Retail Analytics

In: Business and Management

Submitted By drkhustar
Words 821
Pages 4
In understanding consumer preferences and purchase behavior, describe the evolution of path to purchase? What type of analytics is prevalent in understanding online v/s offline path to purchase?

1. Importance: The path to purchase is a cyclical decision making process that connects consumer demand to what shopper will actually buy. Shoppers face countless options along their journey, and each one forces them to make a decision before they can move forward. But with rapid development in technology, digital touch points in the path have outgrown the physical ones. This has caused the “path to purchase” to change dramatically and become condensed into as little as a few minutes with more and more customers shopping from the comfort of their homes with mobile phones. There is a need to invest, strategically and financially, in tools and platforms that measure beyond traditional web analytics to understand engagement activity (e.g. search, review and cross-shopping, mobile/tablet engagement) before, during, and after the customer conversion. Hence all marketing must acknowledge this shift in behavior and ensure that it is designed to create demand and include the means to satisfy that desire instantly.

2. Focus, landscape assessment & key trends: Consumers actively curate their own journeys and expect brands to use all data at their disposal to personalize every interaction. Delivering the right message at the right time is more important. The ‘evaluation of products and services’ is arguably the step along the path that has undergone the most dramatic change. Technology now allows us to crowd source purchasing decisions to a widely distributed network of people. Customers can compare features and prices, view videos of the product and hear how other people use it.

3. Focus area, Business dimensions and Solutions: Extreme fragmentation of the path to…...

Similar Documents


...CUSTOMER INSIGHT: Managed Analytics INDUSTRY: RETAIL CASE STUDY: Leading Eastern European Department Store Boosts Loyalty THE CHALLENGE Revamping customer relationships challenged by the economic crisis This client is one of the largest department stores in the Eastern Europe region with 55 stores serving 28 million customers. Following the economic crisis in 2001, the client faced declining profits. Revenue fell and costs increased. Meanwhile, the company’s loyalty program was in disarray because customers did not find the program valuable. As a result, the company’s reputation was in jeopardy. The client decided a one-to-one customer relationship strategy was necessary to create a customer-centric business model and improve loyalty. THE WORK Data-driven valuation for a relaunch The client engaged Peppers & Rogers Group to help build customer-based strategies, specifically around the relaunch of its loyalty card. Peppers & Rogers Group worked with the client to define the value proposition, determine business requirements, identify performance evaluation metrics, and design a process for card management. The Managed Analytics team conducted customer value and needs assessments, placing customers into value tiers and needs segments for a better understanding of each customer portfolio. From those portfolios, the client could design and implement appropriate customer acquisition programs. As part of the needs analysis, common and unique customer needs were......

Words: 422 - Pages: 2

Retail Loss Prevention: Doing More with Analytics

...[pic] Retail Loss Prevention: Doing more with Analytics February 2009 Abstract T he retail industry is in the middle of an unprecedented economic crisis. All retailers are trying to figure out how to cut costs, retain customers, conserve cash and more importantly stay in business. Recently, the National Retail Federation (NRF) polled readers of its SmartBrief asking them what was on top of their mind. Loss Prevention (LP) came in second only to the overall economy! It is no surprise given that every dollar saved from retail shrink is a dollar added directly to the bottom-line. Looking back in history, we have seen tough times like these are conducive for higher shrink numbers. This is mainly due to retailers cutting down loss prevention staffing and store personnel, slowdown in technology investments, and increase in theft owing from people who cannot handle the economic pressure. LP organizations are at different stages of evolution when we look at their capability to harness the power of analytics – From basic reporting on shrink to understanding the key drivers with high correlation to shrink and managing by exception with the help of predictive models. There is a need to utilize available data assets effectively by building capabilities to report, analyze and predict shrink accurately. This article reviews the trends in retail shrink, its sources and how analytical techniques can help attack shrink in a cost effective manner. Retail Shrink......

Words: 2545 - Pages: 11


...Harlene Santos ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- Analytic geometry From Wikipedia, the free encyclopedia Analytic geometry, or analytical geometry, has two different meanings in mathematics. The modern and advanced meaning refers to the geometry of analytic varieties. This article focuses on the classical and elementary meaning. In classical mathematics, analytic geometry, also known as coordinate geometry, or Cartesian geometry, is the study of geometry using a coordinate system and the principles of algebra and analysis. This contrasts with the synthetic approach of Euclidean geometry, which treats certain geometric notions as primitive, and usesdeductive reasoning based on axioms and theorems to derive truth. Analytic geometry is widely used in physicsand engineering, and is the foundation of most modern fields of geometry, including algebraic, differential, discrete, and computational geometry. Usually the Cartesian coordinate system is applied to manipulate equations for planes, straight lines, andsquares, often in two and sometimes in three dimensions. Geometrically, one studies the Euclidean plane (2 dimensions) and Euclidean space (3 dimensions). As taught in school books, analytic geometry can be explained more simply: it is concerned with defining and representing geometrical shapes in a numerical way and extracting numerical information......

Words: 5082 - Pages: 21

Business Analytics

...Business Analytics- * Exploring data to find new patterns and relationships (data mining) * Explaining why a certain result occurred (statistical analysis, quantitative analysis) * Experimenting to test previous decisions (A/B testing, multivariate testing) * Forecasting future results (predictive modeling, predictive analytics) Answers the Important Questions Such As: -Why did it happen? Will it happen again? What will happen if we change x? What else does the data tell us that never thought to ask? By Using: Statistical/Quantitative Analysis Data Mining Predictive Modeling Multivariate Testing Microsoft Excell- Microsoft Excel is a spreadsheet application . It features calculation, graphing tools, pivot tables, and a macro programming language called Visual Basic for Applications. It has been a very widely applied spreadsheet for these platforms,  SAS- SAS is a software suite developed by SAS Institute for advanced analytics, business intelligence, data management, and predictive analytics. It is the largest market-share holder for advanced analytics. SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it. It is widely used in insurance, public health, scientific research, finance, human resources, IT, utilities, and retail, and is used for operations research, project management, quality improvement, forecasting and decision-making. JMP Pro- JMP is used...

Words: 495 - Pages: 2


...The current issue and full text archive of this journal is available at IJLM 22,3 Retail supply chain management: key priorities and practices Wesley S. Randall Department of Marketing and Logistics, College of Business, University of North Texas, Denton, Texas, USA 390 Brian J. Gibson and C. Clifford Defee Department of Supply Chain Management, College of Business, Auburn University, Auburn, Alabama, USA, and Brent D. Williams Department of Supply Chain Management, Sam M. Walton College of Business, University of Arkansas, Fayetteville, Arkansas, USA Abstract Purpose – The purpose of this paper is to investigate the unique supply chain strategies employed by retailers. Design/methodology/approach – A mixed methods approach was employed involving analysis of depth interviews with 27 retail supply chain executives combined with a follow-up survey capturing over 200 responses. Findings – In light of uncertain economic conditions, retailers appear to be developing more agile/responsive supply chain management (SCM) strategies. Additionally, retailers are putting greater emphasis on maintaining a balance of cost versus service than the cost-centered focus found in a prior study. Research limitations/implications – This study focused on US retailers and therefore results should be cautiously extended to the retailing environment in other countries. Practical implications – Retailing is not a “one size fits all” business, and...

Words: 5824 - Pages: 24


...maintain their loyalty to the product. Kraft Foods Inc. changed its name to Mondelez International, Inc. after spinning-off its North American grocery operations to shareholders (present). Weaknesses Vegemite’s main ingredient is yeast extract, therefore it does not contain vitamin b12 and has a high concentration of glutamic acid. Kraft Australia and its best asset “vegemite” is popular only in Australia. Opportunities The name "Vegemite" was selected out of a hat by Fred Walker's daughter, Sheilah. The winners, local sisters Hilda and Laurel Armstrong (aged 18 and 20 at the time) of Albert Park, Victoria were known as 'The Vegemite Girls’. Threats New Australians and immigrants had no relationship with the vegemite. Analytics Solution A. Descriptive * Kraft Australia is headquartered in Melbourne and has sales revenue of over $650 million. (1926)   * Vegemite considered an Australian national icon. (1926) * The ‘Happy Little Vegemite’s jingle is adapted into a television commercial featuring famous marching band. (1950 – 1960). B. Predictive Although Vegemite becomes famous and earn Australians costumers loyalty, Vegemite should have deeper insights in immigrants like their language, culture, hobbies, and personal backgrounds so that vegemite will not be only patronize by local customers but also other country. C. Prescriptive Conclusion and Recommendation...

Words: 385 - Pages: 2


...INTRODUCTION TO BUSINESS ANALYTICS Sumeet Gupta Associate Professor Indian Institute of Management Raipur Outline •  Business Analytics and its Applications •  Analytics using Data Mining Techniques •  Working with R BUSINESS ANALYTICS AND ITS APPLICATIONS What is Business Analytics? Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Evolution of Business Analytics? •  Operations research •  Management science •  Business intelligence •  Decision support systems •  Personal computer software Application Areas of Business Analytics •  Management of customer relationships •  Financial and marketing activities •  Supply chain management •  Human resource planning •  Pricing decisions •  Sport team game strategies Why Business Analytics? •  There is a strong relationship of BA with: •  profitability of businesses •  revenue of businesses •  shareholder return •  BA enhances understanding of data •  BA is vital for businesses to remain competitive •  BA enables creation of informative reports Global Warming Poll Winner Sales Revenue Predicting Customer Churn Credit Card Fraud Loan Default Prediction Managing Employee Retention Market Segmentation Medical Imaging Analyzing Tweets stylus ...

Words: 952 - Pages: 4


...Forum TCS - Retail Journal July 2015 | Issue 6 Omni-Channel Supply Chain: From Backend to Forefront Foreword Pratik Pal President & Global Head Retail, CPG, Travel, Transportation & Hospitality Welcome to the sixth edition of Forum, the TCS Retail Thought Leadership journal. In this issue, we present our perspective on the changes shaping the supply chain of tomorrow. Retailers all over the world are making the transition from multichannel to omni-channel. The key to delivering the ‘order anywhere, fulfill anywhere’ promise is the supply chain. Supply chain is poised to become the key influencer of the interconnected customer experience. Based on my interactions with leading retailers across the world, it is my view that the battle for omni-channel supremacy will be fought and won on the grounds of supply chain. Retailers across the world are focused on re-architecting and recalibrating their supply chains while maintaining the delicate balance between customer experience and profitability. While earlier, significant investments were directed toward digital customer engagement, in the times ahead, investments will predominantly focus on supply chain re-imagination. In this edition, we discuss the prominent challenges and the response needed across key areas spanning the entire value chain as well as the building blocks for enabling omni-channel supply chain. The ‘plan-buy-make–move–sell’ value chain is no more linear. While the ’sell’ component of...

Words: 7645 - Pages: 31


...Analytics Concepts and Definitions Types of Analytics Descriptive Analytics: * Post Event Analytics * Add features to website and measure its effectiveness in form of clicks, link sharing, page views * Descriptive Analytics Tools -> Google Analytics, Optimizely Diagnostic Analytics: * Post Event Analytics * Analytics used to diagnose why something/phenomenon happened the way it did * It basically provides a very good understanding of a limited piece of the problem you want to solve. * Usually less than 10% of companies surveyed do this on occasion and less than 5% do so consistently. Predictive Analytics: * Used for Prediction of Phenomenon using past and current data statistics * Essentially, you can predict what will happen if you keep things as they are. * However, less than 1% of companies surveyed have tried this yet. The ones who have, found incredible results that have already made a big difference in their business. * Eg:- SAS, RapidMiner, Statistica Prescriptive Analytics:  * Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. * It is considered final phase of Analytics Some Analytics Techniques used Linear Regression In statistics, linear regression is an approach for modeling the relationship between a......

Words: 1288 - Pages: 6

Business Analytics

...Specialty coffee was the highest echelon of quality coffee available in the world. Many people described it as gourmet coffee. There was no one accepted definition in the industry; however, everyone agreed that specialty coffee was of higher quality than basic supermarket brand coffee. It was estimated in 1994 that the specialty coffee industry was growing at a rate of 15 per cent per year and that the basic coffee industry was suffering. Although most consumers only saw this division at the retail level, specialty versus basic coffee was a concept that originated with the coffee grower. SUPPLIERS Specialty coffee companies did not typically deal with suppliers, i.e., coffee farmers, directly. They dealt with exporters instead. About a third of the coffee farms in the world were less than three acres. These farmers did not have the desire, the volume, the money, the expertise, or the connections to export coffee Authorized for use only by Rebekka Pace in Business Analytics at Michigan State University from Sep 02, 2015 to Dec 07, 2015. Use outside these parameters is a copyright violation. 9A98M006 9A98M006 themselves because most countries regulated coffee sales. Coffee processors or exporters regularly visited smaller farmers and bought their coffee1 either in cherry or parchment.2 The coffee would then be moved to a mill where there would be other farmers’ production from the same or different regions. After husking the parchment, the millers......

Words: 10465 - Pages: 42

Big Analytics

...REVOLUTION ANALYTICS WHITE PAPER Advanced ‘Big Data’ Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional analytical model. First, Big Analytics describes the efficient use of a simple model applied to volumes of data that would be too large for the traditional analytical environment. Research suggests that a simple algorithm with a large volume of data is more accurate than a sophisticated algorithm with little data. The algorithm is not the competitive advantage; the ability to apply it to huge amounts of data—without compromising performance—generates the competitive edge. Second, Big Analytics refers to the sophistication of the model itself. Increasingly, analysis algorithms are provided directly by database management system (DBMS) vendors. To pull away from the pack, companies must go well beyond what is provided and innovate by using newer, more sophisticated statistical analysis. Revolution Analytics addresses both of these opportunities in Big Analytics while supporting the following objectives for working with Big Data Analytics: 1. 2. 3. 4. Avoid sampling / aggregation; Reduce data movement and replication; Bring the analytics as close as possible to the data and; Optimize computation speed. First, Revolution Analytics delivers optimized statistical algorithms for the three primary data management paradigms being employed to......

Words: 1996 - Pages: 8


...Introduction to Analytics Hal Hagood u01a1 The article used was found on Forbes and reports how UPS (United Parcel Service) uses predictive analytics to replace routine maintenance. It addresses a problem that UPS, one of the largest logistics operations in the world faces constantly as they deliver millions of packages every day, a feat which is a small miracle in and of itself. If even one of the trucks in their fleet has so much as even a minor breakdown, it can be a big problem with unpleasant consequences. This can result in driver downtime, late packages and angry customers. The data analytics solution used was that of predictive analytics. United Parcel Service, Inc. (UPS) is the world's largest package delivery company and a provider of supply chain management solutions. It is a global logistics company headquartered in Sandy Springs, Georgia, which is part of the Greater Atlanta metropolitan area. UPS delivers more than 15 million packages a day to more than 6.1 million customers in more than 220 countries and territories around the world (UPS, 2015). The challenges associated with this problem and the information that required analysis concerned maintenance of its fleet. In the past UPS used to replace important parts every few years. This was the solution they used to ensure that its vehicles stayed on the road and in good working order. The new approach however, is to collect data from hundreds of sensors in each vehicle. They then use various algorithms...

Words: 769 - Pages: 4


...Spotlight on Making Your Company Data-Friendly Spotlight 64 Harvard Business Review December 2013   Artwork Chad Hagen Nonsensical Infographic No. 5 2009, digital Analytics 3.0 In the new era, big data will power consumer products and services. by Thomas H. Davenport T hose of us who have spent years studying “data smart” companies believe we’ve already lived through two eras in the use of analytics. We might call them BBD and ABD—before big data and after big data. Or, to use a naming convention matched to the topic, we might say that Analytics 1.0 was followed by Analytics 2.0. Generally speaking, 2.0 releases don’t just add some bells and whistles or make minor performance tweaks. In contrast to, say, a 1.1 version, a 2.0 product is a more substantial overhaul based on new priorities and technical possibilities. When large numbers of companies began capitalizing on vast new sources of unstructured, fast-moving information—big data—that was surely the case. Some of us now perceive another shift, fundamental and farreaching enough that we can fairly call it Analytics 3.0. Briefly, it is a new resolve to apply powerful data-gathering and analysis December 2013 Harvard Business Review 65 Spotlight on Making Your Company Data-Friendly methods not just to a company’s operations but also to its offerings—to embed data smartness into the products and services customers buy. I’ll develop this argument in what follows,......

Words: 4608 - Pages: 19


...Download Infographic: Must Read Books in Data Science / Analyt… Resources - Data Science, Analytics and Big Data discussions Home Blog Jobs Trainings Learning Paths 21/07/15 8:48 pm j ADVERTISEMENT Download Infographic: Must Read Books in Data Science / Analytics books data_science datavisualization Manish ! Data Hackers 28d Hey there ! You can think of this infographic as an ideal list of books to have in bookshelf of every data scientist / analyst. These books cover a wide range of topics and perspective (not only technical knowledge), which should help you become a well rounded data scientist. If you have other suggestions, please feel free to add them below: Books related to data science decisioning: When Genius Failed: The Rise and fall of Long-Term Capital Management A fast paced thriller, this book not only brings out how you can compete on data based decisions, but also why you need to keep human behavior in mind while taking decisions on data. Scoring Points: How Tesco Continues to Win Customer Loyalty this book brings out some of the practical challenges faced by Tesco and how they overcame them to create one of the biggest success story of customer loyalty. The Signal and the Noise: The Art and Science of Prediction . From the stock market to the poker table, from earthquakes to the economy, Nate Silver takes us on an enthralling insider’s tour of the high-stakes world of forecasting, showing how we can use......

Words: 1265 - Pages: 6


...Retail Marketing In today's CRM landscape the old analogy comparing the rifle and shotgun approaches to message and / or offer delivery is perhaps more appropriate than ever, as more retail organizations struggle to achieve one-to-one marketing-communications with customers and prospects. Targeting allows a retail enterprise to channel its marketing budget where there is the greatest (and fastest) possibility of Return On Investment (ROI). In terms of overall business strategy, your ability to identify and understand consumers helps you make accurate estimates about the potential for your products and services in a given market, as well as support and direct merchandise development strategies to both new and existing customers. Whether your target is current customers or new prospects, in markets known or unknown, an effective targeting model reduces the risk of any new venture. Blending Demographic, Behavioral, Expenditure and Media Preference data with retailer-specific data and applying data mining technologies produces Zip+4 and postal code level data assets that consistently outperform all other direct marketing techniques. In addition, methodology that should be used must be dynamic to allow the sights to be reset frequently to keep targets in focus consistently. Today's retail marketing managers must: Understand the connections between the lifestyle and expenditure characteristics of customers, their propensity to purchase one product or brand over another, and...

Words: 633 - Pages: 3