20Flowers wanted to make a more attractive customer experience by making customized product suggestions via its digital store.


0scale executed Amazon Personalize, a machine learning service that is specialized to offer real-time recommendations to users depend on different factors, including card addition, purchase history and more.


0scale executed Amazon Personalize, a machine learning service that is specialized to offer real-time recommendations to users depend on different factors, including card addition, purchase history and more.

AWS Services

Executive Summary

Founded in 2002, 20Flowers are the dealer of primarily to DIY customers and professionals who purchases different type of flowers in bulk amount for special events and productions, that includes photo shoots, weddings, birthday celebrations and more.

With the facilitation of 0scale, an Amazon Personalize launch partner, 20Flowers developed a proof of concept for machine learning driven recommendation engine that boost the online shopping experience. New and current customer are not come up with the personalized product recommendation depend on loads of data points.20Flowers is a wholesale flower distributor that deals with the global network of flower farm collaborator.

The Challenge

20Flowers asked us to develop more engaging digital experience for clients that would enhance both revenue generation and shopper satisfaction. The retailer had an idea of the solution that is machine learning an artificial intelligent. Therefore, they needed to have cost efficient and effective technologies to get benefit of it.

Between recruiting a data scent team, training models from beginning and acquiring modified datasets, there was so much that demanded to occur before 20Flowers could achieve its dreamt end state. Ultimately, the leadership decided to have a look for a capable partner like 0scale that could create a proof of concept and provide guidance in the proper manner to the company. 0scale, an Amazon Web Services (AWS) Premier Consulting Partner with an extensive machine learning experience was the best choice for this concern

The Solution

0scale found the best path ahead for 20Flowers that includes Amazon Personalize, a machine learning service that allows businesses to provide real time customized recommendation to users. With the help of Amazon personalize, developers are able to develop build and sustain sophisticated personalization engines with no special machine learning experience.

Before, dive into another part with the complete scale execution, 20Flowers wanted to have a Proof of Concept. The 0scale formed a roadmap comprised of the mentioned components:

Search engine prototype
Transfer learning prototype
Recommendation engine prototype
Notification personalization prototype

By collaborating with 0scale and AWS, 20Flowers could make a convincing recommendation engine proof of concept to increase the end user experience for its valuable floral customer. The organization would be able to provide product recommendation that is consistent with the preference of buyer thus making more enhancing buying opportunities and boosts revenue potential.

The new recommendation is completely automated and managed by AWS. They do not need to maintain an in-house data team to take benefit of two decades of Amazon intelligence that is the most detailed retail dataset in the globe.

The 20Flowers team is able to personalize the database frequency for ingest new information and flex with modification in domain, particularly given how much seasonality affects the business model. The solution integrates analytical insights in real time that includes global retail trend data combined by Amazon to enhance prediction transparency. By depending on AWS, the organization has decreased the burden of administration and made additional ability for in house developers. Not only is 20Flowers' new solution efficient and effective but it is simple to manage.

The engine is able to handle different customer engagement channels on its own and able to send customized notification across several mediums. Moreover, it can rerank search engine results to show the recent information accessible. The other major advantage is that the new personalization engine also woks for new site visits. As compare to other machine learning solutions, it can use the attitude of previous customers to create accurate prediction regarding new shoppers.

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