Our Big Data experts automated the demand forecasting model of our client and analyzed the data obtained from multiple parameters.
About the Industry
The retail industry is witnessing changes constantly. With the exponential increase in customers’ digital engagement and exposure, their demand has also increased. The retail market strategy is leaning towards omnichannel retailing and mobile marketing.
In the digitized era, the concept of personalized shopping is gaining momentum. A personalized retail experience and exhaustive e-commerce engagement are leading to the expansion of the market and the creation of new marketing channels. The incorporation of Artificial Intelligence, Cognitive Computing, and Augmented Reality have shaped the future of the retail industry. The Internet of Things and connected devices will simplify the shopping experience and make things more convenient for customers.
About the Client
Our client is one of the largest US-based retailers. Currently, they operate more than two thousand retail outlets and department stores. They have a vast geographical bandwidth, with a strong presence in countries in the Middle East, Africa, Europe, and some parts of Asia.
The company isengaged in the business of retailing a range of household and consumer products through department store facilities under various formats.
The Business Challenges
Our client faced the following business challenges: -
To mitigate our client’s challenges, we followed this three-fold approach: -
We integrated all the factors that affected demand and precisely calculated demand forecasting. We gathered the historical data from several sources and analyzed it against several parameters like season, time, festivals, and end-of-season promotions.
To consider all the relevant parameters and make forecasts accurate, we deployed a multiple time series model. Along with this model, ML-based algorithms improved the accuracy and credibility of the forecasted results by ingesting several data sets and data points.
Our demand modeling technique delivered insights for better operational planning. The reporting of our analysis projected future demand based on current sales trends. It helped the merchandisers prioritize their business plans.
• Analysis of historical data and the customer demand
• Lack of business model that could analyze retailing trend
• Lack of automation in demand forecasting.
• Demand gauging using time series and ML-based models.
• Gathering and analysis of the historical data.
• Enhanced demand forecasting using holistic demand intelligence tool.
• 90% accurate demand forecasting
• Streamlined process
• 5% growth in revenue
We resolved card and payment related issues and prevented their further occurrence. About the Industry...
February 18, 2020
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