How Data Analytics in Retail Industry Can Help You Predict the Future

In retail industry, fashion styles and trends in apparels and accessories keep changing dynamically, and it is difficult to predict what will sell and what not unless you have an optimized inventory for each change by efficiently utilizing the power of predictive analytics

Unfortunately, it isn’t as it sounds. But with data analytics in retail, they can predict trends based on their customer’s behavior and know what product would do well in a particular season and plan strategies for communicating to their customers.

Data plays the most critical role for analyzing customer behavior, but it can get tedious and challenging to channelize data and filter relevant data from the influx that comes in every day. Making an analysis based on a trend or pattern can make your task easy.

Knowledge is power, but there is a deluge of information on the different ways you can use predictive analytics in the retail industry. Along with trends and styles, you also need to decide the pricing. Optimizing price is influenced by many factors such as margin you can charge on each item, your customer base and how much are they willing to pay for a particular item, overheads of running your business, how much your competitors are charging, and so on.

We have tried to filter a few things that might help you to understand how data analytics in the retail industry can help you. Let’s go through them:

Predictive Retail Analytics – The Magic Crystal Ball for the Retail Industry

Using retail analytics, you can analyze all the data available to you which include inventory and sales records, a performance report of the products based on previous year’s analysis, and which items were loved and preferred by your customers.

Analyzing trends and patterns only based on your customer data might not be that useful; you even would have to study the industry as a whole, do competitor analysis and much more.

Social media is growing extensively, and it is giving the ability to the customer to express their opinion and review every product they buy, which provides you with access to a lot more valuable insights. But, this data is unstructured and is not readily accessible through conventional methods.

And there comes, Big Data.

Using Big Data, you can capture relevant data for your industry across the different competition, sources, and platforms, across the globe which is in massive volume that it requires a lot of computing power to process. A set of technologies are used in Bid Data to capture, store and analyze this data.

You would require deployment and integration of many different resources and includes enormous investment to process Big Data, which is why most of the retail firms outsource it to third-party retail analytics providers to help them tackle this immense information source and create meaningful reports they can use.

Why Retail is Embracing Predictive Analytics

In this epoch of multi-channel, omnichannel and cross-selling strategies, knowing your customers more intimately can give you a competitive edge and promote your business results by making your plan more customer-centric. By collecting data from across all available sales channels and from across the industry, you get the advantage of creating a successful master plan as the consumer expect the brand to be more intuitive in responding to their individual needs immediately. Which leaves the retail market to data-driven decisions to enhance the customer experience, boost sales and strengthen loyalty.

Predictive analytics can help retailers and suppliers by giving them excellent clarity to make better business decisions in areas such as marketing strategies, product assortments, and forecasts. At the same time, you would require determining what part of the data you need to keep and what to discard which cannot be based solely on sales data. You can hence replace uncertainty with probability with future-oriented predictive intelligence using these data insights.

Predictive Data Analytics in retail combine business data, product data and consumer data which helps the companies to connect the dots and understand the pattern related to sales and customer behavior and retail process. Predictive analytics can specifically help retail corporations to foresee trends in the industry, identify customers and optimize pricing by making sense of consumer data from all possible channels, which would even include in-store sales, social media engagement and online browsing.

You can make a customer’s shopping experience more personalized and relevant by predicting their spending habits and by anticipating what shoppers want, you can boost sales and maximize lifetime value per customer, and reduce supply chain, inventory and shipping cost.

Diverse Retail Companies Use Predictive Analytics

In the intensely competitive grocery sector, to gain competitive advantage, retail, wholesale food market, and Krogers use predictive analytics. Even E-commerce leader Amazon use data analytics to track the products people buy, look for and return. To make decisions on its product combination strategy, Amazon uses those these profound, data-driven insights. Amazon maximizes its sales by using predictive analyses to stock their endless aisles online with the merchandise shoppers want.

To satisfy the increasing demand of customers, suppliers are embracing predictive analytics as the consumers expect products delivered exactly when promised. Suppliers manage to deliver orders on time with the help of Big Data innovation.

Experts predict by 2020 almost 70% of manufactures will adopt predictive analysis. Predictive analytics is being used even by small retail companies to broaden their time horizon when developing a strategic plan to achieve more accurate demand forecasting.

Conclusion

Predictive analytics is a huge boost to the retail industry to anticipate their customer’s needs with greater certainty. Expand your understanding of your relationship with individual consumers, to ensure the best of predictive analytics is procured and help your company make shoppers feel special, understood and willing to buy by using statistical algorithms, data mining, and machine-learning techniques. By segmenting shoppers, you can send them personalized emails, so you don’t spam them, and keep only the interested ones engaged.

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