Sanjna Parulekar, Einstein & Data Cloud Marketing at Salesforce opened with, “ I spend a lot of time with customers just like all of you in this room, talking about the data problems that you want to solve. And the number one thing that stands out is that you want to connect the wealth of data that you have in different systems to your critical business actions. The time it takes to do this. It’s too costly. It’s too time intensive. You want to be able to do this with low code.”
The Secret Behind Effective Customer Interactions: Instant, Accurate Data
Now more than ever, customers want seamless and personalized experiences when interacting with businesses—across sales touchpoints, service issues, and marketing communications. They expect relevant information and instant support—highlighting the importance of real-time, accurate data.
In a visionary demo of Einstein One and Data Cloud, Parulekar painted a vision for the art of the possible.
The screen above shows rich, unified data being surfaced to a Salesforce user about a particular individual. Users can see the customer’s lifetime value score and even their likelihood to buy based on their historical behavior. The visual also shows a detailed activity feed about how the customer is interacting with the business across different channels.
Parulekar adds that “all of this is possible with Data Cloud deeply embedded inside of Einstein One.” While this dashboard view is the stuff marketers, sellers, and service agents dream of, how much of it is realizable today remains to be seen.
Nonetheless, her demo gives us an exciting glimpse into Salesforce’s thinking and future roadmap.
Integrated AI and CRM
The fusion of AI with Customer Relationship Management (CRM) offers the potential to customize communications and enhance customer experiences like never before—and this journey starts with connecting data sources.
Parulekar says, “We’ve been talking a lot about low code today. And with no code at all, you can connect to any data within your organization, whether it’s a Salesforce Cloud, an external data source like Amazon S3, or Google Cloud, or even a wide variety of connectors with MuleSoft Anypoint Exchange.”
She then shows us how easy it is to connect data by selecting a New Data Stream.
To maximize the utility of the data, it needs to be harmonized into a single data model. Diverse systems might represent the same data differently, leading to discrepancies. Harmonizing ensures that the data “speaks the same language” irrespective of its source.
“Why does this matter? This matters because when we have data in different systems, it uses different ways in different schemas to refer to a single individual. And when we bring all of this data onto the platform, now all of our data speaks the same language,” Parulekar remarks.
She goes on to say that many Trailblazers (Salesforce users) have been building powerful business process automations in Salesforce with Flows. Which brings us to Data Cloud-triggered flows.
Lucy Mazalon of SalesforceBen explains how Salesforce can trigger flows with dynamic, rich customer and company data in a matter of clicks. In sum:
When specific conditions are met in the Data Cloud’s Data Model Object (DMO) or Calculated Insight Object (CIO), Data Cloud-Triggered Flows are initiated. This happens when there’s a change in a specific data point or a calculated metric meets the trigger’s conditions.
- Data Model Object (DMO): This is a component of the Data Cloud architecture. The flow is as follows: Data Source → Data Stream → Data Source Object → Data Lake Object → Data Model Object. DMOs are akin to Salesforce objects, providing a standardized data model with preset attributes.
- Calculated Insight Object (CIO): CIOs are used to compute data, resulting in metrics at different levels including profile, segment, and population. They can process data as it streams in, almost instantly.
- Data Space in the Data Cloud: A concept that organizes your data. It divides data, metadata, and processes into categories like brand or region, letting users interact with data relevant to their specific category.
Bringing Unified Data to Powerful Workflows
Parulekar gives a low-code example of a proactive service alert, but goes on to say that with Data Cloud on Salesforce’s powerful metadata framework, dashboards are better and display real-time customer information. She adds that that makes your applications better.
Next, activating and surfacing customer data to business users without the traditional load on IT teams and data scientists.
Tableau Pulse Surfaces Real-Time Business Insights
With Data Cloud in the Einstein One platform, data across systems is immediately available in any business intelligence (BI) tool.
In the demo, Parulekar highlights Tableau Pulse.
Typically, gathering insights like the ones displayed above, take time. Your data team needs to do complex ETL, data processing, and visualization. But the problem is that by the time the data gets to business users, the insights are stale and therefore less actionable (and by extension, trustworthy).
If Salesforce is able to realize their product roadmap for Data Cloud and Tableau Pulse, they can make insights like the example above available to business users in real-time.
Conversational Data Interactions
Parulekar goes a step further by implying that the future of data interaction is not just constrained to visualization. It’s also conversational. Users can ask questions of their data with simple conversational prompts. Not unlike the options users see when they Google something and choose to expand on AI-driven results by clicking on conversational buttons.
Data Cloud embedded in the Einstein Platform is a revelation for business intelligence and personalized customer experience. However, it remains to be seen how and when companies will take advantage of these recently released and on-the-horizon features.