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The Role of a Salesforce Metrics Dictionary in Promoting Team Cohesion

To understand Salesforce metrics challenges, let’s evaluate a common situation. Your executive leadership asks Sales, Marketing and operations to present last quarter’s results. Everyone shows up with slides and reports pulled from Salesforce or a Business Intelligence platform like Tableau. Frustration grows, as presented numbers and statistics may not align or contradict each other. Instead of discussing strategy and tactical adjustments to improve performance, time is wasted asking for clarification on the validity of information. If this sounds like your experience you are not alone. Prioritized, correct, and consistent information does not happen overnight. In this article we will explore our approach to help create a better foundation, working with the people, process, and technology you already own.

Salesforce Metrics Meeting

Most enterprises have multiple sources and approaches to acquire data and transform it into information. We love Salesforce because of the relative speed and ease to build and make changes to process, with clear and easy reporting. There are over 150K organizations like yours that have standardized marketing, sales and/or revenue operations on the Salesforce platform. So why would a team with a system of record and  “source of truth” from Salesforce still struggle reporting and understanding and maintain continuity of information as change happens?

Avoiding people, process, and communication blame game

If you have been a part of reporting and analytics initiative that goes sideways, it’s sometimes based on these factors:

  • Flawed requirement gathering
  • Change management or lack thereof during implementation
  • Incomplete or incorrect definitions
  • Lack of consensus across lines of business for goals and metrics
  • Data completeness, availability, and quality

Building an inventory of metrics and KPIs can be an exhaustive process leading to gaps in requirements as a result of not having the right people or experience on hand. In other cases, data quality and availability becomes a friction point that leads to failure. Modern data and analytics technology will help you move faster, dig deeper, model and blend data but not solve un-resolved definition and alignment problems.

In many organizations, there isn’t a solution in place to maintain a unified record and historical log of goals, metrics and data relationships together. Documents, PowerPoints and Excel are typically the system of record for metrics and KPIs until they are coded into data and analytics tools.

If your previous data lake, analytics, and business intelligence initiatives fell short, the blame is all to often put on process, people, and communication often encapsulated sometimes as “poor requirement gathering”. Experienced and tenured data and analytics leaders understand this excuse wont fly in 2024, so our team learned into these challenges to see how we can help!

Our DataTools metrics glossary approach

1. How do we capture and encapsulate the previous work that has happened inside of Salesforce to understand existing metrics and KPIs are adopted and in-use?

2. From this understanding, what is the knowledge that we need to capture and resulting information assets that we need to produce and distribute? One of those key information assets is Salesforce Metrics Documentation

.3. Eliminate most if not all of the manual and redundant work that typically occurs between teams that can be easily extracted from Salesforce metadata?

4. Knowing that this is a live, organic, information asset how do we understand and surface changes that stakeholders should be aware of?

From those questions, we constructed our vision of a metrics glossary that not only captures the metrics but all of the relationships that stem from those metrics.

Lean more about DataTools Pro

Automated Salesforce Metrics Glossary

We took these questions and built a Metric Analyst tool that attempts to automate most of the process.

Live Salesforce Metrics Documentation

One of the important pieces of information that anyone in your enterprise wants to know is “what’s important”? A metric and KPI glossary can exist as a word document, spreadsheet, email, or application that organizes the business definitions. Salesforce metrics documentation should inventory the definitions semantics for metrics where data originates in Salesforce. This document should serve as a knowledge asset and guide to to help cross organization collaboration for business, data, analytics, and technology teams. When properly implemented it should ensure everyone speaks the same, specific language in business terms. A metrics glossary can also include technical / data details to help understand some lineage details.

What are Salesforce metrics?

Salesforce metrics are quantifiable measurements that track business processes, and activities that occur in Salesforce. Salesforce is much more than a customer relationship management platform. Some companies run their entire end to end operations on Salesforce. A metric can encompass anything from sales pipeline health to customer support resolution times. However, with a vast amount of data and numerous metrics available, ensuring consistent understanding and interpretation becomes crucial. Learn more: Analytics, Metrics and AI. Oh My!

Why do you need a Salesforce metrics dictionary?

Let’s revisit the scenario at the beginning of this article. If we take a simple measurement for “Lead conversion”, you can imagine the many variations and iterations of this metric. For example marketing could consider a marketing qualified lead, where sales considers “sales qualified” leads. Conversationally they can be interchanged, but at an organizational level, this misunderstanding could be simple semantics and labeling. A Salesforce metric dictionary acts as source of truth ensuring everyone speaks the same language when clarity and precision is mandatory.

  • Standardization: Defines clear and consistent definitions and calculations for all metrics.
  • Improved Communication: Eliminates confusion and fosters better collaboration across teams.
  • Enhanced Data Accuracy: Reduces errors by ensuring everyone uses the same metrics and formulas.
  • Streamlined Analysis: Makes data analysis faster and more efficient by providing a central reference point.

What Does a Salesforce Metric Dictionary Include?

An effective Salesforce metric dictionary should encompass the following key components:

Mandatory definitions that are managed and governed across lines of business

Metric Name: The name of the metric, clear and concise. There should be 1, official name that ties to a definition. If there are multiple names for the same metric, that is captured and tracked independent of the official name.

Definition: In simple terms what is the metric measuring. This definition may require some detail to how it is calculated but should be readable and understandable to business information consumers and owners.

Ownership: Who is the person ultimately responsible for the metric? The premise is that if there is no clear ownership and accountable person to sign off or accountable for the metric then it shouldn’t be managed.

Important context and ownership information to support usage of definitions

Description (optional): A detailed explanation of what the metric measures and its significance to your business goals. In a world with AI agents, my recommendation is the longer the description and the more context, the better!

Calculation (optional): The specific formula or steps used to calculate the metric. This ensures everyone understands how the value is derived. This work can be time consuming and requires salesforce admins to acquire these definitions.

Target Value/Benchmark: (optional): A target or benchmark to measure your metric against is common practice. Not all metrics will have a target, but a KPI absolutely should!

More reading on metrics, OKRS and KPIs: Analytics, Metrics and AI. Oh My!

Salesforce Metrics Dictionary Template

While Salesforce doesn’t provide a built-in metric dictionary, you can create using a spreadsheet tool like Microsoft Excel or Google Sheets, and now a live connected Metric Dictionary like DataTools Pro. The following table showcases a sample structure:

Additional Tips for Managing Salesforce Metrics

  • Maintain and Update: Schedule regular reviews to assess the dictionary’s accuracy and completeness. As Salesforce evolves and your business needs shift, update metric definitions, calculations, and target values to reflect these changes. This is an important component for information stewardship, governance, and safeguarding the integrity of your organization’s management information systems.
  • Access and Distribution: Don’t let your metric dictionary become a hidden and outdated document. Share it widely with all Salesforce users – sales reps, marketing teams, customer service agents, and anyone who interacts with your CRM data. This is a big part of fostering a culture of data literacy and ensures everyone interprets metrics consistently.


By implementing a Salesforce metric dictionary, you empower your organization to leverage the true potential across teams and lines of business using a language that should be universal (business performance and outcomes). Standardized metrics ensure clear communication, accurate analysis, and ultimately, data-driven decision-making that fuels business success. Here are some resources to help you take control of your Salesforce metrics today and unlock the key to a more informed and strategic CRM strategy.

author avatar
Ryan Goodman Founder
Ryan Goodman has been in the business of data and analytics for 20 years as a practitioner, executive, and technology entrepreneur. Ryan recently created DataTools Pro after 4 years working in small business lending as VP of Analytics and BI. There he implanted an analytics strategy and competency center for modern data stack, data sciences and governance. From his recent experiences as a customer and now running DataTools Pro full time, Ryan writes regularly for Salesforce Ben and PactBub on the topics of Salesforce, Snowflake, analytics and AI.