Let’s pretend we’re a contestant on Jeopardy!, and the answer is:
This software company was Data Cloud customer zero, giving it access to 1.2 billion customer records.
What is Salesforce?
Before becoming the fastest-growing home-grown product in Salesforce history, Data Cloud was used internally to create one unified profile of Salesforce customers from over 60 different data streams, including transactions, communication history, service requests, and more. The company reduced duplicate records and consolidated over 52% of its customer profiles.
You may be thinking, if it worked for Salesforce, then it can work for me, too. And while that may be true, a lot of the success we see companies have with Data Cloud comes from using best practices when implementing it. Here we share some of the practices that we’ve seen work for Silverline’s clients.
1. Begin with the end in mind
Starting with the end goals for your data strategy in mind is crucial for success. Gather all key stakeholders and decision makers to align on the user needs, business goals, and desired outcomes you want to achieve. Common goals we see are improving customer experiences through personalization, increasing marketing ROI through better segmentation, and driving efficiencies by consolidating data sources.
With executive sponsorship and alignment on goals, you can then work backward as a team to map out the user workflows, systems, and data required to enable those future experiences. Document the current challenges and gaps that need to be addressed and start to define high-level requirements. By centering the discussion around the human experiences you want to enable first, you lay the right foundation to then select the optimal technologies like Data Cloud to support that vision.
2. Determine where your organization is for digital transformation
Before deciding on AI and automation solutions, assess your company’s overall digital maturity. Key indicators include the state of core platforms like CRM, level of cloud adoption across business units, and data infrastructure maturity. Companies still early on their digital transformation journeys often lack the historical data, system connections, and supportive processes required to take advantage of cutting edge capabilities like predictive insights from Einstein.
Alternatively, organizations with mature clouds, APIs, and data lakes are primed for generative AI that can streamline operations. Ensure executive sponsors understand the current barriers and prerequisites needed to unlock future innovation. Then craft a multi-year modernization strategy across platforms, infrastructure, and talent to set the stage for AI augmentation over time.
3. Match your digital maturity curve with how Data Cloud can help
There are Data Cloud opportunities at all levels of the digital maturity curve for cleaner data and faster segmentation and activation. If your company is building its foundation and starting the Salesforce journey, then you need to consider unifying your data before broader implementation within the Salesforce 360.
Companies looking to take the next step with their existing Salesforce footprints should use Data Cloud to clean up and unify data for better segmentation, activation, and automation. If you’re in the more advanced stages of the digital maturity curve, then you’ll likely be using Data Cloud for efficiency gains and the “glue” to maximize ROI.
4. Review design and technical considerations
We recommend that our Silverline clients look at these four areas before they start a Data Cloud engagement:
- Individual ID: This enterprise-wide unique identifier is utilized as the Identification Number in Identity Resolution, such as the CRM Contact ID or MDM ID.
- Attributes: Verify that all the data your marketers need related to use cases is ingested into Data Cloud. Align data sources with the standard model and identify gaps to design an extended data model.
- Primary keys: If a data source does not contain a primary identifier required for a data model object, then create a fully qualified key using a formula field upon ingestion.
- Formula fields: Transform data during ingestion to optimize identity resolution, segmentation, and activation and explore calculated insights for more complex, aggregate scenarios.
5. Learn the difference between Data Cloud’s “Party” and “Party Identification”
“Party” is an attribute (field) in many DMOs. The field typically represents a foreign key to the Individual ID and is used by Data Cloud to connect that DMO’s data back to the Individual. An example is the Marketing Cloud Contact Key.
“Party Identification” is a Data Cloud Standard Data Model Object. It is used primarily for Identity Resolution rules based on Party Identification and requires mappings of:
- Party Identification Name
- Party Identification Type
- Identification Number
- Party (to relate back to the Individual)
An example of “Party Identification” is a driver’s license number, external customer ID, or MDM ID.
6. Identify your Data Cloud user experience use case
Rather than starting with the technology, anchor initial Data Cloud projects around tangible user experiences and pain points. Identify workflows disrupted by duplicate records, experiences limited by data discrepancies across systems, or decisions impacted by partial customer profiles.
Speak to frontline teams directly about their biggest obstacles and talk to customers about areas of friction. Map the user journeys and pinpoint root cause data issues. This enables you to define a targeted use case like “call center agents need household 360s during service calls to resolve customer issues faster.” Trace required data elements, integrate relevant sources, and start assembling comprehensive profiles that will transform experiences. Over time you can tackle additional use cases, proving value and driving adoption across the business.
For example, a user experience in the healthcare industry could be with a provider’s call center. The call center is receiving a high volume of calls about failed logins to its online portal, which is taking time away from more serious issues and is raising its cost to serve. Data Cloud could be used to activate digital campaigns to help deter calls from the call center, such as a website pop-up or an email journey about “need help logging in.”
7. Start small but think big
The most successful Data Cloud journeys balance quick wins with long-term vision. Resist the urge to over-engineer an elaborate data model or tackle multiple systems in early phases. Identify contained datasets like digital engagement or sales interactions to assemble an initial customer 360 foundation. As you ingest additional sources in later phases, Data Cloud’s flexible data model and management capabilities easily adapt to expanding requirements.
Focus initial user experiences on high-value roles like service agents who require unified profiles to drive material outcomes. Celebrate small milestones that accrue value while evaluating expansion areas across other teams, data domains, and use cases. Share measurable benefits with executives to secure investment in the broader program. Enlist project advocates to spread adoption. With this “thin slice” approach you thoughtfully mature Data Cloud in sync with digital transformation, scaling seamlessly from a departmental hub to an enterprise customer data platform fueling omnichannel experiences.