Online consultants. A good specialist always takes into account the specifics of sales in his industry better than any machine algorithm, that’s a fact. However, consultants are not able to recommend something until the buyer contacts them himself. And as our own research shows, even top online retailers are not ready to spend resources on training sales consultants – as a result, this recommendation has to be pulled out of them with hot pliers.
Product recommendations
Modern recommendations: they give the user exactly those products that meet his needs (the very same personalization). Recommendations are generated automatically for everyone. A serious disadvantage is that recommendations that take into account the specifics of the industry are only available to large retailers.
Actually, why? A personalized recommendation system that works on your hong kong phone number data site is dozens of complex mathematical
models in one program, analysis of the characteristics of a specific user on the fly, calculation of recommendations in milliseconds. In short, it’s a complicated thing.
Developing recommendation algorithms for each industry is such a noticeable “add-on” and long hours of work. Accordingly, only market leaders have the budget for this.
Among the ready-made recommendation systems used by small and medium-sized stores, only a couple of solutions take into account industry specifics: HookLogic (relevant in the West) and REES46 (relevant for Russia and neighboring countries).
How to turn problems into advantages
Universal recommendations do not work equally well for different industries. But this is not a problem, but a chance to win even more.
Let’s look at examples of the specifics of online sales in specific industries and what benefits can be derived from this for business.
Their key feature is the low
cost of a single item. Also, people usually buy the same products with a certain frequency. It turns out that one basket is a set of products that changes little from time to time.
How a recommendation system corrects the situation:
Recommends products that were in previous baskets. Thus, if the buyer forgot something necessary, the system will remind him.
Recommends trying higher-quality and more expensive analogues. At the same time, the products are recommended “increasingly” gradually, so as not to scare off the buyer.
Recommends products only in the price range that is comfortable for the how much more do sellers earn on prime day on amazon? buyer. The system remembers what purchases he made in the past. Thus, those who are used to saving will not see the most expensive products in the recommendations, and those who are used to kuwait data spending a lot will not see cheap ones.