RETAIL MARKETING CHALLENGES BIG DATA ANALYSIS

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1 Volume 118 No ISSN: (on-line version) url: RETAIL MARKETING CHALLENGES BIG DATA ANALYSIS Mr A Jai Kumar Assistant Professor Ms Y. SRAVANTI, Vishwa Vishwani School of Business PGDM: Major (Human Resources), Minor (Business Analytics) April 26, 2018 Abstract Big Data and Retail are a powerful combination especially in an Omni Channel world today. Location, location, location were once the three most important ingredients to retail success. But today it is data, data and data. And thats no punch line. Because in an Omni-channel world, getting better at every aspect of the big data and retail analytics game holds the key to thriving in a brave new world of retail. Digitization has given power to consumers and today they are called digital customers or Omni customers. They dont care what channel it is as far as its convenient and matches their expectations. In order to survive or keep running in the race, retailers must stop selling only the things and start selling the experience along with the thing. Amazon, the worlds largest electronic commerce company is the massive adopter of big data analysis. Whenever a user browses their website, based on the users behaviour, it starts sending recommendations of the similar products on which the user has shown interest or based on an algorithm prediction of what the user may like. Research says around 30% of their sales are from the recommendations. Therefore, big data is helping Amazon to increase their sales percentage immensely. 1

2 Key Words:Retail, Big Data, Omni Channel, Digitization 1 INTRODUCTION Retail is the sale of goods and services from businesses to an end user (called a customer). Retail marketing is the process by which retailers promote awareness and interest of their goods and services in an effort to generate sales from their consumers. Retailers use various advertising and communication tools to grow awareness and considerations with future customers. Finding the right marketing mix can lead to a profitable growth and a higher return on investment. Retailers serve as purchasing agents for consumer and as sales specialists for producers and wholesaling middleman. They perform many specific activities such as anticipating consumers wants, developing product assortments and financing. With the retail market getting more and more competitive by the day, there has never been anything more important than the ability for optimizing service business processes when trying to satisfy the expectations of customers. Channelizing and managing data with the aim of working in favour of the customer as well as generating profits is very significant for survival. Data Analytics in Retail Industry For big retail players all over the world, data analytics is applied at all stages of the retail process from taking track of popular products that are emerging, doing forecasts of sales and future demand via predictive simulation, placements of products and offers through heat mapping of customers. With this, identifying the customers who would likely be interested in certain products depending on their past purchases, finding the most suitable way to handle them by using targeted marketing strategies and then coming up with what to sell next is what data analytics deals with. What is Big Data? Big data, the name itself defines it is a huge information set. Big data describes the large volume of data both structured and unstructured that inundates a business on a day to day basis. But its not the amount of data thats important. Its what organizations do with the data that matters. Big data can be analyzed for insights 2

3 that lead to better decisions and strategic business moves. Big data analytics helps enterprises to obtain tie ups and resolution from the large data using powerful tools to extract conclusions from both systematic and non systematic data to provide insights. Big data analytics provides three major benefits, they are: It can be used in companys strategic planning. Help the company to enhance their decision making skills. Reduce companys costs and refine the efficiency of performance. What is the impact of Big Data in the Retail Industry? The big data analytics helps retailers to better interpret their customer or potential customers behaviour. Its effective technique has helped both online and offline retailers to embrace analytics solutions to effectively target their audience and upgrade their supply chain operations. The retail industry has come a long way and this analytics helped them to not only identify their customers but also know their customers inside out. Leveraging the fast transforming digital world, retail industries are now able to grab a deeper insight using big data analytics. This is only possible if you have the appropriate tools, a flawless strategy, and manpower who can extract most benefits from the Big Data Analytics. Retailers that embrace big data analytics yield a 60% boost in margins and a 1% improvement in labour productivity. Mckinsey Strategic areas in Data Analytics for Retailers There are few areas where the retailers can identify the usage of big data which helps in the growth of the retail industry. Price optimization is no more a choice, its a need: Big data analytics plays a vital role in price control. Algorithms provide a deep insight on the demand predictions, inventory status and help retailers to have a smart peep on their rivals. Depending on the data, price optimization helps them to decide when to drop and when to raise the price. Price optimization is popularly known as markdown optimization. Earlier retailers used to change their merchandise prices depending on the change of the season. Like at the end of any 3

4 season, having the assumption of less demand, they used to reduce the prices. However, after implementing big data, retailers will have information that are data driven and prices that are optimized based on the real time demand. Now with the updated and analyzed big data, prices can be changed any minute as per the demand. Prediction for future trends and demand: Big data helps retailers to contemplate the present situation and envision the future. There are various algorithms that are used to predict the upcoming trend and compare them to calculate the trend and demand with the help of social media and upgrading technology. The gathered customer data will help retail industry to forecast the product demand and target their users accordingly in a particular category. Pick the highest Return On Investment (ROI) Opportunities: Retailers use data driven intelligence and predictive risk filters after having a good understanding of their potential and existing customer base, for modelling expected responses for marketing campaigns, depending on how they are measured by a propensity to buy or likely buy. Offer smart experience to the customers: Customer experience and customer satisfaction is always on the priority strategy list. Each transaction or the activities that the customer does will be recorded and will have a unique ID, which can be accessed by the retailers. After all the transactions are recorded and billed the customer will have access to give the feedback. Based upon the feedback the predictions can be inferred for any improvements. Big data analytics plays an essential part here to ensure which customer showed interest in what product. Adding New Customers: Another most beneficial application of big data analysis for retailers is adding new customers. Retailers such as Amazon, Flipkart, ebay, Alibaba are relying on recommendation engines to give options to the customers on the basis of previous buying history. They are refining customers data on the basis their age, income and other variables to showcase and sell the goods accordingly. 4

5 Supply Chain Optimization: Retailers are using analytics to evaluate the ROI and all related supply chain optimization processes. It also helps retailers to identify fraud transactions and any other flaws specifically in suppliers and delivery services. Market Basket Analysis: Understanding Customer Behaviour What is it? Market Basket Analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. The set of items a customer buys is referred to as an item set, and market basket analysis seeks to find relationships between purchases. In retail, most purchases are bought on impulse. Market basket analysis gives clues as to what a customer might have bought if the idea had occurred to them. It is one of the most common and useful types of data analysis for marketing and retailing. The purpose of market basket analysis is to determine what products customers purchase together. A store could use this information to place products frequently sold together into the same area, and determine the layout of their catalog and order form. It also helps to improve the effectiveness of marketing and sales tactics using customer data already available to the company. Limitations for Retail business while using real time analytics Data identification: Data classification is the essential and primary step of the planning phase. You will also have the knowledge of what to include in the data and what crucial information is required to growing your business. It is essential to involve the stakeholders and collaborator in your business decision making who can take strategic decision according to the business direction and can take immediate necessary actions if and when the need arises. Data security: Most of the companies know the significance of security, but many ignore it due to the complexity it creates. The big data in the initial stages will secure only small sources of information of the customer information for future analysis. However, in the later stage when bulk data is gathered the 5

6 big data analytics secures it from all the internal as well as external risks. A secured platform is what is expected in the real time analytics, which will improve customer loyalty and strengthen the bonding. Big data governance strategy: The resources from where you collect data should be true blue and genuine, so that it can be trusted by users. Big data analytics used as retail analytics should be stored with the highest security. Data utilization: Business is measured in terms of numbers. The final result and decision can be taken depending on the numbers. The entrepreneur should have the ability to understand which data is necessary and how to utilize that data with the previous data sources. For example, you are selling glasses, so your target customers must be those who love eyewear such as specs or lens. The first criteria to identify is who your target customers are and the scope of your business in the competitive market, or else, the result can be terrible. 5 Ways Walmart Uses Big Data to Help Customers Walmart relies on big data to get a real-time view of the workflow in the pharmacy, distribution centers and throughout our stores and e-commerce. Big data is an essential part of the strategy of many companies and Walmart is analyzing the data in distinct ways. TO MAKE WALMART PHARMACIES MORE EF- FICIENT: Walmart uses the simulation at the stores to find out how many bills are filled in a day and to determine the busiest times of the month or season. This helps the staff to infer the schedules and reduce the amount of time it takes a prescription to be filled. TO IMPROVE STORE CHECK OUT: Walmart uses big data to check on the store check out improvement. By using predictive analysis, stores can anticipate the demand at certain hours and determine how many associates are needed at each counter to associate with the customers. TO MANAGE THE STEPS OF A SUPPLY CHAIN: Walmart uses simulation to track the number of steps from 6

7 the dock to the store or from the warehouse to the delivery centre. This allows the company to optimize the routes to shipping dock and track the number of times a product gets touched and the frequency of the product used. It also uses the data to analyze the transportation lanes and routes or the companys fleet of trucks. The data helps Walmart keep transportation costs down and also schedule driver times. TO OPTIMIZE PRODUCT ASSORTMENT: Through the analysis of customer preferences and shopping patterns, Walmart can accelerate decision making on how to stock shore merchandise. Big data provides insight o new items, discontinued products and which private brands to carry. TO PERSONALISE THE SHOPPING EXPERIENCE: Big data allows Walmart to identify a shoppers preferences to develop a consistent and delightful shopping experience. If a user is shopping for baby products, Walmart can use data analysis to personalize mobile rollback deals for parents. 2 CONCLUSION According to a research conducted by two market giants, IBM and Accenture, if a retail business is increasing revenue, leading in competition and growing fast, it is unquestionably gasping big data solutions. Big data is helping in increasing store sales all along satisfying customers. McKinsey reported businesses that are investing in big data for past five years as a part of their sales and marketing programs, are gaining per cent ROI and retail is one of the biggest sectors which are taking advantage of big data. Big data is helping to improve the operations in the following areas: 62 per cent retailers for merchandising 60 per cent for marketing 44 per cent for developing multichannel for business growth 29 per cent for supply chain item[ ]item[ ] 25 per cent for store management 7

8 14 per cent for business operations Big Data Sources Alibaba, ebay, Amazon, Flipkart and many other ecommerce and retail giants are using big data to improve revenue. They are gathering data from inventory, customer loyalty cards, RFID and local demographics to progress business operations and customer experiences on the whole and to compete and sustain in the market. FINDINGS The objective of this paper is to throw a light on how the analytics on Big Data can affect the buying trends of customers in the retail industry. The prominent areas where big data can be used are price optimization, inventory management, supply chain management and so on. The retailers who use big data applications efficiently are certain to make huge differences in their business. References [1] [2] [3] blog.walmart.com [4] 8