A Guide to Flawless CRM Data Management and Migration
Managing a business’s customer interactions and relationships call for specialized tools like a CRM or Customer Relationship Management system. Staying on top of the customer experience and being able to extend relevant interactions with both current and potential customers while managing processes and increasing profitability is the primary objective of a CRM system.
With an effectively managed, consistently performing CRM system, businesses can expect to benefit from well-organized contact and sales management processes, strengthened productivity, and improved business relationships.
Well managed CRM databases include:
- Storage and management of information about customer contact details, customer interactions, and communications, sales, survey and research findings, receipts, inquiries, customers complaints, and more.
- from various customer engagement channels, like the phone, internet, social media, an informational source for potential customer leads and features crucial information d email.
- Increased likelihood of accurate information to support business decision making.
Managing a CRM database is resource intensive, expensive, and demands skilled technicians to maintain the database, and make necessary modifications and updates. Additionally, many CRM projects revolve around some form of data migration that arises from legacy systems being replaced with new digital infrastructures, so migration has to be executed in a way that’s testable and repeatable for an optimal outcome.
Businesses are opting for a more economical alternative for CRM data management best practices by outsourcing to professional outsourcing agencies so that they can devote their energy to core tasks and execute successful CRM strategies in marketing.
Tips for Effective Data Management
Effective data management is a necessity rather than a practice. Data in unstructured form is a burden on the storage system which doesn’t fulfill the objective of keeping volumes of data which is aimed at giving insights for future decision-making. So, data management starts from the data collection stage through segregation and then the storage part. Following tips would be helpful for streamlining the data management process:
Automation of Information Update
Organizations need to maintain pace with their information governance but may be unable to do so effectively because of a lack of internal resources like budget, specialized skill sets, or staff.
Data Management complexity is increasing due to:
- The diversification and growth of business needs
- Technological advances
- Regulatory and compliance standards
- Information applications and usage
- Changing business priorities
Organizations need to invest in the tools, technologies, and services that relieve the burden of a CRM implementation strategy from their internal teams.
De-Clutter the Database
It’s challenging to maintain neat and orderly data considering all the factors that go into storing, retrieving, and interpreting data. Data enhancement processes that focus on reducing the inherent chaos within massive quantities of information can go far to decrease clutter and offer insight into the data you already are working with.
Keep Your Data Up to Date
There are a few basic steps involved in maintaining your database:
- Centralized data Processes are simplified when you only have to search and update one location to assemble all of your data in one file or program
- Keep a summary Use precise names, data definitions, and tabs, and segment target groups.
- Add new data Databases are kept whole by constantly and immediately adding new data instead of saving it in a different location.
- Monitor outcomes Closely watch your email results and process opt-outs, bounces, and mutations, and clean up your database after every email that’s sent.
- Use Web forms Others can keep their own data up to date, such as address and contact information, through the use of web forms.
- Database enrichment Good marketing software automatically can enrich relation details, depending on the outcome of each campaign that you execute.
- Double opt-ins Avoid spam complaints with double opt-ins. People can sign up through a web form, or you can send an email requesting confirmation of their subscription.
Monitor the Last Review Data
- You can prevent version control problems when working collaboratively on files by agreeing on a logical, consistent naming convention at the start of a project.
- Add metadata for further context so you can understand the data on an ongoing basis.
Establishing an organization system for your files:
- Access your files, prevent duplication, and ensure that data can be backed up with the development of a logical structure for folders.
- Assemble files within folders, so data about a specific subject is in one location.
- Evaluate any preexisting or established approaches in your department or team that you can utilize.
- Folders should be named after relative areas of work rather than individual researchers or personnel to avoid confusion in shared workspaces.
- Begin with a minimal number of folders for more general topics and then create more specific folders within the general ones.
- Separate older documents from current ones when creating folders and files so that ongoing and completed work is distinctive.
- Your “My Documents” folder should be maintained for files that are actively being worked on and move files that you’re not working on to a different folder or location.
- Always backup your files.
Use Data Enrichment Tools
- Data Enrichment: Applies critical customer geographic, demographic, and psychographic data to existing records to gain a complete picture of your customer segments and how to effectively target them.
- Demographic Data: To gain customer insights, you should start with demographic data that helps you to discover any missing details like their age, job title, or gender.
- Geographic Data: Can be used in combination with some demographic variables, which are converged and become geo-demographic data to pinpoint buyer behaviors depending on their location.
- Psychographic Data: Preferences, habits, interests, and hobbies that are crucial to personalized marketing such as when trying to figure out what products will attract which people.
How and When to Audit CRM Data
Companies who are dealing with sizable databases, and smaller companies who lack resources, can benefit from outsourcing CRM data audits to a third-party service provider.
Account for Your Data
Data assets are stored within the tools that your business uses for customer interaction. Likewise, there’s probably a lot of data that can be stored in your CRM but isn’t.
Accounting for your data in various locations has a process:
- Business data assets Figure out which company applications contain customer data, such as eCommerce software, social media, POS purchase data, CRM systems, and email lists.
- Key data handlers Interview managers and team leaders who handle data including how it’s used, its location and any issues that are arising with the data.
- Emphasize data Evaluate which data points offer the best commercial value, whether it’s email addresses, phone numbers, or social media followers.
- Data usage Promote data accessibility to those operations where data can provide the most commercial value.
- Restrict access Data and data management tool access should be on an as-needed basis so that only relevant team members can access, edit, or remove it.
Clean Up CRM Data
Bad data can completely undermine a new CRM management system and can cost companies about $100.00 for every duplicate record. So, enforcing clean, quality data is important.
- Analyze Data Start with analyzing and benchmarking data quality in your database with assessments of duplication issues, where these issues are originating from, and other actionable information.
- Standardize Data Normalizing data creates an organized, consistent, and enforceable environment for data that’s being entered into your CRM.
- Duplicate Data Removal This is one of the most important steps in cleaning an existing CRM and preventing duplicate data from re-entering your system.
- Add Missing Data There are numerous data markers within CRM’s which are a roadmap for filling in any missing data.
- Validate Data Data validation enables CRM systems to operate on correct and useful data so frequently check data validity by creating validation rules and routines.
- Secure Data Data will continue to decay if you don’t utilize a protection tactic. Information like emails and phone numbers change, so if personnel is entering this information into your CRM, then duplicate, and inconsistent data is being created.
- Enhance Data Explore unlimited data potential by adding additional information to existing data for a complete view of customer contact information.
Fine-Tune Data Entry Parameters
Regardless of the pace of CRM usage, audits need to be conducted often.
- Eschew free text fields There is an increased chance of error the more data that needs to be entered manually when using copies of your naming conventions. Utilize multi-select fields and drop-down menus as often as possible.
- Required fields Before a record is saved, CRM admins can make certain fields mandatory depending on the record. Required fields help ensure that all saved records have a minimum data level.
- Validation rules Specific data entry formats are imposed with rules, and an inaccurate format will cause an error message to appear.
- Workflow rules If a specific version of a phrase is entered into the CRM, an automatic field update occurs. A workflow can be created with preferred nomenclatures, such as U.S.A. to the USA.
How to Create a Data Management Plan
Creating a data management plan helps in following the standard practices for collecting, organizing, data back-up, and storing the data your system is expected to generate. Ideally, keeping a data management plan in place should be the first step when you eye for starting a project.
Establish Naming Conventions
Creating versioning policies and naming conventions helps monitor project data files and documents and ensures that every file is unique.
Before starting a project, specify naming conventions and versioning policies in a README file or spreadsheet.
Different labeling methods include:
- Camel-case: ProjectName
- Underscores: Project_Name
- Lowercase text without spacing: projectname
- Combination: ProjectName_datafile1.dta
Versioning policy naming conventions can be specified as follows:
- ProjectName_datafile1_ version1.dta
- ProjectName_datafile1_ codebook_version1.pdf
Establish Data Entry Rules
Data management success is directly dependent on data entry practices, so remember this if you want to develop an organized and valid set of data in specific formats that can be evaluated and used quickly and easily.
- Establish data standards Create data entry standards that are compatible with the unique project, including the data sources and the type of data entry system being used.
- After data entry storage Save your data entry project in a universal format, like Unicode or ASCII, that can be read by any application and resist using any proprietary output formats.
- Be aware of bad data entry practices Ensure that data entry operators can identify and recognize all types of data entry errors including inconsistent formats or applying different types of data in one column.
- Descriptive file and column names Excel-based data entry should have descriptive names for files and columns without special characters or spaces, which can cause issues when the file is used for analysis.
- Consistency Data needs to be entered consistently in a single data sheet rather than in blocks located in various places.
- Missing Data Missing data in data sets can cause huge losses if unidentified, so address this by:
- Assigning a No Value to an empty field.
- Entering a distinctive value to indicate a missing number in a numeric field.
- Use NA in the missing data field.
- Place data flags in a separate column for missing values.
- Fill data lines All cells in a single line in a spreadsheet in a field need to be filled without any empty cells for appropriate sorting.
- Maintain a log Provides a record of issues and errors that are accounted during data entry and every project should have an entry log.
- Automate Use automation for large data entry volumes.
Create Deduplication Rules
Single-table deduplication rules enable you to deduplicate records from the same data entity type, and multi-table deduplication rules allow you to deduplicate records from different data entity types.
To create a new single-table deduplication rule:
- Go to Audience Tools and click Data Tools.
- Then choose New Deduplication Rule.
Or you can click New Deduplication Rule on the right panel.
Configure Deduplication Rule Details:
- Add a name to the Deduplicate Rule Name field.
- You can also add a description to the Deduplicate Rule Description field.
- Choose the Deduplicate Rule Table drop down and the type of data entity that offers data source for the rule, such as contacts, companies, or prospects.
- Choose Save and the new deduplication rule will save under the provided name and the page expands to feature other parameters that you can set, like Filters.
Third-party Data Management Tools
Choose a data management platform software solution that aligns to your business objectives. You may want to seek assistance from an experienced customer relationship management process and data management service provider to help ease the process while providing technical and business support. They can also offer specialized support to provide solutions for CRM for small business.
Top data management platform solutions:
Companies can collect, unify, and activate data across touchpoints and users can merge data from any sources with AI and Machine Learning to better reach target audiences.
The largest independent vendor in the data management marketplace with the main product layered over a data quality platform that enables users to integrate, improve, and govern enterprise data.
Analytics tools built into the platform allows users to discover insights into audience segments that a company creates and includes manual and automated optimization tools.
Businesses can create robust user profiles with information combined with first and third-party sources like advertising, social, and mobile.
Ideal for tracking campaign performance in real time, connecting with customers, and optimizing data distribution to marketing platforms. Though it doesn’t have multi-user support.
Adobe Audience Manager
Create unique audience profiles to identify valuable segments for email and social campaign initiatives.
Ideal for customizing, managing, and analyzing audience data with over 60K audience segments for personalized advertising and content delivery.
Simple to use with considerable customer support and features many integrations and third-party tools. It’s not a full stack solution but does have numerous benefits.
Challenges When Using Data Migration Tools
- Parallel Run During the planning stage, the migration strategy is decided, and a parallel run migration strategy should be cautiously executed because of the effort involved in maintaining two systems in parallel.
- Data Cleansing Data cleansing is necessary to establish data quality KPI objectives in a destination system during migration projects.
- Different Coding Source Systems and Unique Data: Certain instances occur where the same data exists in multiple systems with different coding and no link between them. Migration options:
- Group data in one master data.
- Clear data and don’t migrate it.
- Retain both master data and design a reporting hierarchy.
- ETL Tool Destination systems need to be analyzed during the analysis stage to identify tech requirements for loading data and identification of reference models.
- Custom systems load data directly in database and COTS (Commercial of the Shelf) have their own import/export data tools.
- Validation and Test Migration: A UAT test ensures that migration activities are correctly identified, sequenced, and included in a checklist of steps.
Standardize Your Data Entry with Data Management Service Providers
With all of the important functions data serve for today’s businesses, maintaining data is one of the most valuable and critical tasks. When the data is so important, it’s always wise to take the assistance of an experienced data management service provider. Partnering with the right data management and migration service provider, you can be befitted with the following:
- Increased businesses profit
- Drive sales and other competencies
- Allows you to prioritize core business functions
- Reduces overhead expenses concerning infrastructures, salaries, compensation and additional personnel that required for internal data processing
- Risk reduction
- Enhanced efficiency and productivity
Dos and Don’ts of CRM Data Migration
Initial Data Quality Assessment
- Check data as it enters the system
- Add missing data
- Remove duplicate records
- Maintain current records
- Govern data from IT
- Govern data in silos
- Use pointless metrics
- More analysis and testing
- Adopt a scientific approach
- Define technology and organizational KPI’s
- Utilize model-driven engineering
- Underestimate the magnitude of the issue
- Leave data migration as a retrospective action
- Assume it’s going to be simple
Development of Migration Scripts
- Comprehend legacy system use cases
- Prepare users for forthcoming changes
- Prepare target CRM with the right structure
- Determine cutoff dates and record and field exclusions
- Review data maps at a business level
- Assume that data migration is easy
- Leave data migration solely to the tech team
- Migrate historical information
- Adopt bad habits moving forward
- Identify entities
- Determine the order of entity validation
- Determine the cutoff date
- Field to field mapping sheet
- Avoid disruptions to the user during migration
- Forget to anticipate likely glitches that may occur during migration of the live system
- Perform in-depth analysis
- Plan for cleanup time
- Double check your data
- Utilize better templates
- De-dupe without using internal business rules
- Do it once and forget about it
- De-dupe without team verification
- Lock down the system to avoid bad data which will hurt your sales and marketing
- Review source and target CRM
- Spend more time on data mapping
- Compare CRM structures
- Develop migration tools
- Perform a few test runs
- Forget to see if archive data is available in the UI
- Forget that data validation is crucial
- Forget to focus on all custom fields in the source CRM
- See if any substitutes can be found in the new CRM
Must-Dos Before CRM Transfer
A successful data import encompasses certain tasks that need to be performed before migration:
- Remove and clean up outdated and unnecessary records.
- Prepare a backup strategy in case the need to restore records arises.
- Set goals to understand what you want your system to do and discuss what improvements you’d like from your new system.
- Migrating your CRM is important and impacts numerous departments, so get your teams on the same page and prepared for changes and training.
- Develop an integration roadmap and facilitate buy-ins and ensure everyone understands their role.
- Customize fields need to be managed to be helpful for your business.
- Emails, attachments, and history record migration may be more complex, so use migration tools to ensure you understand how this type of data transfers.
- Double check to make sure that nothing was forgotten during migration.
- Conduct multiple testing rounds with your team to address any problems before going live.
- Be sure to partner with an experienced expert who can help you during the migration process and advise or train your team.
- Websites For every migrated domain, check web applications installed via Plesk homepage address and addresses in https://DOMAIN/RELATIVE_URL for relative links on the homepage.
- Mail For every migrated domain, resource records have to be transferred to the source server to the destination server and the following test conducted:
- Main DNS records from source server compared to those from the destination server to ensure that none are missing.
- Main DNS records are retrieved on destination server so that record resolution can occur.
- Databases For every migrated domain, databases on the source server had to be present on the destination server and checked to make sure that its registered in Plesk and the list of databases tables from the source server is compared to the destination server to ensure that none are missing.
- System Users For every migrated domain, system users that are present on the source server have to be present on the destination server and for every system user, log through the destination server is conducted through FTP and SSH.
Steps Required for Cost-Effective Data Migration
- Analyze Overall business needs and an examination of existing data.
- Assess Consider if specific records should not be included in the migration and if there needs to be a history cutoff date.
- Normalize Take redundant fields and create structured relations in the CRM database.
- Customize Can include everything from adding new fields to developing custom tables for preparing validation rules.
- Map After you’ve determined which data to keep and the structure of the new CRM system, prepare a data map which maybe a spreadsheet.
- Extract Before the test data migration occurs, data should be extracted from the existing CRM system or contact manager. It might have to be taken from a legacy format and stored into an SQL server or MySQL.
- Transform Transform legacy data before it is entered into a CRM system, and old values may need to be transformed into new replacement values.
- Merge If there is a lot of duplicates in the current database, then you may have to merge duplicate records during data migration.
- Test Before the final data migration occurs, conduct a partial import to ensure that the data is entering the right places in the new CRM system.
- Migrate This is the final step before going live and is where some refinements can occur.
- Cleanup Post data migration cleanup may be necessary including running scripts against records or conducting manual record re-duplication.
Well-Managed CRM Database Performance
It’s a common misconception that data migration for a customer management system is a basic technical task of mapping and transferring data from old fields to new fields, but it usually requires numerous business decisions and shouldn’t be left on the shoulders of the technical team.
The many components involved in CRM data migration and Customer Relationship Management strategy, including understanding legacy system uses cases, preparing teams and business users for the change, structuring the CRM properly, evaluating data maps at the business level, extracting and prepping source data, merging records, and performing tests, ensures that the new system is integrated successfully and isn’t rejected by users.
Data Entry Outsourced (DEO) has earned a reputation for being reliable data management and data migration service provider. A decade of experience in the forefront of the industry and a proven history of successfully executed data management campaigns for a global clientele makes DEO a trusted international data management partner.
– Data Entry Outsourced
Disclaimer:All the product names, logos, trademarks, and brand names are the property of their respective owners. All the products, services, and organization names mentioned in this page are for identification purpose only and do not imply endorsement.