Friday, June 1, 2018

The Impact of Big Data Analytics in the Retail Industry

Retail is one of the fastest-moving non-contractual business sectors. It is highly competitive and is an industry where innovation is constantly transforming the landscape. If you are part of the retail industry, you may have experienced constant changes and business models once celebrated becoming obsolete overnight.Image result for The Impact of Big Data Analytics in the Retail Industry
You may, however, be delighted to know that proactive investment in technologies such as machine learning, big data, and artificial intelligence can give your business a competitive edge to stay ahead of the competition.
Artificial intelligence has the potential to facilitate better customer experiences through machine learning, augmented reality, virtual reality and voice processing. According to Gartner, by 2020, AI will manage 85% of the customer interactions in retail.
The application of machine learning and AI systems in retail was recently demonstrated by companies like Amazon using the "Amazon Go" grocery store and by Walmart through "Shelf Scanning Robots."
If these are just brushed aside as proofs of concept today, we don’t know yet how far these technologies will take us. Companies like Amazon and Walmart are large but are not complacent, and they constantly invest in technology, thereby creating huge competition for the relatively smaller online stores and brick-and-mortar stores.

Challenges Faced by Small- and Medium-Sized Retailers

  • Small- and medium-sized retailers are struggling to offer a better shopping experience and provide customer satisfaction with limited budgets.
  • Small- and medium-sized retailers are unable to invest in understanding customer perceptions, leveraging strengths, and addressing weaknesses so much so that some do not even identify their customer using a lightweight loyalty program.
  • Small- and medium-sized retailers do not assign enough resources to identify profitable customers and tailor marketing and service efforts nor do they identify customers or prospects with future high potential.
  • Many of them have not had the time to try to increase marketing ROI for every dollar spent.
  • Personalization and product recommendations are offered by large companies at an individual customer level to enhance conversions, and minimizing cart abandonment is difficult for small- and medium-sized retailers.
  • Small- and medium-sized retailers also do not have resources to build solutions that optimize inventory planning for perishable/semi-perishable goods nor to ensure the availability of the right products for end customers.

How Small- and Medium-Sized Retailers Can Compete

By empowering individuals across the organization to make decisions accurately and confidently by harnessing big data, these retailers can perceive customers more deeply and uncover hidden trends that reveal new opportunities. Big data analytics has applications at every stage and can help with predicting trends (seasonal and otherwise) and demand, thus isolating customer interest and understanding and predicting customer behavior.
Let's take a look at some common techniques that are useful for the retail industry.

Customer Behavior and Predictive Analytics

You can use data analytics to find your potential customers, the key drivers that motivate them to buy more, and the best way to reach them. There is an opportunity to interact with customers through multiple channels like social media, e-commerce, or in-person at the store. In addition, location analytics can be used in-store to help better understand people’s purchasing behavior and to monitor consumer traffic. A customer’s purchase and browsing history (both in-store and online) can be used to predict the needs and interests and personalize promotions for them.

Operational Analytics and Supply Chain Analysis

Retailers can use analytics to optimize supply chains and product distribution to scale back prices. You can potentially combine structured data with unstructured data and then use this data to discover outliers, runtime series, and root cause analyses, and parse, remodel and visualize data.
A few other data-driven approaches include:
  • Text mining algorithms to arrive items and order quantity automatically.
  • Deep learning techniques such as convoluted nets for recognition and analysis of images obtained from refrigerator cameras and automation of order placement.
  • Use of text mining to conduct customer sentiment analysis.
  • Customer lifetime value (CLTV) scores to identify specific customers who need to be targeted or reactivated.
  • Use of look-alike modeling on third-party databases to identify profiles similar to high-value customers (obtained through CLTV analysis or based on the share of wallet maximization).
  • Creation of unique customer personas.
  • Past purchase behavior and timing analysis to identify potential products that customers are most likely to purchase.
  • Suggest more relevant product recommendations based on customer personas and purchase behavior.
  • Enhanced end-user experience by suggesting the right products at the right time of the day.