Mid Term SubmissionAssignment Retail Analysis: Scope and Applications of IT Submitted to Ms Gulnaz Banu Submitted By: Sagrika Padha MFM/14/28 National Institute of Fashion Technology, Bangalore 10th March 2015 Contents IT in the Retail Industry Introduction Business Intelligence Point of Sale Third Party Logistics E-Commerce Warehouse Management Systems Retail Analysis and Data Mining Techniques of Data Analysis Scope and Application of Retail Analysis and Data mining IKEA’s IT system: PIA Conclusion References Page 2 Page 5 Page 6 Page 11 Page 12 Page 12 1 . To quickly respond to the ever changing market and increase its flexibility and speed.Retail Analysis. has enabled the dramatic transformation of business processes in the past. to help serve better to each segment. Today. customer experience. Benefits for retailers include enabling the use of real-time data to monitor inventory levels. Global Data Synchronization: Enabled by radio frequency identification/electronic product coding. and loss prevention. a new wave of opportunity exists for retail industries to improve margins through the use of information technology. 2 . permitting communication between people and devices anywhere and without cables. Improvements have been made in areas such as supply chain management. Radio frequency identification tagging also positions the company to better safeguard its shipments by enabling the tracking of products from manufacturer through the supply chain. the entire supply chain is becoming more intelligent.Scope and applications of IT IT in the Retail Industry Introduction Technology has been an area of intense focus in retail industries as a way to accomplish both goals. The retail industry needs to improve their IT capabilities for the following reasons: To collect and analyze customer data. and continues to do so. Customer Data: Information overload is a challenge for retailers because they need to collect and sift through data to convert it into useful information in a customer-centric industry. inventory management. Wireless technology. To effectively manage all stores across areas and ensure proper stock and business To have an optimum supply chain To sell across various channels (online and offline) To improve Customer Relationship Management The retail industry faces the following IT management challenges: Transparency and Tracking: Retailers need greater transparency between systems and better tracking to integrate systems from manufacturer through to consumer to obtain customer and sales information. Provides objective metrics and ensures excellence in customer experience based on key performance indicators (KPIs) for customer relationship management 3.. 1.Point of Sale is a place where actual sale of goods or services occurs. In this paper the author will put some light on third-party supply chain management provider (3PSCM) or supply chain management service provider (SCMSP) also. The user can analyze sales data and figure out how well all the items on the shelves sell.e. services that integrate parts of the supply chain. or all of their supply chain management functions. Acts as a barometer for customer service and customer satisfaction 2. Quicker and more reliable checkouts mean that less manpower is needed. Sales reports help to maximize the inventory levels and control costs. Third Party Logistics & Connectivity.Business Intelligence & CRM: Learning from International Markets . 4. It often refers to the physical cash transfer that takes place between the customer and the seller or the service provider. i.Third Party Logistics also sometimes known as 3PL or TPL that has grown not only in India but as well as in other countries around the world is a type of firm that provides service to its customers of outsourced (or "third party") logistics services for part. 3 . Categorizes and tracks information on suggestions. and adjust purchasing levels accordingly. 1. 2. Offers accurate analysis for on-time delivery ratios to help define and measure objectives with minimum deviation. good client references and new customer acquisition. A retail POS system can help in increasing the profits in many ways. Often.Customer service and customer satisfaction are the backbone of customer relationships. The marketer can improve pricing accuracy by integrating bar-code scanners and credit card authorization ability with the POS system. these services go beyond logistics and included value-added services related to the production or procurement of goods. warehousing and transportation services that can be scaled and customized to customers' needs based on market conditions and the demands and delivery service requirements for their products and materials. it is easier to make appropriate corrections and ensure customer retention. If an organization can accurately monitor and measure customer service factors and customer satisfaction. complaints and claims to help gauge and minimize the gravity of various customer satisfaction risks and issues POS (Point Of Sale) . Third party logistics providers typically specialize in integrated operation. Warehouse Management Systems (WMS) . Maintain detailed purchase and distribution records 3. “ebay” etc. With the introduction of E-Commerce websites like “Flipkart”. order management. “Snapdeal”. and complete accounting systems. Gain visibility into key supply and demand measurements with role-based dashboard and reports 4 . It helps to 1. Streamline warehouse administration 4.The evolution of warehouse management systems(WMS) is very similar to that of many other software solutions. Manage real-time physical and virtual inventory 2. the role of WMS is expanding to including light manufacturing.E-Commerce and Online Retail. transportation management. the impact of information technology in retail has increased manifold. Initially a system to control movement and storage of materials within a warehouse.Electronic commerce is the buying and selling of product or service over electronic systems such as the Internet and other computer networks. Simplify receiving and tracking with easy-to-use handheld units 5. prediction (including forecasting). enhance goods consumption ratios design more effective goods transportation and distribution policies and reduce the cost of business. Different data mining techniques used for analysis of retail data are: 1. Regression: Regression is a kind of statistical estimation technique used to map each data object to a real value provide prediction value. improve the quality of customer service. modeling of causal relationships. availability and popularity of the business conducted on web or e-commerce. achieve better customer retention and satisfaction. 4. Common tools used for classification are neural networks. Demand forecast is a typical example of a forecasting model. Uses of regression include curve fitting. In other words. Forecasting: Forecasting estimates the future value based on a record’s patterns. Common tools for sequence discovery are statistics and set theory. 5 . It relates to modeling and the logical relationships of the model at some time in the future. 6. Examples of visualization model are 3D graphs. 7. and testing scientific hypotheses about relationships between variables. Sequence discovery: Sequence discovery is the identification of associations or patterns over time. there are no predefined clusters. Common tools for forecasting include neural networks and survival analysis.else rules.Retail Analysis and Data Mining Retail industry collects large amount of data on sales and customer shopping history. Its goal is to model the states of the process generating the sequence or to extract and report deviation and trends overtime. 5. especially due to the increasing ease. It deals with continuously valued outcomes. Classification: Classification is one of the most common techniques in data mining. Common tools for association modeling are statistics and apriori algorithms. It is different to classification in that clusters are unknown at the time the algorithm starts. discover customer shopping patterns and trends. Retail data mining can help identify customer behavior. Market basket analysis and cross selling programs are typical examples for which association modeling is usually adopted. The quantity of data collected continues to increase. Hygraphs and SeeNet. Common tools for regression include linear regression and logistic regression. 3. It is used in conjunction with other data mining models to provide a clearer understanding of the discovered patterns or relationships. Visualization: Visualization refers to the presentation of data so that users can view complex patterns. decision trees and if then. Association: Association aims to establishing relationships between items which exist together in a given record. Common tools for clustering include neutral networks and discrimination analysis. 2. It aims at building a model to predict future customer behaviours through classifying database records into a number of predefined classes based on certain criteria. Clustering: Clustering is the task of segmenting a heterogeneous population more homogenous clusters. Also market 6 . This can be done through product portfolio analysis and then selling the products that are missing from typical portfolios. Customer Relationship Management Customer Segmentation: Customer segmentation is an important part of a retail organization's marketing plan. Hence it is absolutely essential to identify customers with high potential before deciding what the best way to realize that potential is through the right marketing stimuli. Campaign/ promotion effectiveness analysis can answer questions like: •Which media channels have been most successful in the past for various campaigns? •Which geographic locations responded well to a particular campaign? •What were the relative costs and benefits of this campaign? •Which customer segments responded to the campaign? Customer Lifetime Value (CLV): Not all customers are equally profitable. and deducting the value of servicing the customer. this greatly helps in understanding what goes into a successful marketing campaign. fashions and trends. To develop effective customer retention programs it is vital to analyze the reasons for customer attrition. CLV attempts to calculate some projected relative measure of value by calculating Risk Adjusted Revenue (probability of customer owning categories/products in his portfolio that he currently doesn‘t have). For example it can help classify customers in the following segments: • Customers who respond to new promotions • Customers who respond to new product launches • Customers who respond to discounts • Customers who show propensity to purchase specific products Campaign/ Promotion Effectiveness Analysis: Once a campaign is launched its effectiveness can be studied across different media and in terms of costs and benefits. as well as Risk Adjusted Loss (probability of customer dropping categories/products in his portfolio that he currently owns) and adding to some Net Present Value.Scope and Application of Data Mining and Analysis in the Retail Industry 1. It can offer insights into how different segments respond to shifts in demographics. Customer Potential: There are customers who are not very profitable today but may have the potential of being profitable in future. Business Intelligence helps in understanding customer attrition with respect to various factors influencing a customer and at times one can drill down to individual transactions. Cross Selling: Retailers use the vast amount of customer information available with them to cross sell other products at the time of purchase. Customer Loyalty Analysis: It is more economical to retain an existing customer than to acquire a new one. which might have resulted in the change of loyalty. retailers can develop sophisticated price models for different products. Using data warehousing and data mining. which can establish price . Demand Forecasting: Complex demand forecasting models can be created using a number of factors like sales figures. With cash registers equipped with bar-code scanners.basket analysis can be another food method for effective cross selling. Target Marketing/Response Modeling: Retailers can optimize the overall marketing and promotion effort by targeting campaigns to specific customers or groups of customers. Target marketing can be based on a very simple analysis of the buying habits of the customer or the customer group. The data collected for this purpose can provide deep insights into the dynamics of the supply chain.sales relationships for the product and how changes in prices affect the sales of other products. the role of suppliers in specific product outages can be critically analyzed. basic economic indicators. Supply Chain Management & Procurement Supply chain management (SCM) promises unprecedented efficiencies in inventory control and procurement to the retailers. lot size. environmental conditions. delivery time. If correctly implemented. Look-a-like modeling is yet another strategy where model is produce that produce some quantitative measure of affinity of the customer to a specific product. retailers can now automatically manage the flow of products and transmit stock replenishment orders to the vendors. etc. Product Pricing: Pricing is one of the most crucial marketing decisions taken by retailers. 2. Analyzing the movement of specific products . etc. On-time replenishment orders are very critical for these products.can help in predicting when there will be need for re-order. etc. quality of products delivered. Vendor Performance Analysis: Performance of each vendor can be analyzed on the basis of a number of factors like cost. thereby helping in both operational and strategic decisions relating to the inventory. safety stock. payment lead time. Product Movement and the Supply Chain: Some products move much faster off the shelf than others. In addition to this. lot size. Often an increase in price of a product can result in lower sales and customer adoption of replacement products. a data warehouse can significantly help in improving the retailer’s relations with suppliers and can complement the existing SCM application 7 . can be generated from the data warehouse. but increasingly data mining tools are being used to define specific customer segments that are likely to respond to particular types of campaigns. Inventory Control (Inventory levels.using BI tools . and lead time analysis): Both current and historic reports on key inventory indicators like inventory levels. Storefront Operations The information needs of the store manager are no longer restricted to the day to day operations. and finds out which stores are similar in terms of product or customer dimensions. But in real life. The objective is to achieve maximum profitability from a category. Category Management: It gives the retailer an insight into the right number of SKUs to stock in a particular category. 4. grouping products to be sold in a single package deal. Data warehousing and data mining can help the manager gain this insight.3. Web logs and Information forms filled over the web are very rich sources of data that can provide insightful information about customer's browsing behavior.has forced the 'Bricks and Mortar' retailers to quickly adopt this channel. Following are some of the uses in storefront operations: Store Segmentation: This analysis takes the data that is common for different stores. too few SKUs would mean that the customer is not provided with adequate choice.e. Out-Of-Stock Analysis: This analysis probes into the various reasons resulting into an out of stock situation. Next step is to build the profile of the customers that buys from specific store. 8 . This analysis has various uses in the retail organization. It goes without saying that effective category management is vital for a retailer's survival in this market. Typically a number of variables are involved and it can get very complicated. One very common use is for in-store product placement. Market Basket Analysis: It is used to study natural affinities between products. i. Their success would largely depend on how they use the Net to complement their existing channels. Today’s consumer is much more sophisticated and she demands a compelling shopping experience. One of the classic examples of market basket analysis is the beer-diaper affinity. An integral part of the analysis is calculating the lost revenue due to product stock out. Alternative Sales Channels E Business Analysis: The Internet has emerged as a powerful alternative channel for established retailers. which states that men who buy diapers are also likely to buy beer. and too many would mean that the SKUs are cannibalizing each other. This is an example of 'two-product affinity'. market basket analysis can get extremely complex resulting in hitherto unknown affinities between a number of products. For this the store manager needs to have an in-depth understanding of her tastes and purchasing behavior. In other words – what stores are similar based on products that are sold quickly or more slowly in comparison to rest of the stores. Other uses include designing the company's e-commerce web site and product catalogs. Increasing competition from retailers operating purely over the Internet commonly known as 'e-tailers' . Another popular use is product bundling. 5. Product Recommendation: If someone buys product A which other product he may buy. Product – Channel Affinity: Some product categories sell particularly well on certain channels. Following are some of the uses in finance: 9 . Referrer Analysis: An analysis of the sites. This can help in solving the errors and making the browsing experience more pleasurable.purchasing patterns. Many companies. peer dynamics and wisdom of the crowds. many organizations have embraced a free information architecture. Usually there are 3 different angles to exploit when setting up recommendation engine: natural product affinities.friendly. customers’ affinities and preferences. This analysis is primarily required to optimize the operations over the Internet. which are very prolific in diverting traffic to the company’s web site. Finance and Fixed Asset Management Financial reporting is no longer restricted to just financial statements required by the law. Data mining can help identify hidden product-channel affinities and help the retailer design better promotion and marketing campaigns. and whether it makes sense for the retailer to continue building up expertise in that channel. Error Analysis: An analysis of the errors encountered by the user while navigating the web site. have integrated financial data in their enterprise wide data warehouse or established separate Financial Data Warehouse (FDW). likes and dislikes. It also includes an analysis of the most popular pages in the web site. across industries. whereby financial information is openly available for internal use. Many analytics described till now use financial data. Also. The decision of continuing with a channel would also include a number of subjective factors like outlook of key enabling technologies for that channel. This can significantly help in site optimization by making it more user. Channel Profitability: Data mining can help analyze channel profitability. etc. Keyword Analysis: An analysis of the most popular keywords used by various users in Internet search engines to reach the retailer’s e-commerce web site. It typically includes following analyses: Site Navigation: An analysis of the typical route followed by the user while navigating the web site. increasingly it is being used to help in strategic decision making. The main types of analysis done on the web site data are: • • • • • • Web Log Analysis: This involves analyzing the basic traffic information over the ecommerce web site. processed and diffused both inside and outside the organization. a graphic user interface (GUI) and a series of applications that allow calculations and other operations on the input data. The ability to drill down and join inter-related reports and analyses – provided by all major OLAP tool vendors – can make ratio analysis much more intuitive. administration of the product range structure and 4. As a consequence. from photo-pictures to product drawings. liquidity ratios.). It can also be used to allocate budgets for the coming financial period. product descriptions. IKEA’s IT system PIA: a facility for product development IKEA expects its IT systems to offer support for development projects that require large amounts of information and data to be collected. Among IKEA’s many IT systems.Budgetary Analysis: Data warehousing facilitates analysis of budgeted versus actual expenditure for various cost heads like promotion overruns can be analyzed in more detail. total lease cost vs. It would typically involve measures like profitability per sq. 3. IKEA’s Intranet. brands. Profitability Analysis: This includes profitability of individual stores. from measures to materials. from supplying units to components. From a technical point of view. 2. it was meant to take a central role in the management of relevant product-related information. information is extracted by the product developers of IKEAoS and is exchanged with both internal and external units. inside PIA. label drawings. etc. and individual SKUs. and externally connected to other IKEA databases and IT systems (IKEA’s Website. When PIA was introduced in 1998. This production facility is made of various databases that are both internally connected. product categories. etc. administration of product documentation. etc. the ‘‘Pricetag’’ retail system. PIA is the central source from which a number of information bearers (some of which are directly attached to products) can be generated: price tags. departments within the store. from technical descriptions (TEDs) to prices. the IKEA catalogue and IKEA’s pricelists. Financial Ratio Analysis: Various financial ratios like debt-equity. can be analyzed over a period of time. administration of development projects. PIA is particularly relevant for development activities. profitability. The four central functions of this information facility are: 1. During such projects. Making PIA 10 . administration of product information. Fixed Asset Return Analysis: This is used to analyze financial viability of the fixed assets owned or leased by the company. PIA is composed of a series of databases. foot of store space. i. among other issues. administration of development projects. economic calculations and required investments in production facilities. This must happen six weeks before product launch. who set broad requirements and specifications for the project to be translated into a product prototype. internal reports. Passive users (including all visitors to IKEA’s Website) can access different levels of PIA’s databases. To handle them. PIA literally mimics a product development guide that IKEA-oS introduced in 1994 as a template to sustain project planning and management.. They not only provide the IT facility with input data. The fourth PIA function. Individuals outside IKEA are not granted direct access to PIA-borne information. ‘‘inscribed’’ and constantly updated into PIA.g. the processed data. 11 . Every development project that is launched at IKEAoS is supposed to be registered. First buy requiring (1) technical specifications for the involved suppliers. Contract review with supplying units to formalise. Presentation to the product council who assesses the match with the original project goals. For this purpose. Follow-up on the new or improved product in retailing. e. technical requirements into documents called TEDs. both inside and outside IKEA. A wealth of other ‘‘passive users’’ (up to all of IKEA’s 65 000 employees) can also access the system via IKEA’s Intranet interface to PIA. but they are also expected to be the main users of the outcome. 7. IKEA has specified a series of routines that require product developers to provide PIA with input data. News about the developed product is produced and communicated to all of IKEA’s retail stores before they can place any order. Interacting with PIA is considered the most information-rich task that project developers are required to perform during development projects.g. such issues as how input is created and under whose responsibility became crucial.e. 6. via other connected IT systems. In fact. 3. clearly indicates its role in the management of development projects. Sale start after orders from retail stores have been collected and fulfilled.. TEDs and supplier indexes). distribution and production. 4. during their development assignments.. 2. to either simply browse for information or create specific documents (e. Project assignment to a specific product developer and his project team. 5. PIA includes a particular series of applications and databases that represent the seven milestones in this project guide: 1.into a key information source for a number of business units. Product developers and their project teams can therefore be considered as central in the provider–user interface of PIA. price tags. of how resources are combined and recombined. (2) complete product information for consumers and (3) detailed forecasts of local markets’ expected needs. allowing the CRM team to target them for retention. etc. make marketing new products and services more profitable and also improve their supply chain References • • • • http://www. Retailers can encourage the right purchase behavior.in/2011/09/data-mining-in-retail-industry. The best source of information for retailers is POS (point of sale). customer loyalty cards.html 12 .edu/3323863/INFORMATION_TECHNOLOGY_IN_RETAIL_IN DUSTRY http://www.blogspot. high-value customers who can be accelerated to that value through marketing programs. Data mining and analysis can identify valuable customers who are likely to defect to a competitor.com/in-en/Pages/service-retail-information-technologysummary. to product sales based on seasons.Conclusion Retailers have reoriented their business around the customer.aspx http://www. company owned credit cards.academia.accenture. Indian Retail Industry is the most promising and emerging market for investment.academia. to demographics. Retailers collect large amount of information every day – anything from transactional data.edu/2447358/Data_Mining_Techniques_used_in_Retail_Industry http://goranxview. It also points out potential long term.