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How to Use AI in eCommerce to Increase Online Sales

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Artificial intelligence is quickly changing how customers discover products, compare their options and decide what to buy.

For eCommerce businesses, the opportunity is much larger than using AI to write product descriptions or answer customer enquiries. AI can improve almost every stage of the customer journey, from attracting better-qualified visitors to helping customers find products, increasing conversion rates and encouraging repeat purchases.

However, adding more AI tools does not automatically produce better results. The greatest value comes from identifying where customers encounter friction and applying AI to solve specific commercial problems.

This guide explores how retailers, wholesalers and manufacturers can use AI to increase online sales, improve the customer experience and operate their eCommerce businesses more effectively.

Why AI Is Becoming Important in eCommerce

Traditional eCommerce platforms rely heavily on rules.

Customers are placed into predefined segments. Product recommendations are based on simple relationships. Search engines match keywords. Marketing campaigns are sent according to fixed schedules and workflows.

AI can make these experiences much more responsive.

Instead of treating every customer within a segment in the same way, AI can analyse behaviour, purchasing history, product data and real-time intent to determine what an individual customer is most likely to need.

This can help an eCommerce business:

The objective is not simply to automate more activity. It is to make the online buying experience more useful, relevant and commercially effective.

1. Improve Product Discovery with AI-Powered Search

On-site search is one of the highest-value areas in which an eCommerce business can use AI.

Customers who search are often demonstrating strong purchase intent. They know what they want, or at least understand the problem they need to solve. If the search experience returns irrelevant results, the business risks losing a highly qualified customer.

Traditional search engines generally rely on keywords, product names and manually configured synonyms. AI-powered search can interpret the customer’s meaning rather than looking only for an exact phrase.

For example, a customer might search for:

These searches contain context, intent and requirements that may not appear together in a product title.

An effective AI search implementation can interpret those requirements, evaluate available product data and return a more relevant selection.

AI search may also support:

The quality of the result will still depend heavily on the quality of the underlying product information. AI cannot reliably recommend the correct product if important attributes, compatibility details or technical specifications are missing.

2. Deliver More Relevant Product Recommendations

Many online stores use product recommendations, but the recommendations are often based on simple rules such as related categories, commonly purchased products or manually selected items.

AI can make recommendations much more relevant by considering a wider range of signals, including:

This allows the business to move beyond generic “You may also like” recommendations.

For a consumer retailer, AI could recommend a complete outfit based on the product being viewed and the customer’s previous purchases.

For an automotive business, it could recommend compatible components for the customer’s vehicle.

For a B2B supplier, it could predict which products an account is likely to reorder and present those products immediately after login.

Recommendations should help customers make better decisions. If they are intrusive, inaccurate or driven entirely by margin, they can reduce trust rather than increase sales.

The most effective recommendation strategies are measured against commercial outcomes such as conversion rate, average order value, attachment rate and revenue per visitor.

3. Personalise the Customer Experience

Personalisation has traditionally involved dividing customers into broad groups and showing each group different content.

AI allows eCommerce businesses to create more individualised experiences.

A returning customer might see products related to a previous purchase. A new visitor arriving from an industry-specific campaign might see content designed for that industry. A trade customer could see frequently ordered products, account pricing and stock availability for their preferred location.

Personalisation can be applied to:

The goal should not be to personalise every element of the website. It should be to identify situations where greater relevance makes it easier for the customer to buy.

Businesses should also be transparent about how customer data is used and ensure their personalisation practices comply with relevant privacy requirements.

4. Increase Conversion with AI Shopping Assistants

AI shopping assistants can help customers navigate large or complex product catalogues.

Unlike a basic chatbot, an effective shopping assistant should understand the product catalogue, ask useful questions and narrow the available options based on the customer’s requirements.

A customer purchasing a technical product might need help determining:

An AI assistant can guide the customer through these decisions conversationally.

This can be particularly valuable for businesses selling complex products, including automotive parts, electrical products, industrial equipment, workwear, technology and building supplies.

However, accuracy is critical. An assistant should not provide confident answers when the necessary information is unavailable. Product data, integration architecture, response controls and escalation processes need to be considered before the assistant is made available to customers.

5. Reduce Cart Abandonment

AI can help identify customers who are likely to abandon their purchase and determine the most appropriate response.

That response might include:

This is more effective than automatically offering every customer a discount.

Discount-led abandonment campaigns can condition customers to delay purchases and reduce margin unnecessarily. AI can help distinguish between a customer who needs more information, a customer who is comparing products and a customer who is genuinely price-sensitive.

The business can then respond according to the likely cause of abandonment.

6. Improve Email and Lifecycle Marketing

AI can improve email marketing by helping businesses determine what to send, who should receive it and when it is most likely to generate a response.

Potential applications include:

For B2B eCommerce, AI could identify an account whose ordering pattern has changed and prompt the account manager to make contact.

For a consumer retailer, it could predict that a customer is likely to need a product replenishment and send a reminder before the product runs out.

The most valuable AI marketing strategies are built on connected customer, product and order data. If important data remains fragmented across the eCommerce platform, ERP, CRM, point-of-sale system and email platform, personalisation will remain limited.

7. Use AI to Improve Product Content

AI can accelerate the creation and improvement of product information, particularly for businesses managing large catalogues.

It can assist with:

This can be extremely useful, but AI-generated product content should not be published without appropriate controls.

Inaccurate dimensions, compatibility information, materials or performance claims can create customer-service problems and expose the business to unnecessary risk.

A better approach is to use AI within a structured product-information workflow. AI can prepare, classify and enrich information, while validation rules and human review protect accuracy.

The underlying product information should ideally be managed through the eCommerce platform or a product information management system, depending on the size and complexity of the catalogue.

8. Prepare Products for AI Search

Customers are increasingly using AI platforms to research products, compare alternatives and decide what to purchase.

This creates a new discovery challenge for eCommerce businesses. Products need to be understandable not only to traditional search engines, but also to AI systems interpreting customer questions.

Improving visibility in AI-generated answers may involve:

AI visibility is closely connected to data quality.

An AI platform is more likely to understand and recommend a product when its purpose, attributes, applications, limitations and relationships to other products are clearly documented.

Businesses should therefore treat structured product data as a growth asset rather than an administrative requirement.

9. Improve Pricing and Promotion Decisions

AI can analyse sales history, demand, inventory levels, seasonality and customer behaviour to support better pricing and promotional decisions.

This does not necessarily mean changing prices for every customer in real time.

More practical applications might include:

AI should support a clearly defined pricing strategy. Poorly controlled dynamic pricing can confuse customers and damage trust, particularly if prices change unexpectedly across channels.

10. Forecast Demand and Improve Product Availability

Customers cannot purchase products that are unavailable.

AI can improve demand forecasting by analysing historical sales, seasonality, campaigns, lead times, external conditions and changes in customer behaviour.

Better forecasting can help a business:

For an omnichannel retailer, this becomes even more valuable when inventory data is available across stores, warehouses and suppliers.

The eCommerce experience should reflect accurate availability and realistic delivery timeframes. This requires strong integration between the website, ERP, inventory systems, point-of-sale systems and logistics providers.

11. Automate Customer Service Carefully

AI can handle a significant proportion of repetitive customer enquiries, including:

This can reduce response times and allow customer-service teams to focus on more complex issues.

The customer should still have a clear path to a person when the AI cannot resolve the enquiry. Businesses should also monitor conversations to identify inaccurate answers, unresolved issues and recurring sources of customer frustration.

Customer-service automation works best when it is connected to live business systems. A chatbot that cannot access an order, confirm stock or understand account information may provide little more value than a searchable FAQ page.

12. Find Conversion Problems More Quickly

AI can help eCommerce teams analyse large volumes of behavioural and operational data.

It may identify:

The value comes from turning these observations into action.

AI might identify that mobile customers are leaving at a particular checkout step, but the business still needs the technical and commercial capability to investigate the cause, prioritise the change and measure the result.

Where Should an eCommerce Business Start?

The best starting point is not choosing an AI tool. It is identifying a commercial problem worth solving.

A practical process is:

1. Identify the opportunity

Look for areas where customers are struggling or where the business is losing revenue.

This may include poor search results, low category-page conversion, incomplete product information, repetitive service enquiries or weak repeat purchasing.

2. Establish a baseline

Measure current performance before introducing AI.

Depending on the project, relevant measures might include conversion rate, revenue per visitor, average order value, search-exit rate, support volume, repeat purchase rate or product-content completion.

3. Review the available data

Determine whether the business has the product, customer and operational data required to support the intended experience.

Data quality is often the greatest constraint on an AI implementation.

4. Start with a focused use case

Choose one project with a clear commercial objective instead of attempting a business-wide AI rollout.

5. Integrate the required systems

Connect the eCommerce platform with the systems needed to provide accurate information and complete useful actions.

6. Test against the existing experience

Run controlled testing wherever possible. The AI experience should demonstrate a measurable improvement over the current approach.

7. Monitor accuracy and performance

AI implementations require ongoing supervision. Review both technical accuracy and commercial results.

Choosing the Right AI eCommerce Partner

An AI project should not be treated as a standalone technology experiment.

The implementation may involve the eCommerce platform, ERP, CRM, PIM, search technology, analytics, marketing platform and customer-service systems. Decisions made in one part of the architecture can affect the entire customer experience.

When choosing a partner, look for experience across:

The right partner should be able to connect the proposed AI capability to a specific customer need and a measurable business result.

AI Should Make Buying Easier

AI presents an enormous opportunity for eCommerce businesses, but its value should be judged by outcomes rather than novelty.

Does it help customers find the right product?

Does it make a complex purchasing decision easier?

Does it remove friction from the customer journey?

Does it improve conversion, order value or retention?

Does it save time without reducing accuracy or trust?

Businesses that answer these questions clearly will be better positioned to turn AI into sustainable online growth.

OSE helps Australian retailers, wholesalers and manufacturers use AI, eCommerce technology and connected business systems to create better digital buying experiences. Our team works across Adobe Commerce, Shopify, systems integration, product data, cloud platforms and AI to help businesses identify and implement commercially valuable opportunities.

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