The CAC Problem in E-Commerce: Why Your Feed Is Part of the Fix

E-commerce customer acquisition costs have roughly doubled since 2019. The causes are well documented: platform CPCs rising with advertiser competition, signal loss from iOS privacy changes degrading targeting precision, and a broader market where more brands are bidding for the same consumer attention across the same channels. Most growth teams responded by optimising bids, refreshing creative, and testing new audience segments.

Those are reasonable responses to a demand-side problem. The issue is that a significant portion of e-commerce CAC inflation is a supply-side problem, and the supply side in Shopping advertising is the product feed. When the feed is structured poorly, products appear for irrelevant queries, click-through rates are low, conversion rates on the clicks that occur are poor, and the algorithm deprioritises the listings in subsequent auctions. Every one of those outcomes raises CAC. None of them are addressable through bid management.

A home goods brand running 8,000 SKUs across Google Shopping spent six months testing bid strategies, audience layering, and creative formats before a feed audit identified that 28% of their active products had titles with no colour, material, or size attributes. The algorithm was matching those products to broad, low-intent queries because it had no specific attributes to work with. After restructuring titles for the affected product group, Shopping CAC on that segment dropped 23% within eight weeks. The bids had not changed. The budget had not changed. The feed had changed.

Why E-Commerce CAC Keeps Rising

Before making the case for feed quality as a CAC lever, it is worth being precise about what is actually driving the broader CAC inflation trend. The dynamics are structural, not cyclical, which means they do not reverse when market conditions ease.

Platform CPC inflation is the most visible driver. More advertisers bidding on the same set of high-intent keywords pushes auction clearing prices up independently of individual bid strategy quality. A brand that was paying $0.80 per Shopping click in 2019 may now pay $1.60 for the same placement. The conversion rate on that click has not doubled. The CAC has.

Signal degradation from privacy changes has reduced the precision of audience targeting and dynamic product remarketing. Campaigns that previously relied on granular user-level data to serve highly relevant ads to warm audiences are now working with probabilistic matching and modelled conversions. The result is higher spend to reach the same number of qualified buyers, because the precision that allowed tight targeting has been structurally reduced.

Inventory saturation is the least discussed but increasingly important factor. The number of brands advertising on Google Shopping has grown significantly faster than the available ad inventory in most categories. This is a structural supply and demand imbalance that does not resolve without either new inventory creation or advertiser attrition. In the meantime, it functions as a persistent tax on acquisition costs.

These three forces are real, they are ongoing, and they are largely outside the control of any individual advertiser. The response to them cannot be purely bid-based because bidding higher into an increasingly competitive auction is a strategy with a ceiling. The more productive question is how to improve acquisition efficiency within the auction constraints, which is where feed quality enters the picture.

The Feed’s Role in Acquisition Efficiency

Google’s Shopping auction does not simply rank products by bid. It evaluates the relevance of each product to a given search query, scores that relevance against competing products, and uses the relevance score to determine both eligibility to appear and position within the auction. Bid determines position within a relevance tier. It does not override relevance entirely.

This means that a product with a well-structured feed that is highly relevant to a query competes from a stronger position than a product with an equivalent or even slightly higher bid but poor feed data. Feed quality is, in effect, a silent co-bidder. It raises your competitive position without requiring a higher CPC, and it lowers your effective cost per relevant impression by reducing spend on queries where your product appears but was never going to convert.

The mechanism is direct. A poorly structured title, missing attributes, or stale product data causes Google to match the product to a broader, less specific set of queries. Some of those queries are relevant. Many are not. The brand pays for clicks from both groups. The conversion rate across the mixed traffic is depressed, the ROAS signal the algorithm receives is weak, and the algorithm responds by reducing the product’s competitiveness in the auctions where it actually performs well. The poor feed creates a negative feedback loop that bid adjustments cannot break.

Three Feed Improvements That Directly Impact CAC

Not all feed improvements have equal impact on acquisition cost. The three improvements below address different points in the auction and conversion process, and together they produce a compounding effect on CAC rather than an additive one.

How Feed Quality Affects Auction Competitiveness

The connection between feed quality and auction competitiveness operates through Google’s Quality Score equivalent for Shopping, sometimes referred to as product quality score, which factors into the effective CPM paid for impressions and the position achieved at a given bid level.

When a product consistently generates strong CTR relative to its impression volume for a given query set, Google interprets this as a positive relevance signal and begins prioritising the product in future auctions for similar queries. The reverse is also true: products with chronically low CTR relative to category benchmarks are progressively deprioritised, which means they need to bid higher to achieve the same position, increasing effective CPC over time.

Title quality directly influences this dynamic because CTR is partially a function of how well the title text matches the shopper’s query intent. A title that contains the exact attribute the shopper searched for generates a higher CTR than a generic title for the same product at the same position and price. CTR improvement from title optimisation therefore compounds: better CTR improves the product’s quality signal, which improves its competitiveness in future auctions, which allows it to maintain position at a lower CPC over time.

This compounding effect is why feed quality improvements tend to deliver lasting CAC reductions rather than one-time adjustments. A bid change alters performance immediately and can be reversed immediately. A structural improvement to feed data changes how the algorithm evaluates the product, and those changes persist and compound through subsequent auction cycles.

Measuring Feed-Driven CAC Improvement

Attributing CAC improvement to feed changes rather than to other concurrent optimisations requires a structured measurement approach. Without it, the CAC reduction from a feed improvement is invisible in aggregate reporting and the work goes uncredited in future budget conversations.

Isolate the test group. Apply feed improvements to a defined product segment tagged with a custom label, and maintain a control group of similar products with unchanged feed data running in parallel campaigns or ad groups. This is the only way to separate feed-driven performance changes from market fluctuations, seasonal effects, and other campaign-level changes happening simultaneously.

Track the right metrics in sequence. Feed improvements affect metrics in a specific order: impression share on target queries improves first as relevance scoring adjusts, followed by CTR as the improved title matches shopper intent more precisely, followed by conversion rate as the query-to-landing-page match tightens. CAC improvement is the downstream outcome of all three. Measuring only the final metric without tracking the upstream changes makes it impossible to understand which feed improvement drove which result.

Use a four-week minimum measurement window. Google re-indexes product feeds and adjusts auction behaviour over days to weeks following a feed change. Performance measured in the first week after a feed update reflects a transitional state, not the stable outcome. Four weeks of post-change data compared against four weeks of pre-change baseline gives a reliable signal of the actual impact.

Platforms like EnhanceFeed maintain a continuous connection between feed attribute changes and downstream performance metrics, making this measurement process systematic rather than manual. For teams managing frequent feed updates across large catalogues, the ability to trace a specific CAC change to a specific feed modification is the difference between feed quality being a recognised performance lever and an invisible background process.

The Cheapest Way to Lower CAC Is to Fix Your Data

The conventional response to rising e-commerce CAC is to spend more carefully: tighter audience targeting, smarter bidding, better creative. These are legitimate optimisations and they produce real results. But they operate within the constraints set by the product data the campaign is built on. If that data is vague, inconsistent, or structurally misaligned with how buyers search, no amount of bid intelligence resolves the underlying mismatch.

The brands that have navigated the CAC inflation environment most effectively are not uniformly those with the largest budgets or the most sophisticated bidding technology. Many of them are brands that at some point recognised that their feed was a performance asset requiring active management, not a static configuration file uploaded once and forgotten.

Feed quality is not glamorous work. It does not come with the immediate feedback loop of a bid change or the visible impact of a creative refresh. But its effects are structural and compounding, and they change the economics of every auction the catalogue enters. In an environment where the demand-side costs of acquisition are rising independently of anything a brand does, improving the efficiency of the auction entry point is the highest-leverage intervention available.

The data is the bid. Make it work harder.