- This playbook is designed for beginner to intermediate CRM administrators.
The objective of this playbook is to:
- Develop a CRM data management strategy
- The DemandTools modules referenced in this playbook are for versions 5.X.X. Please be sure to update your software.
- We recommend setting up a sandbox environment to test data manipulations prior to implementing them in a production environment. You don’t want to make changes that adversely affect your CRM data quality.
- If you are trying to solve a specific data problem, review the product training documentation in the Validity Help center or get answers to your questions from Validity’s data experts during office hours.
- For technical issues regarding your software, please contact Validity support.
A data management strategy isn’t just for large companies with big budgets. Startups and smaller organizations can benefit greatly by establishing a clear strategy that allows you to scale and has flexibility to handle new data requirements as they arise.
- If you have an established data management strategy that works for your business, a quick review of this playbook can help you determine how to get the most value from DemandTools and GridBuddy Connect.
- If you are looking for help to develop a data management strategy, apply the guidelines in this playbook based on what makes sense for your business. Consider your present situation and needs, but also think about how the guidelines can support you as your business evolves and grows.
While there are other aspects of CRM data management such as data architecture and data security, this playbook is focused on developing the organizational structure, data policies, and procedures to obtain and maintain a clean CRM database.
A clear data management strategy will help to improve your CRM data quality and support achieving your desired business outcomes. With a clean CRM database, many businesses achieve better:
- Accuracy in sales forecasts and reporting
- Data privacy compliance
- Targeting for marketing
- Operational efficiency
- Creating a CRM data management strategy
- Leadership support
- Data governance
- Data quality and management
- What to do next
- Data governance: Developing and implementing data policies and procedures to support business goals.
- Data quality: Accurate, complete, reliable, and actionable data.
- Data standardization: Applying a common and consistent data format.
- Data hygiene: The process of cleaning data to reduce errors and improve data quality.
There isn’t a one-size-fits-all data management strategy because data needs vary based on your industry, business model, company goals, privacy laws, company size, and other factors. But there is a data strategy framework in which a data management strategy can be built upon for businesses of all sizes. Your data management strategy will be built on three foundational pillars:
- Leadership support
- Data governance
- Data quality and management
Without buy-in from every level of your organization, you’re at higher risk of data management processes becoming derailed by leadership-directed initiatives and team misalignment. When all parties within the business are clear on the need for high quality data and trust the process in place to maintain it, you’re less likely to run against challenges in your quest to have first-class data.
- Identify leaders within your organization who can help champion your CRM data strategy efforts. Look for senior managers, executives, and department managers within marketing, sales, customer service, finance, and product.
- Your leaders should have a vested interest in improving the business outcomes related to having a clean CRM database.
- Talk to the leaders about building a CRM data management strategy and strive for consensus and cooperation on aligning the strategy with company goals.
- Most leaders understand the importance of clean, quality CRM data and aligning your strategy with company goals will likely get more buy-in.
- Create a basic strategic framework and project plan based on the guidelines within this playbook to share with senior managers and executives.
- A plan with specific steps to take and clearly defined objectives and roles will likely get more buy-in.
Your data governance strategy starts by forming one or two governance teams. Each team has different roles in your data management strategy, and it is up to you to determine what governance structure is feasible for your business.
- Create a data management steering committee
- A steering committee is comprised of senior managers or executives who are responsible for determining strategic data policy and provide oversight and support for your data quality initiatives. They oversee and support the data governance team.
- A steering committee allows important business requirements to be prioritized and communicated to the data governance team. Should a strategic direction change occur, information is shared with the governance team to help determine what process and data management changes are required.
- Create a data management governance team
- Your data governance team is comprised of stakeholders from departments such as marketing, sales, customer service, finance, and product. Each team member provides input and feedback on current and future processes and helps to define team data needs.
- A collaborative process gets people involved in making decisions that impact their departments, helps to increase buy-in, and increases the likelihood your data policies and procedures are followed.
Your governance team will:
- Define and align data management goals with company goals
- Ensure compliance with data privacy laws
- Define data requirements
- Create data management processes
- Work with the CRM administrator on implementing data quality procedures
- Prioritize data initiatives
If it isn’t feasible to have a separate steering committee and governance team, strive to add at least one senior manager or executive to your governance team as an executive level sponsor. Their role will be to provide governance team oversight, communicate with executives on the team’s behalf, and ensure there is alignment with your company’s goals.
Governance team operations
Your governance team needs to decide how it will operate. Data governance requires an ongoing commitment because your business and data needs evolve and grow. At your first few governance meetings, be sure to:
- Discuss your governance team goals so you are all aligned as to the team’s purpose.
- Decide on meeting frequency. In the beginning, you may want to meet more frequently until you have a firm and clear strategy in place.
- Determine a communication plan for disseminating important information within the governance team, to each department, and to the steering committee.
- Review your data strategy framework and project plan. You may want to discuss:
- How to achieve team alignment and improve CRM adoption.
- How trustworthy your data is in its present condition and what steps you can take to make it more trustworthy.
- Where your organization wants to be in the future.
- How to train employees during the implementation phase.
- Reasonable timelines given any resource constraints and other ongoing company projects.
- Discuss an ongoing operating system. Your operating system determines how data initiatives and change requests are reviewed, decided on, and prioritized.
- Create a dashboard to show stakeholders what is being worked on and what has been completed. It provides visibility to all stakeholders and helps to prioritize data initiatives.
- Consider using an iterative approach where work is implemented in phases and split up into manageable 2 – 4 week working segments within each phase.
- Discuss how your data management initiatives and change requests will be requested and tracked.
- Discuss how urgent data requests will be managed.
Define data management goals
Discuss your data management goals with your data governance team and align them with your company’s business goals. Use current KPIs to help measure your data management strategy’s effectiveness.
For example, your company goals may be:
- Increase market share
- Improve sales forecasts and pipeline management
- Increase customer satisfaction
- Decrease churn
Your data management goals may be:
- Improve CRM adoption
- Enable effective decision making
- Build scalable and effective processes
- Improve data accuracy and operational efficiency through automation
Understand data privacy laws
Complying with data privacy laws must be considered when developing your data management strategy. Depending on the law, certain information about the business relationship, consumer consent, and consumer requests for data deletion (among other data requirements) may be required in your CRM. The following data privacy laws may apply, but you should contact your legal counsel for a comprehensive understanding of which data privacy laws apply to your business.
- General Data Protection Regulation (GDPR)
- Canada’s Anti-Spam Law (CASL)
- Controlling the Assault of Non-Solicited Pornography and Marketing Act (CAN-SPAM)
- California Consumer Privacy Act (CCPA)
- China’s Personal Information Protection Law (PIPL)
- Brazilian General Data Protection Law (LGPD)
When determining your data requirements, be sure you include your list of data required by any applicable privacy laws. This list of data requirements can also help you prioritize data management initiatives.
Determine data requirements
As you begin the requirements gathering process, it is important to first gain a full understanding of what the current state of your data issues are and how the data is used through your sales cycle and customer journey.
- Is the data trustworthy?
- Are reports accurate and reliable?
- Are you capturing the right data?
- Are you asking for too much or too little data?
- What level of data cleanup is required on a day-to-day basis?
Once you have a good understanding of your current state, you need to decide what the data requirements for each stakeholder will be going forward that aligns with your business goals. During this process, it is important for each member of your governance team to communicate what data is important to them and discuss why it is needed.
- If you are unsure where to start, use your list of current data used in your business. Organize the list by CRM object, department, or other use categories like customer engagement, financial, business decision support, and marketing segmentation.
- Review the list and discuss the data requirements for moving a prospect through each stage of your sales cycle.
- Note any new data requirements based on your review.
- Note any data that isn’t needed.
- Note which data is required or “nice to have”.
- After creating your list for the sales cycle, repeat the same process for moving a customer through each stage of its post-sale journey.
- Include any data required by applicable data privacy laws.
Most businesses want to have as much data as possible, but it’s important to think about how the data will be input and managed. The more data you have in your CRM that is missing or invalid can work contrary to your business goals, so strive to minimize data requirements as much as possible.
Expect your data requirements to evolve with your business, so be sure to review requirements regularly in your data governance meetings to ensure they are supporting your business goals.
Determine data management processes
Once you have your list of data requirements, you need to develop the processes for managing them. And everyone in your organization has a role to play in the data management process. A guiding philosophy of data management involves both proactive and reactive strategies.
- Proactive data management involves quality controls put in place for when the data is input into your CRM.
- For example:
- A picklist of correctly formatted states or provinces is provided for someone to select instead of having a text field where the state or province is entered manually.
- When selecting the state, the country and region automatically populate in the correct format.
- For an opportunity to be entered into your CRM, your CRM requires certain fields like the account name and business address (etc.) to be entered into the system. If any of the required fields are not populated, an opportunity cannot be created.
- Your CRM administrator creates role-based field permissions access which restricts field editing capabilities to reduce the likelihood of data editing mistakes.
- Your CRM administrator or marketing representative uses the DemandTools Import module to insert leads from a recent trade show and standardizes the state, country, and phone numbers as they import, making sure the data is clean and consistent.
- For example:
The goals of proactive data management processes should be to make it easy for people to input and manage their data, and to use your CRM’s technical configuration and business rules to enforce data standards.
- Reactive data management involves data quality procedures put in place after data is input into your CRM.
- This work is usually performed by your CRM administrator, but it may also be done by people in marketing, sales, customer success, and finance for specific accounts, leads, or contacts as they manage their day-to-day activities.
- For example:
- The primary contact on an account leaves the company and the customer service representative changes the primary contact to another person on the account.
- A sales representative notices that an opportunity has an incorrect currency and changes it to reflect the correct currency.
- Your CRM administrator uses DemandTools to identify duplicate contacts and accounts and uses the Dedupe module to merge duplicate records.
- Your sales, marketing, and customer service teams use GridBuddy Connect to improve CRM adoption, productivity, and simplify data management for existing opportunities, accounts, and contacts.
Strive to have clear roles, responsibilities, and user workflows when developing your processes.
- Start with your sales cycle and go through each stage of the process.
- Use your list of required and “nice to have” data requirements.
- Identify the role of the person inputting data and the technology used to support them.
- In some cases, your prospect or customer may be responsible for inputting data into a form or providing information verbally.
- Apply a proactive or reactive label to the different points in the process if needed.
Consider scalability as much as possible for the current growth stage of your organization and where you plan to be in the future. It’s not always easy to predict, so expect things to change as your company evolves and try to be proactive when recognizing processes that need to change.
You will want to know the answers to these questions:
- What data is required at each stage to move a prospect through the sales cycle?
- What data is required at each stage to move a customer through their post-sale journey?
- How and when will data be cleaned and maintained by your CRM administrator going forward?
- What can be automated both proactively and reactively using your CRM, DemandTools, and GridBuddy Connect?
- Which GridBuddy Connect data productivity grids are needed for each department or role?
- Can we improve mobile friendliness to help with CRM adoption?
- What reports can we produce from the data?
- How will we train our staff on new processes and procedures?
Tip: Prior to implementing processes and automation in your CRM production environment, test out your ideas in a CRM sandbox environment. You want to be sure that all changes work as expected. Testing may also help identify additional opportunities to improve workflows.
Data quality and management
While everyone in your business has a role to play to effectively manage your CRM data, the CRM administrator has most of the responsibility for cleaning and maintaining the data on a day-to-day basis.
The processes created with the data governance team will guide you on how and when data should be cleaned and maintained. As you go through the process of cleaning data, be sure to note the types of problems you encounter that should or could be addressed proactively during data entry. You will want to share this information with the governance team to discuss changes to procedures, required fields or improve employee training.
Start implementing your data management processes to proactively and reactively manage your data using your CRM, GridBuddy Connect, and DemandTools.
Configure or update your CRM based on the new data requirements and processes.
- Create new data fields and deactivate old data fields that are no longer of use.
- Enforce required fields needed to effectively move a lead through your sales cycle and a customer through its post-sale journey.
- Add system automation rules to pre-populate data fields.
- Add picklists to reduce manual data entry errors.
- Create new or edit existing workflows.
- Create new or edit existing reports.
Provide easy access to important business information so malformed or missing data can be fixed before it negatively impacts your business.
- Create your data productivity grids using GridBuddy Connect ReadyGrids for the different departments or roles involved in proactive data management (e.g. sales, sales operations, marketing, customer service, and finance).
- ReadyGrids are pre-configured grids that you can use right away and may only require minimal customization to meet your business needs.
- You also have the option of creating your own custom data productivity grids.
- Implement monthly data quality checks for each role.
Data quality issues occur in every organization no matter how good you are at proactively managing data. The good news is that DemandTools can automate fixes for many common data problems, which allows you to focus less on fighting data fires each day and more on helping your business grow. Use DemandTools to manage your data quality proactively and reactively.
- Implement your new data import procedures.
- Define, provide access, and train roles responsible for importing data.
- Apply data standardization to imported leads.
- Merge duplicate records upon import.
- Automate regular imports, updates, or upserts.
- Assess your data quality health using the Assess feature at least once per month or quarter to identify new data problems across objects and track your data cleanup efforts.
- Standardize your data based on internal naming conventions and formatting policies.
- Standardizing data will fix some malformed data problems.
- Deduplicate your records
- Ensure your data governance team has agreed on what a duplicate record means for your business. You don’t want to merge duplicate records incorrectly and miss out on sales.
- Fix malformed data including invalid email addresses
- Address missing data
- Automate data cleaning scenarios
- Implement your data management strategy as determined by your data governance team.
- Assess your CRM data health and verify email addresses.
- Clean your data using our data playbooks.