According to Forrester Consulting, more than 66% of polled CDP users thought their firm should implement Customer data platforms (CDPs) as a key priority. Today, data is the lifeblood of organizations, and with the correct methodology, businesses may uncover insights that significantly improve the consumer experience.
Business leaders who need help getting the data they need to ensure the success of their organizations can be dealing with an even more difficult problem: managing the data they already have.
CDPs have recently gained significant appeal thanks to their seductive promise of bringing together your sizable, incompatible, and constantly changing datasets—effectively purchasing your way out of the data problems posed by outdated technologies.
Top Customer Data Challenges
To move away from fragmented (or outsourced) methods to data management, marketing solutions must adopt a continuous, cohesive fabric with customer identification at its core, insights acting as the brain, and activation acting as the arms and legs. The CDP capability needs to be integrated into your marketing cloud rather than added as an afterthought if you want to get the fuel into the car.
A technology system called a customer data platform aids businesses in centralizing, managing and using their customer data. CDPs can help to:
- Gather and maintain customer information from numerous sources in one place.
- Combine customer data from several systems and programs, such as marketing automation, ERP, and CRM platforms.
- Give customers a unified view of their behavior, preferences, and history across all channels and platforms.
- Enabling firms to segment and personalize client data might result in more specialized and efficient marketing strategies.
- Real-time data should be made available to organizations so they can react swiftly to client needs and help with decision-making.
The following are some of the most significant issues that businesses encounter when trying to manage consumer data that CDPs address:
1) Data Silos
Data isolation within particular departments, systems, or applications is called "data silos." This may occur when many teams or departments within an organization independently gather and store data without sharing or integrating it.
Due to a lack of visibility and access to data, it may be challenging for organizations to fully comprehend their operations, clients, and performance.
Examples of data silos include:
-
Departmental silos
When various departments within a company gather and store data independently, keeping it private from other departments.
-
System silos
When several systems inside a company gather and store information on their own without connecting to other systems.
-
Geographical silos
You can create geographical silos when several locations or subsidiaries within a company gather and store information independently without collaborating with other locations or subsidiaries.
-
Data format silos
Data format silos are created when data is kept in various types and formats across several systems, making it challenging to combine and analyze.
Data silos can cause various issues, including ineffective resource usage, poor decision-making, and a lack of confidence in the data. Additionally, it might make it challenging for companies to effectively segment and target their client base. To eliminate these data silos and combine data from several sources into a single, cohesive perspective, CDP is a solution.
2) Data Inconsistency
The status of data differing or contradicting across several systems or applications is called data inconsistency. This can occur when data is manually entered or transmitted across systems without being adequately validated or reconciled.
A lack of trust in the data can result from data inconsistency, making it challenging for organizations to base choices on the data.
Examples of data inconsistency include:
- Duplicate records
When different information about the same product or customer is recorded more than once in separate systems.
- Inconsistent data formats
When data is saved in several forms across various systems, such as when a date field is stored as mm/dd/yyyy in one system and dd/mm/yyyy in another.
- Inconsistent data values
When information is entered into systems differently, a customer's name is entered as "John Smith" in one system and "J. Smith" in another.
- Inconsistent data structures
Data is stored in various ways across many systems, such as when a customer's address is divided into different fields in one system and stored as a single string in a different system.
A lack of trust in the data and erroneous reporting are only a few issues that might arise from inconsistent data. Additionally, it might make it challenging for companies to effectively segment and target their client base.
3) Data Activation
The practice of leveraging data to influence business outcomes and take action is known as data activation. It entails gathering, combining, and analyzing data from multiple sources to guide and enhance corporate choices, procedures, and consumer interactions.
Businesses may take action based on their understanding of their customers, operations, and performance thanks to data activation.
Examples of data activation include:
- Personalization
Utilizing data to enhance and personalize consumer interactions, such as product recommendations, online experiences, and email campaigns.
- Segmentation
Using data to segment customers and send them individualized offers and messages.
- Predictive modeling
Analyzing data to develop predictions about consumer behavior, such as which customers are most likely to leave or which goods will sell well.
- Optimization
Using data to improve logistics, supply chain management, and other company activities such as inventory management.
- Real-time decision making
Utilizing data in real-time to guide and enhance commercial choices, like pricing tactics and advertising campaigns.
Data activation enables businesses to make more meaningful use of their data, resulting in more focused and efficient marketing, better customer experiences, and better financial results.
Overcome Data Challenges With Customer Data Platforms (CDP)
Companies may gather consumer data from various sources, including website interactions, social media platforms, and CRM systems, and keep it in one place, thanks to CDPs. As a result, data silos are no longer necessary, making it simpler for businesses to access and analyze customer data.
CDPs use sophisticated data management techniques, including data normalization, deduplication, and data validation, to ensure that consumer data is accurate and consistent across all sources. By doing this, data discrepancies are removed, and it is ensured that consumer data may be used to make accurate and actionable decisions.
Thanks to CDPs, companies may target and segment their consumer bases depending on their behavior and interests. As a result, businesses may tailor their marketing messages to their target audiences and offer a more relevant and interesting consumer experience.
1) Data Integration
Using CDPs, companies can gather and combine customer data from numerous platforms, including social media, email, websites, and CRM. As a result, there is no longer a requirement for data to be stored in silos and a more comprehensive perspective of the client is now possible.
Customer data integration (CDI) is the act of bringing together customer data from several sources into a single, unified view without the need for human data merging and reconciliation.
This can involve gathering information from many sources, converting it into a standard format, and adding it to a central repository. This can incorporate data from various resources, including social media, marketing automation platforms, and CRM systems.
By offering a more thorough and accurate understanding of the customer and by making it simpler to take actions based on that understanding, CDI aims to enhance the customer experience.
Personalizing messaging, spotting cross-selling opportunities, and enhancing customer service are a few examples of what this might entail.
2) Data Normalization
Data silos are reduced because of CDPs' cleaning and normalization of the data, ensuring consistency and correctness. This could entail eliminating pointless data, standardizing field names, and deduplicating records.
The practice of arranging and organizing customer data in a dependable and standardized format is known as customer data normalization. This procedure is often used to verify that complications brought on by differences in format or structure can be avoided when combining and comparing data from several sources.
Standardizing field names and data types, eliminating redundant or inconsistent data, and combining data from several sources into a cohesive view are all normalization chores.
Increasing the accuracy and dependability of data through normalization can result in better decision-making and enhanced consumer experiences. Normalizing data can improve decision-making and company performance by ensuring data integrity, enhancing data quality, and simplifying access and analysis.
3) Single Customer View
CDPs combine data from all sources to produce a single consumer perspective. This enables businesses to comprehend market categories and specific clients more thoroughly.
An all-inclusive and unified view of a customer's interactions, transactions, and demographics across all channels and touchpoints is known as a single customer view (SCV). It represents all client data that a company has gathered, combined, and cleaned up.
A single customer view is intended to give organizations a comprehensive picture of a customer's behavior, preferences, and history. This can help businesses make better decisions, enhance the customer experience, and increase revenue.
Data from multiple sources, including CRM, ERP, social media, website analytics, and other systems, can be combined to produce SCV. In addition, data integration, warehousing, and governance strategies can be used.
4) Data Governance
Data silos are reduced thanks to CDPs' governance and data management processes, guaranteeing data quality, privacy, and compliance.
To ensure that customer data is gathered, maintained, and used responsibly and legally, firms must put rules, procedures, and practices in place. These practices are referred to as customer data governance.
Making sure the data is precise, thorough, timely, and consistent falls under this category. Along with managing data security and quality, it also involves the management of data architecture, data models, and metadata.
Regulations governing data privacy, security, and retention policies are a few examples of this. Additionally, it guarantees that client data is ethically utilized and in accordance with applicable rules and regulations and that information is accurate, complete, and up-to-date.
Customer data governance aims to safeguard consumers' rights and privacy while enabling businesses to use such data to enhance their goods and services.
5) Data Matching and Deduplication
CDPs employ cutting-edge algorithms and machine learning approaches to match and deduplicate client records across various data sources, guaranteeing that the data is accurate and consistent.
Deduplication and customer data matching are procedures used to find and eliminate duplicate customer records inside a database or many databases. Duplicate records can cause confusion, mistakes, and inefficiencies, so avoiding them is crucial.
Customer data matching entails comparing several customer records' data fields, such as name, address, and email address, to ascertain whether they pertain to the same individual or company. Different methods, including exact matching, phonetic matching, and probabilistic matching, can be used to accomplish this.
Customer data deduplication eliminates duplicate records after they are found by combining or removing redundant data. Depending on the amount and complexity of the data set, this operation can be done manually or automatically.
6) Data Quality and Validation
Organizations can quickly address data quality issues thanks to CDPs' ability to validate, verify, and report any inconsistent data. The techniques used to verify that consumer data is correct, complete, and consistent are data quality and validation.
This is crucial since insufficient data can result in mistakes, inefficiencies, and challenges when making wise business decisions. Data quality checks are utilized to find and fix errors, omissions, and inconsistencies in customer data. This could entail validating data formats, finding duplicate records, and checking missing fields.
Data validation using regex, patterns, or lists of permissible values ensures that the data entered complies with a set of rules or standards. In addition, automated data quality and validation tools can be used to automate the process, improving its accuracy and efficiency.
7) Data Enrichment
To fill in any gaps in the data and maintain consistency, CDPs can enhance the data by including additional data sources such as third-party data providers, weather, location, and demographic data.
Enriching customer data is adding new information to existing customer data to increase its value and use. This can comprise psychographic information, such as interests, hobbies, and lifestyle, and demographic information, such as age, gender, and income level. Along with the customer's past purchases and preferences, it may also contain details on their internet activity and social media presence.
Data enrichment can be done in several ways, including internal data like customer service interactions and purchase histories, external data like public records, web crawling and data scraping from third parties.
8) Data Management
With CDPs, customer data can be managed and stored centrally while also being conveniently accessed by other systems. Customer data management is the act of gathering, preserving, and using customer information to enhance the customer experience, raise revenue, and enhance overall corporate performance.
Examples of this are information about customer demographics, purchasing patterns, and preferences. Regular data entry and upkeep are necessary for efficient customer data management, as is the usage of tools for data analysis to gather knowledge and make defensible decisions.
9) Data Segmentation
Companies can use CDPs to divide consumer data into several groups according to behavior, purchase history, or demographics. As a result, marketing, sales, and customer service may be more focused and tailored.
Customer data segmentation divides a client base into smaller groups depending on specific criteria, such as demographics, behavior, or previous purchases. This enables companies to target and effectively communicate with particular consumer base segments with their marketing initiatives and product offerings.
There are different methods of customer segmentation, including:
- Demographic segmentation: dividing customers according to attributes, including age, gender, money, and education.
- Behavioral segmentation: dividing customers based on their behaviors, such as past purchases, brand involvement, and loyalty.
- Geographical segmentation: dividing customers according to their geography, such as a city, region, or nation. After additional analysis, each sector can be targeted with particular marketing initiatives and product offerings.
For example, a company may establish a targeted campaign for consumers who usually buy outdoor gear while developing a different campaign for customers who are primarily interested in fashion items.
10) Real-time Data
Organizations can react swiftly to changes in customer behavior and preferences thanks to CDPs' real-time ability to process and analyze data.
Real-time customer data is information about customers that is gathered and examined immediately rather than archived and analyzed afterward. This can include information about your browsing and purchasing history, physical location, and how you engage with a company's website or customer support personnel.
Making business choices in real time and personalizing the customer experience is possible with real-time customer data.
Accelerate Business Growth With Customer Data Platforms
The more data is gathered, created, and used by employees, the more crucial it is for businesses to uphold sound data governance. Organizations implementing thorough and well-thought-out data governance programs will be most equipped to negotiate the shifting business environment as more companies migrate to the cloud.
In addition to being data centralization solutions, CDPs are strategic assets closely related to the daily requirements of the business, such as increasing revenue, creating better experiences, and assisting in digital transformation.
Read More: 5 Ways Customer Data Platform (CDP) Supports Marketing Challenges