Abstract

Sanjeev and his friends, excited about the opportunities that existed in Big Data, decided to quit their jobs in MNCs to pursue a tech start-up in retail analytics. The initial surveys among stores managers and shoppers showed very positive response to their idea. They registered iTrolley solutions, to be developed on a “Big In-Memory Database” platform of a “Large Enterprise” as their partner to offer retail solutions. It was a unique solution catering to both shoppers and retailers with great benefits. Retailers could avail better inventory management and integration of multiple channels and shoppers could select the best offers in a store through the iTrolley app. The application syncs with data warehouse managers through POS/CRM, and provides inbound analytics to ensure better maintenance of inventory levels. The partner, a “Large Enterprise” firm and their Solution Architects approved iTrolley’s idea and provided a credit of $1,100 to build the solution on their platform. However, it was still expensive to build the solution because it required complex data analytics and WebApp Genie capabilities. The team was also not sure about the pricing. They built a minimum viable product using the POS data and past purchase history of the consumers. The team used algorithms for connecting the in-store customer data along with the in-store products and customer purchase activity data. The approval of overall data architecture and the solution was validated and certified by the “Large Enterprise Partner”. The team also received two certifications from the “Large Enterprise Partner” worth € 20,000.

Once validated, the team presented their solution to the head of a large retail chain and his team. They were excited and promised to arrange a meeting with the top management. It was important to run a pilot using their POS data to develop a proof of the concept. Sanjeev and team explored alternative market opportunities using their networks to connect to various retailers, both large and small. However, the responses were mixed. There were delays in getting to run pilot projects in the retail stores and funds were becoming scarce, time was running out, expenses started increasing, initial investments were dwindling and pressure from the families increased. The initial enthusiasm was slowly ebbing out. The team got an opportunity to pitch to the panel at NSRCEL, IIM Bangalore’s incubation center. They received enthusiastic feedback and the panel offered a few subsidized workstations at the center which the team accepted. In the meantime, the team continued to meet retailers to gain access to their POS data and to run pilot projects. They also used the service of a lead generator company to connect them to top brasses of retail stores. While the store managers or even owners could see the benefit of their product, the vendors resisted to provide access to the data. The team pitched to accelerators and venture capitalists for funding and mentoring. All of them asked for either a proven revenue model or a proof of concept. The team members grew more and more impatient. After meeting multiple stakeholders, it was suggested that the way forward may be to build the product using open platforms like Hadoop and Mapreduce. The challenge was that the team didn’t have such expertise, and had to hire Hadoop professionals who charged quite a huge salary and equity from startups. They were at cross roads.