Data is driving powerful changes in the operational practices of a retail business today. One of the key aspects of digital transformation in retail is to collect, clean, process, and analyze legacy data through an advanced data management infrastructure resulting in an exciting customer experience.
Operational Impact of Digital Transformation in Retail
The operational impact of data driven digital transformation depends on factors like the operational stages an organization follows, its data collection mechanism, and affinity to technology adoption. However, the operational stages can be broadly categorized to assess the value of digital transformation in various stages of the retail industry.
Planning and Procurement
Planning depends on customer segmentation, trend analysis, and price analysis. Historical data like sales data, customer data, or external market research data are collected through separate data funnels across various geographies. Data pipelines are built to clean and store them into an enterprise-grade cloud. Business intelligence engineers can then process these data to obtain powerful insights about the market demands of a product in a season or demography.
Information processed from various internal logs, reports, and reviews on the internet can also help to determine the average delivery time of a vendor, pricing in comparison to the market, quantity of goods received damaged or expired, client satisfaction rate etc. This can help in listing the preferred vendor partners for procurement.
Inventory Management
Once the goods are procured it is important to manage the inventory as per the sales pattern. The product is logged into the inventory management system, which tracks its location, quantity, expiry and other relevant information. The warehouse staff ensures proper storage and organization of the product, typically in designated bins, shelves, or pallets as per an inventory mapping software.
A centralized inventory data management system helps to track expiry or declining trends in demand of a product. Locations of products are changed to maintain them in a designated temperature or ensure they are moved out first. Products can be moved from a physical store in a region of low demand to one of high demand. For online stores a product can be procured to the nearest inventory saving logistics time.
Visual Merchandising
Digital transformation in retail promises seamless buyer experience both online and offline. Data from physical stores can be obtained using sensors like eye tracking or foot traffic devices to answer some vital questions like
- where does shoppers’ attention go first on entering the store?
- what do shoppers look at in low or high traffic aisles?
- What display grabs attention at the end of the shelf (the one on the right or the one on the left)?
- Does shelf 3 or shelf 4 draw more attention?
These information points if the product placement is flattering the shoppers. This can help to place products in an easy to access rack preventing congestion.
Depending on the customer buying habit one product can be offered with another or placed close if they are frequently bought together. All these goes in creating a tailored in-store experience for shoppers.
Marketing and Promotion
From trend analysis to targeted marketing data shapes today’s marketing and promotional strategies. Data driven marketing is a significant part of retail marketing strategy.
Customer Segmentation
Big data in retail can be used to segment customers into different groups based on their demographics, purchase history, and other factors. This information can then be used to target customers with personalized marketing campaigns.
Recommendation Engines
Data engineering can build recommendation engines that suggest products to customers based on their past purchases and browsing behavior. This can help to increase sales and improve the customer experience.
Marketing Campaign Optimization
Marketing strategy is a continuous process of trial and perfection. Feedback data after sending out various marketing messages, offers, and channels can help to build a better strategy.
E-commerce
E-commerce is a heavily data driven business. A virtual inventory of an e-Commerce platform is stored in a centralized database. Customers can browse through and order products and the inventory is updated in real time. Cloud based architecture of eCommerce helps to maintain a real time inventory across the system and synchronizes inventory planning.
E-commerce also attempts to build an omnichannel strategy for the customers. Based on the information from the browsing history of a customer, strategic offline campaigns are made for individual customers with attractive offer.
Collecting data on customers browsing history, time spent on a product, geographic location, and other factors helps to draw a clear picture of user demographics. This information can be exploited with the help of some analytics services like Power BI to build niche marketing strategy and procurement planning.
Customer experience and feedback data also goes to contribute in feature development of an eCommerce application. This is one of the important stages of an eCommerce product development cycle.
Other areas of data driven retail transformation
Analysis of data brings in lot of interesting changes to retail operations. Some other areas that gains value from leveraging retail data are:
Fraud detection
Data can be used to observe patterns of fraud. Several layer of access authentication with the help of personal data can be created for users. Sales and support data of customers can be analyzed to profile who frequently return products or replace some components.
Customer lifetime value prediction
Data analysis helps to predict the customer lifetime value of buyers. CLV is an important metric for businesses because it can help them to make better decisions about how to allocate their resources. For example, a business that knows that its customers have a high CLV may be more likely to invest in customer retention programs.
Customer support
Facility to instantly pull out customer data from a centralized CRM improves after sales customer support. Recorded entry of issues coming with a product enables the support team to immediately jump to the solution for an issue of similar nature.
Conclusion
It is evident that digital transformation in retail depends on the ability to gather and utilize industry related data. As more and more data driven technologies like AI, ML, and Business Analytics get adopted to a retail business there will be an increased implementation of data driven features. Data and digital transformation are intertwined in today’s retail environment.
