Transform your insights into business outcomes by harvesting demand intelligence

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. 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 has 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 is engaged 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: –

  • Analysis of large historical data to obtain accurate results.
  • Identifying patterns to ascertain the current customer demand was getting difficult.
  • The organization urgently needed a business model that could use the historical data to identify past retailing patterns
  • Demand forecasting techniques were not automated and lacked real-time reporting.

The Business Solution

To mitigate our client’s challenges, we followed this three-fold approach: –

  • We precisely gauged the demand

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.

  • We gained insights and transformed them into results

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.

  • Improved operational planning for better business plans

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.

Key Results

  • We obtained more than 90% accuracy in demand forecasting.
  • We streamlined the budgetary process, sales planning, warehouse portfolio, and operations, and thus gained better visibility and accuracy.
  • The revenue saw a 5% growth, which further improved their stock market value.

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