Category Archives: Data Governance

Data Governance: Salesforce Objects

Data Governance: Salesforce Objects

This article was posted by Sales Hacker on October 10, 2019. You can view the original article here.


Reliable data is everything…

Without it, your sales team will struggle, the pipeline will dry up, and revenue will plummet.

Any change to the business — whether its a simple change to your product’s pricing or packaging, or an acquisition requiring the integration of new CRM data with your existing data — means your system needs to be adapted.

The challenge? Being able to scale easily.

It’s not an easy task…

Which is why I’m going to outline some key concepts that should be considered and some pitfalls to avoid when designing the CRM process for a lead-to-cash cycle.

Here’s what we’re going to cover:

  • Salesforce objects
  • Why bother?
  • Sales process stages

Fair warning: This gets technical, so hold onto your seat.


Data Governance & Salesforce Objects


Whether your selling model is transactional, self-service, enterprise, or a combination of these, you will need to set up some objects in Salesforce that support the sales and eventual retention strategies of your business.

Salesforce objects can be categorized as either Transactional or Customer Records.


Customer Record Objects


Customer Records store firmographic and technographic data. Firmographic data is relevant for customer segmentation, with information about geographic area, number of employees, annual revenue, and industry. Technographic data records the types of technologies used by the business.

Some examples of Customer Record objects:

  1. Accounts
  2. Contacts

While relatively static, customer information degrades in quality at a rate of just over 5% per month, or 70.3% per year.

After a year without any activity logged on an account or contact, there is a 7/10 chance the information is no longer correct. It is likely the person has changed roles within the company, moved to a different company or location, or the company could have merged or re-branded.

Tip: A policy needs to be established around when and how frequently the accuracy of Customer Record objects are verified.

This is a non-negotiable requirement for any sales organization that intends to maintain a usable database. Come up with a plan to check and refresh existing Customer Records on an ongoing basis.

Transactional Objects


Transactional objects capture transient customer interactions throughout the sales process.

Sales cycles vary from customer to customer since budget, decision process, and timing are different in every sale. That being the case, the current state of the buyer should be reflected in the data collected throughout the sale.

Examples of transactional objects:

  1. Campaigns
  2. Leads
  3. Activities
  4. Opportunities
  6. Orders
  7. Subscriptions (custom object)
  8. Contracts

The tendency in modern B2B SaaS orgs is to record most of the transactional data in the opportunity or account objects. People often think it is extra work to separate relevant data into objects, and salespeople are commonly perceived as incapable of recording meticulous data across different object types and seeing larger purpose in the CRM system.

In reality, salespeople today are technologically adept and spend up to 30% of their time dealing with the results of inconsistent data governance. Enforcing the perceived extra work is worth it.

Sales reps would be 30% more effective if they didn’t have to navigate duplicate accounts, incorrect prospect information, and other preventable scenarios where the root cause is an incomplete or inconsistent data structure.

This is just the icing on the cake — building a scalable data model allows for accurate analysis and predictable revenue generation.

The Right Object for the Right Process


When designing this process from the ground up, flexibility should be built in. Using the right object for the right process is a key element of setting up a Salesforce instance.

Usually people are clear that leads are someone who has shown interest in the product at a particular point in time, and that they come from marketing campaigns. But sales professionals sometimes think of leads in terms of customer contact information.

The difference is that a lead object in Salesforce represents how a prospect acted at a particular point in time, whether they attended a webinar, downloaded a whitepaper, or filled out a web form.

Any good sales rep knows they should probably respond to this activity within about 20 minutes for the best chance of generating a sales opportunity.

Time for a quick math problem…

You assign a list of gated content leads with name, company info, phone number, and email addresses that downloaded a whitepaper at least 6 months ago.

After the SDR reaches out to all 100 of them, Salesforce task records show 10 people replied something analogous to “no thank you” and another 2 responded asking for meetings.

What was the response rate?


Answer: The trap answer is to calculate 12/100, for a reply rate of 12%. But remember, after 6 months of sitting on the shelf, the quality of the customer information has degraded by 30%, even though these are lead objects.

So here’s how to calculate:

You can safely assume 100 * 0.3 = 30 of your leads didn’t get the message. So subtract those 30 leads from the 100 you sent: 100 – 30 = 70 leads.

Then 12/70 = 17.1% reply rate.


This is an example of where data hygiene can impact sales process analysis.

Tip: Leads should be considered as separate objects from accounts and contacts. Assigning an SDR old leads is similar to assigning them detective work, which takes away from selling time.

Remember, a lead represents a moment in time as well as customer information, so it is a transactional object. Be careful to avoid creating duplicate accounts when converting a lead object to an account and contact object. It is okay to purge old leads!

Actions performed by SDRs and AEs are usually tracked with activities like tasks and events. These objects are essential for tracking rep activity and performance and are often used as key performance indicators (KPIs) for SDRs.

Opportunity objects are created when a prospect enters the sales pipeline. Setting up the quote object will allow for flexibility and historical context around pricing and packaging. Most are familiar with the concept of a quote and contract to finish a sale, and recording this data in Salesforce objects is vital for reporting metrics.

The concept of an order is usually brushed aside in the software industry and reserved for brick and mortar businesses.

If your business is only selling software, the system will probably work exactly as intended without the concept of an order object. But if you are selling software and services and more than one product type, orders and subscriptions will be useful for seamlessly passing “inventory” data through the system.

Most software companies sell professional services, subscriptions, or both. Companies that grow often acquire other companies, so your chances of acquiring a Salesforce instance with a different structure than your own seems likely. Incorporating these objects in the system early in a way that allows for different product types later will pay dividends.

Why Bother?


Say you’re selling a subscription-based software and your CRM process only uses lead, opportunity, and contract objects. It seems more efficient, right?

But let’s say your company is doing so well, you acquire another company that sells software and services. Now you need to merge their Salesforce account with yours in some meaningful way, and theirs was set up with custom objects to represent their particular products and services, and they export this data in a spreadsheet for finance to assign commissions and invoices.

Reconciling all of these transactions in one unifying system will take many hours. In this case, the entire process was made more difficult by both sides omitting the concept of an order object in Salesforce.

Integrating an external Salesforce instance can take 4–6 months or longer, and companies may have to seek outside consultants when there’s no bandwidth internally.

In general, the way a modern software company’s offering is packaged and priced will change over time. The underlying architecture of the sales process should be dynamic and robust from the start.

When a Salesforce instance is set up, it is usually done with a sales-centric view, for obvious reasons. It makes sense to optimize the instance for the current sales process or even for the sales process in general.

So the account and opportunity objects are heavily relied on to record customer data throughout the buyer journey, since this option offers the least complexity up-front and puts less pressure on sales reps to update the system.

The buyer’s journey is not just a sales process, though. It usually starts with some kind of campaign and ends with an exchange of cash, all managed by teams beyond sales.

Tip: You need an organizational perspective when setting up the process to record customer and transaction data as the business operates. Relying solely on the account and opportunity objects leads to chaos later on.


Sales Process Stages


Now, let’s look at a typical transactional sales process that starts with a marketing campaign and ends with a paying customer. Notice that objects to the left are highly volatile, and further to the right objects are relatively stable, maintaining their integrity for a longer time.

Salesforce Objects

In order for the lead-to-cash cycle to close, the following stages of data transfer need to be managed:

  1. Campaign > Lead
  2. Lead > Opportunity
  3. Opportunity > Order
  4. Order > Invoice
  5. Order > Commissions

There are multiple departments involved throughout the process. Marketing runs campaigns and generates leads, the sales team handles opportunities, and finance teams run invoicing and commissions. Each of these teams should be able to easily access the data they need.


How This Works for Account-Based Sales


If your company is using an account-based selling strategy, the process would need to be modified. Usually, leads are passed to an SDR team, who also have a pool of accounts and contacts to work.

Account-based selling should start with a clear picture of the accounts that will be assigned to SDRs, including how and when they will be passed leads from marketing.

It requires an even more meticulous data structure and adds operational complexity since the account objects would need to be created and filled with appropriate contacts near the beginning of the process for assignment, or created by the SDR.

Tip: Never overwrite SDR data! SDR data is typically the most reliable since they are on the front lines and their data is the most current, but they are most often junior-level employees who need training to implement a well-defined procedure.

The stages might look something like this:

  1. Campaign > Lead
  2. Lead > Opportunity
  3. Contact > Opportunity
  4. Opportunity > Order
  5. Order > Invoice
  6. Order > Commissions

Notice that the stages are the same except for number 3.

This adds a layer on the back end since contacts need to be associated with an account, which would need to be created ahead of time instead of waiting until the account management phase.

A recommendation for predictable revenue would be:

  1. Conduct a total addressable market (TAM) analysis to help guide segmentation into territories or market segments
  2. Assign the SDR teams accounts strategically

For this reason, switching to an account-based selling strategy requires extra careful preparation from an operational standpoint.


Back to You


These are the types of scenarios you need to consider when you’re trying to set up a scalable sales process for your business.

To build a dynamic and flexible system that allows you to effectively scale a business at some point in the future, you need both creativity and intuition. An attitude of, “I’ll worry about it when you get there,” will cost you.

Is data governance a priority in your org? What’s your biggest challenge with keeping your data updated?


Data Policies: Don’t Fix Your Bad Data

It’s estimated that bad data costs companies between 15 and 25 percent of their total revenue. With clean data, companies could earn up to 25 percent more each year. On a per-unit basis, bad data costs anywhere between $10 - $100 a record. Most CRMs have over 100,000 records...that’s up to $10m in lost income.

A less obvious but just as significant expense is time. Bad data wastes 50 percent of workers’ time in finding, correcting, and confirming information. Analysts spend even more time - 60 percent - cleaning and organizing faulty information. Essentially, productivity could be doubled if CRM managers didn’t have to spend time cleaning their database. Even reps suffer. Inside sales reps waste 27 percent of their time dealing with inaccurate records, which totals 546 hours per year, per rep. That’s valuable time that could be spent selling. 

The reasons for cleaning databases are obvious. More money, more time, more effective selling. But fixing bad data isn’t really the solution. In fact, these statistics are related to the cost of fixing it, not just the bad data itself.

What if there was a way to not just clean your CRM but keep it clean?

CRM databases grow at a rate of 40 percent per year, with 20 percent of the database being dirty. That means inaccurate information grows each year, making it more expensive to clean and confirm. 

Most solutions are centered around enrichment and cleansing. But as your CRM grows, the cost to continually update your data increases with the growth of your database. Instead, there needs to be a solution that builds policies and rules that govern data and prevent bad data from entering and staying in your CRM. 

The most important thing you can do for your CRM is to build rules and processes that prevent bad data from entering and stagnating in the first place.  Within CRM, we have the ability to build logic, rules, and required fields to stop errors like account duplication, missing fields, and retaining dead leads. Treating our CRM as a breathing exchange of information incentivizes us to not let data rot away as teams spend hours updating it. Instead, we bring fresh, accurate information in and expel old information out in real-time, saving ourselves from the expensive time and effort of fixing errors.

Let’s talk about what information is stored in your CRM and how we can think about creating processes and policies to keep it clean.

Accounts and Contacts

Accounts are the cornerstone of your CRM database and your go-to-market strategy. The number of accounts and their relationship with each other defines how you allocate coverage, territory, and ownership. To manage account data, create data rules that give you consistent definitions. 


    1. How to define an account and its minimum data requirements?
    2. What are the lifecycle statuses of accounts in relation to your company? 
    3. How do you deal with duplicate accounts?

These policies define how you treat an account, who creates it, and the minimum data requirements for a valid account. Policies define the buying behavior of an account (i.e. prospect, customer, attrited) and reduce account duplication.

You need a similar approach when creating and managing contacts.  Contacts change roles and leave organizations, requiring almost constant updating. Build policies to define and govern contact lifecycles that match your buying cycle. 

Account Hierarchy and Industry Taxonomy 

Integrating third-party data sources enables you to have accurate information when building account hierarchies and industry taxonomy. However, each data provider has its own taxonomy and account hierarchy structures that differ from your go-to-market plan.  Invest in creating a custom hierarchy and taxonomy model to match your go-to-market plan. This allows you to segment accounts accurately and provides a coverage plan to support your go-to-market plan.

Addressing these CRM policies enables more actionable data in your CRM investment and creates processes for keeping records current and accurate.  Rather than fix bad data on the backend, CRM policies reduce errors that enter and stagnate in CRM. 

Changing how we think about database management will ultimately solve our bad data problem. We must go from reactive cleaning to proactive maintenance to save our companies the growing expense of wasted time and money. 

Three Priorities of Focus For Sales Transformation

Nancy Nardin of Smart Selling Tools interviewed Founder and CEO Dharmesh Singh for her series on Transforming Sales. Dharmesh explains how trends in sales ops are driving growth. Nancy is the founder of Smart Selling Tools which reviews sales tools and provides resources for sales professionals.  You can read the original interview here.


DHARMESH: I think the first priority is taking control of the tool stack. There has been an explosion of tools in the sales ops world and we feel that today, sales operations professionals are spending more time integrating systems than performing sales operations.  It’s death by a thousand tools. Moreover, most tools aren’t integrated with each other, so you get point solutions that solve a niche need but are not really helping teams grow. Sales organizations are dynamic by nature and if they invest in tools that do not accommodate and align an evolving sales team, teams will outgrow the tool. This has tremendous impacts on sales teams.

The second transformation is Data Governance. CRM is now the source of corporate truth. It aligns and integrates all customer-facing functions from pre-sales to post-sales. CRM should be treated as an enterprise database, and as with most enterprise databases, it’s only as good as the data within it. Teams looking to transform sales organizations need to invest the time to define and enforce policies for data entry and data lifecycle within CRM. Today most CRM instances are in the wild west age of data governance. The lack of trust in the data is holding sales organizations back from taking advantage of their CRM investment to its fullest capacity.

Finally, thinking about integrating operations with your go-to-market plan. Today, operations teams responsible for daily sales execution chores are disconnected from the overall sales go-to-market team. These are two separate functions in most organizations and it’s imperative to integrate and ensure they are working in cadence as the go-to-market evolves. Successful organizations align their resources rapidly to meet the requirements of an evolving go-to-market as change cycles become shorter and competition in the marketplace increases. Companies that build agility in their go-to-market with execution ability will succeed.


DHARMESH: Take a data-driven approach to making decisions. Sales operations teams are typically working in silos, disconnected with the needs of the executive suite. Successful sales organizations use sales operations as a strategic lever for growth. An investment in sales operations can exponentially help scale sales beyond just adding headcount.

We have created a GrowthOps Framework that we think can help teams think through metrics to drive alignment from the CXO to the sales ops team. The metrics in the “Grow” row are the top-most metrics for most organizations. They should be the north star for teams working on metrics in the “Optimize” row to drive alignment.


DHARMESH: I recommend folks to go back to the metrics that matter and track to see if they are driving the right behavior. Focus on the process and policies that you want to enable before picking a tool. We have seen success for organizations that take the time to define process and policies before jumping into tools. The tool is a means to an end. Many teams make the mistake of signing up for the newest, shiny toy without taking the time to see how it fits into their go-to-market plan and what policies it will enforce.


DHARMESH: Our platform approach allows teams to bring their sales planning and sales operations together for the first time.  We now have a unified view of how each team is supporting the overall sales motion.

Teams leveraging’s platform are finding that they are able to:

  • Shorten their sales planning cycles and react faster to market changes
  • Build integrated, collaborative sales plans that drive transparent decision making
  • Reduce dependency on IT with the ability to make CRM changes
  • Cut down on ad hoc custom code in Salesforce systems
  • Enforce sales policies consistently across the organizations, resulting in cleaner data and better decision making


DHARMESH: has built a community for sales and sales operations leaders. We’ve hosted meetups around the country and plan to host more in the coming months. These meetups have covered everything from career growth in sales operations to best practices in sales planning. We source our community for topics and listen for pain points we can address at our events. On a more daily basis, we’ve launched the Growth Ops App and a LinkedIn Sales Ops Community group for those looking to connect with other sales operations professionals, ask questions, and share ideas. You can find the Growth Ops App in the app store and request to join the LinkedIn group here.  As one of our community members put it, sales ops professionals often stumbled into their role and make it up as they go, so we’re committed to providing resources to empower sales operations teams and help them unlock growth in their companies.

Data Is The New Oil – Only If You Can Capitalize On It

I was thinking about data and an Economist article I read a while back that had compared a new data center to the oil wells of the last century. The article made a point that in the current environment, data is the new currency and companies able to monetize data will be the leaders of the future. If you have not read it, you can find the link here.

Nothing earth-shattering for us in tech, but something to ponder. Does having access to data alone makes the data worthwhile, or is it only useful if you can capitalize on it?

I ask the question in the context of the data sitting in CRM systems. Companies have been acquiring data for years and they are always hesitant to purge it. There is another article from Deloitte Insights that shared an analysis of how incomplete and inaccurate most personal data is, despite reams of marketing data collected as we all browse and shop online.

Biznology did a study that shows B2B data decays an average of 5% per month or 70.3% per year. Think about it – 70% of your data on accounts and contacts decays in one year. Most CRMs have data that has been sitting around for years. There has been no cleanup and no governance process to manage the lifecycle of this data.

Companies not investing in proactively managing the lifecycle in their CRM systems will adversely impact the way their organizations engage with customers. If 70% of your contact data is old in a year, then chances are 7 times out of 10 you are responding to a “dead” contact.

We were working with an organization that has invested in tools like Lean Data to rout leads, yet had to manually route leads to the right person since the underlying data was unreliable. Another customer wants to drive more coordinated engagement among marketing, sales, and support by engaging account and contacts in the context of their journey with the company. They have the data on the accounts and contacts, but it’s not been cleaned and there is no governance structure around managing data. Result: low ROI on campaigns.

Companies owe it to their sales and marketing teams, and to their customers, to get a better handle on the data they have within CRM systems. Cleaning data without a specific goal in mind is not the best use of anyone’s time. Here are a few things to consider:

  1. Set goals about what you want to achieve. Consider the various tools ingesting data into the CRM system and run a full inventory to see if all are needed. Identify what data you absolutely need to have to meet the needs of marketing and sales teams and ruthlessly set rules to purge everything else. Understand how your marketing automation data flows into your CRM system, for example.
  2. Make rules for purge and storage. Once you know where the data is coming into the CRM system, work across teams to create a “store” or “purge” list. Purging contacts is important. You will be surprised how often people change roles, companies, or get promoted. Simple flags like no activity over a year, no email address, bad phone number, etc. are reasons to purge records.

Investing in Establishing Data Governance Policies

At, we believe cleaning and purging is not the solution to get a handle on this problem. It is the first step to drain the swamp. As you set about putting rules for the purge, you need to establish policies around data governance. When the data faucet gets turned on, you need to have automation that manages the lifecycle of the key data elements around which you engage with your customers. We believe that selling is a series of events, and each event is supported by a policy. You need to invest in creating these policies and enforce them consistently at the time of data ingestion into the CRM.

Keep your CRM current by applying policies that help you build a self-regulating CRM system. Having data is not enough. Making data work for you so you can disperse leads/accounts/opportunities/cases to the right person is important. You need good data to run a more targeted reach to your customers and engage in the context of their journey with you. It’s rude to reach out to a contact who is no longer an active contact.

It’s a disservice to your sales teams to assign them accounts that can no longer make a buying decision, or have them sell into accounts that may not be in their patch legally because someone else owns the parent account, but your CRM account hierarchy does not reflect that relationship. It’s not fair to your sales planners who struggle to create fair and balanced territories if they are working off an unreliable set of data for accounts.

We would love to learn from your experience on how you handle this at your organization.

Feedback welcome.

Call to action: You need to look at your data if you have done any of the following:

  1. Changed your GTM and ICP.
  2. Entered new markets
  3. Purchased lists
  4. Ingested data into your CRM from third-party tools that push account or contact and activity data into your CRM