Increasing conversion rates by 25% through AI strategy and implementation for a Manchester-based online retailer.
Bloomfield Commerce, a Manchester-based online retailer, achieved a 25 percent increase in conversion rates after ajairu.ai implemented AI-powered product recommendations and personalised search. The fixed-price engagement delivered working AI systems in 8 weeks, with the client's team trained to manage the platform independently.
Bloomfield Commerce is an online retail business based in Manchester, selling homeware and lifestyle products to customers across the UK. Founded by Priya Sharma, the company had grown steadily over five years through strong product curation and social media marketing. However, their e-commerce platform was running on standard Shopify templates with no personalisation, no intelligent search, and no automated product recommendations. As competition in online retail intensified, Bloomfield needed to work smarter to keep growing without doubling their marketing spend.
Priya knew that larger retailers were using AI to personalise shopping experiences, recommend products, and optimise pricing, but she had no idea where to start or what was affordable for a business of her size. Her team was small and focused on day-to-day operations. They had experimented with a few Shopify plugins promising AI-powered recommendations, but the results were disappointing: generic suggestions that did not reflect how customers actually browsed and bought on their store.
The key challenges were:
The engagement began with an AI strategy engagement to map Bloomfield's customer funnel, identify where AI could create the most value, and build a prioritised implementation roadmap. The strategy phase revealed three high-impact opportunities: intelligent search, personalised product recommendations, and automated merchandising. Each was assessed for feasibility, cost, and expected ROI before being prioritised.
Following the strategy, ajairu moved into AI implementation with a phased approach:
Throughout implementation, Priya and her team were trained on how to interpret the analytics dashboards, adjust recommendation logic, and understand what the AI was doing and why. Full documentation was provided so the team could manage the system independently after handover.
Within four weeks of the full system going live, Bloomfield Commerce saw their conversion rate climb from 1.8% to 2.25%, a 25% increase. The intelligent search reduced search bounce rates by 40%, as customers were finally finding what they were looking for. Personalised recommendations increased average order value by 18%, as customers were shown relevant cross-sell and upsell products at the right moments as they shop.
The automated merchandising freed Priya's team from manually curating homepage and category layouts every week. The AI continuously optimises product placement based on performance data, which means the best-converting products always get prime visibility without manual intervention. The team now spends that time on product sourcing and content creation instead.
After three months of operation, the system continued to improve as it gathered more data on customer behaviour patterns. The recommendation engine became increasingly accurate, and the merchandising layer adapted automatically to seasonal trends and stock changes.
The commercial impact is straightforward. At Bloomfield's average order value, the conversion and average order value uplifts delivered roughly £210,000 of additional online revenue in the first year. Against the combined strategy and implementation fee, the system paid for itself within two months. The automated merchandising also returns around 6 hours a week to the team, time now spent on product sourcing and content instead of manually curating category pages.
The AI strategy engagement completely changed how we think about our online store. Conversion rates are up 25% since implementation. What impressed us most was the plain-English approach, no jargon, no hype, just results.
This engagement combined two of our core services:
The engagement succeeded because the strategy came first. Rather than jumping straight to implementation and bolting on AI plugins, the strategy phase identified the three areas where AI would have the biggest impact on Bloomfield's specific business metrics. This meant every hour of implementation time was spent on work that directly moved the needle on conversion rate and average order value.
The phased approach also mattered. By deploying intelligent search first, Bloomfield saw measurable improvement within three weeks, which built confidence in the AI before the larger recommendation and merchandising components were deployed. Priya and her team could see the value at each stage rather than waiting months for a single big launch.
The system was built to be owned and operated by Bloomfield's team. Full documentation, training, and a 30-day support period meant they could adjust recommendation logic, interpret analytics, and extend the system independently after handover.
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