Posted by The food trading industry is one of those green-fields, where digitalization hasn’t really taken off in full force. Against this backdrop, about a year ago, Ramón Sánchez-Ocaña and Angeles Vitorica, Co-founders of Claire Global, contacted BigML with an idea that could turn the industry upside down. After more than 20 years as owners of a food trading business, they had all the domain knowledge about how to best achieve digitization in their industry and provide a valuable service to their peers. Now, their collaboration with BigML is helping them turn their ideas into reality.
Claire is a marketplace devoted to the B2B food trading business. It is purpose-built to implement Machine Learning-driven solutions to optimize the buying and selling processes that are core to the wide-reaching global food industry. Since its launch in January 2020, the marketplace has been in ‘Open BETA’ with interest from a diverse set of companies. Check out the introductory video below (or this one in Spanish) to find more about this innovative project pushing the envelope as far as B2B marketplaces can go.
The project is focused on increasing customer conversions and, most importantly, customer engagement. This becomes possible by adding valuable new functionality to the platform while actively supporting a highly heterogeneous group of user personas. All this to promote more activity on the platform and capture all the relevant inputs from customers, products, and transactions facilitated. This data, in turn, allows the team to implement the Machine Learning capabilities that add further value to the platform and its users.
Here are some of the most interesting optimization Machine Learning use cases we are exploring in this next-generation commerce environment:
- Automated Product Recommendations: Selections based on customer data (e.g., prior transactions, web navigation patterns, user segmentation) and product data (e.g., product attributes, product similarities, purchase history) are key in making personalized offers to customers. By better “knowing” your users, you can provide them with highly relevant B2B information to further enhance their purchasing experience. This results in more repeat usage and customer loyalty over time.
- Optimal Pricing Suggestions for Sellers: Finding the optimal price point is a common problem in retail due to the high amount of parameters that can be considered when doing so, e.g., competitive dynamics, customer feedback, seasonality, current demand. There are also a wide variety of pricing strategies that can be chosen depending on the objectives of the retailer. For instance, maximizing profitability, accessing a new market, implementing dynamic pricing, etc. The use of predictive models for price optimization is quite attractive to cover all these possible different pricing scenarios.
- Stock Management for Buyers and Sellers: Historical sales data can be very useful in order to extract sales trends and seasonality effects. This, together with some external data such as upcoming events or geographical location, can provide a producer with the best information on how to distribute its products among different warehouse locations according to the quantities it will sell in different areas. Supermarkets or hotel chains can also benefit by predicting the optimal quantity of a certain product they must acquire per location.
- Anomaly Detection: This is a key technique for tasks such as flagging suspicious customer behavior to prevent fraud, checking for data inconsistencies by spotting pricing mistakes and other data integrity issues going otherwise unnoticed.
In short, we enthusiastically invite those of you in the food industry to actively participate in this new and exciting digital endeavor!
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