E-commerce has become one of the most competitive arenas for companies worldwide. With customers able to compare products and prices in seconds, small improvements in user experience can translate into meaningful gains in revenue. Artificial intelligence (AI) offers retailers a way to personalise journeys, optimise operations and drive more purchases. In a recent case, an online cosmetics distributor saw an 18.8% increase in conversion rate after implementing AI technologies. This article examines what strategies led to that boost and how other businesses can apply similar lessons.

Understanding the 18.8% uplift

The cosmetics retailer's goal was to improve how customers find and buy products. Before AI adoption, shoppers relied on basic keyword search and category filters. Many left without purchasing because they could not easily discover relevant items or felt overwhelmed by the choices.

To address this, the company deployed AI-powered product recommendation and intelligent search systems. The search engine used natural language processing and vector embeddings to understand queries and match them with product attributes. Meanwhile, recommendation algorithms analysed browsing history, purchase patterns and contextual signals (such as seasonality or trending colours) to suggest complementary products.

The impact was swift. Shoppers found what they wanted faster, and the personalised suggestions encouraged them to explore more items. Average order value rose as customers added recommended accessories, and bounce rates declined. Overall, the conversion rate increased by 18.8% compared with the same period before deployment.

Lesson 1: Invest in intelligent search

Traditional keyword search often fails when customers use colloquial phrases, synonyms or multi-word queries. AI-driven search engines use machine-learning models to parse the intent behind a query, even if it doesn't match exact product names. They can also rank results based on relevance and user behaviour rather than simple keyword frequency.

For example, when a shopper types "hydrating red lipstick for winter," the search engine interprets that they want a moisturising lip product in a specific colour for the cold season. It then ranks lipsticks with hydrating properties higher, even if their product names don't include the exact words "hydrating" or "winter."

Implementing such search typically involves:

Building or licensing vector-based search tools that embed product descriptions and queries in the same semantic space.

Continually training the model on click-through data and user feedback to improve relevance.

Integrating search analytics into merchandising decisions: top queries reveal gaps in inventory or content.

Lesson 2: Personalise recommendations thoughtfully

Recommendation engines are ubiquitous, but quality matters. The cosmetics case highlights a few guiding principles:

Contextual relevance. Algorithms should consider browsing context (what category the user is in), current trends and purchase history to avoid recommending random or repetitive items.

Complementary pairing. Suggestions that pair well—such as a moisturiser with a foundation or a mascara with an eyeliner—encourage customers to build a complete routine. This increases basket size while delivering value.

Diversity. Displaying a variety of recommendations prevents fatigue and introduces customers to new products they might not find on their own. This is particularly important for returning visitors.

Transparency and control. Allow customers to see why an item is recommended and let them remove unwanted suggestions. This builds trust and reduces the risk of showing irrelevant products.

Effective recommendation systems require accurate data about product attributes and customer behaviour. Retailers should invest in enriching product metadata, cleaning transaction logs and respecting privacy by anonymising user data and honouring opt-out requests.

Lesson 3: Optimise content and layouts with AI

Beyond search and recommendations, the cosmetics distributor used AI tools to test and optimise site content:

Dynamic content sequencing. Algorithms determined the order of banners, product collections and promotions based on user segments and real-time engagement metrics. For example, first-time visitors might see brand stories and how-to guides, while returning customers see new arrivals and loyalty offers.

Visual search and tagging. Image recognition models automatically tagged product photos with attributes like texture, finish and colour, making it easier to surface them in search and filter results. Visual search also enabled users to upload a picture and find similar products.

A/B testing automation. AI platforms ran continuous experiments on button colours, wording and layout variations, automatically rolling out the best-performing version. This accelerated the optimisation cycle and removed human bias.

Combined, these initiatives reduced friction in the shopping experience and made the website feel tailored to each visitor. The result was not just a higher conversion rate but also increased customer satisfaction and repeat purchases.

Lesson 4: Balance automation with human insight

While AI drove significant gains, the company didn't rely solely on algorithms. Merchandisers and marketers reviewed recommendation outputs, curated special collections and created campaigns that aligned with brand identity. Customer-service teams monitored feedback to identify when the AI misinterpreted intent or produced irrelevant suggestions.

This human oversight prevented "black box" decisions and ensured the AI reinforced, rather than replaced, the retailer's strategy. Periodic audits of models for bias, fairness and performance were part of the workflow, and adjustments were made when certain products were over- or under-represented.

Lesson 5: Build a data and governance foundation

Any AI initiative needs quality data and governance. The retailer invested in:

Unified product catalogues. Consistent naming conventions, rich attributes and high-quality images formed the backbone of search and recommendations.

Consent and privacy management. Clear opt-in mechanisms allowed customers to control how their data was used. Anonymisation and encryption protected personal information.

Cross-functional teams. Data engineers, machine-learning specialists, marketers and domain experts collaborated to define goals, measure outcomes and iterate on models.

These foundations ensured the AI tools delivered value without compromising ethical standards or customer trust.

Conclusion

The 18.8% conversion boost experienced by the cosmetics e-commerce company underscores the power of AI when applied strategically. Intelligent search, context-aware recommendations, content optimisation and a robust data infrastructure come together to create a seamless shopping experience. For other retailers, the key lessons are to start with clear objectives, invest in data quality, blend automation with human expertise and always prioritise the customer's needs and privacy. With these elements in place, AI can turn browsers into buyers and drive sustainable growth.