Recommendation Engines
Personalised recommendations that lift conversion, basket size and loyalty — the right product to the right shopper, in real time, across every channel.
What is Recommendation Engines?
Recommendation Engines at MindCraft Solution help shoppers discover what they want and retailers sell more. We build personalisation that learns from behaviour and context to surface the right products — on the homepage, product pages, search, email and app — in real time, lifting conversion and average order value.
We go beyond simple 'customers also bought': combining collaborative filtering, content signals and real-time context, handling cold-start and long-tail catalogues, and respecting privacy. The engine is tuned to your commercial goals — whether that's conversion, margin, or moving specific inventory.
Integrated with your storefront and data, and continuously measured through A/B testing, the result is personalisation that demonstrably grows revenue, not a black box you have to take on faith.
What Recommendation Engines includes
Everything you need to take recommendation Engines from idea to a dependable, owned capability.
Personalised product recommendations
Tailored suggestions across home, product, search, email and app.
Real-time personalisation
Adapts to each shopper's behaviour within the session.
Cold-start & long-tail handling
Useful recommendations for new users and niche products.
Goal-based tuning
Optimise for conversion, margin or inventory goals.
A/B testing & measurement
Prove uplift with experiments, not assumptions.
Privacy-aware design
Personalisation that respects consent and data rules.
Outcomes we target
Typical results from MindCraft retail & Commerce engagements.
Our delivery model
A clear, low-risk path from first call to a running, optimized solution.
- 1
Understand goals & data
We define the commercial goal and assess behaviour and catalogue data.
- 2
Build & tune models
We build recommendation models tuned to your goal and catalogue.
- 3
Integrate
We deliver recommendations via API into your storefront and channels.
- 4
Test & optimise
We A/B test, measure uplift, and continuously improve.
From problem to outcome
The pressures we see in retail & Commerce — and how we fix them.
- Challenge
Generic, low-converting experiences
How we solve itPersonalized recommendations for every shopper.
- Challenge
Stockouts and costly overstock
How we solve itDemand forecasting by SKU and location.
- Challenge
Disconnected online & in-store data
How we solve itA unified Customer 360 across channels.
The stack we use
What you get
Concrete, owned artifacts — not just advice.
- Recommendation API & models
- Channel integrations
- A/B testing framework
- Personalisation dashboards
- Tuning & enablement
Questions, answered
Built-in widgets are generic and hard to tune. We build personalisation tuned to your catalogue and commercial goals, handle cold-start and long-tail well, and prove uplift through A/B testing — typically outperforming off-the-shelf widgets.
We handle cold-start with content signals, popularity and context, so new users and products still get useful recommendations from the first interaction.
Yes — we tune the engine to your goal, whether that's conversion, margin, or moving specific inventory, balancing relevance with commercial outcomes.
Yes — we design for consent and data-protection rules, using first-party behaviour responsibly and avoiding reliance on data you can't lawfully use.
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Ready to talk about Recommendation Engines?
Tell us where you are and what success looks like. We'll bring the right people, stack and plan — and reply within one business day.