How TIMUS Built Its First D2C Revenue Channel — and Found the Checkout Problem That Costs Indian Brands Lakhs Before Anyone Notices

Established offline brand. Known product. B2B distribution across the market. And a D2C website that had never generated a single rupee from paid advertising. When the co-founder of TIMUS onboarded in June 2025, their one word to describe their feelings toward their current marketing situation was not frustrated or anxious. It was: Inspired. They could see where this could go. What they hadn't built yet was the system to get there. Within two weeks of launching ads, ₹21,000 had been spent. There were 100+ add-to-carts on the account. There was exactly one order. The problem was not the audience. It was not the creative. It was two structural issues hiding beneath the surface — one specific to India's COD economy, one sitting directly at the checkout — that had to be diagnosed and resolved before a single rupee of ad spend could compound into real D2C revenue. Two months after going live, TIMUS had its first ₹2.5 lakh month from D2C. Zero before. One lakh after.

BRAND SNAPSHOT

• Industry: Luggage & Travel Accessories (D2C)

• Category: Premium hard luggage and backpacks — Starlite, Neolite, Leolite series (hard luggage); Atomic, Paris, London, Sydney, Chile, Here I Am (backpacks)

• Geography: India

• Stage: ₹0/month D2C → ₹2,50,000/month D2C online revenue

• Pre-Arlox Context: 7–10L/month in total offline and marketplace revenue (B2B model); zero D2C website sales

• Services: Meta Ads Strategy, Creative Development, COD Fraud Management, Website CRO, Catalog Retargeting, Google Search Ads

THE PROBLEM

TIMUS is a luggage brand built on the offline market. Distributors. Retailers. B2B relationships that generated 7 to 10 lakhs in monthly revenue without a single D2C sale. The product had earned its place in the market. What hadn't been built was a direct channel to the end customer.

The decision to go D2C was not born from desperation. The founders came in inspired — clear-eyed about the opportunity, committed to building something with a partner who understood performance marketing. The goals were ambitious: 50 lakhs to 2 crore in online revenue. The KPIs: revenue, conversion rate, high ROI, high brand awareness.

Two structural realities complicated the launch.

First: TIMUS had no D2C customer data. Offline, they operated B2B — goods moved through distributors, not directly to consumers. There were no phone numbers, no email lists, no purchase history from real end buyers. Cold-audience targeting would have to work from scratch, without the lookalike advantage that brands with even a modest customer database could build from.

Second: The website had been positioned as a discount destination. A "70% Off Sitewide" tag was running on the homepage. For a premium luggage brand looking to compete on quality and design, this was signalling the opposite of what the brand stood for before the first ad had ever gone live.

Both of these were resolved before the campaigns launched. But what no pre-launch audit revealed were the two deeper problems waiting on the other side of the first rupee spent.

WHY IT WAS HAPPENING

The add-to-cart to purchase gap was the real bottleneck — not awareness or traffic. Within two weeks of launch, the account had accumulated 100+ add-to-carts. These numbers looked like early proof of demand. They were. But they were also concealing the fact that nearly none of those sessions were completing the purchase. The cost per add-to-cart was ₹782 — within benchmark. The cost per checkout initiated was over ₹1,000 — slightly elevated, but manageable. The problem was that even customers who reached checkout were not converting. Something at the checkout level was breaking the session.

COD orders from high-risk delivery zones were flooding the account and distorting the data. India's COD model carries a specific risk that digital-first brands learn about the hard way. When ads reached tier-3 cities and high-risk delivery pincodes, orders came in — but many were from buyers who either didn't answer confirmation calls, gave invalid contact information, or simply never intended to accept delivery. These orders looked like conversions in the ad dashboard. In reality, they were dead-end sessions burning budget and feeding the algorithm the wrong signal. "We are receiving COD order from High Risk Area and irrelevant order from town and village," the TIMUS team flagged in late June. "As we have come across 70K ads spent till yesterday not getting relevant orders." The traffic was real. The purchases were not.

Creative resonance was not yet matched to the product. The initial hold rate on ads — the percentage of viewers who watch past the first few seconds — was 5%. For a visually driven premium product category, this meant creatives were not stopping the scroll. The audience was seeing the ad and moving on. Video quality, hook design, and angle specificity all needed to improve before the account could compound toward meaningful ROAS.

There was no offline customer data available to seed lookalike audiences. When the team asked for offline customer contact information to build lookalike audiences from actual buyers, the answer was clear: "Offline data is difficult to get as we are into B2B — not direct to customer." Cold audiences would have to be built from first principles, using interest targeting and behavioural data until the pixel accumulated enough purchase signals from D2C buyers to feed lookalikes organically.

THE SOLUTION

Mythos — Creative Advantage:

The initial creative set launched across hard luggage. When product misalignment was flagged — the team had initially included soft luggage, which the brand confirmed was already performing well through other channels — the focus shifted immediately: Starlite Ivory White, Neolite, and specific backpack SKUs became the creative priority. Product-specific ad sets were built around the categories with the strongest D2C conversion potential.

As the engagement progressed, the creative strategy evolved through two phases. The first was UGC-first: Instagram Reels the brand had already produced — shot by the TIMUS team for organic channels — were identified and repurposed into paid ad formats. These outperformed the initial static and video sets because they carried a human quality that brand-produced content often lacks. The second was AI-assisted creative testing: a new generation of creatives built around the "bag as an investment" angle, developed through iterative creative briefing. The outcome on this angle was measurable — the AI-powered backpack creative generated 2 sales in 5 days and delivered a 40% lower cost per add-to-cart than the previous creative iteration.

Seasonal campaign layers were added to build purchase urgency at natural inflection points: a Rakhi offer campaign with a specific discount code (₹299 off on purchases above ₹2,000), and an Independence Day Freedom Sale. Each used brand-consistent messaging rather than generic sale language.

Sentinel — Scientific Media Buying:

The account launched under constraint. Meta's learning phase requires time and transaction volume before the algorithm can optimise effectively, and with no prior pixel data, the first weeks were a deliberate accumulation of signals rather than an expectation of profitable ROAS from day one.

The COD fraud problem was the first system-level fix. The Arlox team flagged the pattern, identified the high-risk delivery zones draining budget, and worked with TIMUS to configure OTP verification on COD orders and partial COD requirements for high-risk customers — while keeping standard COD active for low-risk buyers to avoid cutting off real purchasers. "I have updated the rules and criteria in COD and Partial COD," the TIMUS operations team confirmed after the fix was implemented.

Product focus was refined through live data. The debate between soft luggage and hard luggage was settled not by opinion but by what the data said about where D2C demand concentrated: hard luggage (Starlite, Neolite) and specific backpack SKUs (Atomic, Paris) became the campaign anchors once early signals revealed category preferences.

The ROAS trajectory captured the compounding effect of these fixes across July. Over the 7-day window from July 15–21, total ad spend was ₹11,540 against ₹8,045 in sales — a 0.70x ROAS. In the final 4 days of that same window, the same ₹8,045 in revenue came against only ₹4,954 in spend — a 1.62x ROAS. The account was improving within the same reporting period as fixes compounded.

Catalog-based retargeting was layered in to re-engage the high-ATC but low-purchase sessions. Microsoft Clarity was integrated to record session behaviour up to the add-to-cart stage and identify the friction points in the pre-checkout funnel. Google Search Ads were added in August to capture high-intent search traffic — buyers actively looking for luggage and backpacks — operating alongside the Meta brand-awareness and retargeting stack.

Vault — Brand Value Engine:

The "70% Off Sitewide" tag was removed before ads went live. For a premium luggage brand, a blanket discount signal at the homepage level undermines price positioning before a buyer has seen a single product. Removal was immediate.

The website audit went deeper. A full Figma-based CRO review of the hard luggage product detail pages was produced and delivered — identifying specific changes needed to closing copy, comparison tables, delivery expectation clarity, CTA placement, and PDP visual hierarchy. The team also recommended introducing an extended warranty offer for online purchasers as a trust-building conversion lever unique to the D2C channel — something offline and marketplace competitors couldn't easily replicate.

THE RESULTS

• ₹0 → ₹2,05,000/month — D2C online revenue built from zero in 2 months

• 1.62x ROAS (4-day trailing window) — July 2025, improving from 0.70x in the prior 7-day window

• 40% lower cost per add-to-cart — AI-powered "bag as investment" creative vs. prior creative iteration

• ₹782 cost per add-to-cart — within KPI benchmark, confirming product-market demand existed from the start

• COD fraud eliminated from high-risk zones — partial COD and OTP verification implemented without blocking legitimate buyers

• 2 ad channels live by end of engagement window — Meta (brand awareness + retargeting) and Google Search (high-intent capture)

• "70% Off" discount positioning removed — brand repositioned at full premium price before first paid impression

LESSONS FOR SIMILAR BRANDS

• "High add-to-carts means our ads are working." One hundred add-to-carts with a single order is not a success signal. It is a diagnostic. Traffic was reaching the product pages. Something between the product page and the confirmation screen was killing the session. For TIMUS, the culprits were two: COD fraud from high-risk pincodes injecting false conversion signals into the algorithm, and a checkout experience that was not yet optimised for a buyer encountering the brand for the first time online. Add-to-cart is interest. Purchase is conversion. The gap between them is where most D2C campaigns bleed.

• "COD is just a payment option — it doesn't need special management." In India's D2C market, COD is not just a payment option. It is a fraud vector if left unmanaged. Small towns and high-risk delivery zones generate orders that look like conversions in your ad account but result in returns, undelivered parcels, and wasted logistics spend. The buyers who flood these orders are often not genuine purchasers. The damage is compounded because the Meta algorithm reads these as purchase signals and starts optimising your budget toward the same high-risk zones. TIMUS lost tens of thousands of rupees to this pattern before it was identified and fixed. OTP verification and partial COD requirements for high-risk buyers resolved it without blocking genuine D2C customers.

• "We can use our offline customers to seed our D2C strategy." TIMUS's offline business was B2B — goods moved through distributors, not directly to consumers. There was no customer database to build lookalike audiences from. Every cold-audience assumption that a brand with an existing D2C customer list could shortcut had to be built from scratch. For offline-dominant brands transitioning to D2C, the audience architecture starts at zero. The pixel needs time and real D2C purchase data before it can optimise effectively. The first two to four weeks are a data accumulation phase — not a revenue phase — and the budget deployed in that window is buying signal, not just sales.

CHALLENGES WE FACED

• COD fraud disrupted the optimization signal before it could be identified. The first weeks of the account accumulated what appeared to be purchase signals — but a large portion were from COD orders placed in high-risk delivery zones that were never going to convert to real deliveries. These signals were training the algorithm toward the wrong audiences. By the time the pattern was clear enough to diagnose, the account had spent over ₹70,000 into a distorted signal pool. The fix required configuring COD rules, partial COD gating, and OTP verification at the checkout level — none of which had been in place at launch.

• Insufficient checkout visibility limited funnel diagnosis. Microsoft Clarity records session behaviour up to the add-to-cart stage, but cannot capture the post-ATC checkout journey due to privacy restrictions. With 100+ add-to-carts and a single order in the first two weeks, the exact moment of checkout abandonment was not directly visible in the session recordings. The team had to infer the drop-off cause from indirect signals — order patterns, COD zone analysis, and checkout initiation rates — rather than watching the session break in real time.

• Product category debates slowed early focus. The initial campaign included soft luggage alongside hard luggage, despite the brand's own offline data showing soft luggage was already selling well through other channels. Resolving the product focus — hard luggage and selected backpack SKUs only — required back-and-forth between the creative team, the account manager, and the brand, and cost days that could have been spent in-market testing the right creative mix. The data ultimately settled the debate, but the delay had a cost.

• No offline customer data meant cold audiences had to be built entirely from first principles. With a B2B offline model and no D2C purchase history, there was no customer list to seed lookalike audiences from. This extended the learning phase and meant early campaigns relied entirely on interest and behavioural targeting while the pixel accumulated D2C-specific purchase signals. Brands transitioning from offline-dominant to D2C should expect this gap and plan the initial budget accordingly.

BELIEFS CHANGED

• "Offline revenue gives us an advantage when launching D2C." TIMUS had 7–10 lakhs in monthly offline revenue and an established B2B distribution network. None of that translated into a D2C head start. The offline buyers were reached through distributors, not directly. Their data didn't exist in a form that could be used. The brand's offline credibility was real — but it did not shorten the D2C cold-start period. The D2C channel required building its own customer acquisition infrastructure from scratch, as if the offline business did not exist.

• "Getting people to add to cart means the product-market fit is validated online." The 100+ add-to-carts in the first two weeks were a genuine demand signal. The product was right. The category had interest online. But the signal was also being contaminated by COD fraud orders that looked like purchases, and a checkout experience that was not yet converting genuine interest into confirmed orders. Add-to-cart volume is a leading indicator of demand. It is not a lagging indicator of revenue. The gap between them is operational — not creative.