Off-the-shelf recommendation widgets on e-commerce platforms tend to be generic — "customers also bought" logic that works reasonably well for large international catalogs but performs poorly for smaller, locally-specific retail catalogs where the customer base and buying patterns look nothing like the platform's training data.
A custom recommendation feature built on a retailer's actual sales history can factor in details generic tools miss — seasonal buying patterns specific to the Ghanaian market, complementary product pairings unique to the catalog, and inventory awareness so it never recommends something out of stock.
The realistic starting point isn't a fully personalized AI engine — it's rule-based recommendations backed by actual sales data (frequently bought together, trending this week) which often outperform a more "intelligent" system that hasn't seen enough data yet to make genuinely personalized suggestions.
This is a feature that gets better with data over time — a retailer should expect the recommendations to be decent at launch and meaningfully better after a few months of real purchase data, not perfect immediately.