Recommendations you can rely upon for conversion
Our research has revealed that traditional approaches to retargeting can be limited in their conversion potential.
Simply displaying the product last viewed to a consumer is insufficient to drive conversion at scale.
Consequently, Merchenta includes a range of sophisticated product recommendation strategies which may be drawn upon by each campaign.
Conversion Probability Strategies
These strategies reveal the products the consumer is most likely to purchase, given their personal browsing history and the interests of people ‘like them’.
Product Affinity Strategies
These strategies recommend products which are relevant – but unfamiliar – to the consumer. They evaluate product characteristics and descriptions to find products relevant to the consumer’s browsing history. Ideal for long-tail retailers.
These strategies may be combined with others to emphasise products which are expiring soon (eg deals), approaching stock-out (eg ends of line), on sale etc. Especially powerful when combined with ‘countdown’ creative.
Interest Based Strategies
Focuses upon those products most consistently, frequently or recently browsed by the consumer. These are Merchenta’s simplest product recommendation strategies and are often combined with other strategies to produce more sophisticated approaches.
Best Seller Strategies
Useful in specific scenarios – for example, promotions – where a real-time selection of best-selling products is required. Often blended with other strategies for effect.
Yield Optimisation Strategies
These strategies use a merchant’s margin preferences to balance higher margin product selections against conversion probability. This results in higher margin products selections that convert. Generally applied in conjunction with other strategies to optimise AoV.
Focussed on identifying products placed in a cart/shopping basket and then subsequently abandoned. Typically combined with other strategies for effect.
Dynamic Classification Strategies
These strategies enable products to be selected across a range of attributes which may vary with behaviour. In the example above, “floppy summer hats with stripes or patterns”. A travel example – “hotels with TripAdvisor rating >=4 and located in Berlin with local star rating of at least 3*”.
Random Selection Strategy
This strategy is used primarily for A/B testing purposes where the effectiveness of another strategy is being compared against a generic, random product selection.
Each of these marketing strategies may be blended or combined for maximum effect.