DealSmash, a smart shopping discounts and rewards application startup based out of Technology Incubation Center-NUST Islamabad, has raised a funding of PKR 8.8M from National ICT R& D fund.
The raised funds will be utilized to develop an artificially intelligent shopping recommendation system which will recommend live deals and discounts to buyers according to their previous shopping trends.
DealSmash is a mobile-centric technology startup that promises to bring traditional consumer retail to the digital age. At the heart of it, is its namesake mobile app, DealSmash, that uses business/social analytics to provide value to shoppers through personalized offers at their favorite stores based on their interests and buying patterns at no cost to them.
From the perspective of consumers, this not only saves them money but also enhances customer experience. From the perspective of retailers, this not only enables customer loyalty and increase their revenue but also enables them to get insights into consumer behavior, inventory control, etc DealSmash mobile app is currently operating in Islamabad and has recently been launched in Karachi, offering discount coupons at various stores across the city. It also has a Rewards System for most loyal customers who can earn cash-back simply by shopping and dining at any store/ restaurant.
The funding from ICT R&D will enable DealSmash to build our (Artificial Intelligence-based) recommender system to handpick for our customers the most relevant offers based on their interests and their buying patterns. This way, we won’t spam the customers or waste their precious time with pointless/ irrelevant offers.” There is a dearth of smart intelligent mobile applications, DealSmash application is hoping to change theirs through their smart analytics-based system.
The startup has currently 10 brands on board for its reward and discounts system but is aiming to double this number in the next few months. The central component of the DealSmash recommender system is an intelligent, adaptive software-based system that can learn, both offline and online, the preferences, shopping behavior, geographic attributes and demographics of the customer base, in return providing a personalized shopping experience to each customer by suggesting only relevant offers to his or her mobile device in a context and location-aware manner.