How many times we realise we shop much more happily in a certain store rather than in others because the person in front of us (seller, sales assistant…) understands our needs and advises us better! The product may even be the same, but it is the seller that makes the difference in the way we are guided through our shopping experience, on the basis of our history or our similarity with other customers.
The same concept can be applied to digitali tools for omnichannel sales, which we can consider real digital sales assistants, capable of advising the most adequate product, thanks to complex systems like the recommender systems.
Regarding suggestion typology and sales targets, 4 macro-categories are distinguished:
“STARS” Recommendations
They are a very widespread tool in “social” logic: inside this category we can find suggestions of the most “popular” products in the market our customer or a customer subgroup addresses; they constitute an excellent tool in marketing support, making demand trends visible.
“PULL” Recommendations
They suggest the products providing more added value for the company, in terms of margin inside the order/trolley or because they are strategic to the company to obtain new market segments.
“CORRELATED” Recommendations
They suggest correlations on the basis of what has been previously acquired, both in the “I usually buy this” or “other users buy this” logics.
“PUSH” Recommendations
They promote products the company would like left their warehouse (for example, close-out ones or products nearing expiry date), thus reducing warehouse costs or preventing the risk of future surplus and, at the same time, guaranteeing a more accessible price for the customer, who knows that item sells well.
