CRM Database: What it is and How to Make the Most of Yours
Customer data has become complex. Customers communicate through a large number of channels and have new expectations about speed and personalization — such as the personalized experience Amazon provides.
Many modern CRM platforms are providing the kinds of solutions that businesses need, but these platforms are only as good as their data. The CRM database is the key to not only an effective CRM tool, but also to optimizing customer data for new business insights.
What is a CRM database?
A CRM (customer relationship management) database is a resource containing all client information collected, governed, transformed, and shared across an organization. It includes marketing and sales reporting tools, which are useful for leading sales and marketing campaigns and increasing customer engagement. CRM systems differ in their abilities to pipe data from other applications, functionalities, performance, and ease of use.
CRM databases sort data into three different categories: operational, analytical, and collaborative.
- Operational CRM streamlines business sales, marketing, and service tasks
- Analytical CRM helps business leaders plan and decide how to serve customers better
- Collaborative CRM or Strategic CRM share customer information with various business units as they need to do their jobs
Salesforce, for example, provides a customizable platform that can be an operational, analytical, or collaborative CRM depending on the modules selected by Salesforce business customers. Companies access the Salesforce’ products using the internet, as Salesforce hosts all client data in the cloud. Therefore, unlocking Salesforce or any other CRM database requires setting it up well.
Benefits of a good CRM database
A CRM’s power lies in the ability to get the right information from a trustworthy source, to the right customer, at the right time. Three benefits from a good CRM database include:
Personalized customer experience
Effective marketing requires knowing whom to target for what product or service featured in tomorrow’s campaign, discount, or reward. Those insights come from detailed, integrated, governed CRM data. A CRM database that provides a single source of truth for every customer or lead can enable marketing teams to create the kind of personalized experiences that people have come to expect.
Speedy sales process
Good CRM systems consistently organize contact information well. Salespeople and marketers on the move have access to client data all in one place. Maintaining up-to-date customer data in real-time requires a CRM database that can handle multiple data streams, and integrate all that data efficiently.
Compliant governance ensuring privacy and appropriate access
Customers and regulators expect careful stewardship of information to maintain privacy and to have access to their collected data. If a client hears of a data breach, not only does the company need to compensate for the leaked data, it needs to restart a more positive relationship.
A company has to know what customer data it holds and how that data has been transformed to comply with stricter regulations, such as General Data Protection Regulation (GDPR) and the California Consumer Privacy Act. Non-compliance results in steep fines. A good CRM database lets you track all of your customer data, from start to finish, providing necessary access and privacy to a client’s information.
Data migration: 4 steps to getting started with a CRM database
Customer data lives in many physical and technical channels, such as emails, spreadsheets, Post-It notes, Facebook, LinkedIn, and Twitter. A CRM system needs to have all this data accessible in one place in order to have meaningful client conversations and analysis. Setting this up, and maintaining it in real-time, requires efficient, secure data migration.
Moving data to the CRM database may seem daunting at first; however, these tasks breaks down into easily manageable steps:
- Data migration planning - Stakeholders, managers, and technical staff meet to determine the scope of data to be transferred and how the newer system meets business and data goals. The data governance team will manage the data migration program and determine roles and responsibilities.
- Data migration analysis and design - Data migration analysis tests the data migration plan to see if it is good to go or needs some amendments. Team members evaluate data quality to correct any data with improper formatting or corrupted data, and how to secure data from start to finish in compliance with regulations. Technical staff hash out data environments and migration details, including staging, testing, and production.
- Data migration implementation - Data migration implementation starts with a step-by-step procedure and creation of a staging area for the data to be migrated. Start with a small subset of data before completing the full data migration. After thorough testing, operations needs to transfer over to the new system.
- Data migration closeout - This stage specifies successes and lessons learned from the data migration project. The migration team documents any knowledge needed for training or success for future projects.
On-going migration—from any of the myriad possible data streams—is the same. It may seem daunting at first, but data migration tools make the process easily manageable.
CRM database example: Uploading 120 data feeds
Age UK, the largest charity in the United Kingdom, uses Microsoft Dynamics CRM to better understand clients and personalize their experiences.
Age UK integrates and imports data from 120 data feeds through a data quality tool. As a result, Age UK has a better, more timely understanding of its clients and needs fewer resources to quickly clean the data for a single feed processes. Age UK gets accurate information in almost-real-time.
4 ways to optimize your CRM data
A CRM systems’ value hinges on having trustworthy and high-quality data. When more CRM data comes into the system at a higher speed, this data needs to be maintained so that it remains relevant to the business, accessible to those who need it, respectful of an individual’s privacy, and available to explore and discover new business opportunities.
Try these four steps to optimize your CRM data:
- Invest in data governance. Data governance describes a collection of practices and processes that formally manage data assets — including CRM data. Data governance clarifies ownership of data, data procedures, and communication, to ensure the CRM database has quality and security.
- Invest in a data automation tool. Rather than manually correcting records with the same value, find a way to automate (e.g. group edits). Vendors have tools within their suites to simplify data quality tasks. This saves time maintaining a CRM Database.
- Report metrics using CRM data. Ask the business (e.g. sales and marketing) to design reports that will help them succeed. PAT RESEARCH lists some examples to assess business efficiency and effectiveness. This provides information about the CRM database’s performance.
- Identify a quick win with CRM data. Bernard Marr has developed a data use template to identify business and data strategy, and narrow it down to a specific goals. From these use cases, identify quick wins, see if any can make some use of the CRM system, and market this goal.
CRM database example: A fully integrated CRM
Lectra, a world leader in integrated manufacturing processes, makes the most of its CRM database by connecting to its product lifecycle management (PLM) solution, through an integration tool. As a result, business units share consistent customer data — for example, supplier lists and nomenclature.
Cloud CRM and the future of customer data
A cloud CRM describes a customer relationship management system hosted by a third-party and accessible through the internet. Cloud CRM is a kind of software as a service (SaaS), reducing the cost and time in dealing with CRM technical set-up and maintenance.
Modern businesses need to consider a cloud CRM to catch up with customer demands. While many retailers, for example, have yet to capitalize on their customer data, using the cloud, it will become a must. Retail growth using cloud CRM will continue to increase over the next year and a half, while the vast majority of these businesses will either be initiating a new cloud CRM or adding one on to augment their products.
Take the next step with your CRM database
Businesses need to keep up with their customers by maintaining a high-quality and high-performance CRM database. A CRM database should excel at collecting, governing, transforming, and sharing customer information. Its power lies in getting in the right information from a trustworthy source to the right customer at the right time.
Vendors have made this goal achievable by integrating multiple data feeds and data systems into the CRM database. This makes sales and marketing operations, analysis, and collaboration easier, as all data is accessible in near-real-time and trustworthy.
The key to an optimized CRM database is successful data migration, followed by air-tight data governance and the highest data quality. Talend Data Fabric is a suite of apps that shortens the time to trusted CRM data through governed, self-service, and automated IT tools. It connects to all industry-leading CRM systems — from open source tools to Windows, from Apache Kafka messaging to Amazon cloud—pipelining that data effectively.
When your organization is ready to launch or upgrade your CRM database, use Talend Data Fabric to start by making sure your data is secure and the tools are in place for a seamless and quick data migration. Start your Talend Data Fabric free trial today.
Ready to get started with Talend?
More related articles
- What is MySQL? Everything You Need to Know
- What is Middleware? Technology’s Go-to Middleman
- What is Shadow IT? Definition, Risks, and Examples
- What is Serverless Architecture?
- What is SAP?
- What is ERP and Why Do You Need It?
- What is “The Data Vault” and why do we need it?
- What is a Data Lab?
- Understanding Cloud Storage
- What is a Legacy System?
- What is Data as a Service?
- What is a Data Mart?
- What is Data Processing?
- Understanding data mining: Techniques, applications, and tools
- What is Apache Hive?
- Data Munging: A Process Overview in Python
- What is a Data Source?
- Data Transformation Defined
- SQL vs NoSQL: Differences, Databases, and Decisions
- Data Modeling: Ensuring Data You Can Trust
- How modern data architecture drives real business results
- Data Gravity: What it Means for Your Data
- Data Conversion 101: Improving Database Accuracy