How Retailers Uses Big Data

With all the personalized ads popping up on Facebook and Google, It should come as no surprise that big box retailers like Wal-Mart and other franchises use big data too. Their store networks track data about customer purchases to make the store more efficient and tailored to customer needs.

If you’ve ever been shopping and picked up more than you really needed to buy, it might be retail analytics at work! But big data isn’t useful in its raw form – AI uses algorithms to identify key trends in the information gathered, like price points of customer purchases and shipping routes that goods take to reach the stores. There are several ways retailers use this data, including:

  1. To determine staffing needs – retail analytics of department store locations can be useful in assigning shift work to employees. Since the data can tell the end user when the store is most busy, staffing can be adjusted accordingly to add more staff during busier times, and less during slower times.
  1. To decide what types of checkouts are more efficient – automated and human tellers have both advantages and disadvantages for retailers, and the same is true for customers. Some types of customers like a person behind the till, while others prefer to check out on their own.                                                                                                             There will likely be locations where it will make more sense to have more automated tellers, where other locations will benefit from more human tellers, depending on the demographics of the customers. Data on how many customers use each can help store owners decide how many of each to have at a particular location.
  1. To help with merchandizing – data on what purchases are generally made together can help merchandisers decide what to put on endcaps. If you know people often buy suntan lotion and travel toothbrushes in the same purchase, it’s helpful to display them together somewhere in the store, even if they’re usually in separate aisles. This data can help drive the layout of the store as well, determining what to put in specific aisles to maximize sales.
  1. To determine pricing strategy/sales items – data on when customers make specific purchases can also drive when certain sales will hit, or what price points to put certain products at. It can also factor into decisions such as what products go on sale at the same time, and what products can have higher price points.
  1. To maintain brand identity – of course, it’s not just big box stores using retail analytics; many brand owners use MAP (Minimum Advertised Price) monitoring to ensure that their products are selling at a specific, predetermined price point to maintain their brand identity. This means that retailers can’t get away with selling at lower than the minimum advertised price – MAP monitoring systems will identify this and help brands put a stop to it.

Of course, this list isn’t exhaustive. Retail analytics also drive things like advertising, store features, and brand identities as well. People who can parse the end results of big data will be in higher demand as these techniques become more and more common. Soon, retail analytics will be as essential as computers themselves in conducting business.

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