Data Engines Delivering the Value for Consumer Support: Refreshing Retail & Consumer Goods
Customer data helps gain insights into consumer behavior and emerging market trends.
With organizations moving toward the intelligent consumption of data, the demand for smart and cost-effective cloud and data structure infrastructure is rising, shares Shiva Mathur, VP of retail & consumer product goods at HCL Technologies. Not just data management, an efficient data engine also ensures the safety of the data and its compliance with data protection laws against cyberattacks or system crashes.
“Knowledge is power in business, and data is the fuel that creates this power.”
Retail is an industry that develops swiftly and constantly. In this business category, customers are the end users who buy services and products for personal use. Forming a personal connection with the customer is vital for success in such an industry. With 40% of consumers expected to switch brands, a thorough understanding of their changing needs and preferences is essential to develop targeted, effective marketing strategies and fresh product ideas.
The retail industry relies heavily on customer data to gain insights into consumer behavior and emerging market trends. With millions of consumers worldwide, and e-commerce sales flourishing across industries, this industry generates massive volumes of data. If optimally used, this data can provide retailers with a significant edge over competitors. Enterprises are now investing in advanced data engines to manage this data deluge.
See More: Nine Tips for Scalable Data Masking for Growing Companies
Understanding Data Engines
A data engine is a built-in key-value store (KVS) specializing in sorting and indexing data. It is often referred to as a storage engine, which is software that performs basic store-management operations, like creating, reading, updating, and deleting (CRUD) data. However, as this underlying system exceeds its traditional function as a storage engine, it is referred to as a data engine to cover its wider scope and use cases. By creating defined structures and mechanisms for storing and manipulating information, data engines enable the retail industry to manage consumer data to generate valuable insights optimally. Not just data management, an efficient data engine also ensures the safety of the data and its compliance with data protection laws against cyberattacks or system crashes.
The Scope of Data Engines
The scope of data engines in delivering consumer support to present-day retail and consumer goods manufacturing is immense:
- Merchandising and manufacturing: From the insights obtained from the curated data, retailers can gauge the frequency at which consumers buy products and the type of products that are bought together. This enables retailers to promote and manufacture particular products according to the customers’ needs and preferences. This is essential for customer retention and increased revenues.
- Personalized advertising and product recommendations: From the curated data extracted from the customers’ purchase history, insights can be obtained to help understand their behavior and purchases. Analyzing the choices of customers to provide product recommendations can help retailers enhance sales and even develop new trends.
- Forecasting of consumer demand: From the data collected from the customers, retailers can predict the future demand for products and services as well as emerging market trends. Precise prediction of demand can strengthen an enterprise in multiple sectors like retail and manufacturing. Next-generation technologies and techniques like time series analysis enable a thorough understanding of market trends, randomness, and seasonality, all from the data.
- Churn prediction: Customer churn is the phenomenon when an existing customer stops using the products and services offered by a company. This makes enterprises lose money as retention costs are five times lower than that of acquiring customers. With the collected data, algorithms can determine patterns and predict the churn rate, enabling retailers to pinpoint shortcomings in their setups.
- Analysis of customer sentiment: From the data accumulated from online services and social media feedback, retailers can analyze customer sentiment through natural language processing (NLP) and analysis of text for the extraction of positive, negative, and neutral sentiments. This enables retailers to provide superior customer service.
The Benefits of Data Engine Implementation
To stay relevant in the highly competitive retail industry, retailers must adopt an efficient data engine. The various advantages offered by this model are as follows:
-
- Cost optimization: The analysis of data helps in the proper determination of prices. The algorithms track inventory levels, demand, and competitors for real-time responses to market challenges. This helps in gaining insights into when the prices should be dropped, popularly referred to as “markdown optimization.” Prior to the use of data analytics, retailers would drop prices after the end of a buying season when the demand started diminishing. After the introduction of analytics, it was understood that a gradual price drop after demand started decreasing helps in the highest increase in revenues.
- Enhanced customer experience: A data engine helps in enhancing customer experience through three means. The first way is providing personalized recommendations and reward programs on multiple levels based on data of online purchases. The second way is sentiment analysis based on product reviews and customer feedback. The third and final way is predictive analysis on all online and offline channels and devices.
Driving Intelligent Consumption
With organizations moving toward intelligent data consumption, the demand for smart and cost-effective cloud and data structure infrastructure is rising. It then requires a holistic approach toward infrastructure requirements keeping in mind product investments and architectural designs for future growth parameters.
In addition to maintaining services, brands must keep quality high, despite ever-shorter time-to-market deadlines and increasingly complex products and processes. Regulations and standards add an extra layer of difficulty, as does pressure from customers for service deceptions.
How are you leveraging data engines to drive value for your customers? Share with us on Facebook, Twitter, and LinkedIn.
MORE ON RETAIL
