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CartApp is an application made to enhance the experience for a barista and the customer at the sales point. 

How do we enhance the experience of the point of sales, for both - baristas and customers?



The system will be able to manage the order complexity. 


Empowered and comfortable while executing the tasks


Have a simple conversation and place the order.

Targeted Audience

The sales-point involves two primary stakeholders.

1. Barista

2. Customer


STEP 1: Initial Interviews

As a preliminary survey we interviewed baristas and customers in popular coffee shops in Pasadena. We also interviewed the dining staff and student community at ArtCenter. 

Young Barista

STEP 2: Pain Point Exploration

Through these surveys, we collated fine grained data about several pain points prevalent at Points of Sale. These included the complex interfaces of POS systems, large order volumes, and high number of touch points required to complete each user journey.

Casual Meeting

STEP 3: Barista Persona

Synthesizing all the survey insights and 

pain-points, we generated user persons for the barista to pin-point the exact problems faced by the stakeholder.

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STEP 4: Customer Persona

Synthesizing all the survey insights and 

pain-points, we generated user persons for the customer to pin-point the exact problems faced by the stakeholder.

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STEP 5: User Journey 

We then ideated an ideal user flow that retains the experience of talking to your barista, but enhances the efficiency of the ordering process. By introducing facial recognition and voice modeling, we realized we could increase the efficiency of recognizing customers, ordering and billing. This, in turn, reduced the touch points needed

per order.

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STEP 6: Lo-fi Wireframes

The initial interfaces for the POS system and the customer app were drawn on paper. Users were given different printouts of the interface cards and their natural affordances were observed.

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STEP 7: User Feedback

In the first round of user testing, we understood that we needed to optimize our layouts and stick to a consistent information hierarchy. We then generated our mid-fidelity prototypes to further test our interfaces.

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STEP 8: Mid-Fi Wireframes

Using only gray scale design palette, we created the wireframes, linked them and tested across multiple users. All their interactions were recorded.  We then inferred the necessary changes to further improve the design.

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STEP 9: User Feedback Round 2

After the second round of testing, we validated that our design had better efficiency. After incorporating the final set of feedback, we generated the high-fidelity prototypes.

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Facial recognition and voice modelling will help in real-time recognition of the customer and their order. The refined interface of both the customer app and the POS system will further ease the billing and ordering process. 


The customer can remotely order using the customer app. Frequent orders will be collated within the 'Usual' section. Users, with 1 tap, can order from the home-screen itself using the OS popup interface.



The POS system is equipped with a camera and a microphone. This will enabe facial recognition and voice modeling. Upon recognition of the customers face, "usual orders" and "order history" is shown to the barista. New orders are recognized via the voice modeling module of the POS system.



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