4 Ways Predictive Analytics Can Help Retailers

03 Jun. 2015 Big Data

We live in a hyper-competitive retail space today, where the consumer is not shying away from comparing prices with competitors and posting reviews over the internet. In the past, retailers had to scout out customer information and data to improve their customer service. Now, the issues faced by many retail stores is the overload of information on their customers and looking for ways to make sense of it. A large volume of structured and unstructured data is floating around the web, predictive analytics in retail helps piece the puzzle together.

Big data analytics insight to study the market trend

Big Data for Market Analysis in Retail Sector

To be successful in the retail space, enterprises will need to know what data to compile to generate relevant insights that help predict shopping patterns & consumer behavior. Some of the ways through which a business can succeed using big data analytics in retail are:

  1. Strategic Thinking : Before carrying out data analysis, retailers need to identify the real purpose behind conducting it. The goals of the analysis should be tailored to the business requirements. For instance, when a retailer wants to boost web sales, they should appraise the effectiveness of its e-marketing campaigns and performance of the supply chain network for delivering the goods sold. In contrast, another retailer maybe looking to improve the revenue from a particular product category.
  2. Perfect Data Use : Viewing data from a multifaceted lens is necessary if retailers want to benefit from it. Gathering information about consumers from different points such as its eCommerce store, mobile app purchases and in-store POS systems helps retailers understand consumer preferences. This understanding assists them in designing better product offerings and change store layouts to better serve their customers.
  3. Capitalize on Loyalty : Majority of retailers offer loyalty schemes that reward shoppers for their repeat business, helping brands track their buying patterns & behavior. Besides making product recommendations, data gleaned from loyalty programs can be analyzed to improve the consumers buying experience as well as meeting their needs in real-time.
  4. Put Data to Work : Consumer data can go beyond just deciding about what promotions should be offered. Once retailers decide to execute the data at hand, they can plan operational and merchandising activities based on the same. Data-driven insights aid retailers to determine product trends helping them better manage their inventory for their products.

To summarize, retailers cannot afford to ignore Big Data anymore. While it isn’t rocket science, there is patience and investment involved. Inciting ‘desired’ consumer behavior is not an impossible task since the data you need on them is already out there.

Are you a retailer who wants to make most of predictive analytics for your business?

Or, are you a small business owner who’d like to understand how to win with retail big data analytics?

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