kjhf MIS 510 sharethisdeal - Final Project Report Team Members:- Damini Akash Krittika Karan Khurana Agrawal Patil Dhingra

Transcription

1 kjhf MIS 510 sharethisdeal - Final Project Report Team Members:- Damini Akash Krittika Karan Khurana Agrawal Patil Dhingra 5/14/2014

2 Introduction Shoppers often come across offers that require them to purchase multiple items of the same kind. For example, apparel stores contain buy 2, get 2 free offers. While every shopper wants to avail this discount, most of them don t want to purchase more than one item of the same kind. This is often the cause of a shopper losing out on an item of his/her liking and the store losing out on a potential sale. ShareThisDeal is a Web and Mobile based discount and deal portal that focusses on allowing shoppers to share bulk-buying deals (including, but not limited to, buy-2-get-3 type offers) in real-time, with other patrons in their immediate vicinity. It captures a user s geographical location and studies her buying patterns, to retrieve the most relevant and closest deals for that user. Users can post deals by scanning barcodes or entering details and images of the product they want to share, have a conversation with interested shoppers, and consequently split costs, save money and build their own shopping community. Further, the hotspots for shopping in a city can be analyzed and provided to customers who can use this information to decide where they would like to go shopping. The application can cater to multiple domains like apparel, groceries, travel and services. Market Landscape Market Competitors Groupon is an established player in the deal and coupon based shopping space. It provides and promotes discounts on local restaurants, shops and online entities. Deals are driven by groups i.e. a deal becomes active only when a certain predetermined number of people sign up. The Find app locates products nearby stores. Users can search for a specific product and the using GPS, the app locates it at a nearby store. The location specific aspect works very well, giving comprehensive lists. The objective here is to compare prices at local stores and online. It includes a barcode scanner and a deal finder. LivingSocial is a deal-based application similar to Groupon. It provides deals from local and national vendors. Users can view deals and buy via their smartphone. Also offers incentives for users to get free deals by getting additional members to sign up.

3 Competitor Analysis Business Case Revenue Model Our revenue model is divided in three parts: Phase 1: Build user base In the first phase of our business model we are planning to build a user base by generating awareness for the application. In order to do this we will use the following strategies Market products to users during major shopping seasons (e.g. Black Friday) Use social media plugins to advertise the application Use referral & reward point systems to generate user interest in the application Phase 2: Pay-to-promote business partner deals Once the user base is built, we will get businesses to promote their deals on our recommendation list (Curated list) For every promoted deal redeemed we take a fee We will also provide targeted deals for users based on their purchase history to provide recommendations to users

4 Also, we will use the data collected within the application and provide feedback to businesses as a part of the business partner deal in order to provide insight into user behavior. This can be used by businesses to provide better deals to users Phase 3: Premium memberships for additional features We will offer a premium membership to users and restrict the following features to only premium members Provide hotspots (Heatmap) of deals of the day Provide option to create custom groups of community members based on shared interests Provide exclusive deals to users based on membership status Additionally, we will expand the analytics engine to provide additional value to the user Promotion & Marketing Plan Pre-app Launch Teaser Video Free Pubic Forums Google Ad Sense Blog Posts as they are indexed by Google At Launch Cohesive Presence on Social Media Platforms Facebook, Twitter, Instagram Share it with Friends via Post Launch User Experience Videos Tech Blogs and Publications Reach out with Download Statistics Customer Testimonials Include Developer Contact Information

5 Key Scenarios Example User Scenario Key Scenarios There are three key scenarios we will discuss and summarize for the Sharethisdeal application Web User Location-Based Search The first scenario web user might engage in is a typical location-based search via a web browser. This scenario is a standard case in which the user wants to see all the deals posted around him When a user first comes to the site, he or she might wonder what the application does. A helpful and short slider is prominently suggested at the top of the page, to allow the user to quickly familiarize herself with the site s functionality as shown in Figure 1.Upon entering the site, the user is prompted to allow the application to use the current location, which is a capability common to all modern browsers, as shown in Figure 2. Figure 1: Helpful Landing Page with the Map

6 Deals around the User Once the location of the user is got, all the deals around the users are shown along with the twitter feeds of sharethisdeal users that are being shared currently. The user also has an option to sign up for the newsletter for our app which will give him constant updates based on his likings. Details of the Deal Posted Once the user clicks on the deal, he can get more information regarding the deal such as the person who posted the deal, how many people are required to share the deal, location, product details such as category etc. Post a deal When a user of the application wants to share a deal, all he has to do is scan the barcode of the product if applicable, or post the details of the deal by themselves and the deal will be posted to other users who are close to the vicinity of the deal and also to his/her friends in the recommendation page. Recommendations The user can also get deals based on our analysis which will be explained in detail at a later stage. The user can see the recommended deals and also the hot deals places i.e. a heat map which shows the intensity of the deals that are being posted from various locations around the user. Novelty: A connected shopping experience True SoLoMo Social: Connect with other users o The website allows users to share deals live with other users based on their location o Sharethisdeal has a chat feature integrated into the website that allows users to chat with each other in order to collaborate and share deals based on similar interests o The website incorporates social information from providers such as facebook & twitter for social networking and user authentication o This data will also be used by the analytics engine, developed for the website, to show hotspots of deals around the local area

7 Local: Local deals o One of the main features of sharethisdeal is that it allows users to search and post for deals, in real time, around the user s current location o The location of the user is first captured as the user logs in and the application keeps track of the user location throughout the user session to accurately show deals around the user o This location is also fed into the analytics engine to create deal hotspots around a user s local area Mobile: Mobile platform o Sharethisdeal is also available as a mobile application o Sharethisdeal is centered around a user being mobile as it considers users current location when showing deals o Through a mobile application, sharethisdeal allows users to share deals and allow users to connect with each other in real time at the store location immediately find a deal Cloud based architecture Our website is hosted using Amazon EC2. We are using Microsoft SQL Server for the database. The below figure summarizes the main feature of the architecture. Analytics The sharethisdeal website also implements advanced analytics based on Amazon s recommender system and a heatmap. The purpose of these features is to provide advanced functionality that makes the user spend more time and get more value out of the website. Brief descriptions of the two features are given below.

8 Personalized Recommendations When a user visits a website like Amazon, there are sections like you may also like or people who bought mouse also bought keyboard. These recommendations are based on collaborative filtering by analyzing which users are most similar to the user in question. We are doing the same thing for our website by comparing users based on attributes like age, gender, income bracket and shopping interests. We then use the k-nearest Neighbor algorithm to cluster similar users. We use k=2, which means that for every user we find the two most similar users. Then, we recommend the deals that have been shared or dealt in by the nearest neighbors. We used Weka for performing the k-nearest Neighbor algorithm in real time. Heatmap In the default scenario, a user s list of deals is populated by the deals in the area that he is in currently. However, by viewing the heatmap, the user can also get a bird s eye view of all the deals at a city, state or country-wide level. These deals are represented as heat on the map. An area where a large number of deals are being shared will be represented as a radiating heat dot with its size proportionate to the number of deals in the area. An example is shown below. The heatmap is plotted over a Google map using the HeatMapAPI.

9 APIs Implemented API Functionality We integrated the Google Geocoding and Reverse Geocoding API to get the location and then getting details of a location from latitude and longitude Twitter API was integrated in our website to pull out tweets specific to universities. This will provide users the ability to see what people are tweeting about sharethisdeal RSS feeds API was used to give users feeds regarding deals all around Mailgun API allowed our website to send updates and information to customers who have signed up to our website and become members. Factual API was used to get the details of products once a user scans a product using barcode scanner Google Map API allows users to get geo based location information specific to universities. Google MAP API is integrated in the landing page to give deals around the user as soon as he logs in. Envolve API is customer service/chat API that allows users to communicate when they want to share a deal and also form communities Barcode Scanner API was use to let user get the details of the product immediately by just a click on the barcode in the store

10 Google Places API is a service that returns information about Places where the deals are posted Facebook API allows users to log into the system using their facebook login credentials. Facebook Like and Share options allow users to share content on social media. Tableau is an Analytics tool that allows you to represent data in various graphical visualizations. We used tableau in our project to represent specific statistical information and results of where most of the deals are posted. Heat Map visualization is done using Tableau Team Members and Contributions Akash Agrawal o List of APIs Reverse Geocoding API Google Places API Google Maps API o EC2 website hosting o WEKA analytics Karan Dhingra o List of APIs Facebook Login API Facebook Like API Facebook Share API o Development of business logic o Website design o Database connections Damini Khurana o List of APIs Envolve chat API RSS feed API Factual API Barcode scanner API o Database design

11 o Module integration Krittika Patil o List of APIs Twitter post API Mailgun o Concept development o Website design o Business logic development Future Extensions With the growing importance of crowd-based systems and user-generated content, the site provides wide scope for expansion. The expansion has been studied both in terms of improvements and extensions to current project scope, and monetizing opportunities that can be considered. Product Recommendations Targetted Advertisements Cross-Platform Deployement Use customer history to recommend deals Based on Association Rule Mining Provides customized user experience Leverage user's shopping pattern Provide 'curated' deals taking nominal fee from local business Unique opportunity to capture shoppers in the vicinity 'right now' Expand to ios and Windows Platforms Improve and perfect Mobile Platform