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:
Behind these targeted suggestions are mathematical models and functions, as we explained in more detail in the articles “Recommender System: who knows you better than yourself?” and “Mathematics behind Aton’s B2B e-commerce“.