Mid Term Submission

Assignment

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

(Page 2Page 5Page 6Page 11Page 12Page 12)

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

IKEAs IT system: PIA

Conclusion

References

Retail Analysis- 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. Improvements have been made in areas such as supply chain management, inventory management, customer experience, and loss prevention. Wireless technology, permitting communication between people and devices anywhere and without cables, has enabled the dramatic transformation of business processes in the past, and continues to do so. Today, a new wave of opportunity exists for retail industries to improve margins through the use of information technology.

The retail industry needs to improve their IT capabilities for the following reasons:

To collect and analyze customer data, to help serve better to each segment.

To quickly respond to the ever changing market and increase its flexibility and speed.

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.

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.

Global Data Synchronization:Enabled by radio frequency identification/electronic product coding, the entire supply chain is becoming more intelligent. Benefits for retailers include enabling the use of real-time data to monitor inventory levels. 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.

Business Intelligence & CRM: Learning from International Markets - Customer service and customer satisfaction are the backbone of customer relationships. If an organization can accurately monitor and measure customer service factors and customer satisfaction, it is easier to make appropriate corrections and ensure customer retention, good client references and new customer acquisition.

1. Acts as a barometer for customer service andcustomer satisfaction

2. Provides objective metrics and ensures excellence in customer experience based on keyperformance indicators (KPIs) for customer relationship management

3. Offers accurate analysis for on-time delivery ratios to helpdefine and measure objectives with minimum deviation.

4. Categorizes and tracks information on suggestions, complaints and claims to help gauge and minimize the gravity of various customer satisfaction risks and issues

POS (Point Of Sale) - Point of Sale is a place where actual sale of goods or services occurs. It often refers to the physical cash transfer that takes place between the customer and the seller or the service provider.

1. The user can analyze sales data and figure out how well all the items on the shelves sell, and adjust purchasing levels accordingly.

2. The marketer can improve pricing accuracy by integrating bar-code scanners and credit card authorization ability with the POS system.

A retail POS system can help in increasing the profits in many ways. 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- 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, or all of their supply chain management functions. Third party logistics providers typically specialize in integrated operation, 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. Often, these services go beyond logistics and included value-added services related to the production or procurement of goods, i.e., services that integrate parts of the supply chain. 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.

E-Commerce and Online Retail- Electronic commerce is the buying and selling of product or service over electronic systems such as the Internet and other computer networks. With the introduction of E-Commerce websites like Flipkart, Snapdeal, ebay etc. the impact of information technology in retail has increased manifold.

Warehouse Management Systems (WMS) - The evolution of warehouse management systems(WMS) is very similar to that of many other software solutions. Initially a system to control movement and storage of materials within a warehouse, the role of WMS is expanding to including light manufacturing, transportation management, order management, and complete accounting systems. It helps to

1. Manage real-time physical and virtual inventory

2. Maintain detailed purchase and distribution records

3. Streamline warehouse administration

4. Simplify receiving and tracking with easy-to-use handheld units

5. Gain visibility into key supply and demand measurements with role-based dashboard and

reports

Retail Analysis and Data Mining

Retail industry collects large amount of data on sales and customer shopping history. The quantity of data collected continues to increase, especially due to the increasing ease, availability and popularity of the business conducted on web or e-commerce. Retail data mining can help identify customer behavior, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, 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. Association: Association aims to establishing relationships between items which exist together in a given record. Market basket analysis and cross selling programs are typical examples for which association modeling is usually adopted. Common tools for association modeling are statistics and apriori algorithms.

2. Classification: Classification is one of the most common techniques in data mining. 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. Common tools used for classification are neural networks, decision trees and if then- else rules.

3. Clustering: Clustering is the task of segmenting a heterogeneous population more homogenous clusters. It is different to classification in that clusters are unknown at the time the algorithm starts. In other words, there are no predefined clusters. Common tools for clustering include neutral networks and discrimination analysis.

4. Forecasting: Forecasting estimates the future value based on a records patterns. It deals with continuously valued outcomes. It relates to modeling and the logical relationships of the model at some time in the future. Demand forecast is a typical example of a forecasting model. Common tools for forecasting include neural networks and survival analysis.

5. Regression: Regression is a kind of statistical estimation technique used to map each data object to a real value provide prediction value. Uses of regression include curve fitting, prediction (including forecasting), modeling of causal relationships, and testing scientific hypotheses about relationships between variables. Common tools for regression include linear regression and logistic regression.

6. Sequence discovery: Sequence discovery is the identification of associations or patterns over time. Its goal is to model the states of the process generating the sequence or to extract and report deviation and trends overtime. Common tools for sequence discovery are statistics and set theory.

7. Visualization: Visualization refers to the presentation of data so that users can view complex patterns. It is used in conjunction with other data mining models to provide a clearer understanding of the discovered patterns or relationships. Examples of visualization model are 3D graphs, Hygraphs and SeeNet.

Scope and Application of Data Mining and Analysis in the Retail Industry

1. Customer Relationship Management

Customer Segmentation: Customer segmentation is an important part of a retail organization's marketing plan. It can offer insights into how different segments respond to shifts in demographics, fashions and trends. 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; this greatly helps in understanding what goes into a successful marketing campaign. 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. 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 doesnt have), 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, and deducting the value of servicing the customer.

Customer Potential: There are customers who are not very profitable today but may have the potential of being profitable in future. 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.

Customer Loyalty Analysis: It is more economical to retain an existing customer than to acquire a new one. To develop effective customer retention programs it is vital to analyze the reasons for customer attrition. 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, which might have resulted in the change of loyalty.

Cross Selling: Retailers use the vast amount of customer information available with them to cross sell other products at the time of purchase. This can be done through product portfolio analysis and then selling the products that are missing from typical portfolios. Also market basket analysis can be another food method for effective cross selling. 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.

Product Pricing: Pricing is one of the most crucial marketing decisions taken by retailers. Often an increase in price of a product can result in lower sales and customer adoption of replacement products. Using data warehousing and data mining, retailers can develop sophisticated price models for different products, which can establish price - sales relationships for the product and how changes in prices affect the sales of other products.

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; but increasingly data mining tools are being used to define specific customer segments that are likely to respond to particular types of campaigns.

2. Supply Chain Management & Procurement

Supply chain management (SCM) promises unprecedented efficiencies in inventory control and procurement to the retailers. With cash registers equipped with bar-code scanners, retailers can now automatically manage the flow of products and transmit stock replenishment orders to the vendors. The data collected for this purpose can provide deep insights into the dynamics of the supply chain.

Vendor Performance Analysis: Performance of each vendor can be analyzed on the basis of a number of factors like cost, delivery time, quality of products delivered, payment lead time, etc. In addition to this, the role of suppliers in specific product outages can be critically analyzed.

Inventory Control (Inventory levels, safety stock, lot size, and lead time analysis): Both current and historic reports on key inventory indicators like inventory levels, lot size, etc. can be generated from the data warehouse, thereby helping in both operational and strategic decisions relating to the inventory.

Product Movement and the Supply Chain: Some products move much faster off the shelf than others. On-time replenishment orders are very critical for these products. Analyzing the movement of specific products - using BI tools - can help in predicting when there will be need for re-order.

Demand Forecasting: Complex demand forecasting models can be created using a number of factors like sales figures, basic economic indicators, environmental conditions, etc. If correctly implemented, a data warehouse can significantly help in improving the retailers relations with suppliers and can complement the existing SCM application

3. Storefront Operations

The information needs of the store manager are no longer restricted to the day to day operations. Todays consumer is much more sophisticated and she demands a compelling shopping experience. For this the store manager needs to have an in-depth understanding of her tastes and purchasing behavior. Data warehousing and data mining can help the manager gain this insight. Following are some of the uses in storefront operations:

Store Segmentation: This analysis takes the data that is common for different stores, and finds out which stores are similar in terms of product or customer dimensions. In other words what stores are similar based on products that are sold quickly or more slowly in comparison to rest of the stores. Next step is to build the profile of the customers that buys from specific store.

Market Basket Analysis: It is used to study natural affinities between products. One of the classic examples of market basket analysis is the beer-diaper affinity, which states that men who buy diapers are also likely to buy beer. This is an example of 'two-product affinity'. But in real life, market basket analysis can get extremely complex resulting in hitherto unknown affinities between a number of products. This analysis has various uses in the retail organization. One very common use is for in-store product placement. Another popular use is product bundling, i.e. grouping products to be sold in a single package deal. Other uses include designing the company's e-commerce web site and product catalogs.

Category Management: It gives the retailer an insight into the right number of SKUs to stock in a particular category. The objective is to achieve maximum profitability from a category; too few SKUs would mean that the customer is not provided with adequate choice, and too many would mean that the SKUs are cannibalizing each other. It goes without saying that effective category management is vital for a retailer's survival in this market.

Out-Of-Stock Analysis: This analysis probes into the various reasons resulting into an out of stock situation. Typically a number of variables are involved and it can get very complicated. An integral part of the analysis is calculating the lost revenue due to product stock out.

4. Alternative Sales Channels

E Business Analysis: The Internet has emerged as a powerful alternative channel for established retailers. Increasing competition from retailers operating purely over the Internet - commonly known as 'e-tailers' - has forced the 'Bricks and Mortar' retailers to quickly adopt this channel. Their success would largely depend on how they use the Net to complement their existing channels. 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, purchasing patterns, likes and dislikes, etc. The main types of analysis done on the web site data are:

Web Log Analysis: This involves analyzing the basic traffic information over the e-commerce web site. This analysis is primarily required to optimize the operations over the Internet. It typically includes following analyses:

Site Navigation: An analysis of the typical route followed by the user while navigating the web site. It also includes an analysis of the most popular pages in the web site. This can significantly help in site optimization by making it more user- friendly.

Referrer Analysis: An analysis of the sites, which are very prolific in diverting traffic to the companys web site.

Error Analysis: An analysis of the errors encountered by the user while navigating the web site. This can help in solving the errors and making the browsing experience more pleasurable.

Keyword Analysis: An analysis of the most popular keywords used by various users in Internet search engines to reach the retailers e-commerce web site.

Product Recommendation: If someone buys product A which other product he may buy. Usually there are 3 different angles to exploit when setting up recommendation engine: natural product affinities, customers affinities and preferences, peer dynamics and wisdom of the crowds.

Channel Profitability: Data mining can help analyze channel profitability, and whether it makes sense for the retailer to continue building up expertise in that channel. The decision of continuing with a channel would also include a number of subjective factors like outlook of key enabling technologies for that channel.

Product Channel Affinity: Some product categories sell particularly well on certain channels. Data mining can help identify hidden product-channel affinities and help the retailer design better promotion and marketing campaigns.

5. Finance and Fixed Asset Management

Financial reporting is no longer restricted to just financial statements required by the law; increasingly it is being used to help in strategic decision making. Also, many organizations have embraced a free information architecture, whereby financial information is openly available for internal use. Many analytics described till now use financial data. Many companies, across industries, have integrated financial data in their enterprise wide data warehouse or established separate Financial Data Warehouse (FDW). Following are some of the uses in finance:

Budgetary Analysis: Data warehousing facilitates analysis of budgeted versus actual expenditure for various cost heads like promotion overruns can be analyzed in more detail. It can also be used to allocate budgets for the coming financial period.

Fixed Asset Return Analysis: This is used to analyze financial viability of the fixed assets owned or leased by the company. It would typically involve measures like profitability per sq. foot of store space, total lease cost vs. profitability, etc.

Financial Ratio Analysis: Various financial ratios like debt-equity, liquidity ratios, etc. can be analyzed over a period of time. 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.

Profitability Analysis: This includes profitability of individual stores, departments within the store, product categories, brands, and individual SKUs.

IKEAs 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, processed and diffused both inside and outside the organization. During such projects, information is extracted by the product developers of IKEAoS and is exchanged with both internal and external units.

Among IKEAs many IT systems, PIA is particularly relevant for development activities. The four central functions of this information facility are:

1. administration of product information,

2. administration of product documentation,

3. administration of the product range structure and

4. administration of development projects.

From a technical point of view, PIA is composed of a series of databases, a graphic user interface (GUI) and a series of applications that allow calculations and other operations on the input data. This production facility is made of various databases that are both internally connected, inside PIA, and externally connected to other IKEA databases and IT systems (IKEAs Website, IKEAs Intranet, the Pricetag retail system, etc.).

When PIA was introduced in 1998, it was meant to take a central role in the management of relevant product-related information, from supplying units to components, from technical descriptions (TEDs) to prices, from measures to materials, from photo-pictures to product drawings. As a consequence, 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, product descriptions, label drawings, the IKEA catalogue and IKEAs pricelists. Making PIA into a key information source for a number of business units, both inside and outside IKEA, such issues as how input is created and under whose responsibility became crucial. To handle them, IKEA has specified a series of routines that require product developers to provide PIA with input data. Product developers and their project teams can therefore

be considered as central in the provideruser interface of PIA. They not only provide the IT facility with input data, but they are also expected to be the main users of the outcome, the processed data, during their development assignments. A wealth of other passive users (up to all of IKEAs 65 000 employees) can also access the system via IKEAs Intranet interface to PIA. Passive users (including all visitors to IKEAs Website) can access different levels of PIAs databases, via other connected IT systems, to either simply browse for information or create specific documents (e.g., internal reports, price tags, TEDs and supplier indexes). Individuals outside IKEA are not granted direct access to PIA-borne information.

The fourth PIA function, administration of development projects, clearly indicates its role in the management of

development projects, i.e., of how resources are combined and recombined. Interacting with PIA is considered the most information-rich task that project developers are required to perform during development projects. Every development project that is launched at IKEA-oS is supposed to be registered, inscribed and constantly updated into PIA. For this purpose, PIA literally mimics a product development guide that IKEA-oS introduced in 1994 as a template to sustain project planning and management. In fact, PIA includes a particular series of applications and databases that represent the seven milestones in this project guide:

1. Project assignment to a specific product developer and his project team, who set broad requirements and specifications for the project to be translated into a product prototype.

2. Presentation to the product council who assesses the match with the original project goals, e.g., economic calculations and required investments in production facilities.

3. First buy requiring (1) technical specifications for the involved suppliers, (2) complete product information for consumers and (3) detailed forecasts of local markets expected needs.

4. Contract review with supplying units to formalise, among other issues, technical requirements into documents called TEDs.

5. News about the developed product is produced and communicated to all of IKEAs retail stores before they can place any order. This must happen six weeks before product launch.

6. Sale start after orders from retail stores have been collected and fulfilled.

7. Follow-up on the new or improved product in retailing, distribution and production.

Conclusion

Retailers have reoriented their business around the customer. Indian Retail Industry is the most promising and emerging market for investment. Retailers collect large amount of information every day anything from transactional data, to demographics, to product sales based on seasons. The best source of information for retailers is POS (point of sale), company owned credit cards, customer loyalty cards, etc. Data mining and analysis can identify valuable customers who are likely to defect to a competitor, allowing the CRM team to target them for retention. It also points out potential long term, high-value customers who can be accelerated to that value through marketing programs. Retailers can encourage the right purchase behavior, make marketing new products and services more profitable and also improve their supply chain

References

http://www.academia.edu/3323863/INFORMATION_TECHNOLOGY_IN_RETAIL_INDUSTRY

http://www.accenture.com/in-en/Pages/service-retail-information-technology-summary.aspx

http://www.academia.edu/2447358/Data_Mining_Techniques_used_in_Retail_Industry

http://goranxview.blogspot.in/2011/09/data-mining-in-retail-industry.html

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