Discovery & Personalisation
PDP bundle recommendations
In one sentence
Frequently bought together surfaces complementary products directly on the product page, turning a single-item purchase into a considered bundle without the shopper needing to go looking for the add-ons themselves.
How it works
On the product page, a module shows other products commonly purchased alongside the one being viewed — a protein shaker with a tub of protein, a charger with a device, a refill with its base product. The shopper can add the suggested items to basket directly from the module without leaving the page. Which products pair together can be driven by purchase-pattern data or curated manually by the brand where a specific pairing is commercially important, and the module's presence and position on the page is configurable per brand.
The problem it solves
For the brand
Complementary products that shoppers would happily buy together often go unsold simply because the shopper doesn't think to look for them separately, leaving basket value on the table that a well-placed suggestion would have captured.
For their customers
Realising after delivery that they needed an accessory, refill or complementary item means a second order, a delivery wait and sometimes a second delivery charge that a single combined purchase would have avoided.
For shoppers
For the brand
Increases average order value through relevant complementary attachment
Average order value, attach rate
Purchase-pattern data reveals natural product pairings
Cross-sell insight for range planning
Manual curation lets the brand promote strategic pairings
Sell-through on curated bundle pairs
Reduces split orders and repeat delivery costs
Orders per customer per week, delivery cost per customer
In practice
A shopper views a protein powder product page.
The frequently bought together module shows a shaker bottle and a scoop commonly purchased alongside it.
They tick both suggested items and add all three to basket in one action.
The order ships as a single delivery, with the accessory need already met before it arose.
Where it lands hardest
Health & nutrition
Strong fit — supplements naturally pair with accessories (shakers, pill organisers) and complementary formulations.
Beauty & personal care
Strong fit — routines naturally bundle (cleanser with toner, primer with foundation), making pairing suggestions genuinely useful.
Food, drink & FMCG
Good fit for pairing consumables with equipment (coffee with a cafetière) or flavour variety packs.
Pet care
Strong fit — food pairs naturally with treats, bowls and accessories bought on the same shopping trip.
Fashion & apparel
Works for outfit-building pairings, though 'other customers bought' and styling-led merchandising often perform better for apparel.
Luxury & premium
Use with restraint — overt cross-sell prompts can read as pushy against a premium positioning; curated, not algorithmic, pairing suits better.
Common questions & objections
Why the platform version wins
Because the module reads from the same catalogue and order data as the rest of the platform, pairings reflect real, current purchase behaviour rather than a static list that goes stale as the range changes.