Sales and marketing teams can unlock all of the benefits of Salesforce Analytics with these seven best practices. To maximize the easy-to-use AI tools for a business, consider intelligent market monitors that process the billions of external buying signals that matter.
What's inside this article:
- The Seven Best Salesforce Analytics Practices to Know
- Find App Structure Through Patterns
- Take Advantage of Unique Salesforce Analytics Capabilities
- Salesforce Analytics With and Without Data Scientists
- Salesforce Analytics Can Go Further
Salesforce Analytics Is Approachable AI
Businesses of any size or industry can use Salesforce Analytics to uncover meaningful insights about their CRM data. By using a set of intelligent tools powered by AI technologies like machine learning, natural language processing, and computer vision, Salesforce offers its users the opportunity to learn more about their teams and clients. When sales and marketing teams have the best tools, they can perform their best work—but everyone from salespeople to team managers needs to know how to use these features correctly. Following a few best practices ensures that everyone is on the same page.
Transform Data Into Insights With Salesforce Analytics
Salesforce Analytics is particularly helpful for transforming data into insights that can be used to face complex business challenges. Managers can build flexible and advanced visualizations to better understand salespeople's performance. And clients receive more custom recommendations for their needs because salespeople understand exactly what to anticipate in the sales process. Suggestions like these are all based on previous deals and client interactions that the AI learns to build predictions from to offer intelligent sales insights.
The Seven Best Salesforce Analytics Practices to Know
Having the right AI tools is important. But that alone won't accomplish anything meaningful for teams or clients. Knowing the seven best practices to Salesforce Analytics is a good first step in unlocking the potential of using intelligent features in the sales process. They're listed below and the following sections go into more detail as to how teams could benefit from them.
- List and rank priorities.
- Empathize with the user.
- Visualize to answer questions.
- Find app structure through patterns.
- Take advantage of unique capabilities.
- Work with quality data.
- Maintain through regular reassessment.
#1: List and Rank Salesforce Analytics Priorities
Before kicking off an AI analytics project, a business needs to identify, list, and rank its priorities. If this is their first time using Salesforce Analytics features, it's the perfect time to clearly isolate the AI goals of your business. The key to starting is finding the dashboards and models that can be built quickly and have the highest sales impact. This is imperative for showing the effectiveness of Salesforce Analytics and fully integrating it into the standard workflow.
Use Design Advice From Salesforce
Director and former Senior Product Designer of Salesforce Analytics UX, Eddie Picot and Anwesha Samanta shared design best practices to a group of trailblazers. They describe a well-functioning analytical app and dashboard design process as having three steps that continually move through each other. Empathy, ideation, and finally, communication and testing. When these intelligent tools can seem overwhelming, simple breakdowns like this help make sense of Salesforce Analytics.