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RESENSEᵀᴹ For AI Driven Retail Analytics

Globally Retail as an industry is in the throes of transformation. With the sudden onslaught of COVID-19 outbreak, the retailers are caught in a quagmire. The COVID-19 outbreak has completely upset the retail cart – what was valid a couple of months ago has lost its relevance today. While some retailers are experiencing unprecedented demand surge, others are witnessing shrinking sales numbers at this current juncture. Ability to predict demand accurately is the key to adapt to the present uncertain situation.

Going by a recent Gartner Inc. observation, it said, “ To mitigate disruptions and revenue reductions caused by the novel coronavirus , the goal is to combine an effective near-term response to the impact of COVID-19 across the end-to-end supply chain with a clear plan that position organizations for success as the economy recovers. Retail organizations need to leverage available data effectively, work closely with suppliers and run a smart workforce.

Also, this is the time to bring in specific technology infusions that will help mitigate the impact and bring in a rapid response model to market realities as retailers quickly realign themselves to the new and for the next normal.

A Retail Insight Ecosystem Powered by AI

Given this backdrop, a need for reinventing the retail forecasting solution becomes all the more relevant. Nihilent’s RESENSETM analytics solution specifically caters to the current unprecedented times, and infuses the much-needed business alignment with extremely volatile market dynamics and helps in navigating uncertain times.

Nihilent developed its retail forecasting solution leveraging years of industry expertise and its deep retail industry domain knowledge. RESENSETM provides the demand forecast by utilizing multiple forecasting algorithms, including univariate and multivariate time series forecasting models, Machine Learning (ML) including deep learning models. These models are chosen to replicate numerous different demand patterns including extreme volatile patterns. The application refines demand forecasting accuracy by segregating actual demand from sales data, which is generally used for demand forecasting.

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