Kick Ads

Industry Insights

Ecommerce advertising should be judged on ROAS.

Getting more traffic is the easy part. Knowing which product and which campaign the next dollar should go to is not. That depends on which products actually produce revenue, on whether the site converts the traffic it already has, and on whether that revenue reaches the account accurately.

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What Makes This Industry Different

Not every product deserves the same budget.

Different products have different demand, margins, stock levels and conversion rates. Treating them equally can leave strong products underfunded while weaker products continue spending.

The pattern repeats at campaign level. Shopping and Performance Max both bid on product data, so a weak feed or a loose product grouping quietly moves budget toward whatever is cheapest to serve, not whatever sells. The account still reports a reasonable blended ROAS while the mix underneath it drifts.

For ecommerce accounts, the job is not simply to increase traffic. It is to understand which products and campaigns are actually creating profitable revenue, then move budget toward them deliberately rather than letting the platform decide by default.

From the Accounts

What the accounts we manage actually show

20% → 36%

Brand Search share of Google spend, against its share of value

3x

Spread in average order value between accounts

+14%

Paid clicks on a Sunday, against an average day

10%

Share of product spend behind 80% of product value

00Headline finding

Brand Search took a fifth of the budget and returned a third of the value.

Share of Google spendShare of conversion value

Account 01

44%
63%

Account 02

27%
58%

Account 03

17%
45%

Account 04

20%
36%

Account 05

18%
31%

Account 06

6%
8%
Six of the 13 accounts, from the strongest brand share to the weakest. The pattern held in all 13

Across the Hong Kong ecommerce accounts we manage, the brand Search campaign takes about 20% of Google spend and produces about 36% of conversion value.

What we do with it

Brand Search reaches people who were already going to buy. Part of a blended ROAS is therefore a measure of brand recognition, and a prospecting campaign has to be judged against non-brand return instead.

01Order value

Basket size is a property of the shop, not of the media

Most accounts
100
Median = 100
42
Lowest
131
Highest

Across the Hong Kong ecommerce accounts we manage, average order value runs about three times higher in the top account than the bottom one, and most accounts cluster in the middle. Within a single account it barely moves. Not by channel, not by device, not across the summer, even while conversion rate moved by half.

What we do with it

Paid media does not raise a basket. When a target needs larger orders, that is a merchandising, bundling or pricing decision, and we would say so rather than promise it from the ad account.

02Weekly rhythm

Sunday brings more clicks in every account. It does not bring better ones.

An average day for that account
1.00

Sunday

1.14

Monday

0.98

Tuesday

0.97

Wednesday

0.98

Thursday

0.92

Friday

1.00

Saturday

1.05

Across the Hong Kong ecommerce accounts we manage, Sunday carries about 14% more paid clicks than an average day, in every account. Thursday is the quietest, again in every account. Sunday clicks also cost slightly less. What Sunday does not do is convert better: people who click on a Sunday buy at about the same rate and spend about the same amount. There are simply more of them.

What we do with it

A day that brings more traffic at the same conversion rate is a budget question rather than a targeting one. We would let budget follow the demand instead of building day-of-week bid rules around a quality difference that is not there.

03Sale periods

Sale periods are a planning problem before they are a bidding problem

Hong Kong ecommerce runs on a crowded calendar: 618, back to school, 11.11, Black Friday, Christmas and Chinese New Year, with a quiet stretch after each one. Auction prices rise while everyone is bidding on the same weeks, and the offer, the stock position and the creative all have to be settled before the traffic arrives.

What we do with it

We plan the sale calendar with the client several weeks ahead: which offer goes live, which products carry it, what the budget looks like on the peak days, and what happens to spend in the quiet weeks afterwards. Raising budgets on the day is the least useful thing to do.

04Catalogue

In most accounts, a small share of product spend produced most of the product revenue

10%Share of product spend
80%Share of product conversion value

In most accounts a small group of products carries the revenue. That group produces 80% of Shopping and Performance Max conversion value on about a tenth of the product spend, and most of the rest goes to products that sell nothing.

What we do with it

Budget set at account or campaign level cannot reach this. Before adding spend we look at which products carry the value, then at whether the feed, product groups and stock let budget follow them.

05Channel roles

Only brand Search returned more value than it cost

Value share equals cost share
1.00

Brand Search

1.79

Non-brand Search

0.74

Performance Max

0.83

In most of these accounts Performance Max returns a smaller share of value than its share of spend. Once brand is separated out, non-brand Search and Performance Max return value in roughly the proportion they cost.

What we do with it

When an account looks like Performance Max is underperforming Search, it is usually the brand campaign talking. We compare Performance Max against non-brand Search and judge it on the value it adds.

How these figures were produced

Based on anonymised Google Ads and Meta Ads accounts managed by Kick Ads, Hong Kong ecommerce, mid-2026. Order value figures are from Hong Kong dollar accounts only.

At a Glance

Across the Hong Kong ecommerce accounts Kick Ads manages, the brand Search campaign takes about 20% of Google spend and returns about 36% of conversion value, and average order value across those accounts spans about three times end to end. Kick Ads manages ecommerce paid media across Google Shopping, Performance Max, Search and Meta Ads, including product feed review, campaign structure, revenue tracking and landing page review.

Shopping + Performance MaxMeta catalogue + retargetingProduct feed + trackingROAS by product / category

Diagnostics

What we check first

01

Products and feeds

Merchant Center, product titles, feed errors, catalogue structure and how products are grouped

02

Campaign structure

Search, Shopping, Performance Max and Meta, including the role of brand, category and prospecting campaigns

03

The conversion action and revenue

What the account counts as a conversion, before GA4, Pixel or CAPI. One account we reviewed counted add-to-cart as revenue, which made an ordinary campaign look like its best

04

Website and checkout

Product pages, landing pages, mobile experience and checkout issues that may be limiting paid traffic

Channels

The campaign mix we usually run for ecommerce

Before they search

Meta Awareness and reach

Support

Run before a launch or a sale period, so the conversion campaigns have an audience to draw on when the offer goes live

Demand Gen

Test

Worth testing where the products are visual and there is enough creative to keep feeding it

While they are searching

Brand Search

Core

Protects branded demand and keeps the message under our control at the moment intent is highest

Shopping and Performance Max

Core

Carries product-level demand. Split by product group, category or margin rather than letting one campaign hold the whole catalogue.

Non-brand Search

Support

Category and product terms. Judge it on its own return rather than on the blended figure the brand campaign is holding up.

While they are comparing

Advantage+ Shopping (ASC)

Core

Meta's main purchase campaign. It reaches new and returning buyers together, so the new-customer budget cap is a decision to make rather than leave at its default.

Catalogue and dynamic product ads

Support

Brings back people who viewed a product or added to cart. The catalogue feed has to be clean before this is worth scaling.

What the results tell us

Revenue tracking and feed health

Purchase value, Merchant Center feed status and product-level reporting, so budget decisions rest on revenue rather than clicks

Informs the next budget decision

The calendar

The Hong Kong retail calendar we plan against

Hong Kong ecommerce runs on a crowded calendar, and the quiet stretch after each peak matters as much as the peak. Offer, stock and creative all have to be settled before the traffic arrives.

  1. Jan to Feb

    Chinese New Year. Gifting, hampers and anything red sells; logistics shut for about a week.

    We front-load the two weeks before, then cut budget hard over the holiday itself rather than paying for clicks nobody can fulfil.

  2. Apr to Jun

    Mother's Day, then 618. 618 is the first real test of the year for most catalogues.

    We build a dedicated 618 campaign rather than lifting budgets across the account, and we decide which products carry it several weeks ahead.

  3. Jul to Aug

    Summer sale and back to school. Two different audiences in the same eight weeks.

    We separate them. A clearance audience and a back-to-school parent do not respond to the same creative or the same offer.

  4. Sep to Oct

    Mid-Autumn, then National Day golden week. Also when 11.11 stock decisions have to be made.

    We use this window to test the creative angles that will run at 11.11, while auction prices are still normal.

  5. Nov

    11.11, Black Friday and Cyber Monday inside four weeks. Auction prices rise because everyone is bidding on the same days.

    We budget for the peak days specifically and accept a higher cost per click, because the alternative is being outbid on the only days that matter.

  6. Dec to Jan

    Christmas, then a genuinely quiet January before Chinese New Year picks up.

    We plan the January dip in advance instead of reading it as a problem, and use it to fix the feed and the product pages.

This calendar comes from running these accounts, not from a measurement. Chinese New Year moves between January and February, so the dates around it shift each year.

One client

A fashion retailer · Hong Kong

The problem

An international fashion brand's Hong Kong account had grown campaign by campaign without a structure behind it, so budget was not following the products that were selling and the bidding had nothing clean to optimise against.

What we changed

We rebuilt the account around three separate jobs: brand demand, product category, and the products actually producing revenue. Brand was split out so it stopped flattering the account average. Categories were separated so a strong one could be funded without carrying a weak one. Product groups were rebuilt off the feed so bidding had clean data to work with, and budget was then set to follow the products producing revenue rather than whichever campaign was cheapest to serve.

What moved

+217%ROAS, year on year
−66%Cost per acquisition, year on year
+194%Conversions, year on year

Common problems

  • Increasing PMAX budget before fixing feed issues
  • Looking only at blended ROAS
  • Treating every product equally
  • Mixing brand and non-brand demand
  • Running Meta without enough creative variation
  • Weak revenue tracking
  • Reading an exceptional day as proof the campaign structure worked
  • Sending paid traffic to weak product or collection pages

Results

Selected ecommerce results

View all results

Beauty Ecommerce · APAC & UK

3 → 6
Markets

Expanded from three markets to six. +45% HK ROAS, +90% AU monthly ROAS.

Home Appliances Ecommerce · Hong Kong

+109%
Google Ads ROAS

+92% paid traffic value uplift

Sports Retail · Hong Kong

10x+
Annual ROAS

17–21x during the summer peak

Where figures are shown, they are based on anonymised campaign data reviewed by Kick Ads.

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Ecommerce AdsGoogle AdsMeta AdsLanding Page Optimisation

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FAQ

Common questions

Want to know where your ecommerce budget should go?

Send us your store and current advertising setup. We can look at product feed, campaign structure, tracking, product priorities and landing pages before recommending what to change.

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