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Customer Data Management: An Introduction to the Basics
Effective customer data management allows businesses to get better insights into their audience, make informed decisions, and ultimately drive growth. In this blog, we'll explore the basics of customer data management, including what it is, why it's essential, and how businesses can improve their processes to make the most of their customer data.
What we'll cover
- What is customer data managment (CDM)?
- Why is customer data management important?
- Improving customer data management
- Common challenges in customer data management
- Speak to a data expert
What is customer data management (CDM)?
Customer data management (CDM) is the general practice of collecting, storing, organising, protecting, and maintaining customer data throughout its lifecycle. It involves managing all the data a business collects about customers, for example, contact information, purchase history and social media interactions.
The main aim of CDM is to ensure that data can be used to create detailed and insightful views of customers, and ultimately better understand their needs, behaviours and preferences. With this information at their fingertips, businesses and marketers can tailor products, services and communications to better meet their customers’ needs.
Effective customer data management ensures that customer data is accurate, complete, and up-to-date, as well as easily accessible to authorised personnel.
Customer data management involves not only collecting and analysing data for marketing purposes, but also ensuring data privacy and protection. By complying with data laws and regulations such as GDPR, businesses can maintain the trust of their customers and avoid legal repercussions. As a result, the CDM process encompasses a range of departments, including legal and IT - which play a crucial role in managing data throughout its lifecycle.
By implementing good customer data management practices, businesses can enhance their customer relationships, drive revenue growth, and gain a competitive advantage in their respective markets.
Why is customer data management important?
1. Customer insights
In today’s digital world, understanding customer preferences, behaviours and needs is vital. Without an understanding of your audience, it’s impossible to connect with them. It’s important to understand what makes every customer tick, from new customers to your most loyal base – it’s key to learn what it is that helps consumers connect with your brand.
Data can be used to create targeted marketing campaigns, improve customer experience, and increase customer loyalty. Essentially, this data can be used to take the business in a direction that works for its intended audience.
2. Personalised experiences
We are living in the age of personalised marketing. Without it, messages often fail to cut above the noise. In fact, McKinsey’s Next in Personalisation 2021 Report reveals that 71% of consumers expect personalised interactions from companies, and 76% of respondents feel frustrated when this isn't the case.
Similarly, a survey from Accenture finds that customers are 91% more likely to buy from brands who offer relevant offers and recommendations.
These figures don’t just suggest that personalisation is nice to have, they demonstrate that it’s a necessity. By implementing effective customer data management practices, businesses can streamline the collection of customer data from various sources, such as website or social media interactions, purchase history, and customer feedback.
For example, by collecting data on a customer’s purchase history, businesses can use this information to recommend relevant products and services that might interest the buyer. Furthermore, businesses can personalise communications to the customer’s preferred channel.
3. Customer retention
Customer data management is essential for customer retention. By implementing CDM processes, businesses can use customer data to create targeted marketing campaigns, experiences, and programmes that encourage repeat custom.
One of the most common ways this is done is through loyalty programmes. Businesses can use customer data to identify and develop targeted loyalty schemes, designed to encourage repeat purchases and referrals.
For example, Tesco pioneered its Clubcard loyalty scheme back in 1995, applying customer data science to track shopper trends and reward loyal customers. The scheme works by tailoring offers and rewards to shoppers based on their purchase behaviours and preferences, ultimately encouraging loyalty and retention.
As the UK’s first supermarket loyalty programme, the Clubcard is often considered the scheme that paved the way, making impeccable use of customer data management processes to improve the consumer experience.
4. Compliance with data laws
Data management goes beyond simply optimising data processes to improve customer insights and operational efficiency, it also ensures compliance with data laws. In fact, failure to adhere to relevant regulations and standards can result in severe legal and financial consequences.
For instance, in industries such as healthcare and finance, where sensitive customer data is involved, non-compliance with data privacy laws like HIPAA or GDPR can result in hefty fines, loss of customer trust, and reputational damage.
By ensuring data quality and compliance, organisations can mitigate risks and demonstrate their commitment to ethical and responsible data practices. This, in turn, can help to build customer trust, enhance brand reputation, and drive long-term business growth.
5. Operational efficiency
Data is integral to everything we do in modern business. From production and supply processes, to marketing strategies and wider business decisions, data is used across all areas of the organisation.
Let's say an energy company provides services to residential and commercial customers. The company maintains a database of customer information, including contact details, billing information, and usage history.
However, due to poor data management practices, the customer data is not accurate or up-to-date, which can lead to billing errors. For instance, incorrect meter readings or incorrect charges can occur due to outdated or inconsistent data.
By implementing data management processes, businesses can avoid spending time amending errors and repeating manual tasks. Instead, spending time on more valuable tasks.
6. Scalability
As businesses grow, they generally acquire more customer data. From email marketing, social media, website conversions and more, businesses acquire data from all directions. Without the necessary management processes in place, this can become difficult to maintain, potentially leading to inefficiencies, errors, and missed opportunities.
It’s important to consider customer data management with a view of supporting future scalability. For instance, is the data you collect relevant to long term goals? Is the data centralised into a singular management system? Is your current technology scalable?
Improving customer data management
1. Define data governance policies
The first step in improving customer data management is to establish a data governance process. This involves defining policies, procedures, and standards for collecting, storing, sharing, and using data across the organisation.
The goal of data governance is to ensure that data is accurate and consistent throughout the business, and that it is used in a way that is aligned with business objectives and regulatory requirements.
Here are some tips to consider when putting together a data governance strategy:
- Identify the types of data that will be included
- Allocate departments and individuals that will be responsible for managing the data
- Establish a data governance team
- Develop policies and procedures that outline how data should be collected, stored, managed, and shared
- Define metrics that will be used to measure the quality of the data, such as accuracy, completeness, and consistency
- Develop a scheme that classifies data based on its sensitivity, value, and criticality
- Define who can access different types of data, and under what conditions
- Establish policies around how long data should be retained, and under what conditions it should be deleted or archived
- Implement technology solutions that support the data governance process, such as data quality and management tools, and data cataloguing tools
- Monitor and measure performance
2. Implement data quality checks
Implementing regular data quality checks is crucial for businesses to improve customer data management because it ensures that the data being used for business operations is accurate, complete, and up-to-date. Regular data quality checks can help identify and correct data errors, such as missing or duplicate records, inconsistencies in data formatting, and outdated information leading to better insights and decision-making.
Furthermore, implementing regular data quality checks can help businesses maintain compliance with data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Failing to adhere to data quality requirements can result in legal and financial consequences.
3. Ensure data security and privacy
Ensuring data security and privacy is essential for businesses to improve customer data management practices. Here are some measures businesses can take to achieve this:
Limit access to customer data to only authorised personnel who need it to perform their job functions. Implement role-based access controls that restrict access to sensitive data based on an individual's job responsibilities.
Use encryption to protect customer data both in transit and at rest. This includes using strong encryption algorithms to protect data stored in databases and ensuring that data transmitted over networks is encrypted using secure protocols such as HTTPS.
: Keep software and systems up-to-date with the latest security patches and updates to prevent vulnerabilities that can be exploited by cybercriminals.
Regularly review access and activity logs to identify any suspicious activity or unauthorised access to customer data.
Define clear data retention policies to ensure that customer data is not kept for longer than necessary and is disposed of in a secure manner when it is no longer needed.
4. Choose the right tools to manage customer data
Data management is only as easy as the tools that allow it. Choosing the right tools allows businesses to streamline the process and even automate parts of it, reducing the time and effort required to manage and maintain the data.
Businesses can use automation tools to handle routine data management tasks, such as data cleansing and data integration. This can significantly reduce the risk of human error and ensure that data is accurate and up-to-date.
Our online data management platform, Online, helps businesses validate, clean, update and enhance data with ease. Find out more about Online.
Common challenges in customer data management
Data silos
Data silos occur when a company's data is stored in isolated systems that are not connected to one another, making it difficult for different departments to access and share data. For example, a retail company that operates both in-person stores and an online store, may have separate teams responsible for managing their physical stores and their e-commerce platform.
Each team collects data about their respective customers, such as purchase history, product preferences, and customer feedback. However, neither of the datasets are connected.
Data silos are common, and can create a number of problems, including:
- , data silos can lead to incomplete and inaccurate customer data. For example, a customer may have different contact information or purchase history stored in different systems, making it difficult for a company to have a complete and accurate view of that customer.
- , data silos can make it difficult for different departments to work together effectively. For example, a customer service representative may not have access to the same information as a sales representative, which can lead to a disjointed customer experience.
- data silos can lead to wasted time and resources. For example, if a company has to manually transfer data between different systems, this can be a time-consuming and error-prone process.
Businesses can solve the issue of data silos by implementing a unified systems for a single customer view that allows for seamless data sharing across different departments.
Data quality issues
Data quality is one of the biggest challenges businesses face when implementing or coming up with a customer data management strategy. Poor data quality can result in inaccurate insights, poor decision-making, and ultimately, a negative impact on the customer experience.
There are several reasons why data quality issues can occur, including:
data can be entered manually, and human error can lead to mistakes in the data.
where data is incomplete or missing important fields, this can make it difficult to gain a complete view of the customer.
data can be inconsistent across different systems, making it difficult to link and merge data across different sources.
information can become outdated over time, and businesses need to ensure that the data is regularly updated.
this occurs when multiple records for the same customer are present in different systems, or even within the same system. This can happen due to data entry errors, technical issues during data integration, or other reasons
To address these common data quality issues, businesses need to implement data quality management processes, such as data cleansing. This process helps to identify data quality issues and fix them in a timely manner.
Technical challenges
Technical challenges are another common issue that businesses may face when implementing or coming up with a customer data management strategy.
These challenges can arise due to the complexity of managing customer data and the technical requirements for storing and analysing data. For example, businesses might lack the necessary software to manage data quality and identify where they need to improve data management practices. Though, trying to tackle things manually can be equally as challenging.
Online data management software, such as Online, is designed to make this easier, putting simple data management into the hands of the business.
Conclusion
In conclusion, effective customer data management is essential for businesses that want to stay competitive in today's market. By utilising the right tools and techniques to collect, organise, and analyse customer data, businesses can gain valuable insights into their customers' needs and preferences, improve their customer engagement and retention, and ultimately drive business growth.
However, it is important to remember that data management is an ongoing process that requires continuous attention and adaptation to changing customer behaviours and trends. By staying informed and up-to-date on the latest data management best practices, businesses can leverage the power of customer data to create more personalised and impactful customer experiences, and ultimately achieve greater success in their market.
Speak to a data expert
Have questions about how data can work to improve your business strategy? Get in touch with our data experts to find out more
