The primary goal of retail analytics is to perform data analytics related to domains like specific marketing, logistics management and customer feedback. This insight acts as a source of data for yearly sales, stock management and customer activity. Consequently, retail data analytics provides a solid ground for making operational decisions for an organization.
Crossroads of retail analytics
At the crossroads of retail analytics lie the fields of big data, omnichannel and personalization. Let us examine this intersection in a deeper detail. The voluminous increase in data and the value attached to it have witnessed a rapid surge in the last few years. This has deeply motivated companies to carry out ecommerce analytics. In the next few years, this field would gain immense popularity as tools of machine learning are being employed to carry out analytics. One example of this is data analytics carried out by Algoscale using machine learning techniques.
Read how – Algoscale harnessed the power of machine learning tools to forecast resale for a diagnostic test kits provider.
The second big thing that lies at the heart of retail analytics is omnichannel. The shift to omnichannel became prominent in the last ten years and we witnessed the integration of physical and online mediums. Soon, artificial intelligence would greatly influence developments in this domain. That said, we would witness retailers experimenting with the groundbreaking technologies like augmented reality and virtual reality.
The third technique that lies at the crossroads of retail analytics is personalization. This technique can prove extremely handy in improving business performance and outcomes. A survey conducted by BCG concluded that a personalized customer journey has the potential of improving sales by about 90%.
Text analytics tools extract patterns after reviewing the literature looking for themes, sentiments, and concepts alongside testing a given hypothesis. These tools require very little human assistance as they execute tasks with the help of NLP and machine learning.
A report published on June 7, 2016 revealed that a leading tech company managed to identify anonymous pancreatic cancer patients by analyzing search queries with a staggeringly low false-positive rate. The controversial success of this innovative attempt speaks for the efficiency and prospects of text analytics. A vertical persuasion regarding the method of this achievement will most certainly help in a better understanding of how text analysis works.
Retail analytics in consonance with omnichannel
The beauty of omnichannel experience is a natural integration between digital and physical interfaces. This is what retail analytics requires: diversity for the customer plus an ease of access at the time and place of his own choosing. There are four prime reasons due to which retail analytics supports omnichannel. The first one is cybersecurity. Cybersecurity has become very important in the current times due to sensitivity of data. Soon, cybersecurity would assume the center stage of all technological progress because intimate customer details could become available online. As such, retailers would give much attention to data security. The second reason is tracking of customer journey which would help in determining a customer’s mindset. The third reason why retail data analytics would support omnichannel is due to the access to real time data to track sales and promotions. This is what will take ecommerce analytics to a new level in the future. Lastly, retail analytics will aid in customer segmentation, which will help to keep track of varied customer needs and suggest appropriate recommendations.
Reaping the benefits of retail analytics in future
First things first, retail analytics would greatly help in increasing revenues. This would happen by better targeting of customers. A highly specialized type of analytics would help in segmentation of customers demographically. Geo-tagging of thousands of products, running of promotional campaigns and channelizing customer feedback to improve service delivery would become possible.
Retail analytics would not only help in layout optimization but also enrich a specific niche of the ecommerce ecosystem. This would help in improving business intelligence in general and business performance in particular. Demand forecasting would see major advances in the times to come. Nouveau forecasting techniques including effective algorithms would be devised. By tracking historical data and facts, behavior of customers and their choices could be predicted.
Throwing caution to the winds
There are formidable challenges that accompany data analytics which cannot be ignored. The first and foremost challenge is that of data security. With the constant expansion of data lakes, the security aspects are slated to grow. In such a case, data protection and data trust need to be the norms rather than an exception. Second in line is data governance. The way through which data is processed, the amount of time for which it is stored, and other compliance requirements are aspects that need to be given much attention to. In addition to this, ethical data utilization needs to be given prominence.
The future of retail analytics in the age of artificial intelligence is very galactic although it is riddled with challenges as well. The challenges need to be addressed and benefits need to be reaped in the times to come.
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Also Read: Retail Analytics: a better way to engage with consumer information