Imagine you’re a company that just acquired a business. You now own a portfolio of products and a large customer base—but how do you unlock growth from what you already have?

As an executive, you’re asking: “What’s the real market opportunity? How many of our existing customers already buy these products? Should we keep the original sales team, or can we consolidate them with our existing reps?”
As a sales leader, you’re wondering: “Which customers already buy one category of products but not another? Who are the best targets for expansion?”
As a sales rep, you want to know: “Where is my easiest win? Who’s never been pitched this product before? Who did we try selling it to last year, and why did they say no?”

These are all fundamentally whitespace questions, and are critical to answer for optimal revenue performance. The issue is that revenue leaders often lack the quality, structured data needed answer them quickly - or at all - and instead find themselves relying on:

Spreadsheets that take too much time to update
Personal memory and intuition
Disconnected systems that don’t communicate

If leadership asks a question like, “Which of our customers have Product A but not Product B, and have never even been pitched Product B?”—the response shouldn’t be, “That’ll take me a few days to figure out.” But too often, it is.

The Value of Structured Data

If your data is structured properly inside a CRM (whether through CPQ/RCA or other data modeling best practices), answers should be instantly available.

Sales reps should be able to quickly see where their best opportunities are.
Sales leadership should be able to refine territories, quotas, and resource allocation based on actual data.
Executives and investors should be able to assess market expansion and acquisition decisions with confidence.

For companies at a certain maturity level, key data is nearly impossible to manage without a dedicated tool like Salesforce CPQ or Revenue Cloud Advanced - which (when adopted and aligned with best practices) establishes a strong foundation for whitespace.

Data Standardization

For companies that struggle with deal inconsistency and missing product data, CPQ and RCA ensure that every deal is structured consistently, capturing:

What was sold (products, services, bundles)
At what price (discounts, renewals, usage-based fees)
When it was sold (contract start/end dates, renewal terms)
Whom it was sold to (customer segmentation, geography, industry)

With this data structured within Salesforce itself, leadership can now confidently assess expansion potential, customer penetration, and renewal risk without requiring complex BI tools. This becomes even more critical as AI and automation evolve—without clean, structured data, businesses won’t be able to generate meaningful insights from AI-driven natural language queries or analytics.

Territory & Quota Planning

With CPQ or RCA in place, sales leaders can quantify whitespace potential within existing accounts by analyzing:

Potential Annual Recurring Revenue (ARR) (New, Expansion, Renewal) by product
Cross-sell and upsell logic (e.g., “If they own Product A, they need Product B”)
Rep and territory-level whitespace (ensuring every rep has sufficient Total Addressable Market (TAM) to hit quota)

For PE firms evaluating portfolio company sales teams, this means fewer territory coverage gaps and more efficient resource allocation.

The “Bad Data” Problem

Many companies inherit messy data—from acquisitions, legacy sales processes, or poor CRM hygiene. Sales leaders looking to professionalize revenue operations need a strategy for backfilling incomplete data into CPQ/RCA. At a minimum, we commonly recommend migrating:

All Active Contracts and Subscriptions
Any accounts that have churned in the last 12 months

If migration isn’t feasible, leveraging Salesforce Data Cloud to unify siloed information is a strong alternative.

The Cost of Incomplete or Inaccurate Data

Companies typically need certain data in order to build and execute sales plans with confidence - historical Opportunity win rate, expected renewal dates, churn, existing ARR, etc..

Imagine a company trying to build their plans, knowing the data they have to go off did NOT come from a standardized and unified system. Annual territory planning happens, but:

Reps lack visibility into what’s been sold, leading to missed expansion opportunities.
Leadership overestimates whitespace, setting quotas that aren’t achievable.
Sales teams spend more time searching for data than selling, slowing growth.
PE firms struggle to predict future ARR, making valuation and investment decisions riskier.

Key Best Practices

Simply buying the tool doesn’t get the job done. It’s both having the tool and adopting key best practices to enable this type of data-driven planning. Here are a few main themes to keep in mind:

Invest in Product Structure

Do: Organize your Products into a clean and categorized taxonomy (e.g., Product Types, Product Families). Establishing key attributes at the Product-level allows the system to cascade to their corresponding downstream twins.
Do: Invest in proper Product setup to ensure you create the right customer contract data (e.g., Subscriptions, Assets, etc.).
Don’t: Utilize a “One Product Family Per Opportunity” models to leave space for bundles and cross-selling expansion as you strive to capture whitespace.
Don’t: Create separate SKUs for New vs. Expansion vs. Renewal business. This results in SKU proliferation, increases administration costs, degrades the seller experience, and ironically can make reporting more difficult!

Master Multi-Year Selling

Do: If you’re going to sell multi-year deals with uplifts (e.g., 50 units for $20k in Year 1, 100 units for $35k in Year 2, etc.), you HAVE to adopt MDQ (CPQ) or Ramps (RCA). This will ensure you have an accurate product-level forecast that factors in quantity, term, probability, current price, and renewal pricing strategy.
Don’t: Create multiple field blocks on the Opportunity to represent different years. And definitely don’t outline the specifics of each term in a free text field or an attached email.

Automate Renewals & Amendments

Do: Automate Renewal Opportunity and Quote generation to have a forecast with actual probability and expected renewal pricing. For more details, check out How to Accelerate Renewal Revenue in CPQ.
Do: Adopt Amendments for expansion and contraction of existing customers to get accurate line-level ARR reporting and enable whitespace strategies such as cross-sell discounts available only to existing customers. For more details, check out Master Amendments in Salesforce CPQ.
Don’t: Create a custom “Upsell” Opportunity type that doesn’t start from an existing Contract.

Get ARR Calculations Right

Do: Use Salesforce as the source of truth for pipeline ARR calculations, as opposed to an ERP. This supercharges ARR reporting capabilities, enabling you to factor in open and lost Opportunities and your total addressable customer base. (It does tend to make things like credit memo adjustments more difficult, but we think it is worth it!) For more on this topic, review How to Build ARR & MRR Reporting for you CPQ folks and this article for those with RCA).
Do: Track revenue at a Quote Line Level, in addition to at the Deal level. This data models data model allows reps to book a renewal transaction with a customer that includes increasing the price of an existing subscription, selling additional quantities of the same product, reducing the quantity of a second product, and cross-selling a third product. All of this can be managed within a single Opportunity, with ARR attribution captured at the individual line level.
PS: We also have created a free ARR for CPQ AppExchange solution to help organizations get this right!

Enable Smarter Sales Execution

Do: Give individual sellers visibility into whitepsace by setting up cross-filtering (e.g., “All customers that have purchased Product A but have never been quoted Product B.”)
Do: Enforce standard usage of CPQ/RCA for all deals to maintain the integrity of your existing data model and avoid risk in the future.
Don’t: Degrade the seller experience by overcomplicating required fields. Finding the right balance is the key.

Migrate Key Historical Data

Do: Migrate all Active Contract and Subscriptions, as well as any that have Churned in the last 12 months. Yes, even those from Mergers, Acquisitions, renewals team working outside of SFDC, etc. These data points will allow customer-facing teams to have a full picture of the customer and increase accuracy of Sales Planning; they’re also a pre-requisite to actually using Amendments.
Do: If data migration is not in the cards, consider Data Cloud to unify and surface relevant information.
PS: CPQ/RCA data migrations can be are always tricky. Check out Salesforce’s Legacy Data Upload for Salesforce CPQ, but also consider giving us a call.

Structuring Data to Capture Whitespace