Business Intelligence in the Retail Sector

Business Intelligence in the Retail Sector

Business Intelligence in the Retail Sector RITU RATHOD JENNIFER DOWNEY WHAT IS BUSINESS INTELLIGENCE Business Intelligence: The practice of collecting, integrating and analyzing data. A process in which raw data can be transformed into useful and meaningful information. Main purpose of BI is to support better decision making. Three industries that widely use BI Oil Retail Apparel Business Intelligence in Retail:

Retail industry continues to accelerate rapidly. Retailers aim to deliver a seamless experience across stores and on their websites. Use of BI enables retailers to gather insight through data. Helps retailers formulate marketing strategies. Predict future requirements of customers. Business Intelligence in Retail Traditionally BI and Analytics has provided actionable insights that can help corporate executives, business managers, and other end-users make more informed business decisions based on historical data. Today, BI and Analytics solutions provide the ability to: Optimize internal business processes Increase operational efficiencies

Identify market trends Drive new revenues Forecast future probabilities and trends Business Intelligence in Retail - Purpose Allows the retailer to gain high quality information through the use of BI tools like data warehousing, data mining, and online analytical processing. BI allows organizations to predict the behavior of their competitors, suppliers, customers, technologies, acquisitions, markets, products and services. BI helps retailers use such as Point of Sale transactions and social media gives unprecedented access to the customers mind. Retailers are also using retail management technologies like Self Checkout POS, RFID and Cloud Computing for them by providing a real time integrated and collaborative information system.

BI further helps the retailer keep a vigilant eye on business activities by estimating the long and short-term demands, notifications of low inventory and monitoring factors that influence customer buying decisions. BI is becoming a mission critical application. Retailers are looking beyond reporting capabilities to applications for syncing information from a wide variety of systems to analyzing performance of sales, margin, supplier delivery times, effectiveness of promotions and allowing them to effectively react to business insights Business Intelligence in Retail Dimensions of Data Customers - Loyalty cards, Credit Card Tracking, IP address, registered user logins, social media, user-generated content, and linking data with a customer-relationship

management (CRM) system. Products - SKU collection, enriched data like brand premiums, similarity links, link customer tie ins that are microtarget to customers, allow for more attributes that increases product modeling. Time - Increases size of collection because real time collection allows for continuous measurement of customer behavior, product assortment, stock outs, in-store displays, and environments.

Location - Use of geo-special location and purchase history can tie products to a region. Data allows for targeted marketing and product placement. This can allow for short-term revenue maximizing especially if customers purchase history is tied to what they are physically near. Channel - Increase is ways that consumers are purchasing products including a tendency research shop (accessing info on one channel but buying through another). Leads Business Intelligence in the Retail Sector

In Store Uses (I)/ E-commerce Uses (E) Marketing Inventory Loyalty Scheme Data (I/E) Track Consumer Preferences Personal Promotional Offer Real Time Sales Data (I/E) Price Decisions Layout heat maps,

customer buying habits (I) Layout Shoppers platform acceptance (E) Consumer Insight to Stock Deployment (I/ E) Replenishment Procurement SKU Analyzation SKU Deployment Optimization

Store Layout Maximization in minimal space. Logistics Optimization Service Level Optimization Business Intelligence in Retail Example of a Data Capture Map Business Intelligence in Retail BI Example of Price Decisions A Look Into Two BI Superusers

WALMART IN STORE AMAZON ONLINE WALMART Dependent on data to make shopping experience memorable. Reasons for use: 1.Check in to pick up an online order at their store, refill pharmacy prescriptions. 2.In-store maps with product locations help finding items easier.

3.Analyze demand and make products available- improved the apps capabilities to ensure products are in stock. METHODOLOGY - HOW WALMART USES BI Helps make Walmart pharmacies more efficient: -BI allows Walmart to understand the number of prescriptions filled in a day. -Determine the peak time during each day/ month. -Scheduling manpower, thereby reducing the time and labor needed to fill perceptions Improve store checkout: -Uses predictive analytics to anticipate store demand (use of data to identify likelihood of future outcomes) -Helps determine how many associates are needed to man registers. Managing the supply chain: -Uses simulations to track number of steps - dock to store. Strategy also

pinpoints the number of times a product gets touched along the way to the customer. -BI helps reveal transportation lanes and routes for the companys truckskeeps transportation costs down. Optimize product assortment: -Analyses customer preferences and shopping patterns that help with stocking shelves and displaying merchandise. Personalize shopping experience: Uses data analytics to anticipate customer needs then creates personalized mobile rollback deals for shoppers. CHALLENGES Walmart has been making big investments in its online business adapt to preferences and compete with rival, Amazon. BI tools can get complicated to use and only a few

individuals know how to use them. Data spread across different systems and software (ERP, CRM, excel spreadsheets), can get difficult to gather. Getting your way around data buried in your systems get data when you need it can be an extremely painful process. Delivering mobile BI; managers and senior executives should be able to access insights easily, as and when required. FUTURE Walmart scan and go:

FUTURE AI and Robotics: - New shelf scanning technology being tested in a few Walmart stores - Simplifies routine work - Uses data and vision technology to find items

that are out of stock, make changes and identify products with missing labels - Helps locate items and check inventory levels on the shelf - Technology is focusing on tasks that are repeatable, predictable and manual to free associates to spend more time serving customers

AMAZON Dependent on data to drive business and create innovative solutions in retail. Reasons for use: 1.Gather information to drive sales and market products. 2.Analyze demand and make products available with improved product monitoring capabilities to ensure products are in stock 3.Optimize Logistics and Supply Chain methods METHODOLOGY - HOW AMAZON USES Personalized Recommendation System: - Uses Comprehensive BI

collaborative filtering engine (CFE) -BI allows Amazon to analyze previous purchase to suggest new items -Uses your recommendations to suggest new purchases to others who bought similar items -Pulls info from your search and wish list to recommend new purchases Book Recommendations from Kindle Highlighting: -Uses social networking services to send Kindle highlighted notes to others for book discussions -Uses highlight function to determine what other books you might like One-Click Ordering / Price Optimization -Auto fills in shipping and payment methods to allow for quick purchase. -Sets prices based on activity on website, competitors prices, product availability, item preferences, order history, expected profit margin. Product prices change every 10 minutes as data is analyzed Anticipatory Shipping Model:

-Uses big data to predict when you are likely to order the same product again and pre-stages the items a dc close so it is ready to ship -Uses predictive analytics to increase product sales by suggesting its time to by or creating personalized sales for items pre-staged Supply Chain Optimization: -Links to manufacturers to track their inventory -Uses analytics to determine warehouse closest to customers -Uses graph theory to help decide best delivery schedule, route and product groupings to reduce CHALLENGES Amazon has been making big investments in its big data analysis they will continue to be industry disruptors, but will find challenges BI tools to increase channel expansion, high growth

pains, and evolving Business Models How to use data to digitize real world experiences to create a sense of wonder for visitors. Expanding their mobile first commerce as trends show that by 2022, $175.4B in retail sales will come from mobile devices Changing government regulations regarding BI especially as they expand their international ecommerce retailers and customers. FUTURE Amazon will use BI for customer reach and optimization. - Expanded go stores - Drone delivery

- VR real world shopping experience Business Intelligence in Retail Benefits to BI The benefits associated with BI adoption in the retail sector include accurate decision making, efficient service delivery and competitive advantage. These benefits include - Better customer focus - Ability to anticipate changes in the market earlier - Ability to manage prices better - More efficient service delivery - More robust forecasting of future trends - More efficient use of resources - Improved sharing of inter-department knowledge

- Easier to manage costs - Strengthens strategic planning - Better quality of information for improved decision making - Stock management optimization Business Intelligence in Retail Measuring Benefits Business Intelligence in Retail Conclusion Using BI tools in the retail industry is the key to revealing relevant insights, increasing profitability, and improving brand awareness. The right BI analytics can help uncover new markets, identify areas for future development, track the responses to marketing strategies,

and much more. In the research it was found that BI and analytics plays a crucial role and the use of integrated BI tools makes decision making easier and faster. For big data in retailing it is important that data quality is improved rather than merely a rise in data volumes that drives improved outcomes. Increases in quality comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. Improved data results in improved forecasts of customer demands, planning, pricing, stock optimization, clear operatives, as well as strategic decision-making Developing and implementing software solutions that help retail businesses to streamline their operations as more and more organizations in the retail sector are calling for the necessity of business intelligence (BI) reporting.

Business Intelligence in Retail Thought Provoking As you keep the reasons and ways that BI is used in retail wrap your mind around this final statistic: Walmart collects around 2.5 petabytes (1 petabyte = 1,000,000 gigabytes) of information every hour about transactions, customer behavior, location, and devices THANK YOU WORKS CITED 1. Hitachi Solutions, director. YouTube. YouTube, YouTube, 26 June 2014, www.youtube.com/watch?v=hDJdkcdG1iA.

2. Olap. What Is Business Intelligence? BI Definition. OLAP.com, 2018, olap.com/learn-bi-olap/olap-bi-definitions/business-intelligence/. 3. Aktas, Emel, and Yuwei Meng. An Exploration of Big Data Practices in Retail Sector. Logistics, vol. 1, no. 2, 2017, p. 12., doi:10.3390/logistics1020012. Das Pratim. Business Intelligence & Big Data on AWS. Business Intelligence & Big Data on AWS, Amazon, 2016. 4. Sisense. Business Intelligence Case Study in Retail: How to Better Your Brand l Sisense. Sisense, 22 Aug. 2018, www.sisense.com/blog/businessintelligence-case-study-retail-better-brand/. 5. Datameer. Five Big Data Use Cases for Retail. Datameer, www.datameer.com/blog/five-big-data-use-cases-retail/. 6. DeZyre. How Big Data Analysis Helped Increase Walmarts Sales Turnover? DeZyre, www.dezyre.com/article/how-big-data-analysis-helped-increasewalmarts-sales-turnover/109. 7. Keyes, Daniel. Walmart Revamps App to Bolster the in-Store Experience. Business Insider, Business Insider, 13 Feb. 2018, www.businessinsider.com/ walmart-revamps-app-to-bolster-the-in-store-experience-2018-2.

8. Chainstorage. Five Ways Walmart Uses Big Data. Chain Store Age, 6 Oct. 2017, www.chainstoreage.com/operations/five-ways-walmart-uses-bigdata/. WORKS CITED 11.Zamba,, Carlington, et al. An Investigation of the Potential Benefits and Challenges of Business Intelligence Adoption in the Retail Sector in Gweru, Zimbabwe. JOURNAL OF SYSTEMS INTEGRATION, vol. 2018, no. 2, June 2018, pp. 2025., doi:10.20470/jsi.v9i2.331. 12.OLEXOV, CECLIA. Business Intelligence Adoption: a Case Study in the Retail Chain. WSEAS TRANSACTIONS on BUSINESS and ECONOMICS, vol. 11, no. 2014, May 2014, pp. 95106., doi:E-ISSN: 2224-2899. 13.Akter, S., & Wamba, S. F. (2016, February 26). Big data analytics in E-commerce: a systematic review and agenda for future research. Institute of Applied Informatics at University of Leipzig, 2016(26), 173-194. *** 14.Sam Fullerton, R. B. (2016). Consumer perspectives on the ethics of an array of

technology-based marketing strategies. Marking Strategies, 1079-1096. 15.Eric T. Bradlow, M. G. (2017). The Role of Big Data and Predictive Analytics in Retailing. Journal of Retailing, 79-95. 16.RANJAN, J. (2009). BUSINESS INTELLIGENCE: CONCEPTS, COMPONENTS, TECHNIQUES AND BENEFITS. Journal of Theoretical and Applied Information Technology, 60-70. 17.Choeh, S. L. (2017). Exploring the determinants of and predicting the helpfulness of online user reviews using decision trees. Management Decisions, 681-700. 18.Jaewon Choi, Hong Joo Lee, Hee-Woong Kim. "EXAMINING THE EFFECTS OF PERSONALIZED APP RECOMMENDER SYSTEMS ON PUCHASE INTENTIONS." Journal of Electronic Commerce Research, (2017): 73-102. WORKS CITED 19.MOHD FAWZY, SHARUDDIN SH, RAJAGDERAN S,WAN ZULKIFLY WZ. "E-COMMERCE ADOPTION AND AN ANALYSIS OF POPULAR E-COMMERCHE BUSINESS SITES." Journal of Internet Banking and Commerce (2018): 50-62.

20.Matthews, Daniel. How Amazon Has Shaped the Big Data Landscape. SmartData Collective, 3 Nov. 2017, www.smartdatacollective.com/how-amazon-shaped-big-datalandscape/. 21.Wills, Jennifer. 7 Ways Amazon Uses Big Data to Stalk You. Investopedia, Investopedia, 20 Oct. 2018, www.investopedia.com/articles/insights/090716/7-waysamazon-uses-big-data-stalk-you-amzn.asp.

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