AI Product Recommendations Manchester
We add product recommendations to online shops: customers also bought, similar items, personalised home pages and recommendation emails. Our Manchester team builds them for WooCommerce and Shopify stores, tests them against simple rules and keeps out-of-stock products off the list.
- Since 2003trading in Manchester
- 20,000+UK businesses helped
- Collect M8Unit 3, 116 Bury New Road
- A personchecks every file
Search, sales and content
Every job is a fixed quote, agreed in writing before any work starts. No obligation.
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Showing each shopper the products they are likely to want
Most shop pages show the same products to everyone. Recommendations change that by using signals you already hold, such as order history, items viewed together and product attributes, to suggest what a shopper may want next.
Two problems trip up most setups. The first is cold start: a new product has no sales history and a first-time visitor has no profile, so the system needs a fallback such as best sellers in the category or matches on description and attributes. The second is stock. Recommending something that cannot be bought wastes the slot, so stock status is checked at display time.
Browsing-based personalisation relies on tracking, so it only runs once a visitor has accepted the relevant cookies; our cookie consent setup handles that side.
Shops that tend to benefit
Recommendations work best where there is enough range for a suggestion to be useful.
- Fashion and workwear shops with many sizes, colours and matching items.
- Homeware and gift stores where shoppers browse without a fixed item in mind.
- Parts and spares sellers, where compatible accessories matter more than lookalikes.
- Trade suppliers with repeat buyers who reorder similar baskets.
- Food and drink retailers whose products pair naturally together.
From order data to live suggestions
Review catalogue and orders
We look at product data quality, order volumes and where shoppers drop off, which tells us which recommendation types are worth building.
Set the simple baseline
We start with plain rules, such as best sellers in the same category, so there is something fair to compare the smarter version against.
Build the recommendation model
Co-purchase patterns, viewed-together data and product text embeddings feed the suggestions, with cold-start fallbacks and stock filters applied.
Connect to your store
Blocks are added to WooCommerce templates or Shopify theme sections, and recommendation feeds are prepared for your email platform.
A/B test and adjust
Half your visitors see the model and half see the baseline, and we compare add-to-basket and order figures before rolling it out.
What is included
- Also-bought and similar-item blocks on product and basket pages.
- A personalised home page section for returning visitors.
- Recommendation data for abandoned basket and follow-up emails.
- Cold-start rules for new products and new visitors.
- Live stock filtering so unavailable items never appear.
- An A/B test report comparing the model with the rule-based baseline.
Search that understands what shoppers mean
Standard shop search matches keywords, so “rain coat for site work” can return nothing if your titles say “shower jacket”. Semantic search turns product descriptions and queries into embeddings, so results match meaning rather than exact words. It uses the same data work as recommendations, so the two are often built together.
We keep it honest, though. Semantic search can surface odd matches, so we test it against your most common real queries from site search reports and add synonyms or boosts where results look wrong. If shoppers arrive from Google Shopping, clean data from Google Merchant Center setup helps both systems.
Frequently asked questions
Do we need lots of orders before recommendations are worthwhile?
Not to start. With few orders, similar-item suggestions based on product attributes and descriptions still work well. Co-purchase suggestions improve as order history grows, so we switch them on once there is enough data for the patterns to be meaningful.
Does this work with WooCommerce and Shopify?
Yes. On WooCommerce we add blocks through the theme and a plugin we maintain, and on Shopify through theme sections and the store API. If you are on another platform, we check what its API allows before quoting.
Will it slow our shop down?
It should not. Recommendations are calculated ahead of time where possible and loaded after the main page content, so product pages render first. We check page speed before and after launch and tune anything that adds noticeable delay.
How do you know the recommendations actually help?
We run an A/B test against a simple rule-based version and compare add-to-basket rate and order value over the same period. If the model does not beat the simple rules for your shop, we tell you and keep the simpler option.
Is browsing data handled lawfully?
Personalisation that relies on tracking only runs after a visitor accepts the relevant cookies, and the data stays within your store systems. Our setup follows published ICO guidance, though your privacy notice wording is a matter for your own legal adviser.
Related services
- AI services: our full AI range in Manchester.
- WooCommerce setup: the store build these blocks plug into.
- Shopify store setup: Shopify themes and apps configured properly.
- AI integration: model APIs connected to your other systems.
- AI data insights: plain-English reporting on sales and stock.
Send us your store link and rough order volumes, and we will suggest where recommendations fit and quote a fixed figure. Ask for a recommendations quote, ring 0161 315 1151 or WhatsApp 07737 902425.
Ready to get started?
Tell us what you need and we'll come back with an honest, fixed-price quote — no obligation.