Best Practices of Data in Salesforce AI

By Miko DiGiacomo-Castillo | Updated November 9, 2020

The six best practices of data start with identifying business goals and focusing on privacy to ensure that a team is getting the most value out of Salesforce AI. Add intelligent market monitors to the Sales Cloud dashboard to maximize CRM investments.

What's inside this article:

Salesforce AI Is Intelligence for Sales Cloud

Salesforce AI powers the Einstein platform for customers interested in using intelligent tools in their CRM. What makes Salesforce AI the perfect addition to the standard CRM experience is its access to the valuable data already being collected. This information is so important that this post is dedicated to understanding why and exploring the best practices on how to use it. First, some reflections on what makes data so relevant these days.

Data Adds Value to Business

The truth is in the numbers. Big Data has seen major deals in the last two years, which speaks to the awareness that companies like Salesforce, Google, and Microsoft have for what's coming next in the future of work. Beyond acquisitions, data can become a valuable component for small businesses who want to grow or for sales teams that want to work more efficiently. Data can become the key to streamlined workflows, increased revenue, and higher client engagement—but only if it's handled correctly.

How Do You Manage Data Effectively?

Effectively managing data means understanding the information collected, having it accessible to the team, regularly updating the process for quality, and using the right data management tools. When teams have the right digital tools, they accomplish their most successful work. The same applies to the best practices of data in Salesforce AI. With the right understanding and coordination, any business can favorably apply Salesforce AI to improve its use of data.

There Are Different Types of Data

The Salesforce Sales Cloud is an example of a CRM that expertly collects appropriate data points so that a business can build positive client relationships. Salesforce has made large investments to ensure that this data is not only collected but that there is a way for customers to meaningfully interact with it. To prepare for the six best practices of data in Salesforce AI, this post will first go over the three types of data that Salesforce customers may interact with on the dashboard. With this understanding, a business will be more equipped for implementing the best data strategies.

#1: Start With Demographic Information

Demographic information is some of the most basic data on CRM. May it be age, location, or gender, businesses can quickly organize customers for better analysis. In terms of Salesforce AI, this data can be used to build custom predictive models for assessing which actions affect certain segments in specific ways. With the right information and intelligent tools, businesses can make their data more valuable.

#2: Behavioral Information Answers More Questions

After demographics, behavioral information gets into more of the nuances of business. These data points can inform marketing teams on campaign engagement with how many times an email is opened or how often content is downloaded. In the Total Guide to Salesforce AI, what it means for Einstein's features to offer data-driven insights is explored. These intelligent recommendations are largely instructed by this type of behavioral information.

#3: Get the Full Picture With External Data

The type of data that could perhaps best equip a team is the external data signals that aren't necessarily available on Salesforce. This data is composed of everything that goes on outside of a business and still affects clients. As mentioned, Salesforce is excellent for collecting demographic and behavioral information, but for the most comprehensive external data, businesses may want to look elsewhere. Intelligent market monitors have seamless integrations into the Sales Cloud dashboard to ensure that their CRM is fully connected to their industry and beyond.

Know the Six Best Practices of Data

Data can elevate workflows and empower employees to make better decisions. Below is a collection of the top six best practices of using data in Salesforce AI.

  • Identify business goals.
  • Ensure data protection and security protocols.
  • Make data accessible to the team.
  • Automate basic processes.
  • Regularly check and update data practices.
  • Use trustworthy software.

Best Practice #1: Identify Business Goals

Always start with the end in mind. What KPI is the team focusing on? How can data help? Truthfully, data will be at the core of almost every use case for Salesforce AI because every intelligent tool starts with quality data.

Best Practice #2: Ensure Data Protection and Security Protocols

Discussions on data aren't far off from conversations about privacy in the digital age. Salesforce has committed itself to ethical data usage with their self-described "robust and flexible Salesforce security architecture". This means customers can confidently use the platform for their CRM needs. This trust should be extended to a business's clients to ensure data best practices.

Salesforce AI Is Ethical AI

While on the topic of making responsible decisions, consider the importance of ethical AI. Salesforce has transparently aligned itself with AI features that are not only valuable to business but accountable and inclusive to all. In the list of Important AI Features for Salesforce Customers, this commitment to customers and a more equitable future of work is highlighted.

Best Practice #3: Make Data Accessible to the Team

The most beneficial tools and techniques are the ones that actually get used. That's why data needs to be accessible to the people who can best work with it. This will depend on the business and may be a team's data scientist or a designated sales manager or member of the marketing team. There will most likely be many people who fit this role—they should each understand the data they're working with, what digital tools are available to them, and how they work.

Best Practice #4: Automate Basic Processes

Embracing automation is the fourth recommendation on the best practices of data in Salesforce AI. With data comes data-entry. Consider a salesperson who spends hours a week inputting relevant client information instead of actively building positive client relationships. By setting up deliberate workflows to automate certain processes, a team could be focusing on more high-value tasks.

Best Practice #5: Regularly Check and Update Data Practices

The key to any efficient work process is regular assessments and readjustments. After first identifying a team's business goals, how well have they been met after a few weeks or months? Is Salesforce AI providing all of the features needed or should the team add external intelligent tools? With regular checkups, it can be ensured that the systems are in working order.

Best Practice #6: Use Trustworthy Software

Wrapping up the best practices of data in Salesforce AI, it's recommended that businesses use trusted digital platforms and tools. Sales Cloud users feel confident in their CRM choice so if Salesforce customers can know they're in a good place to either start or continue the efficient use of client data. As mentioned, intelligent market monitors are excellent additions because they analyze the billions of external signals that otherwise go missed.

Clean Data Leads to Intelligent Insights

With this best practice knowledge of data in Salesforce AI, a brief review of what kind of intelligent insights are available from it is also helpful. Lead scoring is one of the most valuable techniques for salespeople and it's only possible with accurate data. Both demographic and behavioral information is required to create meaningful lead scoring predictions.

Salesforce AI Without Data Scientists

An article about data and Salesforce AI would not be complete without some mention of data scientists. Are they required to use AI? Not entirely. While qualified data scientists do wonders to create meaningful insights, they are not essential to using Salesforce AI tools.

Salesforce AI With Data Scientists

Although data scientists are not required for using Salesforce AI features, they are still valuable additions. Many data scientists spend most of their time cleaning and organizing data. With a platform like Einstein, they can focus more on building custom models and less time preparing data. As mentioned, when people have the right tools, they accomplish their best work.

Data at Its Full Potential

As you adopt the best practices of data in Salesforce AI, consider adding intelligent market monitors to ensure you're using the right tools. At Aptivio we understand how to harness the power of sales AI to fuel efficient growth. Our Aptivio Intelligent Market Monitor for Sales creates simple dynamic digital sales playbooks from billions of buying signals to maximize your CRM investment.

Transform the performance of your business with action cards at the point of impact generated by analyzing 150 signals from billions of external data points. With seamless integration directly into Salesforce, Aptivio is the missing link to creating a digital sales machine. Ready to learn more? Get started for FREE today!

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