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How to Sell More Online Using AI: A Guide for eCommerce Retailers

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AI is creating new opportunities for eCommerce retailers to attract customers, improve conversion rates and generate more revenue from their existing traffic.

The difficulty is knowing where to begin.

There are now thousands of AI products promising to automate marketing, personalise the customer experience and increase sales. Some can deliver measurable value. Others add cost and complexity without addressing an important customer or commercial problem.

The most effective approach is to focus on the areas that directly influence online revenue:

This guide explores how retailers can use AI across each stage of the eCommerce journey and what is required to turn the technology into genuine commercial growth.

Use AI to Attract More Qualified Customers

AI is changing the way people discover products online.

Customers are increasingly asking Google, ChatGPT and other AI-powered services complete questions instead of searching with a few short keywords. They may ask:

These platforms do not simply return a list of matching pages. They attempt to understand the question, evaluate available information and recommend suitable products or businesses.

For retailers, this creates a new form of product discovery.

To appear in these results, your product information needs to be comprehensive, accurate and easy for machines to interpret. This includes:

AI search visibility is closely connected to product data quality. If essential information is missing, contradictory or locked inside an image or PDF, an AI service may be unable to confidently recommend the product.

Retailers should therefore treat product data as a revenue-generating asset rather than an administrative requirement.

Improve On-Site Search

Customers who use site search often have strong purchase intent. They know what they want, or at least know what problem they need to solve.

Unfortunately, many eCommerce search experiences still depend on exact keyword matches.

A customer searching for “waterproof jacket for winter hiking” may receive no useful results because the products are described as “weather-resistant outerwear”. Someone searching for an automotive product may use an abbreviation, informal term or vehicle description that does not match the catalogue.

AI-powered search can interpret the meaning behind a query rather than relying entirely on matching words.

It can help retailers understand:

AI can also improve how search results are ranked. Instead of simply presenting the closest keyword match, results can consider availability, popularity, conversion history, customer location, margin and relevance to the individual shopper.

The objective is not to show customers more products. It is to help them reach the right product with less effort.

Retailers should regularly examine:

This information can improve search configuration, product content, navigation and buying decisions.

Help Customers Choose the Right Product

Customers do not always arrive knowing exactly what they need.

They may understand the outcome they want but struggle to compare specifications, confirm compatibility or determine which product represents the best value.

An AI shopping assistant can guide customers through this decision.

Instead of presenting a generic chat window, the assistant can ask relevant questions and progressively narrow the available options. For example, it might ask:

The assistant can then recommend suitable products and explain why each option may be appropriate.

This can be particularly valuable for retailers selling:

A useful AI assistant needs access to reliable product information. Depending on the retailer, this may include inventory, pricing, technical specifications, compatibility data, promotions, delivery estimates and store availability.

Without those integrations, the assistant risks becoming another layer between the customer and the information they need.

Deliver More Relevant Product Recommendations

Product recommendations can increase average order value, but only when they are relevant to the customer’s current purchase.

Generic recommendation blocks often display popular products regardless of what the individual customer is trying to achieve. AI can evaluate more information and make recommendations that reflect the customer’s behaviour and context.

This may include:

Different recommendation placements should serve different purposes.

On a product page, AI might recommend comparable alternatives or help the customer trade up to a more suitable option.

In the cart, it might identify accessories, consumables or services that complete the purchase.

After the order, it might recommend replenishment products or complementary items based on what the customer bought.

The strongest recommendations solve a problem for the customer. Suggesting the correct cable for an appliance or the appropriate fitting for a plumbing product is more useful than displaying an unrelated high-margin item.

Retailers should measure the effect on:

This helps establish whether recommendations are creating additional value or merely taking credit for purchases that would have occurred anyway.

Create Better Product Content at Scale

Large product catalogues are difficult to maintain.

Descriptions may come from multiple suppliers, use inconsistent terminology or omit the information customers need. Important attributes may be stored in spreadsheets, PDFs or unstructured text. Some products may have little more than a title and an image.

AI can help retailers improve and standardise product content across large catalogues.

It can assist with:

However, AI should not be allowed to invent product facts.

If the source data does not specify that a product is waterproof, compatible with a particular model or covered by a certain warranty, AI should not make that claim.

A robust process uses approved information from systems such as an ERP, product information management platform or verified supplier feed. AI can transform that information into useful customer-facing content, while validation rules and human review protect accuracy.

The commercial value can be significant. Better product content can increase search visibility, improve conversion and reduce returns caused by customers purchasing an unsuitable product.

Personalise the Shopping Experience

Most eCommerce websites present broadly the same experience to every visitor.

AI allows retailers to adapt parts of the experience according to customer behaviour, purchase history and likely intent.

This could include personalising:

A returning trade customer might see products they purchase regularly. A customer who previously bought a coffee machine might receive timely recommendations for filters, cleaning products or coffee. A shopper browsing premium furniture may see different content from someone primarily viewing discounted items.

Personalisation should make the experience more useful without restricting choice. Retailers should avoid creating such a narrow view that customers can no longer discover the wider range.

It must also be supported by clear privacy and consent practices. Customers should understand how their information is being used and retain control over their preferences.

Reduce Cart and Checkout Abandonment

AI can identify behavioural patterns associated with cart abandonment and help retailers respond before the customer leaves.

For example, a customer may repeatedly visit the delivery page, return to the product specifications or hesitate after viewing the final order total. These behaviours may indicate uncertainty about:

The retailer can respond by displaying the relevant information at the point it is needed.

AI can also improve abandoned-cart communications by determining the most appropriate timing, channel and message for each customer.

This does not mean every abandoning customer should receive a discount. Automatically offering incentives can reduce margin and teach customers to wait for an offer.

Before introducing AI, retailers should fix the common causes of checkout abandonment, including unexpected costs, mandatory account creation, limited payment methods, poor mobile usability and slow page performance.

AI is most effective when it builds upon a well-designed checkout rather than attempting to compensate for a broken one.

Improve Customer Retention

Winning a new customer is only the beginning of the commercial relationship.

AI can analyse customer behaviour and identify when someone may be ready to repurchase, interested in a related product or at risk of leaving.

Retailers can use these insights to improve:

A fixed email sent 30 days after every purchase may be inappropriate for many products. AI can estimate likely replenishment timing using the product purchased, order frequency and customer behaviour.

It can also help retailers distinguish between different reasons for inactivity. One customer may be approaching their normal repurchase date, while another may have experienced a service issue or started purchasing from a competitor.

This allows retention activity to become more relevant and less dependent on blanket promotions.

Strengthen Customer Service

Customer service directly influences whether a shopper completes a purchase or returns in the future.

AI can provide immediate assistance with common questions such as:

It can also support customer service teams by summarising account history, locating relevant information, suggesting responses and routing enquiries to the correct person.

The aim should be to resolve simple matters quickly while making human support easier to access when the issue is complex, sensitive or commercially important.

Customer conversations also reveal problems elsewhere in the business. AI can analyse enquiries at scale to identify recurring issues such as confusing product information, delivery delays or compatibility questions.

Fixing these underlying issues can improve both service efficiency and online conversion.

Make Better Merchandising and Inventory Decisions

AI can analyse product performance, inventory, margin, customer demand and seasonal patterns across a much larger dataset than a merchandising team could assess manually.

It can help identify:

These insights can improve purchasing, product placement and promotional planning.

For omnichannel retailers, AI may also help determine where inventory should be located. A product with strong demand in a particular region could be moved closer to those customers, improving availability and reducing delivery time.

Decisions involving pricing and promotions should retain appropriate controls. Retailers need to understand why a recommendation has been made and protect margin, customer trust and regulatory compliance.

Use AI to Improve Marketing Performance

AI can make marketing teams faster, but speed alone does not create growth.

Generating more advertisements, emails and articles is not useful if the content remains generic or reaches the wrong audience.

The greater opportunity is to use AI to improve relevance and decision-making.

This may include:

AI can assist with content production, but the retailer still needs a clear position, strong offers and an understanding of its customers.

The objective is not to sound like every other retailer using the same tools. It is to apply AI using the retailer’s own customer, product and performance data.

Your Data Will Determine the Result

Most eCommerce AI projects are ultimately data and integration projects.

An AI shopping assistant cannot recommend the correct product if important attributes are missing. It cannot provide an accurate delivery estimate without current inventory and fulfilment data. It cannot personalise an experience effectively if customer activity is fragmented across disconnected systems.

Retailers should determine:

For established retailers, the necessary information may be distributed across an eCommerce platform, ERP, PIM, CRM, search platform, marketing platform, data warehouse and physical store systems.

Integrating these systems is often more important than selecting the AI model itself.

How to Get Started

Retailers do not need to introduce AI across the entire business at once.

The best starting point is usually a defined customer problem or revenue opportunity that can be tested and measured.

1. Identify where revenue is being lost

Review analytics, site-search reports, customer enquiries, return reasons and feedback. Look for points where customers cannot find products, lack the confidence to purchase or leave the website unnecessarily.

2. Select one valuable use case

Choose an opportunity that has a clear commercial outcome. This may be improving poor search results, recommending compatible accessories or enriching incomplete product information.

3. Assess the available data

Confirm that the information required to power the experience is accurate, governed and accessible. If it is not, improving the data should become part of the project.

4. Establish a baseline

Measure current performance before introducing AI. Depending on the use case, this may include conversion rate, search exit rate, average order value, return rate or service resolution time.

5. Run a controlled pilot

Test the solution with a limited audience, product category or business process. This reduces risk and makes it easier to determine whether the result justifies further investment.

6. Measure incremental value

Compare the AI-assisted experience with the existing experience. Consider revenue, margin, customer outcomes and operating costs rather than focusing only on engagement.

7. Scale successful applications

Once the use case has demonstrated value, integrate it properly, establish ownership and extend it to other relevant areas.

What Should Retailers Avoid?

Retailers should be cautious of introducing AI without a defined problem or measurable outcome.

Common mistakes include:

A polished demonstration can create excitement, but it does not prove that a solution will perform accurately at scale or deliver a financial return.

The Opportunity for eCommerce Retailers

AI has the potential to improve almost every stage of the eCommerce customer journey.

It can help more people discover a retailer, connect customers with suitable products, improve purchasing confidence, increase order value and create more relevant reasons to return.

However, the technology is not the strategy.

The retailers that achieve the greatest value will be those that combine AI with strong product data, well-integrated systems, commercial discipline and a detailed understanding of their customers.

Start with a genuine source of customer friction. Define the commercial outcome. Ensure the necessary data can be trusted. Test the application under real conditions and measure whether it creates incremental value.

That is how AI moves from an interesting experiment to a meaningful source of eCommerce growth.

Choosing the Right AI and eCommerce Partner

Introducing AI into eCommerce may require more than installing an application. The project can involve customer experience design, data architecture, platform development, systems integration, analytics, privacy and ongoing optimisation.

The right partner should understand how the retailer’s eCommerce platform connects with its ERP, product data, inventory, marketing and customer systems. They should also be prepared to challenge ideas that lack a clear customer benefit or commercial return.

OSE works with Australian and New Zealand retailers to design, build and integrate sophisticated eCommerce solutions across Shopify, Shopify Plus and Adobe Commerce.

We help retailers identify practical opportunities for AI, strengthen the data and integrations required to support them, and deliver solutions focused on measurable improvements in online performance.

If you want to sell more online using AI, begin by identifying the parts of your customer journey where better information, greater relevance or faster decisions could make the greatest difference.

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