Personalisation of recommended products as an important stage in the B2B trade development strategy

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A level of purchases made over the Internet is growing very dynamically not only in the B2C segment. The situation is very similar in the case of the B2B trade. According to experts’ forecasts, revenues from e-commerce between enterprises in 2022 will reach USD 7.88 trillion. At the same time, as many as 35% of the surveyed companies are ready to spend over USD 500,000 in one transaction via Internet.

Characteristics of the B2B trade 

It is worth noticing that B2B trade is often based on long-term relationships, cultivated even at the stage of direct sales. As the process is transferred on-line, the power of personal contact weakens. It is therefore essential to provide a similar experience to e-commerce business customers.

The consumerisation of the B2B segment has long been an exception to e-commerce. Only some retailers have offered this type of approach. Today, however, a similar approach is expected by almost all business customers. They are interested in fast, easy and always available mobile shopping. In addition to an easy access to sales representatives, they also need an immediate on-line quotation.

Not only that, purchasers in the B2B segment also expect personalised experiences. These are users aware of the power of data and modern technologies with artificial intelligence at the forefront. Product recommendations based on complex algorithms bring tangible benefits to individual customers. Now, also, they can prove to be of help in case of the B2B trade. In this way, sellers free up time resources and can improve their shops in the areas such as analytics, search management and user experience.

Types of product recommendations for sale

According to the forecasts of the IDC, by 2026 the use of artificial intelligence to create recommendations will be common. In this manner, approximately 40% of contact points with humans during the conclusion of transactions will be eliminated – their role will be taken over by recommendations. They will occur not only in the case of the B2C sales, but also in sales between enterprises.

Product recommendations appearing on virtually every subpage (home page, categories, search, product pages and during the confirmation of purchases) are based on four methods of operation of algorithms. These are as follows:

  • ‘Those who have purchased this product have also purchased’. The recommendation is therefore based on the behaviour of other customers. The most frequently proposed products complement the purchased goods. Alternatively, the products that have been purchased or viewed by people interested in a given product may be displayed,
  • ‘The most popular products. Recommended products are selected the basis of a conversion rate (view to shopping basket or purchase). They can also be the most viewed or purchased goods. More advanced algorithms are also based on trends, i.e., changes in popularity recently.
  • ‘May also interest you’. In the third case, visual or feature-based similarities are searched for. In this manner, the customer receives suggestions that can even better meet his/her expectations,
  • ‘Recommended for you’. After all, artificial intelligence analysing large amounts of data enables you to create individual proposals for a given customer. For this purpose, all information about previous purchasers and the behaviour of the current customer is used.

 

In this respect, personalisation of the recommended products is similar as in case of individual customers.

What distinguishes recommendations in the B2B realm?

However, there are some significant differences in recommendations for individual and business customers. First of all, in the latter case, we often deal with groups of customers for whom a different visibility of products and their prices will be available. Therefore, the sales platform must apply the entitlements to individual categories so that the offer is customised to the user (individual customers, small entrepreneurs, wholesalers).

When customising the recommendations, you should also take care of a new language layer. The customers are now not individual people, but companies, which should be reflected in the messages. Instead of ‘users also have purchased’, the text ‘other traders have also purchased’ can be used.

While the aforementioned differences between the recommendations for individual and business customers may present some implementation issues, the results of these efforts will certainly have a huge positive impact on sales.

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