How Retailers are Maximizing Returns from their Data

How Retailers are Maximizing Returns from their Data

Today, as business strategies are being rewired to meet the new norms of physical barriers and evolving customer expectations, data has become the equivalent of untapped oil as a resource. However, the real benefit of data can only be realized when it is properly mined for the insights within it.

The retail industry, the foundation of which is built on consumers and their ever-evolving behaviors and patterns, is experiencing massive technology-backed transformations. With advanced technologies such as artificial intelligence, machine learning, cloud computing, edge, and the Internet of Things (IoT) integrating strongly into the business management, increasing volumes of big data and analytics will play a vital role in how businesses function and how they connect with their stakeholders.  Case in point, Gap, Inc.[1], leading global apparels retailer, underwent massive losses due to the pandemic. However, through data optimization and analytics, the company understood the growing need across businesses for essential products such as face masks for their employees; and ventured into online B2B channels to sell reusable, non-medical-grade cloth face masks to both public and private sector companies.

Research[2]states that by end of 2024, global big data analytics for retail is estimated to reach $10.94 billion. The increase in data being generated in retail is corroborated by Walmart, a leading global retailer, that is in the process of developing the world’s largest private cloud system, with an estimated capacity to manage 2.5 petabytes of data every hour[3].

Turning to Data for Strategic Planning

Retailers can reap the benefits of data by capturing and analyzing data in real-time, enabling them to make proactive decisions. This can help businesses target and retain customers, streamline operations, maximize profits by improving pricing and promotions, enhance supply chain, and augment business decisions, resulting in cost savings and increased sales.

A key area of impact is consumer behavior. Predictive analytics is being used as an effective tool for gathering a “360-degree view” of the customer to understand their likes and dislikes, the probability of them using discount coupons and offers, their geographical locations, age, gender, their social media activities, etc. This not only aids companies in narrowing their target area for follow up, but also to build new and effective sales and marketing strategies and refine their cross-sell and up-sell strategies, improving both online as well as in-store customer experiences. We can see this reflected in the strategy adopted by Gap’s Athleta brand[4], an exclusive women’s active-wear brand. During the current crisis, the brand was able to analyse the changing customer behavior and subsequently ventured into providing live workout classes to its customers, accessible on the company’s Instagram page or through the community hub on its website.

Further, when data is being spoken of, the concept of enhanced productivity is not far behind. Employers are evaluating the routine process-related data such as employees’ job tasks, with parameters of when, how, time spent on it, etc., collated for analysis to find the patterns and loopholes in processing common workflows across operations.

While it is not always possible to be fully stocked at all times, the use of data can help businesses in managing their supply chain and product distribution. Use of technological tools such as trackers, active RFIDs, weight sensors, computer vision, and others, can help retailers successfully manage inventory at the product level, and enable personalization to the level of one based on individual customer preferences.

Challenges in applying data analytics

While data plays a crucial role in maximizing business value, there are roadblocks that can slow down the process of securing value from data.

A lack of tools and skillsets to process data can be a major hurdle in optimizing data. Data locked in silos across different solutions, and departments – due to any reason – limits the effectiveness of decision making. Further, data that is either inaccessible or cannot be evaluated in context of other data inputs can only deliver limited or even erroneous insights.

For best results, companies need to implement appropriate data management, architecture and governance to arrive at a single source of truth, irrespective of which level or person is accessing it.

Way forward

Today, retail data is no longer confined to limited information complied over a period of time. Decision-makers across the industry have access to a plethora of real-time data at a mere click of a mouse that can be used to predict trends, forecast demand movements for their products, optimize pricing strategies for a competitive edge and evaluate personalized offers for their customers. It is imperative for business and technology leaders to create a conducive data culture within the organization to maximize value and deliver enhanced customer experiences.