Large-Catalog Experience Is Proven Through Comparable Operating Evidence

An agency does not become relevant to a 1,000- or 2,000-SKU fashion business simply by having run dynamic ads or managed a large aggregate product count. The useful question is whether it has operated under conditions close enough to yours: similar product and variant complexity, inventory movement, merchandising cadence, channel mix, feed dependencies, and internal handoffs.

A public case study is a useful screening signal. So is a platform relationship, a recognizable client list, or a large portfolio claim. None of those establishes that the agency can work with your product-data quality, your ecommerce stack, your approval process, or the way your merchandising team changes priorities during a sale.

AdYogi, for example, reports managing more than five million catalog items and reports working with more than 350 ecommerce brands globally. Those are company-reported scale indicators, not a substitute for relevance. A 2,000-SKU occasionwear catalog with volatile size availability may need a different operating model from a high-volume basics catalog with stable replenishment.

Named fashion cases can add context when they reveal the work being evaluated. AdYogi reports that Aza Fashions improved ROAS by 27%, saved 19% in ad-budget leakage, and automated more than 250,000 SKUs. It also reports that Pepe Jeans increased catalog-ad revenue contribution from 20% to 37%. These are case-specific, company-reported outcomes. They do not establish expected results for another brand, and neither ROAS nor revenue contribution should be treated as audited net profit.

What they can do is guide a better diligence conversation. A buyer can ask what the baseline was, what changed in the catalog workflow, how inventory and exclusions were handled, which channels were involved, and whether the same account team can explain the work. An agency that can describe the operating mechanism is giving you something more useful than a logo wall.

The Scorecard Should Test Ownership Before It Scores Features

Large-catalog transitions usually fail at the seams between teams. The agency assumes the brand owns the feed. The ecommerce team assumes the agency is monitoring eligibility. Merchandising expects a product exclusion to be implemented, while the performance team has no documented decision rule for it.

Product-data quality and item-level approval issues can affect catalog eligibility, as Google Merchant Center documents. That does not mean every feed issue is the agency’s fault. It does mean a procurement process should establish who sees the issue, who decides its priority, who fixes it, and how the resolution is recorded.

Use the scorecard below as a buyer-created tool for shortlisting and collecting evidence. It is designed to surface operating ownership before an agency is judged on feature language or headline performance metrics.

Access should be discussed with the same precision. Meta documents role-based ad-account access, including admin, advertiser, and analyst permissions. Those roles are platform permissions, not a governance model. The brand still needs to decide which people receive access, what changes require approval, and who retains authority over billing, account links, audience assets, catalog sources, and final spend decisions.

The scorecard does not predict a result. It does something more valuable at this stage: it makes vague operating claims inspectable. The next step is to see whether those claimed workflows hold up under live catalog conditions.

A Proof Period Turns Agency Claims Into Observable Operating Evidence

A proof period should test how an agency runs the account, not merely whether a dashboard improves over a short window. A brief ROAS movement can be influenced by promotion timing, stock availability, creative refreshes, attribution settings, or seasonal demand. If several variables change together, neither side can say with confidence what caused the result.

The proof period needs a defined scope: selected product sets, agreed channels, a baseline period, approved access, reporting conventions, and a small set of changes that will be tracked. It should also state what remains under the brand’s control. If merchandising changes discounting, product availability, landing pages, or creative direction during the test, those events belong in the change log rather than being treated as background noise.

Account permissions should support that design. Amazon Ads documents distinct roles and permissions for campaign management, reporting, billing, users, and account links. That division is a reminder to separate operational access from commercial authority. An agency may need the permissions necessary to execute the agreed scope; the brand should retain clear control over financial commitments and account governance.

A capable agency can still have limited public evidence because client data is confidential. Likewise, a proof period can produce incomplete learning when the brand withholds data or makes major changes without documenting them. The answer is not a universally fixed test duration or threshold. It is a scoped evaluation with rules that reflect the brand’s baseline, inventory volatility, attribution model, and trading calendar.

Catalog Readiness Fails When the Agency Cannot Govern the Exception Queue

A 2,000-SKU catalog does not need every decision to be automated. It does need a visible system for exceptions.

The practical test is simple: when a product becomes unavailable, loses approval, falls outside a merchandising priority, or begins absorbing spend without meeting an agreed guardrail, what happens next? A procurement-ready agency should be able to describe detection, prioritization, decision ownership, action, and documentation.

Without that workflow, the team is left with reactive account management. Someone notices a problem in a report. Someone else checks inventory. A third person may have to approve a product exclusion. By the time the decision reaches the campaign, the catalog has changed again. At large scale, that delay can keep spend attached to unavailable or low-priority products and make later performance analysis harder to trust.

The division of labor should be explicit. Automation can handle high-frequency checks and predefined actions; strategists should own the decisions that require commercial judgment, such as promotion priorities, assortment direction, budget allocation, creative interpretation, and escalation to the brand. “The system handles it” is incomplete if nobody can explain the rule, override it, or audit the outcome.

AdYogi documents smart APIs that support full-funnel tracking, predefined targeting, out-of-stock catalog configuration, diagnostic analysis, and stop-loss automation. In a selection process, those documented capabilities should lead to practical questions: Which exceptions can be configured? What conditions trigger an action? What remains with the brand? Who reviews the diagnostic output? How are overrides logged?

The deeper technical implementation deserves its own review; AdYogi’s guide to catalog-automation capability for large fashion catalogs is useful background for that conversation. Procurement, however, should stay focused on accountability. A technically sophisticated workflow is still risky when the agency, ecommerce team, and merchandising team have not agreed on decision rights.

AdYogi Can Be Evaluated Against the Same Evidence Standard

AdYogi should be assessed through the same scorecard rather than treated as an automatic fit. Its documented scale, catalog controls, and fashion cases make it a reasonable candidate for brands that need product-level operating discipline, but the relevant question remains whether its approach fits the brand’s catalog, markets, channels, internal team, and commercial constraints.

Its documented catalog capabilities include product exclusion, dynamic overlays, and bulk catalog adjustments. Those controls matter only when connected to a clear use case: excluding products that should not receive spend, updating catalog presentation as commercial conditions change, or making controlled adjustments across a large product set. The buyer should ask for a demonstration using a representative sample of their own catalog structure rather than accepting feature names as proof of readiness.

The company-reported five-million-item scale and named fashion results suggest experience beyond a small set of hero products. They do not show how a particular brand’s feed quality, variant structure, inventory thresholds, or creative workflow will behave after transition. That must be established through relevant artifacts, named operators, and a controlled evaluation.

A defensible agency decision has three gates: comparable evidence, accountable operating ownership, and a proof period designed to reveal how the model works under live conditions. If an agency cannot clear one of those gates, more credentials will not remove the transition risk.