The cool choice!
With its professional recommendations solution, YOOCHOOSE provides the Nestlé Schöller B2B online store with a service tailored to the B2B environment. The classic challenge in the B2C environment lies in persuading customers to buy the highest quality product possible for what is mostly a one-off purchase. The B2B environment, by contrast, has its own distinctive features: business customers are generally tied to fewer suppliers on a more long-term basis and order larger, very static quantities from the product range offered at regular intervals.
The Recommendation Engine must cater to these differences in order to consistently increase the customer lifetime value and customer loyalty in the B2B environment. This was demonstrated by the use of the YOOCHOOSE Recommendation Engine in the Nestlé Schöller online store.
The project partner
NESTLÉ SCHÖLLER GmbH specializes in ice cream, frozen baked goods, and frozen food for the away-from-home market. The SCHÖLLER DIRECT B2B online store only serves commercial customers from the full-service, quick-service, leisure, catering, trade and transport sectors. In this regard, SCHÖLLER not only sees itself as a supplier, but also as a partner to help you achieve the highest standards. With unique product concepts, top quality, individualized service, and personal support, customers are delighted time and again.
Challenge and objective
Working with integration partner ETECTURE, there were several objectives to be achieved by integrating the Recommendation Engine into the online store. Firstly, the store would be made more attractive for customers and the recommendations feature would make it easier for customers to use the store and find products. Secondly, the recommendations feature should achieve a significant cross-sell and up-sell effect by suggesting products to customers that they have never heard of and, therefore, have never searched for. Thirdly, the Recommendation Engine also enables purchasing behavior to be analyzed and used for marketing ,such as personalized newsletters or the automatic generation of shopping lists.
The first phase involved determining where in the store recommendations would be used and what type of recommendations and filter settings would be used for the selected locations. In addition, user tracking was integrated into the store in order to record user activity, such as clicks, shopping basket activity, or purchases. ETECTURE then began to implement the boxes for displaying recommendations. One special feature of the YOOCHOOSE Recommendation Engine is the fact that the recommendations are not only returned as fully rendered HTML code but can also be retrieved as JSON objects via a REST-API. This means that even conditions or quantities displayed in the recommendations can still be adjusted during runtime using the store system.
In order to make the product catalog with product attributes available for recommendations, a nightly reconciliation of the product catalog was implemented. The flexible YOOCHOOSE REST-API, which has various data import and comparison methods as standard features, was also used here. After some final tests and approval by the customer, it was possible for the store to go live with the newly implemented features five weeks after the project was started. In order to shorten the cold start phase and better conform with the regular purchase cycle typical for B2B with a very high proportion of products being re-ordered, the purchase history for one year was imported from the entire customer base, meaning orders by fax and telephone were also included. This enabled the YOOCHOOSE Recommendation Engine to issue high-quality recommendations right from the start, without having to first determine the user’s click and purchase behavior over a longer period using tracking.
With the YOOCHOOSE Recommendation Engine, Nestlé Schöller has, in a remarkably short time, deployed a tool that enables targeted cross-sell and up-sell recommendations to be displayed in the online store. Due to YOOCHOOSE Recommendation Engine’s flexible and extensive API, the distinctive features of B2B business are also taken into account, without investing lots of time in complex adaptations of the standard solution. In this way, besides the low implementation costs, the future maintenance costs for Nestlé Schöller in particular have been reduced to a minimum. Once again, it is demonstrated that software-as-a-service does not necessarily have to be inflexible; rather the smart utilization of a good API can meet an extremely wide range of requirements.