Contact

Back to Insights

10 eCommerce Quick Wins That Can Increase Revenue

, ,

Major ecommerce growth does not always require a new platform, a redesigned storefront or a multi-year transformation program.

Established retailers often have substantial revenue opportunities hidden inside their existing customer journey: unsuccessful searches, unclear availability, weak product information, poorly timed delivery costs and missed basket-building opportunities.

These problems may appear small in isolation. At scale, they affect thousands of sessions and can create material revenue leakage.

The most effective ecommerce quick wins share four characteristics:

The objective is not simply to increase conversion. Retailers should also consider gross profit, fulfilment cost, customer lifetime value and the effect on other channels.

Here are ten practical opportunities that can influence revenue quickly while creating evidence for longer-term investment.

1. Repair the highest-value failed searches

Customers who use site search are often expressing clear purchase intent. When the search engine fails, retailers lose demand that has already reached the storefront.

Common problems include:

Begin by extracting the most frequent zero-result queries and the search terms with high exit rates.

The fastest improvements may involve:

Do not optimise solely for the percentage of searches returning a result. A search engine can return technically valid but commercially irrelevant products.

Measure:

A retailer should be able to identify how much demand currently reaches a dead end and what proportion can be recovered.

2. Make availability visible before checkout

Customers should not need to add a product to their cart or enter an address before learning whether they can obtain it.

Availability should be visible on product and category pages wherever the underlying data is dependable.

Depending on the retail model, this may include:

This reduces uncertainty and helps customers choose products that can fulfil their immediate need.

Local availability can also improve the productivity of the store network. Customers can be directed towards stock already positioned nearby rather than relying on central fulfilment.

The quick win is not necessarily a new inventory system. It may be improving how existing availability data is communicated, changing when the customer selects a location or removing ambiguous stock messages.

Measure:

Availability should be treated as a revenue metric, not only a supply-chain measure.

3. Put the delivery promise on the product page

Delivery costs and timing frequently appear too late in the customer journey.

A customer may spend several minutes evaluating a product, only to discover at checkout that delivery is expensive, slow or unavailable.

Bringing useful fulfilment information forward can remove this uncertainty.

Product pages should communicate:

The information does not need to be excessively detailed. It needs to answer the customer’s immediate question: “How and when can I get this?”

Delivery has become part of the purchase decision. Australia Post reports that 73% of Australian shoppers are more likely to shop online following a good delivery experience, while 69% want multiple delivery options. Australia Post ecommerce trends

Measure:

The final measure matters. A delivery proposition that increases orders but destroys contribution margin is not a commercial success.

4. Improve the product information that prevents purchase

Retailers do not need to rewrite their entire catalogue to improve product-page performance.

Start by identifying the information customers need in high-traffic, low-converting categories.

Signals may include:

The missing information may be:

The fastest approach is to define the few attributes that materially affect purchase in each priority category, then make them consistent and visible.

Do not hide decisive information in downloadable documents if it can be presented directly on the page.

Measure:

This work also improves search, filters, recommendations, marketplaces and AI-assisted product discovery.

5. Replace generic recommendations with basket-building logic

Many ecommerce recommendations are technically functional but commercially weak.

Carousels titled “You may also like” often display similar products rather than products that help complete the purchase.

A customer buying a power tool may need a battery and accessory. A customer buying pasta may need sauce and parmesan. A customer buying skincare may need a complementary product within the same routine.

High-value recommendation types include:

Recommendations should account for availability and basket context. Promoting an unavailable accessory or a product already in the cart wastes valuable space.

The objective should be incremental contribution—not recommendation clicks.

Measure:

A controlled test is essential. Some recommended products would have been purchased without the intervention.

6. Fix the most damaging filters

Filters become commercially important when retailers sell broad or technically complex ranges.

Poor filters force customers to inspect products individually or abandon the category entirely.

Common problems include:

Begin with priority categories and actual customer behaviour.

Ask:

For some categories, compatibility, size or application may matter more than brand. In others, dietary requirement, colour, material or availability may dominate.

Measure:

Filters should reduce the effort required to reach a confident decision.

7. Recover revenue from unavailable products

Out-of-stock pages often function as dead ends.

Removing the page entirely can waste organic visibility and prevent the retailer from capturing future demand. Leaving it unchanged can frustrate customers who cannot complete the purchase.

A better response depends on the expected availability and product type.

Options include:

Alternatives should be genuinely suitable. Similar colour, price or category membership does not necessarily make a product a valid replacement.

Notify-me functionality can recover future revenue, but it also provides a demand signal. A large number of requests may reveal replenishment, ranging or allocation opportunities.

Measure:

Retailers should quantify the demand reaching unavailable products rather than treating stockouts only as an inventory report.

8. Remove unnecessary checkout friction

Checkout optimisation is often reduced to button colours and field layouts. The larger opportunities are usually procedural.

Common sources of friction include:

Start by segmenting checkout abandonment by device, fulfilment method, payment type and customer status.

Session replays and customer-service logs can help identify what aggregate analytics cannot explain.

Useful interventions may include:

Measure:

The goal is to remove unnecessary work without weakening fraud, compliance or operational controls.

9. Make mobile category and product pages faster

Performance is not only a technical metric. It affects how quickly customers can browse, compare and purchase.

Retail pages frequently accumulate scripts from analytics, advertising, personalisation, reviews, chat, experimentation and tag-management platforms. Each tool may have a defensible purpose, but their combined effect can make the buying journey noticeably slower.

Quick improvements may include:

Prioritise the templates with the most traffic and revenue exposure rather than attempting to optimise every page equally.

Performance should also be assessed on real customer devices and mobile networks—not only on office Wi-Fi or high-powered test machines.

Measure:

The commercial test is whether faster experiences change customer behaviour.

10. Improve cart recovery by addressing the reason for abandonment

Sending more reminder emails is not necessarily the best way to recover abandoned carts.

Retailers should first understand why customers leave.

Common reasons include:

Recovery should reflect that context.

A high-value basket abandoned after a payment error needs a different response from a low-value basket abandoned before delivery information appeared.

Useful recovery interventions include:

Discounting should not be the default response. Automatically rewarding abandonment can train customers to wait for an offer and create avoidable margin leakage.

Measure:

A successful recovery program restores purchases that would otherwise have been lost—not purchases that were merely delayed.

How to identify the right quick wins

The best opportunity will differ by retailer.

A disciplined process starts with four sources of evidence:

Behavioural data

Review search, navigation, product views, checkout steps, device performance and fulfilment choices.

Customer evidence

Use service enquiries, store feedback, reviews, surveys and usability observation to understand why behaviours occur.

Operational data

Include availability, cancellation, substitution, fulfilment, picking and return information.

Financial data

Connect each problem to revenue, gross margin, operating cost, working capital or lifetime value.

This prevents teams from prioritising changes simply because they are visible or easy to discuss.

Rank opportunities by financial exposure

A useful prioritisation model is:

Affected demand × expected behavioural improvement × transaction contribution − implementation and operating cost

For example, a small conversion improvement on a high-traffic category can be more valuable than a larger percentage improvement on a rarely visited feature.

Each initiative should be evaluated against:

This produces a commercially credible roadmap rather than a collection of disconnected optimisations.

Measure incrementality—not correlation

Revenue frequently changes for reasons unrelated to the ecommerce release.

Promotions, seasonality, stock availability, marketing activity and competitor behaviour can all affect results.

Where possible, use:

Conversion alone may not be enough.

A change that increases conversion could reduce margin, create smaller baskets or move customers from stores into a more expensive fulfilment channel. The evaluation should capture the complete commercial effect.

Quick wins should inform the larger strategy

A quick win is most valuable when it does two things:

  1. Produces a measurable improvement.
  2. Reveals what the business should invest in next.

Repairing failed searches may expose structural product-data problems. Improving delivery visibility may reveal limitations in fulfilment rules. Adding better substitutes may identify gaps in inventory accuracy.

These findings should shape the longer-term ecommerce, data and operating roadmap.

The strongest retailers do not treat optimisation as an occasional campaign. They create a repeatable system for identifying revenue leakage, testing interventions and scaling the changes that produce profitable growth.

The standard for an ecommerce agency should be equally clear.

The work should go beyond interface recommendations. It should identify where customer friction affects the retailer’s financial position, establish the value at stake and demonstrate the incremental result.

That is how a series of focused ecommerce improvements can become a material commercial advantage.

PREVIOUS ARTICLE

eCommerce Structured Data: How to Build an Advanced Schema Implementation

Structured data is one of the highest-value technical improvements an eCommerce retailer can make to its website. It is also frequently underdone. Many eCommerce websites have some structured data. A standard theme, SEO plugin or commerce platform might automatically output a Product schema containing a product name, price and availability. Technically, structured data is present. […]