Latest Trends in Retail Analytics

August 11, 2017

Blogger Image

Rajesh Kumar K

Head Presales and Technical Solutions

Expansion of industries and businesses has made it difficult for enterprises to keep track of their sales and inventory. Data Analysis and tracking key business parameters are crucial in business decision making. This includes inventory levels, supply chain movement, consumer demand, sales, etc. Analytics pertaining to demand and supply is being successfully and popularly used for making marketing decisions. Retail Analytics companies provide detailed customer insights which businesses can leverage from.

Analytics has now turned into a powerful tool for the retail industry. It is largely being used to achieve a fact-based, insight-driven decision making process to improve their strategic, operating and financial performance, and earn a strong shareholder value. Retailers today are looking for varied techniques and approach to derive more customer intelligence and operational insights from their data.

Leading retailers are giving priority to analytics with the increasing amount of valuable data available. This is also changing business intelligence norms across the industry. Following are the current trends in Retail analytics:

Analytically Driven Clustering:

Retail enterprises are using advanced analytics to understand customers' merchandise preferences. Breaking down of silos of channels with data analytics helps retail enterprises in looking across channels. This will obtain information on what products are being purchased online? What products being purchased in stores?; And also offer better insights on market opportunity. Taking cue from this, retailers are popularly using analytically-driven clustering techniques to identify and cluster trade zones that are more lucrative.

  • Attribute Analytics:
    Retail industry is drowning in data. Retail enterprises want to make the most of the data and therefore the research attributes are increasing rapidly. The attributes further have specific values that have market opportunity. This segmentation helps in achieving precise data to make improved decisions on Pricing, Promotions, Inventory Management, and Product Assortment.
  • The rise and rise of Omni Channel:
    In order to have a strong Omni channel strategy, retailers need a holistic perspective of data pertaining to commerce channel, supply-chain, and market share. Studying through varied sources of data for each of the required areas is tedious, time consuming and sometimes too overwhelming to derive conclusion from. The demand for an Omni channel experience has resulted in sophisticated tools to connect to data sets as per required attributes. This has further resulted in the adoption of Omni Channel strategies.
  • Improve Assortment Planning:
    Retail enterprises are popularly improving their assortment planning processes with the help of analytical insights. Sorting of where to sell which product is significantly helping the retail industry in improving merchandising and eventually, customer experience and retention.

Building a Retail Analytics Strategy can be daunting. There are a few challenges that retail enterprises could face while executing real-time analytics as given below:

Challenges

  • Data Security:
    Data Analytics can create complexity in security. Security is robust primarily for large amounts of data. However retail enterprises using small amounts of data also need a secure platform for real-time analytics.
  • Governance:
    Overwhelming data, increasing demand for relevant data, and rise of numerous sources for data has resulted in data that might not be true-blue. Utility of data analytics holds good only if the data is true and untampered. Retail Analytics contains valuable customer information and needs a strong governance strategy to ensure the credibility of the derived data.
  • Data Utility:
    Retail industry is overloaded with data. Enterprises strive to deep dive into the data to achieve business goals with precision. It is imperative for retail enterprises to sort the wheat from the chaff and leverage from the right data.

Benefits

Density of competitiveness in the retail industry is increasing rapidly. To thrive and flourish in this retail environment, enterprises need to be consistent in delivering great customer experience. The best way to build a substantial Customer Experience Strategy is with the help of Retail Analytics.

Here are the benefits of Retail Analytics:

  • Smarter Shopping Experience:
    With the use of predictive analytics, enterprises can develop or create products that show an increased demand. It is also critical in identifying trends from various channels and thus derive sales opportunities. Therefore, enterprises can benefit from increased sales with improved margins.
  • Develop a strong sales strategy with future estimation:
    With the use of predictive analytics, enterprises can develop or create products that show an increased demand. It is also critical in identifying trends from various channels and thus derive sales opportunities. Therefore, enterprises can benefit from increased sales with improved margins.
  • Change Pricing:
    Retail analytics helps enterprises in revamping the prices in real-time in accordance with current demand, and anticipated trends. With this, the enterprises can improve margins, increase sales and have a strong hold in the competitive market.

Analytics allows retail enterprises to make well informed decisions. Success of retail analytics begins from the use of right and relevant tools, strategies and selecting the right retail analytics consulting firm. Enterprises that are looking towards retail optimization will need to start with a strong retail analytics strategy.

If you would like to know more about our retail analytics solutions, then do leave an enquiry with us and we will get back to you.

Join the conversation

What are your thoughts on this blog? Drop us a line below. We’d love to hear from you.

© 2017 Nous Infosystems Pvt. Ltd. All rights reserved.