HuntaSale – A Sale Hunting Platform using AI and Blockchain Technologies

PIs

PI Organization

Introduction

Traditional advertising methods to reach the customers about a sale promotion include billboards, email, social media, TV/radio, SMS and newspaper. However, the cost associated with these advertising forms is generally huge. This cost is one of the reasons why many sale promotions go unnoticed or small businesses avoid benefiting from the huge sale volumes by having sale promotions. In addition, there is no feedback mechanism in place or user analytics data available for stores to know the number of users benefiting from various forms of marketing they use for sale promotions. In fact, the traditional advertising methods, i.e., billboards, TV, radio commercials or SMS based marketing, do not provide any feedback to the retailers and brands about the user analytics, i.e., profiles of the shoppers viewing their ads etc. Thus, despite spending millions of dollars on advertisements, the stores, brands and retailers do not know the outcome.

On the other hand, from the customers’ perspective, the sale promotion ads could be annoying especially if one is not interested in the products or stores that they are advertising. Social media and other search engines try to figure out the interests of users by looking at their search histories and interests in the social media platforms but the predictions could be wrong as in most cases the users do not make their choices explicitly. Moreover, to the best of our knowledge, there is no single platform that the shoppers can approach to get the ongoing sales and deals in their locality. There are some platforms that provide this information for specific localities only, however, they rely on their users to populate the deals and thus cannot be considered as a comprehensive source of all the deals.

State-of-the-art

To the best of our knowledge, all the online sale sharing platforms, including LivingSocial, Yipit, Woot, RetailMeNot, Tanga, Ebates, Gilt and Krazy Coupon in North America and Europe, Cudo, Scoopon, Catch, MyDeal.com, Grocery Run, Crazy Sales in Australia, and Dealsmash, Disconto and Jazz Discount Bazar in Pakistan require manual additions of sales/deals, are specific to certain zones only, provide very limited incentives for their users’ time, and do not provide data analytics about the promotional sale to the vendors. Some of these platforms have a huge number of users that add the sales and deals but again new entries are usually done manually. Moreover, the user interest in viewing ads is considerably low because she is not given any incentive to view an ad. Another reason for such low interest is that the users have to find the deals of their choice among the many options available. Similarly, it is noticed that the deals in these platforms are usually focused on some specific regions. For example, the Pakistani platform Disconto is focused on Lahore, Karachi and Islamabad and Jazz Discount Bazar is focused on Islamabad/Rawalpindi region only. In their models and similarly most of the international platform models, the idea is to contact the vendors to get specific coupons, which is not a scalable option as engaging vendors across large regions requires a bigger team that is spread out geographically as well.

Proposed Solution

In order to solve the above-mentioned problems, we propose to develop a global platform for retailers and shoppers that would be automatically populated with latest sales promotions of all stores signed up. There would be a feedback mechanism available for businesses to keep track of user analytics, i.e., number of users benefiting from their advertisement on our platform. The proposed mobile-based platform would provide advertising opportunities to the businesses for free but the main banners on the apps could be purchased at a very nominal price, e.g., 5-10$ a day. This platform would be available to customers for free as well and they can in fact choose the advertising that they are interested in. Moreover, a centralized reward system would be incorporated within the platform where user would get some incentive for spending time on application, i.e., monetize the time spent on the application. Thus, the proposed solution to the above-mentioned problem is a user-friendly, self–populating app/web-based platform that provides cheap and targeted advertising and user analytics feedback system for the businesses and the advertising by-choice solution and incentive for spending time on application to the shoppers.

In this regard, we have already launched the beta version of an android app, Huntasale (available at Google Play Store), which provides a platform to share deals for free for both the shoppers and the businesses. The shoppers have the choice to select the stores and brands that they are interested in. The businesses can choose either to add the deals for free in the regular interface or pay for posting attractive banners on Huntasale, which is the main source of revenue for the company.

 

In the proposed program, we mainly plan to expand the platform to iOS and web to have a wider outreach. Moreover, we plan to develop a novel Natural Language Processing (NLP) and Optical Character Recognition (OCR) based solution that allows us to collect the available sales/deals of stores automatically from their announcement emails, web pages and their social media accounts. This capability will allow us to populate the databases of HuntaSale for deals all around the world and thus we can capture the market share of advertising globally. Furthermore, we intend to monetize user engagement with our application by generating cryptotokens at periodic intervals that users can then transact with on a distributed blockchain-based network. This capability will allow us to increase our user base and incentivize them to use our platform. In addition, we would develop a support mechanism to track user analytics and provide feedback to businesses. We also plan to develop a viable business plan during the course of this project to make our idea commercially viable.