Rolling out BeautyHelper

To ensure that BeautyHelper can be rolled out across the support teams, you've been tasked with testing more customer recommendations and assessing their accuracy.

The Data team at Lotus Skincare has helped compile a dataset of customer product recommendation requests based on transcripts from customer service calls. In addition to each customer's name, age, and skin type, the product type and preferences columns contain the type of product and additional preferences that the customer was looking for.

Name	Age	Skin Type	Product Type	Preferences
Allison	43	Normal	Serum	Cruelty-free
Basma	33	Oily	Night Cream	Sensitive Skin
Charlie	15	Combination	Blemish Control Cream	None
Debra	65	Dry	Moisturizer	Paraben-free

 

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By prompting BeautyHelper, find the recommended product for each customer. If BeautyHelper's recommendation matches what is stated in the possible answers, tick the box for that person so you can assess the accuracy.