In today's digital age, customer data can open a lot of doors for optimization opportunities. Intelligent businesses can collect and leverage their customer information to learn more about their client base and improve their user experiences.
But manually collecting every piece of information and entering it into one place, e.g., a CRM, can be very costly and time consuming.
This is where automated customer data collection tools come in handy.
In this article, we’ll discuss how you can utilise AI and machine learning technology to automatically extract valuable customer information from files, business forms, purchase orders, bookings, and more.
But first, let's start with the basics.
What is customer data?
Customer data is any information that’s related to a customer. This can be personal information such as their name, age, where they are based, phone number, email address, household income, educational level. But also consumer information such as:
- The products they buy?
- How often do they buy from you?
- What social media platforms do they use to interact with your brands?
- What distribution channel do they use to buy from you?
It can also include their product preferences, wishlist, browsing habits, loyalty program information, etc. Customer data can be collected from various sources, including:
- Contact forms
- Questionnaires
- Customer feedback
- Surveys and polls
- KYC processess
Why collect customer data?
Collecting relevant data on your customers can be used to make informed business decisions and gain competitive advantage when it comes to retaining customers and acquiring new ones.
For instance, knowing whether a customer has an interest in sports cars could be useful when deciding which ad campaign would work best for them—if one of the models in an ad was a sports car, that ad might have more appeal to this customer than an ad that shows a pickup or a family car.
Furthermore, in customer relationship management (CRM) functions, collected customer data is made available to customer service representatives in order to improve customer service and overall satisfaction.
Collecting customer data and compliance
While there is no cap on how much data can be collected. There are, however, restrictions on what personal data you can collect and how long you are allowed to keep it. These are commonly known as customer consent, data protection and privacy regulations.
For instance, for any data related to EU customers and users, businesses have to comply with the General Data Protection Regulation (EU GDPR), which states that companies must have the consent of their EU customers before collecting and storing personal data.
What is considered personal data? Any information that could be used to personally identify someone. That can be names, addresses, credit card numbers, social security numbers etc. Anything that could compromise privacy, or expose a person to identity theft or fraud, must be held securely and for the shortest time possible.
Issues with manual customer data entry
Even with the necessary infrastructure and consent in place, extracting compliant customer data and accurately populating digital customer records is easier said than done. For companies just embarking on their digital transformation journey, unlocking the value of trapped data in their legacy customer records and files can be time-consuming, costly and resource intensive, especially if data collection is done manually.
Manual extraction processes are also prone to human error and can compromise data quality and analysis. In some cases, inaccurate customer data can jeopardise the entire business. This is especially evident for financial institutions or law firms that utilise Know Your Customer (KYC) forms as part of their due diligence routines to protect against fraud, corruption, money laundering, and terrorist financing.
This is where automated customer data collection and entry has the edge. It saves time and money, results in fewer errors and massively boosts business efficiency. Automated data entry is the ideal solution for businesses looking to streamline their data processing.
With automated data extraction tools like Parsel, you can extract customer information from PDFs, paper-based customer records, as well as any scanned questionnaires or forms. All captured data can be integrated directly into your CRMs and centralised databases.
Why opt for automated customer data extraction?
In contrast to manual data entry, automated customer data collection tools can yield several efficiency benefits:
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Error reduction: Automation reduces human error or inconsistencies in dealing with customer records. It also ensures that all data is accurate and up to date in one place—so your business won't lose out on potential customers or risk losing current ones because of inaccurate information in their files.
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Time savings: With automated storage and retrieval of customer records, employees spend less time searching for client information. This means that they can focus on other tasks.
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Cost saving: An automated system will lower costs by reducing paper use and manual effort. That's ideal for businesses looking to keep overheads low without sacrificing quality of service.
How to use Parsel to automate customer data collection
Save hours of time by automating your customer data extraction process from start to finish with Parsel. Our advanced AI and OCR work in tandem to identify data in your documents automatically, so you don't have to.
Our advanced parsing algorithms enable you to capture all the tabular and form field data you need directly from KYCs, feedback forms, bookings, registration forms, legacy customer records and more.
Simply Upload your documents via the drag and drop interface on our web application or directly into our API. Export as ready-to-use Excel, JSON, or Text files, or send your data directly to your CRMs and central databases via Parsel Enterprise API.
Want to see it in action? Try us out with a sample of your own data, so that you can check our outputs and accuracy first hand. Sign up for free or schedule a free 1-1 demo with one of the Parsel team members here.