For ecommerce businesses, increasing average order value (AOV) is one of the most effective ways to drive revenue growth without having to attract more visitors or acquire new customers. Yet many retailers and B2B merchants still rely heavily on discounts, promotional codes and price reductions to encourage customers to spend more.
While these tactics can generate short-term sales, they often come at the expense of profitability, particularly when customers become accustomed to waiting for the next promotion.
Artificial intelligence (AI) offers an alternative. By understanding customer behaviour, anticipating purchasing needs and delivering more relevant shopping experiences, AI helps ecommerce businesses increase basket values without sacrificing margin.
The opportunity isn’t simply to persuade customers to spend more. It’s to help them make better purchasing decisions, discover relevant products and recognise additional value in what they’re buying.
1. Delivering More Intelligent Product Recommendations
Product recommendations are nothing new in ecommerce. Traditional approaches typically rely on relatively simple rules, such as displaying bestsellers, recently viewed products or items frequently purchased together.
AI takes this a lot further.
By analysing browsing behaviour, purchase history, product relationships and customer preferences, AI-powered recommendation engines can identify products that are genuinely relevant to individual shoppers.
For example, a customer purchasing a digital camera might be recommended a compatible lens, memory card or protective case. Rather than offering a blanket discount to encourage additional spending, the retailer is presenting products that complement the original purchase.
For B2B merchants, the opportunities are even greater. A customer ordering electrical components might be prompted to consider compatible fittings, installation accessories or associated consumables.
Importantly, AI will learn from previous purchasing patterns and continually refine its recommendations, improving relevance over time.
The result is a more helpful shopping experience that will naturally increase basket values.
2. Using AI to Support Upselling
Upselling involves encouraging customers to consider a higher-value product than the one they originally intended to purchase.
Traditionally, ecommerce websites have approached this through manually configured product relationships or simple comparisons between different models.
AI enables a more sophisticated approach.
By understanding a customer’s requirements and purchasing behaviour, AI identifies situations where a more expensive product offers meaningful additional value.
Consider a customer browsing laptops. Rather than simply displaying a higher-priced alternative, an AI-powered shopping assistant could explain how a model with additional memory, greater storage capacity or improved battery life would better suit their intended use.
The distinction is important. Effective upselling isn’t about pushing customers towards the most expensive product. It’s about demonstrating why an alternative may better meet their needs.
When customers understand the benefits, they’re more likely to make a higher-value purchase without requiring a financial incentive.
For merchants, this approach improves both AOV and customer satisfaction.
3. Increasing Basket Values Through Intelligent Cross-Selling
Cross-selling is closely related to upselling but focuses on complementary products rather than higher-value alternatives.
AI makes cross-selling considerably more effective by recognising relationships between products that aren’t always obvious.
For example, an online retailer selling garden equipment might identify that customers purchasing particular lawnmowers frequently require specific maintenance products, replacement blades or protective equipment.
In B2B ecommerce, AI could recognise that a customer ordering machinery is likely to need particular spare parts, servicing products or associated components.
These recommendations can be presented at appropriate points throughout the customer journey, including product pages, basket pages and checkout.
Timing is particularly important.
A relevant suggestion made when a customer is considering a purchase can be helpful. The same suggestion presented too frequently or without context can become intrusive.
AI helps determine not only which products to recommend, but also when recommendations are most likely to be useful.
4. Creating More Valuable Product Bundles
Product bundles provide another opportunity to increase AOV without relying on discounts.
While bundles are frequently associated with promotional pricing, they don’t necessarily need to be sold at a reduced combined price.
Instead, their value comes from convenience, compatibility and helping customers purchase everything required for a particular task.
AI analyses historical transactions and product relationships to identify combinations that customers regularly purchase together.
A retailer selling home office equipment, for example, could offer a complete workstation package comprising a monitor, keyboard, docking station and accessories.
Similarly, a B2B supplier might create application-specific bundles containing all the components required for an installation or maintenance activity.
AI will also personalise these bundles according to customer requirements, previous purchases or product compatibility.
By simplifying the purchasing process and reducing the risk of customers overlooking necessary items, merchants will encourage larger orders while delivering genuine customer value.
5. Using Conversational AI to Guide Purchasing Decisions
One of the more significant developments in ecommerce is the emergence of conversational AI shopping assistants.
Unlike traditional website search, these tools allow customers to describe what they’re looking for in natural language.
A customer might ask:
“I’m looking for a commercial printer suitable for a small office producing around 5,000 pages per month. What would you recommend?”
An AI assistant could interpret the requirements, identify suitable products and explain the differences between available options.
It could also recommend relevant consumables, maintenance products or accessories.
This creates opportunities to increase AOV by helping customers understand the complete solution rather than focusing exclusively on the initial product.
For B2B merchants, conversational AI can be particularly valuable where product catalogues are complex, technical specifications are important or customers require guidance around compatibility.
Effectively, it provides an additional layer of product expertise, available around the clock.
However, recommendations need to be grounded in accurate product information, stock availability and compatibility data. Poorly implemented AI risks undermining customer confidence rather than improving the experience.
6. Predicting Customer Needs Through Purchase History
AI may also help merchants anticipate what customers are likely to need next.
By analysing purchasing frequency, previous orders and customer behaviour, predictive models identify opportunities to recommend additional products or larger quantities.
For example, a B2B customer regularly ordering packaging materials might be approaching the point at which another purchase is required.
Rather than waiting for the customer to return, the merchant could provide a timely reminder or suggest related products based on previous purchasing patterns.
AI might also identify that a customer purchasing a particular product regularly orders an associated consumable several weeks later.
Presenting that consumable during the original purchasing journey could encourage the customer to consolidate their order.
This approach is especially effective in B2B ecommerce, where purchasing behaviour is often more predictable than in consumer retail.
The objective is to make purchasing more convenient while increasing the value of individual transactions.
7. Improving Product Discovery and Search
Customers can’t purchase products they can’t find.
Poor website search, ineffective filtering and complicated product navigation often limits basket values, particularly on ecommerce websites with extensive catalogues.
AI-powered search improves product discovery by understanding customer intent rather than relying solely on exact keyword matches.
For example, a customer searching for “waterproof outdoor workwear” might be shown relevant jackets, trousers, footwear and accessories, even where individual product descriptions don’t contain those precise words.
AI may also improve product ranking based on relevance, availability and customer behaviour.
By making related products easier to discover, merchants can encourage customers to explore more of their catalogue and identify additional purchasing opportunities.
This is particularly important for B2B merchants managing thousands, or even hundreds of thousands, of SKUs.
8. Measuring Success Beyond Average Order Value
Although increasing AOV is the primary objective, merchants should avoid treating it as the only measure of success.
A higher basket value doesn’t necessarily translate into greater profitability.
For example, recommending products with low margins or introducing friction into the checkout process could undermine the financial benefits of larger orders.
AI initiatives should therefore be evaluated against several commercial measures, including:
- Average order value and revenue per visitor
- Gross margin and contribution per order
- Conversion rates and checkout abandonment
- Recommendation engagement and attachment rates
- Repeat purchasing and customer lifetime value
Testing is equally important. Merchants should compare AI-driven recommendations against existing approaches to establish whether improvements are genuinely incremental.
The most successful implementations will balance increased basket values with profitability and customer experience.
Making AI Work for Your Ecommerce Business
AI offers ecommerce merchants a significant opportunity to increase average order values without becoming dependent on discounting.
Whether through intelligent product recommendations, personalised upselling, predictive purchasing insights or conversational shopping assistants, the underlying principle remains the same: help customers recognise and purchase more of what they genuinely need.
However, achieving these benefits requires more than simply introducing an AI tool.
Product data needs to be accurate, integrations need to be reliable and recommendations must reflect the commercial realities of the business.
Platforms such as Adobe Commerce, BigCommerce and Shopify provide opportunities to introduce AI-powered functionality through native capabilities, third-party solutions and bespoke integrations.
At PureNet, we work with ecommerce businesses to identify opportunities for improving customer experience and commercial performance through technology, integration and intelligent digital solutions.
If you’re looking to increase average order values while protecting your margins, talk to PureNet about how AI could support your ecommerce growth strategy.
