https://github.com/drewmoore/feature_planning

https://github.com/drewmoore/feature_planning

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/drewmoore/feature_planning
• Owner: drewmoore
• Created: 2017-07-17T14:19:34.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-07-17T15:24:14.000Z (almost 9 years ago)
• Last Synced: 2025-03-06T04:46:44.089Z (over 1 year ago)
• Size: 2.93 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Personalized Shopping Suggestions

## What

- Suggest designs for purchase based on authenticated users' social media activity.

## Why

- This can lead to a more personalized shopping experience.

- It can strengthen customers' brand-association.

- It would be a fun task to implement this feature.

## How

### General Overview

- Spoonflower's designs have 'tag' data, as can be seen on results from its new API.

- Twitter posts have 'hashtag' data.

- There will likely be overlaps between these two sets of tags.

- Comparing the users' most-used hastags with tags associated with designs would enable the suggestion of new designs.

### Backend Implementation

Assuming a user has authorized Spoonflower to access Twitter data, and the needed

Twitter account information is stored on the user's db record:

- Implement an API endpoint for retrieving suggested designs for a user.

  - Controller's action will:

    - Query Twitter for the user's Tweets, let's say 50 most recent for now.

    - Reduce Twitter API results to a flat array of hashtags featured in those tweets.

    - Query the designs tags table for records with a tag that is included in the array of hashtags, limit to say, 20 for now.

    - Query and respond with the designs associated with these tags, using the same response format as the 'search' API endpoint for consistency.

- Of course, acceptance, integration, and/or unit tests will be needed!

### Frontend Implementation

- On 'Home' view, include a section of page that is a row for suggestions.

  - This row would be empty for users who have not yet authorized Spoonflower to integrate with Twitter. There could be a button for Twitter and a suggestion for users to integrate.

- On initial loading of view, the React container for the suggestions would dispatch an asynchronous action to fetch the suggestions from an API endpoint.

- On receiving the API response, another action would dispatch

- Test the container's ability to receive and render these results, possibly via Jest.

### Technology to Use

- Twitter API

- isomorphic fetch

### Personnel

- Likely a two-person job: one frontend and one backend.

- If I were working on it, I would likely work on the tasks of designing the React container/component, making and receiving the API request and results, and testing this frontend feature. My reasoning is just that I have less professional experience in this area and therefore wish to build on these skills.

### Tasks:

  - Frontend (~= 8 hours):

    - Implement container/component for rendering of suggestions, empty at first.

    - Implement twitter button that successfully authorizes Spoonflower to interact with user's Twitter account.

    - Implement sending of user token to server for saving to user record.

    - Implement container's fetching of results.

    - Implement reducer's transferring results to app state.

    - Rendering results

    - Tests.

  - Backend (~= 8 hours):

    - Receive and store Twitter user token for accessing account.

    - Implement endpoint for suggestions:

      - Fetch user's Tweets

      - Reduce tweets to tags

      - Query design_tags.

      - Retrieve the designs.

      - Respond with designs.

      - Test backend features.

### Potential Challenges

- Feature efficacy

  - One likely scenario on initial implementation is that users will have suggestions for designs that they may have already purchased.

    - This could be smoothed out in planning another feature for the filtering of suggestions.

    - This would not be entirely inconsistent with other E-Commerce platforms that have similar "rough edges" and such challenges.

  - There may be the possibility of having empty results (user does not use hashtags, or hashtags have no corresponding tags on Spoonflower).

- Scalability

  - Querying Twitter for potentially large amounts of data on a user's posts.

  - Querying through large quantities of designs to match tag data with a user's hashtag-related data from Twitter.

- Guiding users to integrate with Twitter and give permissions to Spoonflower to access data.

  - Users will likely not mind sharing data, but they may need to be convinced that they want to make a few extra clicks and possibly log-in to Twitter, as well.