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Big data and commerce/eCommerce merchandising

As Craig Mundie, Senior Advisor to the CEO at Microsoft mentioned that "data are becoming new raw material about the business", the Omni-channel merchandising are making their decision based on the data and analytics helps them to predict the future demand flow.

The Big data analytics are helping the merchandising decisions retailers are making every day. A few examples where data driven decision are being taken in the etail/retail space regularly. Although, business decisions are very complex and based on a lot more factors, below are the few examples where key matrices are created and helps management to take decisions.
  • Assortment:  Keep product A, drop product B, add product C – these kind of decisions were taken based on the data and sensitivity analysis using big data frame work.
  • Online Marketing and campaign management: Big data can analyze the sentiments of the customers based on the reviews and analyzing the social media and helps marketing team to take informed decisions about the products. Some products need to be de-commissioned or some products need more campaigning.
  • Pricing: Big data can help to analyze and change prices based on pre-defined parameters and thus helps to drive the business. Few of the parameters are competitor's price, stock quantities, future demands etc.
  • Promotions: The big data helps to strategies the promotion for the products. Based on the analysis, merchandisers can decide the time period, when to give, promotion based on local market or global requirements. These helps merchandisers to personalize the promotions.
  • Local Market: Big data analytics helps to curve the adaptation strategy for the merchandiser. It helps analyze if there is a new trends in the local market which not available in the global space by analyzing the marketplace sellers.
  • Product Content: With the power of analysis of large data from different sources, etailers/retailers can decide the product content in the platform. The analytics helps to analyze and predict the future products to be/should be present on the shelves in each of the target segments. In fact, the power of analysis may help to predict if the merchandiser need to look beyond the cliché segmentations and look for niche segmentation.
  • Seller information analysis: Using big data analytics can analyze the performance of the marketplace sellers based on pre-defined parameters. Big data analytics framework can gather and analyze data from difference sources. It'll create dash board for the management about the performance of different sellers. So, management can take informed decisions to on-board a new seller or off-board an existing seller for their platform.
Investment in Big data analytics will not give you a competitive edge, it is a requirement to stay in the market. With the power of social media (word of mouse), it has become a rudimentary requirements for a business to have good market share and stay afloat.​

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