Product-Usage Email Case Study in Pakistan

The Client

An Islamabad-based B2B SaaS company building project and operations management software for Pakistani creative agencies, construction firms, and professional services practices. The platform combined task and project tracking, resource planning, time tracking, and basic invoicing into a single workspace, and it had reached roughly PKR 58M in annual recurring revenue across about 320 paid accounts. Pricing was seat-based with add-on modules for time tracking, invoicing, and resource planning, which gave the business two clean levers for expansion: more seats inside an account, and more modules switched on.

For three years the company had grown almost entirely through new logos. A small outbound team worked mid-market accounts, a free plan and a 14-day trial fed self-serve signups for smaller teams, and paid Google and LinkedIn campaigns topped up the pipeline. New-logo growth was healthy. What was not healthy was what happened after an account signed up. Most accounts landed on a plan, stayed on that plan, and were never expanded. The product team could see that customers were hitting seat limits, running heavy usage on modules they had not paid for, and inviting dozens of collaborators — but none of that signal ever reached the people who could turn it into a conversation about upgrading.

The company had no dedicated lifecycle or revenue marketing function. A generalist marketer sent one newsletter a month to the entire contact file, and three account managers manually worked the top 30 accounts each quarter. The remaining 290 accounts — the long tail where most of the untapped expansion sat — received nothing. When the leadership team mapped it out, they realised expansion revenue had been growing at barely 3% month on month while new-logo revenue was growing at 11%. They approached WeProms Digital to build the kind of email marketing automation lifecycle flows that turn product usage into expansion revenue automatically, instead of leaving it to whichever account manager happened to have capacity.

The Problem

Four gaps were quietly capping expansion revenue every month:

1. Product data and marketing data lived in different buildings. The product team tracked events in a product analytics tool — seat invitations, module usage, login frequency, project creation, collaborator counts — but that data had no path to the email platform. Marketing could see who was on which plan, but not how they actually used the product. Every email was therefore sent in the dark, blind to the single most predictive signal for expansion: what an account did inside the product this week.

2. Expansion was a manual, top-heavy motion. Three account managers worked roughly 30 strategic accounts each quarter. That left around 290 accounts with no expansion touch at all in any given 90-day window. The economics were backwards: the accounts most likely to expand were often mid-size accounts with clear usage signals, and those were precisely the accounts no one was speaking to.

3. Product-qualified leads were generated and then ignored. The company had sketched out a rough list of expansion signals — an account inviting users beyond its seat allowance, a team using a module heavily on a plan that did not include it, an account crossing a daily-active threshold — but there was no system watching for them. A back-of-envelope count suggested roughly 140 such signals surfaced every month, and only about 6% ever became an upsell conversation. The rest evaporated.

4. Poor activation was leaking the top of the funnel into churn. Only about 31% of new signups hit the core activation milestone — creating a first project and inviting a teammate — within 14 days. The rest drifted, never felt the product’s value, and churned at the end of their trial. There was no behaviour-triggered email to catch them, guide them, or recover them, so the pool of accounts that could eventually be expanded was smaller than it should have been.

The net effect was an expansion engine that existed only on paper. The product was generating the signals every day. Nobody was converting them.

Phase 1 — Diagnosis and Data Plumbing (Weeks 1-2)

Before any flow could be written, the signal had to be trustworthy and reachable. The first two weeks were spent plumbing data and fixing the foundation that everything else would stand on.

Auditing the stack and the data model. We mapped every data source: the product analytics event stream, the billing system (plan, seats, modules, renewal dates), the CRM (account owner, industry, size), and the email platform. The most important finding was structural: the company had been treating email at the contact level, but B2B expansion happens at the account level. A single account might contain a power user, a billing admin, and five occasional users — and the upgrade decision belongs to the billing admin, not the power user. We rebuilt the contact model so that every person was tied to an account record with plan, seat usage, and module data attached.

Building the event pipeline. We stood up a customer data pipeline that forwarded the relevant product events into the marketing automation platform in real time: seat invitations, module usage thresholds, login cadence, project creation, file uploads, and collaborator counts. Billing and plan data were joined so that every event could be evaluated against what the account was actually entitled to. This is the step most lifecycle programmes skip, and it is the reason most of them fail — without trustworthy, timely product events, every downstream flow is guessing.

Defining the signals that matter. With events flowing, we formalised the product-qualified lead definitions. An expansion-eligible signal was defined precisely: an account at 80% or more of its seat allowance, a team using a paid module feature five or more times in a week on a plan that excluded it, an account with three or more daily active users for two consecutive weeks, and an account inviting collaborators beyond its plan. Each definition was versioned and reviewed monthly so the scoring model could mature.

Deliverability for B2B inboxes. Pakistani B2B recipients overwhelmingly read mail inside Google Workspace or Microsoft 365, which filter differently from consumer Gmail. We authenticated the sending subdomain (SPF, DKIM, DMARC), separated transactional and marketing streams, and ran monitored seed-list tests against business inboxes to establish a real inbox-placement baseline. The starting placement was 88%, with the worst filtering on Microsoft 365 tenants.

Phase 1 results (by end of week 2):

Diagnostic	Before	After plumbing
Product events reaching email tool	None	14 tracked events, real time
Account-level data model	Contact-only	Account + plan + seats + modules joined
PQL definitions	Informal list	4 versioned, scored signals
Inbox placement (B2B seed test)	88%	96%
Billing admin identifiable per acct	No	Yes, tagged as decision-maker

By the end of the second week, every flow we built afterwards would fire on real, recent usage and reach the person who could actually approve an upgrade.

Phase 2 — Build the Product-Usage Flow Foundation (Weeks 3-5)

With clean signals flowing, we built the lifecycle foundation. The goal of this phase was coverage: attach an automated, behaviour-triggered response to every expansion-relevant moment in the product.

Activation series (4 emails). Expansion starts with accounts that actually reach value. We built a behaviour-triggered activation series for new signups who had not created a first project or invited a teammate within the first few days. The series was contextual: if a signup had created a project but not invited anyone, the email addressed that exact step; if they had done neither, it walked through the single fastest path to a first win. Lifted activation directly grows the pool of future expandable accounts, so this flow was non-negotiable even though it does not ask for money.

Seat and collaborator expansion flow (3 emails). This became the headline revenue driver. When an account reached 80% or more of its seat allowance, or invited a collaborator beyond its plan, the billing admin received a contextual email: the exact usage that triggered it, the next plan up, the per-seat cost, and a one-click path to add seats or upgrade. The email was sent to the decision-maker, not the power user, and it referenced real numbers from that account — “your team invited 3 new collaborators this week and you have 1 seat remaining” — rather than a generic pitch.

Module upsell flow (2 emails). When a team used a paid-module feature heavily on a plan that excluded it, we triggered a consultative upsell offering a 14-day trial of that module rather than asking for an immediate purchase. A trial-first offer reduced the friction of a mid-contract upgrade and let the team feel the value before committing, which is especially important for Pakistani SMB buyers who treat software spend cautiously and often need to justify it internally.

Value-milestone and power-user flow (2 emails). When an account crossed thresholds indicating deep value — 50 or more active projects, or three or more daily active users for two consecutive weeks — it entered a flow that combined a genuine check-in from a named account manager with a soft upgrade nudge. These accounts were the strongest expansion candidates, and the human touch mattered: the email came from a person, offered a short working session, and only then introduced the upgrade option.

Stalled-PQL reactivation. Signals that surfaced but did not convert within a defined window were not discarded. They entered a reactivation flow offering a use-case walkthrough or a brief call, which recovered expansion conversations that would otherwise have gone cold.

Sales handoff for high-value signals. Not every expansion moment should be handled by email alone. We wired the highest-value signals — accounts above a certain seat count showing expansion behaviour — to create a task in the CRM for the account team, so that a human followed up while email handled the long tail. This human-in-loop layer is central to B2B customer journey automation, and it is what kept the programme credible with the sales team rather than competing with it.

Phase 2 results (by end of week 5):

Flow	Status	Early signal (first 2 weeks live)
Activation series	Live	Activation climbing from 31% toward 38%
Seat and collaborator expansion	Live	First seat-upsell orders attributed
Module trial upsell	Live	Trial sign-ups from usage triggers
Value-milestone / power-user	Live	Account-manager meetings booked
Stalled-PQL reactivation	Live	Dormant signals recovering into pipeline

Phase 3 — Optimize and Scale the Expansion Motion (Weeks 4-8)

Once the flows were live and producing revenue, the focus shifted from coverage to performance. This phase overlapped with the tail of Phase 2 because we began optimising each flow as soon as it carried enough volume to read.

A/B testing the high-leverage moments. We ran structured tests on the variables that move expansion most: subject line, sender (account manager versus product team versus founder), offer (immediate upgrade versus module trial), timing relative to the usage event, and the amount of usage data shown in the email. Each test ran to a pre-decided sample size before a winner was declared and locked in. Over the phase this lifted the seat-expansion email’s click-to-meeting rate materially and shifted the module flow decisively toward the trial-first offer.

Targeting the decision-maker, not the user. The single highest-impact change was ensuring upgrade emails reached the billing admin rather than the power user who had triggered the signal. Early versions went to the most active user in the account and performed weakly, because that person rarely held the budget. Retargeting to the tagged decision-maker lifted response rates sharply and is the kind of B2B nuance that consumer-style lifecycle logic gets wrong.

Offer sequencing. Instead of asking every account to upgrade immediately, we sequenced offers by signal strength and account maturity. A first seat-limit trigger offered a low-friction add-seats action; a repeated trigger offered a plan upgrade; a heavy module-usage trigger offered a trial. Matching the ask to the readiness of the account reduced friction and improved conversion across the board.

Activation-to-expansion handoff. We connected the activation flow to the expansion flows so that an account which hit activation milestones progressed cleanly into the usage-tracking layer. This closed the loop between top-of-funnel value and downstream expansion, meaning the work done in Phase 2’s activation series fed directly into Phase 3’s expansion pipeline rather than ending at first value.

Scoring model refinement. With weeks of outcome data, we re-weighted the PQL model. Some signals that looked strong on paper — for example, raw login frequency — turned out to be weak predictors of expansion, while seat-limit proximity and collaborator-overage were far stronger. Retuning the model meant the flows fired on the signals most likely to convert, which reduced noise and improved the sales team’s trust in what reached them.

Phase 3 results (by end of week 8):

Metric	Start of phase	End of week 8
Expansion revenue (monthly)	PKR 1.15M	PKR 1.32M
Email-sourced share of expansion MRR	~9%	22%
PQL-to-upsell conversation	9%	17%
Activation rate (14-day)	36%	42%
Expansion email open rate	24%	35%

Phase 4 — Measure and Compound (Weeks 8-12)

The final phase was about making the expansion engine durable, defensible, and something the company’s own team could run. Revenue from product-usage flows had already overtaken manual account-manager expansion in the mid-market; the task now was attribution clarity and a cadence that kept the numbers climbing.

Attribution and reporting layer. We built a shared dashboard that attributed expansion MRR by source — product-usage email flows, manual account-manager work, organic, and sales-led — alongside PQL pipeline volume, signal-to-conversation rates, and deliverability. This replaced the previous loose sense that “expansion felt better” with a clear, defensible view of which flow earned which rupee, and it gave the leadership team a number it could take into forecasting.

Continuous-iteration cadence. We established a monthly flow-review rhythm: each flow reviewed against target, underperforming variants retired, fatigued creative refreshed, and one new test added. This cadence is what kept expansion climbing past the initial build rather than plateauing after the launch spike that catches so many lifecycle programmes.

Closing the loop with revenue operations. We fed expansion outcomes back into the company’s revenue forecasting so that product-usage expansion became a planned, forecastable channel rather than a surprise. The scoring model and signal definitions were handed to the in-house team with documentation, so the programme did not depend on an external agency to keep running.

The compounding effect showed up clearly. By the 90-day mark, expansion revenue had risen from roughly PKR 1.1M to PKR 1.4M per month, a 27% lift, and email-sourced expansion had grown from a negligible slice to about 28% of all expansion MRR. The accounts being touched each quarter had grown from around 30 to over 220, almost entirely in the long tail that had previously been ignored.

Final Results at 90 Days

Metric	Before	At 90 days	Change
Expansion revenue (monthly)	PKR 1.1M	PKR 1.4M	+27%
Email-sourced share of expansion MRR	~4%	28%	+24 pts
Accounts touched per quarter	~30	220+	+630%
PQL-to-upsell conversation	6%	19%	+13 pts
Module trial-to-paid conversion	n/a	22%	New channel
Activation rate (14-day)	31%	44%	+13 pts
Expansion email open rate	12%	38%	+26 pts
Expansion email click rate	1.4%	6.2%	+343%

Every number traces back to a specific phase: the inbox-placement and data-plumbing gains came from Phase 1, the coverage and first expansion revenue from Phase 2, the conversion-rate improvements from Phase 3’s testing and decision-maker targeting, and the durable attribution and 27% lift from Phase 4’s iteration discipline.

What Made This Work

1. Signal before send. The decisive decision was plumbing product events into the email platform before writing a single flow. Product-usage email only works when it fires on real, recent behaviour, and most lifecycle programmes fail because they skip this step and end up sending educated guesses.

2. Account-level logic, not contact-level. Expansion happens at the account, and the upgrade decision belongs to the billing admin. Rebuilding the data model around accounts and targeting the decision-maker rather than the power user was the change that turned the seat-expansion flow from a weak performer into the headline revenue driver.

3. Human-in-loop for the high end. Email handled the long tail of 290 ignored accounts, while the highest-value signals were routed to the account team for human follow-up. This made the programme an ally of sales rather than a competitor, and it is what let it scale without diluting the mid-market experience.

4. Trial-first offers for cautious buyers. Pakistani SMB buyers treat software spend cautiously and often need to justify it internally. Leading with a 14-day module trial instead of an immediate upgrade ask reduced friction and converted accounts that a hard sell would have pushed away.

5. Attribution made the result real. The dashboard that attributed expansion MRR by source is what turned “email feels like it is helping” into a defensible 28% share of expansion revenue that leadership could take into planning and forecasting.

What Teams Can Apply

For Pakistani SaaS companies that want expansion to earn its keep:

1. Connect your product events to your email platform before you build flows. If your lifecycle programme cannot see what an account did in the product this week, it is sending newsletters in disguise. Trustworthy, timely events are the entire foundation.

2. Model your data at the account level from day one. Know who the decision-maker is inside each account, and send upgrade emails to that person — not to whichever user happens to be most active.

3. Cover the expansion moments that product-usage reveals. Seat-limit proximity, module over-usage, collaborator overage, and value milestones are the four highest-leverage triggers for a B2B SaaS, and they are the flows most teams are not running.

4. Route the biggest signals to a human. Automate the long tail, but let email create the task and have an account manager close the highest-value expansion opportunities. The programme scales further when sales trusts it.

5. Lead with trials, not hard upgrades, for cautious buyers. A module trial or a low-friction add-seats action will out-convert an immediate plan upgrade in a market where software spend is scrutinised.

WeProms Digital has applied this product-usage lifecycle framework across Pakistani B2B SaaS companies in vertical software, HR and payroll, fintech, and marketplace tools — you can see more of the vertical context in our work for digital marketing for SaaS. The specific event definitions, scoring weights, and offers change with each product’s usage model, but the signal-first, account-level, human-in-loop approach stays the same.