

  • A retail company could use a holistic customer 360 view to identify customers who have recently abandoned their shopping carts and send them a reminder email. 
  • A bank could use a holistic customer 360 view to identify customers who are likely to churn and offer them a personalized retention offer. 
  • A healthcare provider could use a holistic customer 360 view to identify patients who are at risk of developing a certain condition and proactively reach out to them with preventive care recommendations. 
  • a fashion retailer can analyze their customers’ purchase history across all channels, as well as their preferences, interests, and demographics to create bundle offers that can only be availed by someone with a loyalty card

The opportunities are limitless but the challenge lies in building a strategy that can help you build this view without having to waste additional time and resources.

In the next section, we cover a basic process on how to get a customer 360 view with minimal resources and manpower.


It’s a given that small and mid-sized businesses struggle the most with disconnected datasets. They often have data stored in silo and are unable to link these records without significant upheaval. But chaos or not, you do need a customer 360 view if you want to achieve any kind of personalization, targeted marketing campaign or revenue building initiatives.

So how do you about building a strategy with minimal resources and time? Here are some basic steps you can take.

Define your project scope: 

Defining a project scope and setting some KPIs will help you navigate certain challenges better. We suggest a 5W approach when building the scope:

  • why you want to build a 360 view?
  • what data sources and data tools would you need?
  • where is the data stored?
  • who is responsible for the data?
  • when (how soon) do you want the view ready?

Once you define your project scope, the next step is to look for a data match tool.

Choosing a data match tool:

A data match tool is the fundamental tool that will tie together this project. You need a solution that has:

  • an easy-to-use interface
  • powerful data match capabilities
  • scalable on large datasets
  • allows for easy integration of multiple resources
  • advanced data cleaning capabilities
  • affordable pricing model

Your choice of a data match solution can make or break the project. Remember data match is the key technology that will link all your records, so make sure you test out different solutions and then make an informed decision based on the given factors.

Assess your data quality:

Once you have identified your data sources, you need to assess the quality of the data. This includes identifying and eliminating duplicate records, filling in missing data, and correcting inaccurate data.

A data match solution like WinPure will also take care of the data quality aspect, giving you different options to clean and deduplicate the data. With WinPure you can:

  • Remove non-printable characters in the dataset
  • Transform and standardize your data if it’s in a mixed case
  • Remove unnecessary numbers and alphabets from data fields
  • Remove extra spaces or null values in the data
  • Standardize different abbreviations into a format you define

Noisy, dirty data impacts match results, causing false matches that can affect your data and insights.

Build a data match scope: 

Now that you have clean data, the next step is deciding what columns to match. For example, you may want to match and cluster all customers from one town to build a location-based campaign. To do this, you would need to match First Names and Last Names with Town or City. 

The matching algorithm will cluster all your contacts living in the same town or city as a collection of records.

Similarly, if you want to match on Zip or Postal codes, you can use exact match algorithms to run a match based on numbers only.

Here are some tips for data matching:

  • Use a variety of data-matching techniques. There are three matching techniques – fuzzy, exact, and numeric. Fuzzy match is great for text matching. Exact match for text fields with exact data attributes. Numeric match for data fields with numbers.
  • Set a fuzzy match threshold at 90%. Fuzzy data match means identifying data attributes based on the similarity between character counts. For example, Mike and Mikel may be attributed as a similar match based on one additional character. A 90% threshold allows the algorithm to cluster all data attributes that may share similar characters.

Merge records and create master records. 

Data matching will also enable you to decide what records to merge (for example combining a customer record that has two different phone numbers). Once the data is merged, select the final records and create a final record. You can then use this data to feed into visualization software for advanced insights.

By following this strategy, you can build a customer 360 view that will help you to better understand your customers, create more personalized experiences and increase sales and profits.


Data matching is an essential part of building a customer 360 view. By matching data from different sources, businesses can get a complete picture of each customer, including their demographics, purchase history, preferences, and interactions with the brand. This information can be used to better understand customers, create more personalized experiences, and increase sales and profits.

There are a number of different data matching techniques and solutions available. Businesses should choose the approach that is right for their specific needs. It is also important to monitor and refine the data matching process on an ongoing basis to ensure that the customer 360 view is as accurate and complete as possible.

Building a customer 360 view can be a complex and challenging task, but it is a worthwhile investment for businesses of all sizes. By understanding their customers better, businesses can make better decisions about how to market to them, what products and services to offer them, and how to provide them with the best possible customer experience.

Here are some additional tips for building a customer 360 view:

  • Start small. Don’t try to match all of your data at once. Start with a small subset of data and then gradually add more data as you go.
  • Use a data matching solution that is designed for customer 360 views. There are a number of data matching solutions available that are specifically designed for creating customer 360 views. These solutions can help you to achieve better results with less effort.
  • Monitor and refine your data-matching process. It is important to monitor and refine your data-matching process on an ongoing basis. This will help to ensure that your customer 360 view is as accurate and complete as possible.

Remember, building a customer 360 view doesn’t require extensive resource investments. All you need is a process, the right person, and the right tool to help you do the job!


Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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