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Salesforce Einstein: Using AI for Sales, Service, and Marketing

In today's rapidly evolving business landscape, staying ahead of the curve requires using new technologies to optimize operations, enhance customer experiences, and drive growth. Salesforce Einstein, the AI-powered engine within the Salesforce platform, helps businesses handle sales, service and marketing work with less manual effort.

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A practical framework for Salesforce Einstein.

Oblytech and Salesforce Field Service Lightning

Write the sales, service or marketing question in one sentence before choosing a prediction, since a vague question produces scores nobody acts on.

Define the question and scope

Record which predictions a sales or service team may act on directly and which ones need a manager to review them first.

Make decisions and ownership visible

Field work tends to stall at the scheduling step, so map who assigns jobs, how technicians confirm arrival and what the customer sees at each stage.

The practical framework

In today's rapidly evolving business landscape, staying ahead of the curve requires using new technologies to optimize operations, enhance customer experiences, and drive growth. Salesforce Einstein, the AI-powered engine within the Salesforce platform, helps businesses handle sales, service and marketing work with less manual effort.

Understanding Field Service Management and Salesforce FSL

Field Service Management (FSM) encompasses the strategies, processes, and tools used to manage and optimize a company's field operations. This includes scheduling and dispatching technicians, tracking work orders, managing inventory, and ensuring customer satisfaction.

Salesforce Field Service Lightning (FSL) is a comprehensive FSM solution built on the Salesforce platform. It provides a unified platform to manage all aspects of field service, from scheduling and dispatching to work order management and customer communication.

Key Features of Salesforce Field Service Lightning

FSL offers a rich set of features designed to streamline field service operations and enhance efficiency:

  1. Intelligent Scheduling and Optimization:
    • FSL uses advanced algorithms and real-time data to optimize schedules and routes, ensuring that the right technician is assigned to the right job at the right time.
    • This can help reduce travel time and fuel costs, and can help improve first-time fix rates.
  2. Dispatcher Console:
    • The dispatcher console provides a centralized view of all field service operations, enabling dispatchers to manage schedules, track technicians, and respond to service requests efficiently.
    • Real-time visibility into technician location, availability, and skills allows for optimized dispatching and resource allocation.
  3. Mobile Workforce Enablement:
    • The FSL mobile app gives technicians access to critical information, including work orders, customer details, knowledge articles, and inventory data, directly on their mobile devices.
    • This enables them to complete tasks efficiently, access relevant information on-site, and provide timely updates to customers.
  4. Inventory Management:
    • FSL provides tools to manage inventory levels, track parts usage, and ensure that technicians have the necessary equipment and supplies to complete their jobs.
    • This can help reduce delays and inventory costs, and can help improve first-time fix rates.
  5. Customer Communication and Engagement:
    • FSL enables proactive communication with customers, providing them with real-time updates on appointment scheduling, technician arrival times, and service completion.
    • This can support customer satisfaction and trust.
  6. Analytics and Reporting:
    • FSL provides comprehensive analytics and reporting capabilities, allowing you to track key performance indicators (KPIs), identify areas for improvement, and make data-driven decisions to optimize your field service operations.

Maximizing the Benefits of Salesforce Field Service Lightning

To get the most out of FSL and achieve maximum efficiency, consider these strategies:

  1. Define Clear Objectives and KPIs:
    • Clearly define your goals for implementing FSL and identify the key performance indicators (KPIs) you'll use to measure success.
    • This will help you track progress and make adjustments to your strategy as needed.
  2. Customize FSL to Your Needs:
    • Configure FSL to match your specific business processes, workflows, and industry requirements.
    • Customize objects, fields, and workflows to tailor the platform to your unique needs.
  3. Integrate with Other Systems:
    • Integrate FSL with your other critical business systems, such as ERP, CRM, and accounting software, to create a unified ecosystem and streamline data flow across your organization.
  4. Equip Your Mobile Workforce:
    • Provide your technicians with comprehensive training on the FSL mobile app and encourage its adoption.
    • Ensure they have access to the latest mobile devices and connectivity to maximize efficiency in the field.
  5. Use Automation:
    • Automate tasks like scheduling, dispatching, and work order creation to reduce manual effort and improve efficiency.
    • Use workflow rules and process builders to automate tasks and streamline processes.
  6. Monitor and Optimize Performance:
    • Continuously monitor your FSL instance's performance and identify areas for optimization.
    • Utilize Salesforce's reporting and analytics capabilities to track KPIs and make data-driven decisions to improve efficiency.
  7. Foster User Adoption:
    • Provide comprehensive training and support to your team to ensure they understand and embrace FSL's capabilities.
    • Encourage feedback and address any challenges to ensure user satisfaction and adoption.

Oblytech and Salesforce Field Service Lightning

Oblytech can help you implement, customize, and optimize Salesforce Field Service Lightning so it supports your business goals. Our team works with you through each stage of the process, from planning the setup to reviewing how it runs in daily use.

Common questions

Questions teams ask about Salesforce Einstein

What should a review establish first?

Start with the question, scope, decision boundary, ownership, and evidence that the reader needs to carry into the next action.

How should the next action be chosen?

Use the article framework to identify the unresolved decision, the responsible group, and the record that will show what happened.

The documented boundary

This article is presented as a practical reference. Apply its recommendations against the specific systems, responsibilities, and evidence available in the organization being reviewed.

Choosing the first Einstein use case

Many Einstein features read the records already in the org and return a prediction, a ranking or a suggested action. That makes the first choice a question about the decision a person has to make and the data that decision depends on. A team that wants better forecasts has a different first step from a team that wants faster case replies, even though both are reading the same platform.

The table sets out how the common first use cases differ. Read each row as a question to answer before configuration starts, not as a feature to enable.

First use caseRecord the output depends onWho acts on the outputSign that it is not working
Opportunity scoring for salesStage history and honest close datesAccount executives reviewing the weekly pipelineScores move every time a stage is edited by hand
Case classification for serviceConsistent categories and resolved case outcomesQueue managers deciding routingAgents keep correcting the category after assignment
Engagement prediction for marketingCampaign membership and consent statusMarketing operations choosing the next sendSegments shrink to a handful of contacts without explanation

A sequence for the first release

A first release goes more smoothly when it follows a fixed order. The steps below keep the scope narrow enough that a problem can be traced back to one cause.

  1. Write the decision in one sentence, including who makes it and how often.
  2. List the fields the output will read and mark each one as complete, patchy or missing.
  3. Choose a small group of users who will act on the output and agree how they will record disagreement.
  4. Run the feature against a sample of past records and check whether its ranking matches what happened.
  5. Release to the small group first, then widen only after the override pattern is understood.

Step four is the one teams often skip. Replaying past records shows whether a prediction would have changed a decision that was already made, and it often reveals fields that look complete but are filled with defaults.

Signs the output is drifting from the process

A prediction can stay technically sound while the business changes around it. Watch for a rise in manual overrides, a group of users who stop opening the feature entirely, or a sudden change in the share of records the model places at the top. Each of these points to a process change, a new product or a data entry habit that the model has not seen. The fix is often a field definition or a rule change rather than a new model.

Questions after the first release

Does a prediction need to be explained to the people who use it?

It needs to be explained well enough that a user can decide whether to trust it on a given record. A short note on which fields drove the output is usually enough for a representative. Managers who approve decisions based on the output need a fuller account of how the feature was trained and tested.

Should every team switch on the same features?

No. A feature should be enabled where the decision is frequent, the data is reasonable and an owner will review the results. Turning features on across all teams at once makes it impossible to tell which one improved anything.

What if the first use case works but nobody uses it?

Adoption is a process problem more than a feature problem. Ask whether the output appears where the user already works, and whether acting on it takes fewer steps than ignoring it.

When is it sensible to wait?

Wait when the sales stages or service categories are still being redesigned, because any prediction built now will need rebuilding. A simple rule that sorts records clearly is a better first step while the process settles.

Where to take the decision next

Before anything is enabled, settle where Einstein features belong in your sales or service process. Related reading includes the Salesforce service workflow design page, which looks at case context, ownership, priority and closure evidence, and the Salesforce automation and workflows page.

A practical next step

Need a clearer operating model?

OBLYTECH can discuss the questions raised by this guide and the next review step. Use the OBLYTECH contact route to start the conversation.

Useful starting pointBring the current question, scope, ownership concerns, and evidence needs into the first review conversation.

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ServiceNow guides on this topic

Guides from the Oblytech ServiceNow library that explain the practice behind this page.