“The next $10 billion privacy company will not sell more software. It will delete more data.”
Investors see one simple pattern: companies that handle deletion requests with speed and proof are closing larger enterprise deals and avoiding seven-figure fines, while everyone else is still arguing with legal about what “erasure” actually means.
The shift is not philosophical. It is commercial. Large buyers now treat “right to be forgotten” execution as a procurement filter. If your product cannot honor Article 17 of GDPR, CCPA delete rights, and similar rules in Brazil, India, and a growing list of markets, your sales cycle stretches, your discount grows, and your legal risk compounds. The market rewards vendors that treat deletion as a product capability, not a support ticket.
For most startups, the catch is simple: their data model was built for retention, not removal. Every acquisition channel, every product feature, and every BI dashboard pulls in one direction: keep more data for longer. Erasure pulls in the opposite direction and exposes every hidden coupling in the stack. Growth teams want lifetime value models. Security wants logs. Marketing wants attribution history. Legal wants demonstrable deletion. The architecture rarely wants any of this, at least not in its first version.
Investors look at this tension and price it. A company with a clean deletion story gets faster security approvals, fewer redlines in DPAs, and better leverage in enterprise negotiations. A company with a vague story sees deals stall at procurement while competitors slide in with cleaner answers and real evidence: deletion logs, data lineage, and retention schedules that match contract terms. The revenue impact is not theoretical. In B2B SaaS, losing a single enterprise deal that would have anchored your logo slide can push a round back 6 to 12 months.
The trend is not fully defined yet, but the signal is getting sharp. Regulators do not only read privacy policies anymore. They ask for deletion workflows. They ask for database diagrams. They ask which vendor held what data for how long. Buyers are not only asking “Do you support the right to be forgotten?” They are asking “Show me the audit trail the last time you deleted a user in Germany.”
The business value sits in the gap between those two questions. If you can demonstrate predictable deletion across production, backups, analytics, and vendors, you earn trust and compress sales cycles. If you cannot, you expand your risk surface and your legal budget. The engineering task looks technical, but the return is commercial.
“Over 70% of EU consumers expect companies to delete their data when requested, but fewer than 40% believe it actually happens.”
Source: Eurobarometer privacy survey
The right to be forgotten is no longer a niche European clause. It is a global sales requirement. The question is not “Should we implement it?” The question is “How do we implement it so that legal, security, sales, and engineering can all sign the same data deletion story and defend it under pressure?”
That is where most teams struggle: turning a legal sentence into a repeatable, measurable flow that works across microservices, data warehouses, SaaS vendors, logs, and backups without killing reporting or product analytics.
What “Right to Be Forgotten” Means In Practice
Legal teams often summarize the right to be forgotten in one slide. Users can ask you to erase their personal data, and in many cases, you must comply. The practical version is longer and more painful.
You need to answer four questions with evidence, not belief:
1. What counts as “personal data” in your system?
2. Where does that data live, both inside your stack and with third parties?
3. When a deletion request arrives, what do you do first, next, and last?
4. How do you prove to a buyer or regulator that you did what you said?
“Most companies underestimate their data surface by at least 30% when they first map for erasure. The missing 30% lives in BI exports, shared drives, and vendor tools.”
Privacy engineer at a Series D SaaS company
The right to be forgotten is not absolute. There are exceptions: data needed for legal claims, tax records, fraud prevention, and contract enforcement often must or may be retained. Investors do not expect zero retention. They expect evidence that you have a rule set and that the product follows it.
From a product and revenue point of view, you want a clear standard that sales can explain, legal can defend, and engineering can implement without fragile one-off scripts. That means you need to translate the abstract legal text into concrete data actions.
From Legal Language To Data Actions
A workable model for engineering and product looks like this:
– For each data element, decide: delete, anonymize, or retain.
– For each operation, define: trigger, time limit, and proof.
For example:
– “Delete email address in all production tables within 30 days of a verified erasure request.”
– “Pseudonymize usage events after 180 days so they no longer point to an identifiable person.”
– “Retain tax invoices for 7 years, isolate them from marketing and product teams, and log any access.”
The business value is clarity. When sales negotiates a DPA, they can state concrete behavior. When growth wants longer attribution windows, they must argue in the open against a defined retention rule, not against a vague fear of “legal risk.”
Step 1: Make Data Deletion a Product Requirement
Deletion usually begins as a support problem. A user emails support. Support emails legal. Legal emails engineering. Someone runs a manual script. Nobody wants to keep doing that, but without pressure, it stays that way.
Investors look for a different pattern: deletion as a product feature.
Why Deletion Needs a Product Owner
Treating deletion as a product requirement changes the economics:
– It moves from ad hoc effort to planned roadmap.
– It receives design thinking around user experience and internal workflows.
– It gains test coverage, monitoring, and documentation.
The ROI comes from three directions:
– Lower legal and regulatory exposure.
– Faster security and privacy reviews in enterprise deals.
– Lower internal support cost per deletion request.
“Companies that formalize privacy features as part of product management cut the cost of compliance projects by 30% compared to teams that run it as ad hoc work.”
McKinsey privacy operations survey
Give one PM or tech lead clear ownership. Their job is not to argue with legal or marketing. Their job is to translate legal requirements into system behavior that does not break growth or reporting.
Defining The Scope: Whose Data Can Be Forgotten
Before building flows, define the subject types in your system:
– End customers or consumers
– Admin users
– Employees or contractors
– B2B client contacts
Each group may trigger different legal obligations and exceptions. For example, you might delete a consumer’s transaction history but retain an admin’s audit logs to preserve system integrity.
This segmentation also affects your pricing and growth story. Enterprise clients often ask: “Can our admin delete their account but leave the audit trail intact?” or “Can we remove a former employee while preserving their contributions to shared objects?” A clear answer reduces friction in security reviews.
Step 2: Map Your Data With Deletion In Mind
Most teams have some kind of data inventory, but few map it explicitly around erasure. You need to see your system the way a deletion request sees it.
Create a Deletion-Focused Data Inventory
For each system that touches personal data, capture at least:
– System name (e.g., “Product DB”, “Stripe”, “Salesforce”, “Amplitude”)
– Data subject identifier used (email, user_id, customer_id, phone)
– Types of personal data stored
– Current retention rules
– Deletion capability (API delete, soft delete, no delete, manual)
This is where your vendor stack often becomes the weakest link. Many tools allow you to export data easily. Fewer offer reliable, documented erasure functions tied to your own identifiers.
Typical Growth Stack Data Map
Here is a simplified view of where user data often lives in a growth-focused SaaS company:
| Layer | System | Personal Data | Deletion Capability |
|---|---|---|---|
| Core | Primary app DB | Profile, credentials, content | Full control via code |
| Billing | Stripe / Braintree | Billing details, last4, history | Limited erasure, legal retention constraints |
| CRM | Salesforce / HubSpot | Contact info, deal history | Record delete / anonymization tools |
| Engagement | Email & in-app messaging | Emails, events, preferences | Suppression lists vs full deletion |
| Product Analytics | Mixpanel / Amplitude | User events with IDs | Per-user delete APIs (varies) |
| Customer Support | Zendesk / Intercom | Tickets, chats, attachments | Record deletion / redaction |
| Data Warehouse | Snowflake / BigQuery | Events, exports, joins | Full control but often manual |
| Logs | Logging / monitoring tools | IPs, headers, debug content | Retention policy based, weak per-user delete |
The business question is simple: if a user in France sends a deletion request, can you reach every row about them in this table, across all these systems, within the required time window?
Step 3: Design Your Deletion Model
Not all data needs the same treatment. A rigid “delete everything” approach often clashes with finance, security, and product analytics.
Delete vs Anonymize vs Retain
A practical model splits personal data into three buckets:
– Direct identifiers: email, name, phone, ID numbers, device IDs.
Typical action: delete or irreversibly hash.
– Indirect identifiers: IP addresses, cookies, location data, usage events tied to IDs.
Typical action: unlink from user, aggregate, or truncate after a set period.
– Legally retained data: invoices, tax records, fraud logs.
Typical action: isolate from operational systems, restrict access, document the legal basis for retention.
You create more long term value when you make these decisions table by table, field by field, instead of leaving it as vague policy language.
Hard Delete vs Soft Delete
Engineering teams often default to “soft delete”: add a deleted_at column and ignore “deleted” rows in queries. That pattern helps recovery and auditing but fails user expectations and some regulatory interpretations if not combined with real removal.
Move toward a layered approach:
– Immediate soft delete in the application to hide the user and cut access.
– Batched hard deletes at the storage level according to a schedule.
– Pseudonymization of references for analytics where full deletion would break metrics.
From a growth angle, this lets you preserve aggregate conversion and retention numbers while reducing the personal footprint.
Step 4: Build a Deletion Request Pipeline
Once your model is clear, you turn legal rights into an operational workflow.
Entry Points For Requests
Common entry points:
– Self-service account settings (“Delete my account”)
– Web form or email to privacy@domain
– Requests relayed from enterprise customers on behalf of their users
Standardize intake. If deletion requests reach you through random support tickets or DMs, you lose tracking and deadlines. At enterprise scale, lost requests can turn into complaints and financial risk.
Verification and Identity
Before deleting anything, you must know who is asking. That requires a balance between security and friction.
Options:
– In-product authenticated deletion for logged-in users
– Email-based verification flow linked to the account
– For B2B clients, admin-submitted bulk requests with signed agreement
You want a procedure that your support team can follow without legal intervention for every single case. Clear rules shrink per-request handling time and reduce the odds of deleting the wrong account.
From Request to Execution
After verification, your internal process should look more like a build pipeline than a loose set of manual tasks:
1. Create a canonical deletion job for the subject (user_id, email, customer_id).
2. Record legal basis, source of request, and deadline.
3. Trigger deletion workers in each system that contains relevant data.
4. Collect success/failure status per system.
5. Generate a final internal log and user-facing confirmation.
Treating this as a job with states, retries, and observability will save you from painful manual audits later.
Step 5: Implement Deletion Across Your Stack
Now the engineering work begins. The difficulty depends on how many systems you already have and how identity flows through them.
Core Application Database
Start where you have the most control.
Steps:
– Identify all tables where the user_id or other identifiers appear.
– Mark which fields hold direct identifiers and which hold behavioral or transactional data.
– Implement a service or worker that, given a user_id, performs the actions you defined: hard delete, soft delete, or anonymize.
Pay special attention to:
– Foreign keys: cascade deletes vs orphan records.
– Shared objects: comments, posts, transactions that involve multiple users.
– Referential integrity: analytics or billing reports that assume user rows exist.
Your goal is a single, well-defined entry point in code that handles a user deletion end to end. Avoid scattered scripts that different teams maintain separately.
Data Warehouse and Analytics
Warehouses are where many deletion projects stall. By the time personal data reaches Snowflake or BigQuery, it has often been transformed, joined, and copied.
Tackle this with two levers:
1. Identity mapping
Maintain a reference table that maps all identifiers used in the warehouse (internal user_id, device IDs, third-party IDs) to the primary subject. When a deletion request comes in, that table tells you where to look.
2. Partitioning and retention
Design tables so that erasure relies on dropping or rewriting partitions instead of hunting every row. For example, store events partitioned by date with user_id as a primary field, then run periodic jobs that remove or hash data for deleted users in recent partitions.
From a business angle, this is where you reconcile privacy with your growth models. You keep aggregate metrics, but you drop the ability to drill down to a particular person after they have asked you not to.
Third‑Party Tools and Vendors
Every vendor in your stack is either an asset or a liability when a deletion request arrives.
You need a vendor deletion playbook:
– For each vendor, record if they support per-user deletion.
– Note the identifier required (email, user_id, vendor-specific ID).
– Note their contractual deletion SLA after your request.
| Vendor Type | Common Identifier | Deletion Support | Risk to Deals |
|---|---|---|---|
| Email marketing | Email address | Delete + suppression | High for B2C at scale |
| CRM | Email / contact ID | Record delete, anonymize | Medium for B2B SaaS |
| Product analytics | User ID | Per-user delete API (varies) | High for data-heavy products |
| Support | Email / user ID | Ticket delete, redaction | Medium, often flagged in audits |
| Ad platforms | Hashed email, device IDs | Tools uneven, often manual | High for marketing-led companies |
For priority vendors, invest early in automation. Build internal adapters that call their deletion APIs when your central deletion job runs. Track responses and failures in your own logs. The goal is to avoid a scenario where a regulator asks about a vendor and your only proof is a support email thread.
Logs and Monitoring
Logs often contain more personal data than anyone expects: IPs, user identifiers, sometimes even payloads with names or emails.
Most logging systems are not built for user-level deletion. The practical strategy is usually:
– Prevent personal data from entering logs in the first place where possible.
– Apply strict retention windows (for example, 30 or 60 days).
– Ensure that your legal and privacy documentation reflects that some data only disappears with log expiry, not targeted deletion.
From a funding and acquisition view, companies that can show their log data is short-lived tend to see fewer red flags during due diligence.
Step 6: Handling Backups, Snapshots, and Archives
Backups are the elephant in the deletion conversation. Many teams hide behind a vague statement that deletion applies “to production systems” while backups age out.
Regulators have signaled some tolerance for this pattern, but only with clear safeguards:
– Backups are not used as active data sources.
– Access is tightly restricted.
– Restores are rare and controlled.
A practical rule set:
– Do not restore a backup and re-expose a deleted user without re-running your deletion pipeline.
– Shorten backup retention schedules where feasible.
– Document the difference between production erasure and backup aging in your privacy documentation.
Investors and acquirers often ask one pointed question: “If you had to restore from a 30-day backup, what happens to users who asked for deletion 15 days ago?” A credible scenario here increases trust quickly.
Step 7: Evidence, Audits, and Buyer Confidence
Deletion is only half the problem. Proof is the other half.
Build a Deletion Ledger
Treat each deletion request as a transaction with:
– Unique request ID
– Subject identifier(s)
– Source and verification method
– Systems touched
– Actions taken
– Timestamps and outcomes
– Operator (if manual steps occurred)
This ledger is not just for regulators. It is a sales tool. Security questionnaires often ask for your erasure process. Being able to say “We can pull anonymized logs of all deletion jobs for the past 24 months” changes the tone of the review.
Internal and External Reporting
Over time, you can track:
– Average time from request to completion
– Percentage of automated vs manual deletions
– Failure rates by system or vendor
These metrics inform roadmaps. If 40 percent of your manual time sits in a single vendor that refuses to build good APIs, that is leverage in renewal talks or a strong reason to switch.
“During acquisition diligence we looked more carefully at deletion logs than at the marketing funnel. Privacy process quality told us more about future risk than CAC did.”
Corporate development lead at a public software company
Step 8: Communicating Deletion to Users and Enterprise Buyers
Legal language alone does not convert. Both users and B2B buyers want clarity, plain speech, and limits.
User-Facing Messaging
A user account deletion flow should answer three questions in a single screen:
– What will be deleted, in what time frame?
– What will remain, and why?
– How can the user follow up if they have concerns?
For example:
– “We will delete your profile, messages, and saved content within 30 days.”
– “We may retain invoice data for tax and accounting rules, but it will no longer be visible or used for marketing.”
– “You will receive an email confirmation once deletion is complete.”
Keeping the wording specific reduces complaints and surprises. It also helps your support team respond consistently.
Enterprise Buyer Concerns
B2B buyers often focus on three areas:
– Multi-tenant data separation: They want to know that deletion of one user’s data does not affect another client.
– Bulk deletion for their own customers: For example, they might need to delete a set of end users to honor their own obligations.
– Jurisdictional handling: They may require stricter behavior for data subjects in certain regions.
Having ready answers and a clear diagram of your deletion flow can shorten long email chains with security teams. That has direct revenue impact when a deal is in quarter-end timing.
Pricing, Contracts, and Data Deletion
Deletion capabilities can affect your pricing model and your legal negotiating position.
Pricing Levers Around Data Retention
Some vendors already price by:
– Retained data volume
– Retention windows for logs and analytics
– Level of custom privacy controls
A simple table of levers:
| Lever | Impact on Costs | Impact on Revenue |
|---|---|---|
| Shorter default retention | Lower storage and infra spend | Higher trust, fewer objections in EU deals |
| Premium custom retention | More engineering, infra tuning | Upsell for regulated industries |
| Audit-grade deletion logs | Engineering and compliance tooling | Faster procurement with large enterprises |
Founders who get ahead of this can pitch privacy controls as part of the value proposition, not as an expense. That story plays well with CFOs and boards that worry about both compliance and infrastructure costs.
Contracts, DPAs, and SLAs
Your data processing agreements and master service agreements should mirror what your system actually does. Overpromising on deletion windows or scope exposes real risk later.
Key contract levers:
– Deletion SLA: Number of days from request to completion.
– Scope of deletion: Which systems, including vendors.
– Proof: What kind of evidence you will provide if asked.
Align these terms with your internal pipeline. If your system relies on batch jobs that run weekly, do not sign a 24-hour deletion SLA without budget for changes.
Building a Roadmap: From Manual to Mature
Most companies will not jump from scattered scripts to a fully integrated deletion platform in one quarter. Treat this as a maturity journey that runs in parallel with growth.
Three Stages of Deletion Maturity
You can think of the path in three rough stages:
| Stage | Characteristics | Business Impact |
|---|---|---|
| Ad hoc | Manual scripts, inconsistent coverage, no central tracking | High legal risk, slow enterprise deals, high support cost |
| Managed | Central process owner, documented flow, partial automation | Predictable response times, improved deal velocity |
| Productized | Deletion as product feature, full audit logs, vendor integration | Competitive edge in privacy-conscious markets, stronger valuation story |
You do not need perfection to unlock value. Even moving from ad hoc to managed often cuts review friction with mid-market buyers.
Prioritizing Work For Maximum ROI
When budgets are tight and engineers are scarce, work in this order:
1. Core product database for consumers in strict jurisdictions (EU, UK).
2. High-visibility vendors that appear on security questionnaires.
3. Warehouse tables used for person-level reporting.
4. Automation of vendor deletions and central logging.
5. UX for self-service deletion and clear messaging.
Tie each step to a commercial milestone: an upcoming enterprise deal, a new region launch, or an expected audit. That turns privacy work from an abstract risk project into a direct revenue enabler in board conversations.
“Privacy work closed our largest deal that quarter. The buyer shortlisted three vendors, and we were the only one who could walk through a live deletion flow during the security review.”
Founder of a Series B SaaS platform