Context
Nap Chief is a South India kidswear brand making organic hosiery from Modal, a plant-based regenerative fibre. Their catalogue included official licensed merchandise — Harry Potter and Looney Tunes — alongside their core organic range.
They were already running Facebook ads and getting 1x to 2x return. That's the level where paid media roughly breaks even and nobody can tell whether it's working. With thin apparel margins, break-even wasn't good enough.
I took over the account and ran acquisition, retention, creative direction, and campaign architecture.
Problem
Three problems stacked on top of each other:
The account was episodic, not systematic. Results came from occasional campaigns that happened to work, with no structure explaining why. The client's own framing was that they wanted to stop relying on "one lucky shot campaign."
Nothing existed below the top of the funnel. Spend was going to cold acquisition. Visitors who browsed, added to cart, or started checkout and dropped were not being brought back. In apparel, that's where most of the recoverable revenue sits.
Margins left no room for inefficiency. At 1–2x ROAS on thin apparel margins, the account was consuming working capital to stand still.
Hypothesis
The account didn't need better ads at the top. It needed a funnel underneath the top. If every stage of intent — browsed, considered, added to cart, abandoned checkout, purchased — had its own campaign, its own audience definition and its own creative, the same traffic would produce several times the revenue, because most of it was currently being bought once and then discarded.
What I built
Full-funnel audience architecture. Segmented the entire audience by intent stage: top of funnel, showed consideration, expressed interest, took action, and existing customers. Each stage got dedicated campaigns rather than sharing one.
Recency-based retargeting windows. Middle-funnel audiences — social engagers, site visitors, and lookalikes built from them — were subdivided by recency at 3, 14, 30, 120, and 180 days. A visitor from three days ago and one from four months ago are different people commercially, and were treated as such.
Bottom-funnel abandonment recovery. Separate campaigns for cart abandoners, checkout abandoners, and payment abandoners. This tier recovered a significant share of otherwise-lost purchases and carried the highest return in the account.
Post-purchase retention campaigns. A dedicated sequence to existing customers — fewer purchases, but the highest ROI of any stage, and the foundation for repeat rate in a category where children outgrow products on a predictable cycle.
Creative testing to a winner format. Tested images, carousels, collections and video against each other. Video won decisively, so I scaled behind video and wrote copy to match the funnel stage and offer rather than running one message everywhere.
Collection pages over product pages. Directed ad traffic to collection pages rather than individual products, specifically to raise average order value by giving buyers a basket rather than an item.
Structured account architecture. One campaign each for mothers and fathers narrowed by parent status, up to six ad sets per campaign, two to three creatives live per ad set at all times. Winning ad sets were duplicated into CBO campaigns at higher manual bid to run discounted sale periods.
Result
Takeover month — Meta Ads Manager
| Metric | Value |
|---|---|
| Campaigns running | 134 |
| Ad spend | ₹197,937 |
| Purchase conversion value | ₹966,285 |
| Purchase ROAS | 4.88x |
| Purchases | 535 |
| Cost per purchase | ₹369.98 |
| Checkouts initiated | 3,104 |
| Checkout-initiated conversion value | ₹4,009,236 |
| Add-to-cart conversion value | ₹1,194,272 |
Top-performing campaigns individually returned 7.25x, 8.49x and 9.06x — the video conversion campaigns and the lookalike stack built on view-content, purchase and add-to-cart audiences.
Ten-month store performance
Oct 1 2020 – Jul 31 2021, compared against Dec 2 2019 – Sep 30 2020.
| Metric | Value | Change |
|---|---|---|
| Total sales | ₹1,29,73,114 (~$164K) | +485% |
| Online store revenue | ₹1,29,68,309 | +487% |
| Total orders | 8,043 | +509% |
| Online store sessions | 580,414 | +476% |
| Visitors | 550,755 | +481% |
Daily revenue across the full period, showing the point where scaling restarted in November 2020 once new stock landed:
The ratio that matters: orders grew 509% against 476% growth in sessions. Traffic and orders scaled together, with orders slightly ahead — meaning conversion rate held while volume grew five-fold. Scaling this hard usually costs conversion efficiency. Here it didn't, because the retargeting and abandonment tiers absorbed the extra traffic instead of letting it leak.
What constrained the result
Inventory. Scaling paused between the May 2020 takeover and November 2020 because stock ran out, which is visible as the flat stretch at the left of the daily revenue chart. The account had already proven 4.88x by then; it simply had nothing to sell. Growth resumed the month new stock landed.
What I'd do next
- Move retention off paid. The post-purchase campaigns had the best ROI in the account, which is a signal that the same sequence belongs in email and WhatsApp where it costs nothing per send. Paid retargeting to existing customers is the expensive version of a flow.
- Build a size-based repeat cycle. Kidswear has the most predictable repurchase trigger in apparel — children outgrow things on a schedule. Segmenting past buyers by child age and timing campaigns to size transitions is a structural advantage this account never used.
- Split licensed and organic ranges. Harry Potter and Looney Tunes buyers are shopping for a character; organic-Modal buyers are shopping for a material. Those are different intents sharing one account and probably different lookalike seeds.
- Forecast inventory against ad spend. The single biggest cost in this engagement was six months of paused scaling. Tying media planning to stock levels would have prevented it.
What this demonstrates
- Turning an account around, not just running one. Inherited 1–2x, delivered 4.88x, with the before-state documented.
- Funnel architecture as the lever. The gain came from building intent-stage structure beneath the top of funnel, not from more budget or better bidding.
- Scaling without efficiency loss. 476% more traffic with orders up 509% — conversion rate held through five-fold growth.
- Commercial framing. Collection pages over product pages to lift AOV; abandonment tiers prioritised because that's where recoverable revenue sits in apparel.